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Sample records for drugs predict subsequent

  1. Global Brain Dynamics During Social Exclusion Predict Subsequent Behavioral Conformity

    OpenAIRE

    Wasylyshyn, Nick; Hemenway, Brett; Garcia, Javier O.; Cascio, Christopher N.; O'Donnell, Matthew Brook; Bingham, C. Raymond; Simons-Morton, Bruce; Vettel, Jean M.; Falk, Emily B.

    2017-01-01

    Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (N = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI...

  2. Global brain dynamics during social exclusion predict subsequent behavioral conformity.

    Science.gov (United States)

    Wasylyshyn, Nick; Hemenway Falk, Brett; Garcia, Javier O; Cascio, Christopher N; O'Donnell, Matthew Brook; Bingham, C Raymond; Simons-Morton, Bruce; Vettel, Jean M; Falk, Emily B

    2018-02-01

    Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (n = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms. We computed the difference in functional connectivity between social exclusion and social inclusion from each node in the brain to nodes in two brain networks, one previously associated with mentalizing (medial prefrontal cortex, temporoparietal junction, precuneus, temporal poles) and another with social pain (dorsal anterior cingulate cortex, anterior insula). Using predictive modeling, this measure of global connectivity during exclusion predicted the extent of conformity to peer pressure during driving in the subsequent experimental session. These findings extend our understanding of how global neural dynamics guide social behavior, revealing functional network activity that captures individual differences.

  3. LSD-induced entropic brain activity predicts subsequent personality change.

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    Lebedev, A V; Kaelen, M; Lövdén, M; Nilsson, J; Feilding, A; Nutt, D J; Carhart-Harris, R L

    2016-09-01

    Personality is known to be relatively stable throughout adulthood. Nevertheless, it has been shown that major life events with high personal significance, including experiences engendered by psychedelic drugs, can have an enduring impact on some core facets of personality. In the present, balanced-order, placebo-controlled study, we investigated biological predictors of post-lysergic acid diethylamide (LSD) changes in personality. Nineteen healthy adults underwent resting state functional MRI scans under LSD (75µg, I.V.) and placebo (saline I.V.). The Revised NEO Personality Inventory (NEO-PI-R) was completed at screening and 2 weeks after LSD/placebo. Scanning sessions consisted of three 7.5-min eyes-closed resting-state scans, one of which involved music listening. A standardized preprocessing pipeline was used to extract measures of sample entropy, which characterizes the predictability of an fMRI time-series. Mixed-effects models were used to evaluate drug-induced shifts in brain entropy and their relationship with the observed increases in the personality trait openness at the 2-week follow-up. Overall, LSD had a pronounced global effect on brain entropy, increasing it in both sensory and hierarchically higher networks across multiple time scales. These shifts predicted enduring increases in trait openness. Moreover, the predictive power of the entropy increases was greatest for the music-listening scans and when "ego-dissolution" was reported during the acute experience. These results shed new light on how LSD-induced shifts in brain dynamics and concomitant subjective experience can be predictive of lasting changes in personality. Hum Brain Mapp 37:3203-3213, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  4. Fixed drug eruption: topical provocation and subsequent phenomena

    Energy Technology Data Exchange (ETDEWEB)

    Mahboob, A; Haroon, T S [Shaikh Zayed FPGMI, Lahore (Pakistan). Dept. of Dermatology; Haroon, T S [King Edward Medical Univ., Lahore (Pakistan). Dept. of Dematology; Iqbal, Z; Iqbal, F [Shaikh Zayed FPGMI, Lahore (Pakistan). Dept. of Medicine

    2006-12-15

    To determine the usefulness of topical provocation in detecting the incriminated drug causing fixed eruption. Three hundred and five, clinically diagnosed cases of Fixed Drug Eruption (FDE) of either gender and of any age were subjected to topical provocation with different drugs by using concentration of 1% (n=203), 2% (n=210) and 5% (n=235) in white soft paraffin. Drug ointment of one strength was applied one at a time on normal skin of flexor surface of right or left forearm. The effects of tests on involved and uninvolved skin were observed for 48 hours. The changes in lesions like erythema, hyperpigmentation, itching, burning or appearance of new lesion were considered a positive response. In case of no change, the patients (n=5) were subjected to oral provocation test, by giving half to full therapeutic dose of the suspected drug depending upon the severity of the initial attack. A patient who exhibited see-sawing phenomenon with 5% metamizole TPT was given oral challenge with same drug. Control topical tests were repeated in equal number of normal persons with various drug ointments and in patients of FDE with white soft paraffin on normal and affected skin. One hundred and thirty-seven patients were males and one hundred and sixty-eight patients were females. Maximum number of patients belonged to third decade. With 1% drug preparations 12 out of 316, with 2% drug preparations 28 out of 422 and with 5% drug preparations, 312 out of 523 TPTs were positive. The comparison revealed a highly significant association (Chi-square 448.1 and p<0.000) among various strengths of preparations and positive response. Sulphamethoxazole was found to be the most commonly incriminated cause of FDE applied in 5% concentration yielded sensitivity rate of 91% compared to 4% with lower concentrations. Positive patch test was also observed with oxytetracycline. Five patients who were given oral provocation with different drugs were found to be positive to tinidazole, dapsone

  5. Fixed drug eruption: topical provocation and subsequent phenomena

    International Nuclear Information System (INIS)

    Mahboob, A.; Haroon, T.S.; Haroon, T.S.; Iqbal, Z.; Iqbal, F.

    2006-01-01

    To determine the usefulness of topical provocation in detecting the incriminated drug causing fixed eruption. Three hundred and five, clinically diagnosed cases of Fixed Drug Eruption (FDE) of either gender and of any age were subjected to topical provocation with different drugs by using concentration of 1% (n=203), 2% (n=210) and 5% (n=235) in white soft paraffin. Drug ointment of one strength was applied one at a time on normal skin of flexor surface of right or left forearm. The effects of tests on involved and uninvolved skin were observed for 48 hours. The changes in lesions like erythema, hyperpigmentation, itching, burning or appearance of new lesion were considered a positive response. In case of no change, the patients (n=5) were subjected to oral provocation test, by giving half to full therapeutic dose of the suspected drug depending upon the severity of the initial attack. A patient who exhibited see-sawing phenomenon with 5% metamizole TPT was given oral challenge with same drug. Control topical tests were repeated in equal number of normal persons with various drug ointments and in patients of FDE with white soft paraffin on normal and affected skin. One hundred and thirty-seven patients were males and one hundred and sixty-eight patients were females. Maximum number of patients belonged to third decade. With 1% drug preparations 12 out of 316, with 2% drug preparations 28 out of 422 and with 5% drug preparations, 312 out of 523 TPTs were positive. The comparison revealed a highly significant association (Chi-square 448.1 and p<0.000) among various strengths of preparations and positive response. Sulphamethoxazole was found to be the most commonly incriminated cause of FDE applied in 5% concentration yielded sensitivity rate of 91% compared to 4% with lower concentrations. Positive patch test was also observed with oxytetracycline. Five patients who were given oral provocation with different drugs were found to be positive to tinidazole, dapsone

  6. Potentially inappropriate medication: Association between the use of antidepressant drugs and the subsequent risk for dementia.

    Science.gov (United States)

    Heser, Kathrin; Luck, Tobias; Röhr, Susanne; Wiese, Birgitt; Kaduszkiewicz, Hanna; Oey, Anke; Bickel, Horst; Mösch, Edelgard; Weyerer, Siegfried; Werle, Jochen; Brettschneider, Christian; König, Hans-Helmut; Fuchs, Angela; Pentzek, Michael; van den Bussche, Hendrik; Scherer, Martin; Maier, Wolfgang; Riedel-Heller, Steffi G; Wagner, Michael

    2018-01-15

    Potentially inappropriate medication (PIM) is associated with an increased risk for detrimental health outcomes in elderly patients. Some antidepressant drugs are considered as PIM, but previous research on the association between antidepressants and subsequent dementia has been inconclusive. Therefore, we investigated whether the intake of antidepressants, particularly of those considered as PIM according to the Priscus list, would predict incident dementia. We used data of a prospective cohort study of non-demented primary care patients (n = 3239, mean age = 79.62) to compute Cox proportional hazards models. The risk for subsequent dementia was estimated over eight follow-ups up to 12 years depending on antidepressant intake and covariates. The intake of antidepressants was associated with an increased risk for subsequent dementia (HR = 1.53, 95% CI: 1.16-2.02, p = .003; age-, sex-, education-adjusted). PIM antidepressants (HR = 1.49, 95% CI: 1.06-2.10, p = .021), but not other antidepressants (HR = 1.04, 95% CI: 0.66-1.66, p = .863), were associated with an increased risk for subsequent dementia (in age-, sex-, education-, and depressive symptoms adjusted models). Significant associations disappeared after global cognition at baseline was controlled for. Methodological limitations such as selection biases and self-reported drug assessments might have influenced the results. Only antidepressants considered as PIM were associated with an increased subsequent dementia risk. Anticholinergic effects might explain this relationship. The association disappeared after the statistical control for global cognition at baseline. Nonetheless, physicians should avoid the prescription of PIM antidepressants in elderly patients whenever possible. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Predicting Subsequent Task Performance From Goal Motivation and Goal Failure

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    Laura Catherine Healy

    2015-07-01

    Full Text Available Recent research has demonstrated that the cognitive processes associated with goal pursuit can continue to interfere with unrelated tasks when a goal is unfulfilled. Drawing from the self-regulation and goal-striving literatures, the present study explored the impact of goal failure on subsequent cognitive and physical task performance. Furthermore, we examined if the autonomous or controlled motivation underpinning goal striving moderates the responses to goal failure. Athletes (75 male, 59 female, Mage = 19.90 years, SDage = 3.50 completed a cycling trial with the goal of covering a given distance in 8 minutes. Prior to the trial, their motivation was primed using a video. During the trial they were provided with manipulated performance feedback, thus creating conditions of goal success or failure. No differences emerged in the responses to goal failure between the primed motivation or performance feedback conditions. We make recommendations for future research into how individuals can deal with failure in goal striving.

  8. Impulsive reactions to food-cues predict subsequent food craving.

    Science.gov (United States)

    Meule, Adrian; Lutz, Annika P C; Vögele, Claus; Kübler, Andrea

    2014-01-01

    Low inhibitory control has been associated with overeating and addictive behaviors. Inhibitory control can modulate cue-elicited craving in social or alcohol-dependent drinkers, and trait impulsivity may also play a role in food-cue reactivity. The current study investigated food-cue affected response inhibition and its relationship to food craving using a stop-signal task with pictures of food and neutral stimuli. Participants responded slower to food pictures as compared to neutral pictures. Reaction times in response to food pictures positively predicted scores on the Food Cravings Questionnaire - State (FCQ-S) after the task and particularly scores on its hunger subscale. Lower inhibitory performance in response to food pictures predicted higher FCQ-S scores and particularly those related to a desire for food and lack of control over consumption. Task performance was unrelated to current dieting or other measures of habitual eating behaviors. Results support models on interactive effects of top-down inhibitory control processes and bottom-up hedonic signals in the self-regulation of eating behavior, such that low inhibitory control specifically in response to appetitive stimuli is associated with increased craving, which may ultimately result in overeating. © 2013.

  9. Predictive toxicology in drug safety

    National Research Council Canada - National Science Library

    Xu, Jinghai J; Urban, Laszlo

    2011-01-01

    .... It provides information on the present knowledge of drug side effects and their mitigation strategy during drug discovery, gives guidance for risk assessment, and promotes evidence-based toxicology...

  10. Is the quality of brief motivational interventions for drug use in primary care associated with subsequent drug use?

    Science.gov (United States)

    Palfai, Tibor P; Cheng, Debbie M; Bernstein, Judith A; Palmisano, Joseph; Lloyd-Travaglini, Christine A; Goodness, Tracie; Saitz, Richard

    2016-05-01

    Although a number of brief intervention approaches for drug use are based on motivational interviewing (MI), relatively little is known about whether the quality of motivational interviewing skills is associated with intervention outcomes. The current study examined whether indices of motivational interviewing skill were associated with subsequent drug use outcomes following two different MI-based brief interventions delivered in primary care; a 15 min Brief Negotiated Interview (BNI) and a 45 min adaptation of motivational interviewing (MOTIV). Audio recordings from 351 participants in a randomized controlled trial for drug use in primary care were coded using the Motivational Interviewing Treatment Integrity Scale, (MITI Version 3.1.1). Separate negative binomial regression analyses, stratified by intervention condition, were used to examine the associations between six MITI skill variables and the number of days that the participant used his/her main drug 6 weeks after study entry. Only one of the MITI variables (% reflections to questions) was significantly associated with the frequency of drug use in the MOTIV condition and this was opposite to the hypothesized direction (global p=0.01, adjusted IRR 1.50, 95%CI: 1.03-2.20 for middle vs. lowest tertile [higher skill, more drug use]. None were significantly associated with drug use in the BNI condition. Secondary analyses similarly failed to find consistent predictors of better drug outcomes. Overall, this study provides little evidence to suggest that the level of MI intervention skills are linked with better drug use outcomes among people who use drugs and receive brief interventions in primary care. Findings should be considered in light of the fact that data from the study are from negative trial of SBI and was limited to primary care patients. Future work should consider alternative ways of examining these process variables (i.e., comparing thresholds of proficient versus non-proficient skills) or

  11. QSAR Modeling and Prediction of Drug-Drug Interactions.

    Science.gov (United States)

    Zakharov, Alexey V; Varlamova, Ekaterina V; Lagunin, Alexey A; Dmitriev, Alexander V; Muratov, Eugene N; Fourches, Denis; Kuz'min, Victor E; Poroikov, Vladimir V; Tropsha, Alexander; Nicklaus, Marc C

    2016-02-01

    Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

  12. The Rise, Fall and Subsequent Triumph of Thalidomide: Lessons Learned in Drug Development

    Science.gov (United States)

    Rehman, Waqas; Arfons, Lisa M.; Lazarus, Hillard M.

    2011-01-01

    Perhaps no other drug in modern medicine rivals the dramatic revitalization of thalidomide. Originally marketed as a sedative, thalidomide gained immense popularity worldwide among pregnant women because of its effective anti-emetic properties in morning sickness. Mounting evidence of human teratogenicity marked a dramatic fall from grace and led to widespread social, legal and economic ramifications. Despite its tragic past thalidomide emerged several decades later as a novel and highly effective agent in the treatment of various inflammatory and malignant diseases. In 2006 thalidomide completed its remarkable renaissance becoming the first new agent in over a decade to gain approval for the treatment of plasma cell myeloma. The catastrophic collapse yet subsequent revival of thalidomide provides important lessons in drug development. Never entirely abandoned by the medical community, thalidomide resurfaced as an important drug once the mechanisms of action were further studied and better understood. Ongoing research and development of related drugs such as lenalidomide now represent a class of irreplaceable drugs in hematological malignancies. Further, the tragedies associated with this agent stimulated the legislation which revamped the FDA regulatory process, expanded patient informed consent procedures and mandated more transparency from drug manufacturers. Finally, we review recent clinical trials summarizing selected medical indications for thalidomide with an emphasis on hematologic malignancies. Herein, we provide a historic perspective regarding the up-and-down development of thalidomide. Using PubMed databases we conducted searches using thalidomide and associated keywords highlighting pharmacology, mechanisms of action, and clinical uses. PMID:23556097

  13. Angiogenic Factors in Cord Blood of Preterm Infants Predicts Subsequently Developing Bronchopulmonary Dysplasia

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    Wen-Chien Yang

    2015-12-01

    Conclusion: Cord blood level of PlGF, rather than VEGF or sFlt-1, was significantly increased in the BPD group. Consistent with our previous report, cord blood level of PlGF may be considered as a biomarker to predict subsequently developing BPD in preterm infants.

  14. Data-driven prediction of adverse drug reactions induced by drug-drug interactions.

    Science.gov (United States)

    Liu, Ruifeng; AbdulHameed, Mohamed Diwan M; Kumar, Kamal; Yu, Xueping; Wallqvist, Anders; Reifman, Jaques

    2017-06-08

    The expanded use of multiple drugs has increased the occurrence of adverse drug reactions (ADRs) induced by drug-drug interactions (DDIs). However, such reactions are typically not observed in clinical drug-development studies because most of them focus on single-drug therapies. ADR reporting systems collect information on adverse health effects caused by both single drugs and DDIs. A major challenge is to unambiguously identify the effects caused by DDIs and to attribute them to specific drug interactions. A computational method that provides prospective predictions of potential DDI-induced ADRs will help to identify and mitigate these adverse health effects. We hypothesize that drug-protein interactions can be used as independent variables in predicting ADRs. We constructed drug pair-protein interaction profiles for ~800 drugs using drug-protein interaction information in the public domain. We then constructed statistical models to score drug pairs for their potential to induce ADRs based on drug pair-protein interaction profiles. We used extensive clinical database information to construct categorical prediction models for drug pairs that are likely to induce ADRs via synergistic DDIs and showed that model performance deteriorated only slightly, with a moderate amount of false positives and false negatives in the training samples, as evaluated by our cross-validation analysis. The cross validation calculations showed an average prediction accuracy of 89% across 1,096 ADR models that captured the deleterious effects of synergistic DDIs. Because the models rely on drug-protein interactions, we made predictions for pairwise combinations of 764 drugs that are currently on the market and for which drug-protein interaction information is available. These predictions are publicly accessible at http://avoid-db.bhsai.org . We used the predictive models to analyze broader aspects of DDI-induced ADRs, showing that ~10% of all combinations have the potential to induce ADRs

  15. Magnetic resonance imaging at primary diagnosis cannot predict subsequent contralateral slip in slipped capital femoral epiphysis

    Energy Technology Data Exchange (ETDEWEB)

    Wensaas, Anders [Akershus University Hospital, Department of Orthopaedic Surgery, Loerenskog (Norway); Wiig, Ola; Terjesen, Terje [Oslo University Hospital, Department of Orthopaedic Surgery, Rikshospitalet (Norway); Castberg Hellund, Johan; Khoshnewiszadeh, Behzad [Oslo University Hospital, Department of Radiology and Nuclear Medicine, Ullevaal (Norway)

    2017-12-15

    Prophylactic fixation of the contralateral hip in slipped capital femoral epiphysis (SCFE) is controversial, and no reliable method has been established to predict subsequent contralateral slip. The main purpose of this study was to evaluate if magnetic resonance imaging (MRI) performed at primary diagnosis could predict future contralateral slip. Twenty-two patients with unilateral SCFE were included, all had MRI of both hips taken before operative fixation. Six different parameters were measured on the MRI: the MRI slip angle, the greatest focal widening of the physis, the global widening of the physis measured at three locations (the midpoint of the physis and 1 cm lateral and medial to the midpoint), periphyseal (epiphyseal and metaphyseal) bone marrow edema, the presence of pathological joint effusion, and the amount of joint effusion measured from the lateral edge of the greater trochanter. Mean follow-up was 33 months (range, 16-63 months). Six patients were treated for contralateral slip during the follow-up time and a comparison of the MRI parameters of the contralateral hip in these six patients and in the 16 patients that remained unilateral was done to see if subsequent contralateral slip was possible to predict at primary diagnosis. All MRI parameters were significantly altered in hips with established SCFE compared with the contralateral hips. However, none of the MRI parameters showed any significant difference between patients who had a subsequent contralateral slip and those that remained unilateral. MRI taken at primary diagnosis could not predict future contralateral slip. (orig.)

  16. Capnography for assessing nocturnal hypoventilation and predicting compliance with subsequent noninvasive ventilation in patients with ALS.

    Directory of Open Access Journals (Sweden)

    Sung-Min Kim

    Full Text Available BACKGROUND: Patients with amyotrophic lateral sclerosis (ALS suffer from hypoventilation, which can easily worsen during sleep. This study evaluated the efficacy of capnography monitoring in patients with ALS for assessing nocturnal hypoventilation and predicting good compliance with subsequent noninvasive ventilation (NIV treatment. METHODS: Nocturnal monitoring and brief wake screening by capnography/pulse oximetry, functional scores, and other respiratory signs were assessed in 26 patients with ALS. Twenty-one of these patients were treated with NIV and had their treatment compliance evaluated. RESULTS: Nocturnal capnography values were reliable and strongly correlated with the patients' respiratory symptoms (R(2 = 0.211-0.305, p = 0.004-0.021. The duration of nocturnal hypercapnea obtained by capnography exhibited a significant predictive power for good compliance with subsequent NIV treatment, with an area-under-the-curve value of 0.846 (p = 0.018. In contrast, no significant predictive values for nocturnal pulse oximetry or functional scores for nocturnal hypoventilation were found. Brief waking supine capnography was also useful as a screening tool before routine nocturnal capnography monitoring. CONCLUSION: Capnography is an efficient tool for assessing nocturnal hypoventilation and predicting good compliance with subsequent NIV treatment of ALS patients, and may prove useful as an adjunctive tool for assessing the need for NIV treatment in these patients.

  17. Does resident ranking during recruitment accurately predict subsequent performance as a surgical resident?

    Science.gov (United States)

    Fryer, Jonathan P; Corcoran, Noreen; George, Brian; Wang, Ed; Darosa, Debra

    2012-01-01

    While the primary goal of ranking applicants for surgical residency training positions is to identify the candidates who will subsequently perform best as surgical residents, the effectiveness of the ranking process has not been adequately studied. We evaluated our general surgery resident recruitment process between 2001 and 2011 inclusive, to determine if our recruitment ranking parameters effectively predicted subsequent resident performance. We identified 3 candidate ranking parameters (United States Medical Licensing Examination [USMLE] Step 1 score, unadjusted ranking score [URS], and final adjusted ranking [FAR]), and 4 resident performance parameters (American Board of Surgery In-Training Examination [ABSITE] score, PGY1 resident evaluation grade [REG], overall REG, and independent faculty rating ranking [IFRR]), and assessed whether the former were predictive of the latter. Analyses utilized Spearman correlation coefficient. We found that the URS, which is based on objective and criterion based parameters, was a better predictor of subsequent performance than the FAR, which is a modification of the URS based on subsequent determinations of the resident selection committee. USMLE score was a reliable predictor of ABSITE scores only. However, when we compared our worst residence performances with the performances of the other residents in this evaluation, the data did not produce convincing evidence that poor resident performances could be reliably predicted by any of the recruitment ranking parameters. Finally, stratifying candidates based on their rank range did not effectively define a ranking cut-off beyond which resident performance would drop off. Based on these findings, we recommend surgery programs may be better served by utilizing a more structured resident ranking process and that subsequent adjustments to the rank list generated by this process should be undertaken with caution. Copyright © 2012 Association of Program Directors in Surgery

  18. Prediction of Parkinson's disease subsequent to severe depression: a ten-year follow-up study.

    Science.gov (United States)

    Walter, Uwe; Heilmann, Robert; Kaulitz, Lara; Just, Tino; Krause, Bernd Joachim; Benecke, Reiner; Höppner, Jacqueline

    2015-06-01

    Major depressive disorder (MDD) has been associated with an increased risk of subsequent Parkinson's disease (PD) in case-control and cohort studies. However, depression alone is unlikely to be a useful marker of prodromal PD due to its low specificity. In this longitudinal observational study, we assessed whether the presence of other potential markers of prodromal PD predicts the subsequent development of PD in MDD patients. Of 57 patients with severe MDD but no diagnosis of PD who underwent a structured interview, olfactory and motor investigation and transcranial sonography at baseline, 46 (36 women; mean age 54.9 ± 11.7 years) could be followed for up to 11 (median, 10) years. Three patients (2 women; age 64, 65 and 70 years) developed definite PD after 1, 7, and 9 years, respectively. The combined finding of mild asymmetric motor slowing, idiopathic hyposmia, and substantia nigra hyperechogenicity predicted subsequent PD in all patients who could be followed for longer than 1 year. Out of the whole study cohort, only the subjects with subsequent PD presented with the triad of asymmetric motor slowing, idiopathic hyposmia, and substantia nigra hyperechogenicity in combination with at least two out of four reportable risk factors (family history of PD, current non-smoker, non-coffee drinker, constipation) at baseline investigation. Post-hoc analysis revealed that additional rating of eye and eye-lid motor abnormalities might further improve the prediction of PD in larger cohorts. Findings of this pilot-study suggest that MDD patients at risk of subsequent PD can be identified using an inexpensive non-invasive diagnostic battery.

  19. Neural correlates of encoding processes predicting subsequent cued recall and source memory.

    Science.gov (United States)

    Angel, Lucie; Isingrini, Michel; Bouazzaoui, Badiâa; Fay, Séverine

    2013-03-06

    In this experiment, event-related potentials were used to examine whether the neural correlates of encoding processes predicting subsequent successful recall differed from those predicting successful source memory retrieval. During encoding, participants studied lists of words and were instructed to memorize each word and the list in which it occurred. At test, they had to complete stems (the first four letters) with a studied word and then make a judgment of the initial temporal context (i.e. list). Event-related potentials recorded during encoding were segregated according to subsequent memory performance to examine subsequent memory effects (SMEs) reflecting successful cued recall (cued recall SME) and successful source retrieval (source memory SME). Data showed a cued recall SME on parietal electrode sites from 400 to 1200 ms and a late inversed cued recall SME on frontal sites in the 1200-1400 ms period. Moreover, a source memory SME was reported from 400 to 1400 ms on frontal areas. These findings indicate that patterns of encoding-related activity predicting successful recall and source memory are clearly dissociated.

  20. Computational prediction of drug-drug interactions based on drugs functional similarities.

    Science.gov (United States)

    Ferdousi, Reza; Safdari, Reza; Omidi, Yadollah

    2017-06-01

    Therapeutic activities of drugs are often influenced by co-administration of drugs that may cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and identification of DDIs are extremely vital for the patient safety and success of treatment modalities. A number of computational methods have been employed for the prediction of DDIs based on drugs structures and/or functions. Here, we report on a computational method for DDIs prediction based on functional similarity of drugs. The model was set based on key biological elements including carriers, transporters, enzymes and targets (CTET). The model was applied for 2189 approved drugs. For each drug, all the associated CTETs were collected, and the corresponding binary vectors were constructed to determine the DDIs. Various similarity measures were conducted to detect DDIs. Of the examined similarity methods, the inner product-based similarity measures (IPSMs) were found to provide improved prediction values. Altogether, 2,394,766 potential drug pairs interactions were studied. The model was able to predict over 250,000 unknown potential DDIs. Upon our findings, we propose the current method as a robust, yet simple and fast, universal in silico approach for identification of DDIs. We envision that this proposed method can be used as a practical technique for the detection of possible DDIs based on the functional similarities of drugs. Copyright © 2017. Published by Elsevier Inc.

  1. Neuroticism Predicts Subsequent Risk of Major Depression for Whites but Not Blacks

    Directory of Open Access Journals (Sweden)

    Shervin Assari

    2017-09-01

    Full Text Available Cultural and ethnic differences in psychosocial and medical correlates of negative affect are well documented. This study aimed to compare blacks and whites for the predictive role of baseline neuroticism (N on subsequent risk of major depressive episodes (MDD 25 years later. Data came from the Americans’ Changing Lives (ACL Study, 1986–2011. We used data on 1219 individuals (847 whites and 372 blacks who had data on baseline N in 1986 and future MDD in 2011. The main predictor of interest was baseline N, measured using three items in 1986. The main outcome was 12 months MDD measured using the Composite International Diagnostic Interview (CIDI at 2011. Covariates included baseline demographics (age and gender, socioeconomics (education and income, depressive symptoms [Center for Epidemiologic Studies Depression Scale (CES-D], stress, health behaviors (smoking and driking, and physical health [chronic medical conditions, obesity, and self-rated health (SRH] measured in 1986. Logistic regressions were used to test the predictive role of baseline N on subsequent risk of MDD 25 years later, net of covariates. The models were estimated in the pooled sample, as well as blacks and whites. In the pooled sample, baseline N predicted subsequent risk of MDD 25 years later (OR = 2.23, 95%CI = 1.14–4.34, net of covariates. We also found a marginally significant interaction between race and baseline N on subsequent risk of MDD (OR = 0.37, 95% CI = 0.12–1.12, suggesting a stronger effect for whites compared to blacks. In race-specific models, among whites (OR = 2.55; 95% CI = 1.22–5.32 but not blacks (OR = 0.90; 95% CI = 0.24–3.39, baseline N predicted subsequent risk of MDD. Black-white differences in socioeconomics and physical health could not explain the racial differences in the link between N and MDD. Blacks and whites differ in the salience of baseline N as a psychological determinant of MDD risk over a long period of time. This finding

  2. Mathematical modeling and computational prediction of cancer drug resistance.

    Science.gov (United States)

    Sun, Xiaoqiang; Hu, Bin

    2017-06-23

    Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of

  3. The Past Is Present: Representations of Parents, Friends, and Romantic Partners Predict Subsequent Romantic Representations.

    Science.gov (United States)

    Furman, Wyndol; Collibee, Charlene

    2018-01-01

    This study examined how representations of parent-child relationships, friendships, and past romantic relationships are related to subsequent romantic representations. Two-hundred 10th graders (100 female; M age  = 15.87 years) from diverse neighborhoods in a Western U.S. city were administered questionnaires and were interviewed to assess avoidant and anxious representations of their relationships with parents, friends, and romantic partners. Participants then completed similar questionnaires and interviews about their romantic representations six more times over the next 7.5 years. Growth curve analyses revealed that representations of relationships with parents, friends, and romantic partners each uniquely predicted subsequent romantic representations across development. Consistent with attachment and behavioral systems theory, representations of romantic relationships are revised by representations and experiences in other relationships. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.

  4. Prior nonhip limb fracture predicts subsequent hip fracture in institutionalized elderly people.

    Science.gov (United States)

    Nakamura, K; Takahashi, S; Oyama, M; Oshiki, R; Kobayashi, R; Saito, T; Yoshizawa, Y; Tsuchiya, Y

    2010-08-01

    This 1-year cohort study of nursing home residents revealed that historical fractures of upper limbs or nonhip lower limbs were associated with hip fracture (hazard ratio = 2.14), independent of activities of daily living (ADL), mobility, dementia, weight, and type of nursing home. Prior nonhip fractures are useful for predicting of hip fracture in institutional settings. The aim of this study was to evaluate the utility of fracture history for the prediction of hip fracture in nursing home residents. This was a cohort study with a 1-year follow-up. Subjects were 8,905 residents of nursing homes in Niigata, Japan (mean age, 84.3 years). Fracture histories were obtained from nursing home medical records. ADL levels were assessed by caregivers. Hip fracture diagnosis was based on hospital medical records. Subjects had fracture histories of upper limbs (5.0%), hip (14.0%), and nonhip lower limbs (4.6%). Among historical single fractures, only prior nonhip lower limbs significantly predicted subsequent fracture (adjusted hazard ratio, 2.43; 95% confidence interval (CI), 1.30-4.57). The stepwise method selected the best model, in which a combined historical fracture at upper limbs or nonhip lower limbs (adjusted hazard ratio, 2.14; 95% CI, 1.30-3.52), dependence, ADL levels, mobility, dementia, weight, and type of nursing home independently predicted subsequent hip fracture. A fracture history at upper or nonhip lower limbs, in combination with other known risk factors, is useful for the prediction of future hip fracture in institutional settings.

  5. Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder.

    Science.gov (United States)

    Gottschalk, M G; Cooper, J D; Chan, M K; Bot, M; Penninx, B W J H; Bahn, S

    2015-08-01

    Although social anxiety disorder (SAD) is strongly associated with the subsequent development of a depressive disorder (major depressive disorder or dysthymia), no underlying biological risk factors are known. We aimed to identify biomarkers which predict depressive episodes in SAD patients over a 2-year follow-up period. One hundred sixty-five multiplexed immunoassay analytes were investigated in blood serum of 143 SAD patients without co-morbid depressive disorders, recruited within the Netherlands Study of Depression and Anxiety (NESDA). Predictive performance of identified biomarkers, clinical variables and self-report inventories was assessed using receiver operating characteristics curves (ROC) and represented by the area under the ROC curve (AUC). Stepwise logistic regression resulted in the selection of four serum analytes (AXL receptor tyrosine kinase, vascular cell adhesion molecule 1, vitronectin, collagen IV) and four additional variables (Inventory of Depressive Symptomatology, Beck Anxiety Inventory somatic subscale, depressive disorder lifetime diagnosis, BMI) as optimal set of patient parameters. When combined, an AUC of 0.86 was achieved for the identification of SAD individuals who later developed a depressive disorder. Throughout our analyses, biomarkers yielded superior discriminative performance compared to clinical variables and self-report inventories alone. We report the discovery of a serum marker panel with good predictive performance to identify SAD individuals prone to develop subsequent depressive episodes in a naturalistic cohort design. Furthermore, we emphasise the importance to combine biological markers, clinical variables and self-report inventories for disease course predictions in psychiatry. Following replication in independent cohorts, validated biomarkers could help to identify SAD patients at risk of developing a depressive disorder, thus facilitating early intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

  6. Pretraining Cortical Thickness Predicts Subsequent Perceptual Learning Rate in a Visual Search Task.

    Science.gov (United States)

    Frank, Sebastian M; Reavis, Eric A; Greenlee, Mark W; Tse, Peter U

    2016-03-01

    We report that preexisting individual differences in the cortical thickness of brain areas involved in a perceptual learning task predict the subsequent perceptual learning rate. Participants trained in a motion-discrimination task involving visual search for a "V"-shaped target motion trajectory among inverted "V"-shaped distractor trajectories. Motion-sensitive area MT+ (V5) was functionally identified as critical to the task: after 3 weeks of training, activity increased in MT+ during task performance, as measured by functional magnetic resonance imaging. We computed the cortical thickness of MT+ from anatomical magnetic resonance imaging volumes collected before training started, and found that it significantly predicted subsequent perceptual learning rates in the visual search task. Participants with thicker neocortex in MT+ before training learned faster than those with thinner neocortex in that area. A similar association between cortical thickness and training success was also found in posterior parietal cortex (PPC). © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  7. Malaria overdiagnosis and subsequent overconsumption of antimalarial drugs in Angola: Consequences and effects on human health.

    Science.gov (United States)

    Manguin, Sylvie; Foumane, Vincent; Besnard, Patrick; Fortes, Filomeno; Carnevale, Pierre

    2017-07-01

    Microscopic blood smear examinations done in health centers of Angola demonstrated a large overdiagnosis of malaria cases with an average rate of errors as high as 85%. Overall 83% of patients who received Coartem ® had an inappropriate treatment. Overestimated malaria diagnosis was noticed even when specific symptoms were part of the clinical observation, antimalarial treatments being subsequently given. Then, malaria overdiagnosis has three main consequences, (i) the lack of data reliability is of great concern, impeding epidemiological records and evaluation of the actual influence of operations as scheduled by the National Malaria Control Programme; (ii) the large misuse of antimalarial drug can increase the selective pressure for resistant strain and can make a false consideration of drug resistant P. falciparum crisis; and (iii) the need of strengthening national health centers in term of human, with training in microscopy, and equipment resources to improve malaria diagnosis with a large scale use of rapid diagnostic tests associated with thick blood smears, backed up by a "quality control" developed by the national health authorities. Monitoring of malaria cases was done in three Angolan health centers of Alto Liro (Lobito town) and neighbor villages of Cambambi and Asseque (Benguéla Province) to evaluate the real burden of malaria. Carriers of Plasmodium among patients of newly-borne to 14 years old, with or without fever, were analyzed and compared to presumptive malaria cases diagnosed in these health centers. Presumptive malaria cases were diagnosed six times more than the positive thick blood smears done on the same children. In Alto Liro health center, the percentage of diagnosis error reached 98%, while in Cambambi and Asseque it was of 79% and 78% respectively. The percentage of confirmed malaria cases was significantly higher during the dry (20.2%) than the rainy (13.2%) season. These observations in three peripheral health centers confirmed what

  8. Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

    Science.gov (United States)

    Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

    2013-11-01

    The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

  9. Alpha Oscillations during Incidental Encoding Predict Subsequent Memory for New "Foil" Information.

    Science.gov (United States)

    Vogelsang, David A; Gruber, Matthias; Bergström, Zara M; Ranganath, Charan; Simons, Jon S

    2018-05-01

    People can employ adaptive strategies to increase the likelihood that previously encoded information will be successfully retrieved. One such strategy is to constrain retrieval toward relevant information by reimplementing the neurocognitive processes that were engaged during encoding. Using EEG, we examined the temporal dynamics with which constraining retrieval toward semantic versus nonsemantic information affects the processing of new "foil" information encountered during a memory test. Time-frequency analysis of EEG data acquired during an initial study phase revealed that semantic compared with nonsemantic processing was associated with alpha decreases in a left frontal electrode cluster from around 600 msec after stimulus onset. Successful encoding of semantic versus nonsemantic foils during a subsequent memory test was related to decreases in alpha oscillatory activity in the same left frontal electrode cluster, which emerged relatively late in the trial at around 1000-1600 msec after stimulus onset. Across participants, left frontal alpha power elicited by semantic processing during the study phase correlated significantly with left frontal alpha power associated with semantic foil encoding during the memory test. Furthermore, larger left frontal alpha power decreases elicited by semantic foil encoding during the memory test predicted better subsequent semantic foil recognition in an additional surprise foil memory test, although this effect did not reach significance. These findings indicate that constraining retrieval toward semantic information involves reimplementing semantic encoding operations that are mediated by alpha oscillations and that such reimplementation occurs at a late stage of memory retrieval, perhaps reflecting additional monitoring processes.

  10. Interaction between hippocampal and striatal systems predicts subsequent consolidation of motor sequence memory.

    Directory of Open Access Journals (Sweden)

    Geneviève Albouy

    Full Text Available The development of fast and reproducible motor behavior is a crucial human capacity. The aim of the present study was to address the relationship between the implementation of consistent behavior during initial training on a sequential motor task (the Finger Tapping Task and subsequent sleep-dependent motor sequence memory consolidation, using functional magnetic resonance imaging (fMRI and total sleep deprivation protocol. Our behavioral results indicated significant offline gains in performance speed after sleep whereas performance was only stabilized, but not enhanced, after sleep deprivation. At the cerebral level, we previously showed that responses in the caudate nucleus increase, in parallel to a decrease in its functional connectivity with frontal areas, as performance became more consistent. Here, the strength of the competitive interaction, assessed through functional connectivity analyses, between the caudate nucleus and hippocampo-frontal areas during initial training, predicted delayed gains in performance at retest in sleepers but not in sleep-deprived subjects. Moreover, during retest, responses increased in the hippocampus and medial prefrontal cortex in sleepers whereas in sleep-deprived subjects, responses increased in the putamen and cingulate cortex. Our results suggest that the strength of the competitive interplay between the striatum and the hippocampus, participating in the implementation of consistent motor behavior during initial training, conditions subsequent motor sequence memory consolidation. The latter process appears to be supported by a reorganisation of cerebral activity in hippocampo-neocortical networks after sleep.

  11. Early gross motor skills predict the subsequent development of language in children with autism spectrum disorder.

    Science.gov (United States)

    Bedford, Rachael; Pickles, Andrew; Lord, Catherine

    2016-09-01

    Motor milestones such as the onset of walking are important developmental markers, not only for later motor skills but also for more widespread social-cognitive development. The aim of the current study was to test whether gross motor abilities, specifically the onset of walking, predicted the subsequent rate of language development in a large cohort of children with autism spectrum disorder (ASD). We ran growth curve models for expressive and receptive language measured at 2, 3, 5 and 9 years in 209 autistic children. Measures of gross motor, visual reception and autism symptoms were collected at the 2 year visit. In Model 1, walking onset was included as a predictor of the slope of language development. Model 2 included a measure of non-verbal IQ and autism symptom severity as covariates. The final model, Model 3, additionally covaried for gross motor ability. In the first model, parent-reported age of walking onset significantly predicted the subsequent rate of language development although the relationship became non-significant when gross motor skill, non-verbal ability and autism severity scores were included (Models 2 & 3). Gross motor score, however, did remain a significant predictor of both expressive and receptive language development. Taken together, the model results provide some evidence that early motor abilities in young children with ASD can have longitudinal cross-domain influences, potentially contributing, in part, to the linguistic difficulties that characterise ASD. Autism Res 2016, 9: 993-1001. © 2015 The Authors Autism Research published by Wiley Periodicals, Inc. on behalf of International Society for Autism Research. © 2015 The Authors Autism Research published by Wiley Periodicals, Inc. on behalf of International Society for Autism Research.

  12. Predicting drug?drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge

    OpenAIRE

    Takeda, Takako; Hao, Ming; Cheng, Tiejun; Bryant, Stephen H.; Wang, Yanli

    2017-01-01

    Drug?drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigorous evaluation of drug candidates. The primary mechanisms of DDIs are based on pharmacokinetics (PK) and pharmacodynamics (PD). This study examines the effects of 2D structural similarities of drugs on DDI prediction through interaction networks including both PD and PK knowledge. Our a...

  13. Analysis of the value of post-radiation prostate biopsy in predicting subsequent disease progression

    International Nuclear Information System (INIS)

    Benda, R.; Shamsa, F.; Meetze, K.; Bolton, S.; Littrup, P.; Grignon, D.; Washington, T.; Forman, J.D.

    1997-01-01

    Purpose: To analyze the value of Transrectal ultrasound(TRUS), Color flow doppler(CFD) and Prostate specific antigen(PSA) in identifying residual disease in the prostate status post external beam radiation therapy and to determine the value of this pathologic information in predicting subsequent disease progression. Materials and Methods: As part of four prospective protocols, 146 patients had scheduled TRUS guided prostate biopsies 6-25 months status post radiation therapy. The stage distribution was: 13% T1, 51% T2, and 36% T3/T4. Fifty six percent had neo-adjuvant hormones. Conformal photon or mixed neutron/photon irradiation was given to a median 2 Gy/fraction equivalent dose of 77 Gy(range 74 to 84 Gy). Following treatment, patients were assessed by digital rectal exam (DRE), PSA and TRUS guided biopsies at 6, 12 and/or 18 months. The ultrasound and CFD results were scored as normal, suspicious or abnormal. Sextant biopsies were obtained as well as ultrasound guided biopsies from any abnormal ultrasound or doppler area. The biopsies, all read by one pathologist (DG), were graded as negative, marked, moderate, minimal therapeutic effect or positive. The median followup post radiation therapy was 33.6 months and post biopsy was 25.3 months. Comparisons were done by Kappa index with corresponding 95% CI, chi square and Fisher's exact tests. Results: Twenty-eight patients had biopsies at both six and 12-18 months. Overall 35% of patients had all negative cores, 30% had at least one core showing a marked therapeutic effect, and 35% had at least one core showing moderate or minimal therapeutic effect or were positive. Although CFD correlated with a positive biopsy in 9% and a suspicious doppler identified cancer in 15% of cases, an abnormal TRUS identified cancer in 29.5% biopsies ((49(166))). However, a serum PSA >1.5ng/ml at the time of biopsy predicted 61% of positive biopsies ((23(38))). A negative biopsy was associated with low stage (≤T2c, p=0.001), low pre

  14. A classification framework for drug relapse prediction | Salleh ...

    African Journals Online (AJOL)

    mining algorithms, Artificial Intelligence Neural Network (ANN) is one of the best algorithms to predict relapse among drug addicts. This may help the rehabilitation center to predict relapse individually and the prediction result is hoped to prevent drug addicts from relapse. Keywords: classification; artificial neural network; ...

  15. Modulating effect of the nootropic drug, piracetam on stress- and subsequent morphine-induced prolactin secretion in male rats.

    OpenAIRE

    Matton, A.; Engelborghs, S.; Bollengier, F.; Finné, E.; Vanhaeist, L.

    1996-01-01

    1. The effect of the nootropic drug, piracetam on stress- and subsequent morphine-induced prolactin (PRL) secretion was investigated in vivo in male rats, by use of a stress-free blood sampling and drug administration method by means of a permanent indwelling catheter in the right jugular vein. 2. Four doses of piracetam were tested (20, 100, 200 and 400 mg kg-1), being given intraperitoneally 1 h before blood sampling; control rats received saline instead. After a first blood sample, rats we...

  16. Prediction of potential drug targets based on simple sequence properties

    Directory of Open Access Journals (Sweden)

    Lai Luhua

    2007-09-01

    Full Text Available Abstract Background During the past decades, research and development in drug discovery have attracted much attention and efforts. However, only 324 drug targets are known for clinical drugs up to now. Identifying potential drug targets is the first step in the process of modern drug discovery for developing novel therapeutic agents. Therefore, the identification and validation of new and effective drug targets are of great value for drug discovery in both academia and pharmaceutical industry. If a protein can be predicted in advance for its potential application as a drug target, the drug discovery process targeting this protein will be greatly speeded up. In the current study, based on the properties of known drug targets, we have developed a sequence-based drug target prediction method for fast identification of novel drug targets. Results Based on simple physicochemical properties extracted from protein sequences of known drug targets, several support vector machine models have been constructed in this study. The best model can distinguish currently known drug targets from non drug targets at an accuracy of 84%. Using this model, potential protein drug targets of human origin from Swiss-Prot were predicted, some of which have already attracted much attention as potential drug targets in pharmaceutical research. Conclusion We have developed a drug target prediction method based solely on protein sequence information without the knowledge of family/domain annotation, or the protein 3D structure. This method can be applied in novel drug target identification and validation, as well as genome scale drug target predictions.

  17. Externalizing Problems in Childhood and Adolescence Predict Subsequent Educational Achievement but for Different Genetic and Environmental Reasons

    Science.gov (United States)

    Lewis, Gary J.; Asbury, Kathryn; Plomin, Robert

    2017-01-01

    Background: Childhood behavior problems predict subsequent educational achievement; however, little research has examined the etiology of these links using a longitudinal twin design. Moreover, it is unknown whether genetic and environmental innovations provide incremental prediction for educational achievement from childhood to adolescence.…

  18. Modelling irradiation by EM waves of multifunctionalized iron oxide nanoparticles and subsequent drug release

    International Nuclear Information System (INIS)

    Wang, Feng; Calvayrac, Florent; Montembault, Véronique; Fontaine, Laurent

    2015-01-01

    Thermal transport in the environment close to the periphery of the nanoparticle, from a few angstroms to less than a nanometer scale, is becoming increasingly important with the advent of several biomedical applications of multifunctional magnetic nanoparticles, including drug delivery, magnetic resonance imaging, and hyperthermia therapy. We present a multiscale and multiphysics model of the irradiation by electromagnetic waves of radiofrequency of iron oxide nanoparticles functionalized by drug-releasing polymers used as new multifunctional therapeutic compounds against tumors. We compute ab initio the thermal conductivity of the polymer chains as a function of the length, model the unfolding of the polymer after heat transfer from the nanoparticle by molecular mechanics, and develop a multiscale thermodynamic and heat transfer model including the surrounding medium (water) in order to model the drug release. (paper)

  19. Drug-Target Interactions: Prediction Methods and Applications.

    Science.gov (United States)

    Anusuya, Shanmugam; Kesherwani, Manish; Priya, K Vishnu; Vimala, Antonydhason; Shanmugam, Gnanendra; Velmurugan, Devadasan; Gromiha, M Michael

    2018-01-01

    Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  20. Predicting drug-target interactions using restricted Boltzmann machines.

    Science.gov (United States)

    Wang, Yuhao; Zeng, Jianyang

    2013-07-01

    In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Software and datasets are available on request. Supplementary data are

  1. Prenatal drug exposures sensitize noradrenergic circuits to subsequent disruption by chlorpyrifos.

    Science.gov (United States)

    Slotkin, Theodore A; Skavicus, Samantha; Seidler, Frederic J

    2015-12-02

    We examined whether nicotine or dexamethasone, common prenatal drug exposures, sensitize the developing brain to chlorpyrifos. We gave nicotine to pregnant rats throughout gestation at a dose (3mg/kg/day) producing plasma levels typical of smokers; offspring were then given chlorpyrifos on postnatal days 1-4, at a dose (1mg/kg) that produces minimally-detectable inhibition of brain cholinesterase activity. In a parallel study, we administered dexamethasone to pregnant rats on gestational days 17-19 at a standard therapeutic dose (0.2mg/kg) used in the management of preterm labor, followed by postnatal chlorpyrifos. We evaluated cerebellar noradrenergic projections, a known target for each agent, and contrasted the effects with those in the cerebral cortex. Either drug augmented the effect of chlorpyrifos, evidenced by deficits in cerebellar β-adrenergic receptors; the receptor effects were not due to increased systemic toxicity or cholinesterase inhibition, nor to altered chlorpyrifos pharmacokinetics. Further, the deficits were not secondary adaptations to presynaptic hyperinnervation/hyperactivity, as there were significant deficits in presynaptic norepinephrine levels that would serve to augment the functional consequence of receptor deficits. The pretreatments also altered development of cerebrocortical noradrenergic circuits, but with a different overall pattern, reflecting the dissimilar developmental stages of the regions at the time of exposure. However, in each case the net effects represented a change in the developmental trajectory of noradrenergic circuits, rather than simply a continuation of an initial injury. Our results point to the ability of prenatal drug exposure to create a subpopulation with heightened vulnerability to environmental neurotoxicants. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  2. Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures.

    Science.gov (United States)

    Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y

    2013-01-01

    The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. A data-driven predictive approach for drug delivery using machine learning techniques.

    Directory of Open Access Journals (Sweden)

    Yuanyuan Li

    Full Text Available In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists of a dynamic model incorporating both the drug concentration and pathogen population into distinct states. These states are then analyzed using a temporal model to describe the drug-cell interactions over time. The dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy. Incorporated into the system is the ability to adjust the sensitivity and specificity of the learned models based on a threshold level determined by the operator for the specific application. As a proof-of-concept, the system was validated experimentally using the pathogen Giardia lamblia and the drug metronidazole in vitro.

  4. Brain Activation during Associative Short-Term Memory Maintenance is Not Predictive for Subsequent Retrieval

    Directory of Open Access Journals (Sweden)

    Heiko eBergmann

    2015-09-01

    Full Text Available Performance on working memory (WM tasks may partially be supported by long-term memory (LTM processing. Hence, brain activation recently being implicated in WM may actually have been driven by (incidental LTM formation. We examined which brain regions actually support successful WM processing, rather than being confounded by LTM processes, during the maintenance and probe phase of a WM task. We administered a four-pair (faces and houses associative delayed-match-to-sample (WM task using event-related fMRI and a subsequent associative recognition LTM task, using the same stimuli. This enabled us to analyze subsequent memory effects for both the WM and the LTM test by contrasting correctly recognized pairs with incorrect pairs for either task. Critically, with respect to the subsequent WM effect, we computed this analysis exclusively for trials that were forgotten in the subsequent LTM recognition task. Hence, brain activity associated with successful WM processing was less likely to be confounded by incidental LTM formation. The subsequent LTM effect, in contrast, was analyzed exclusively for pairs that previously had been correctly recognized in the WM task, disclosing brain regions involved in successful LTM formation after successful WM processing. Results for the subsequent WM effect showed no significantly activated brain areas for WM maintenance, possibly due to an insensitivity of fMRI to mechanisms underlying active WM maintenance. In contrast, a correct decision at WM probe was linked to activation in the retrieval success network (anterior and posterior midline brain structures. The subsequent LTM analyses revealed greater activation in left dorsolateral prefrontal cortex and posterior parietal cortex in the early phase of the maintenance stage. No supra-threshold activation was found during the WM probe. Together, we obtained clearer insights in which brain regions support successful WM and LTM without the potential confound of the

  5. Brain activation during associative short-term memory maintenance is not predictive for subsequent retrieval.

    Science.gov (United States)

    Bergmann, Heiko C; Daselaar, Sander M; Beul, Sarah F; Rijpkema, Mark; Fernández, Guillén; Kessels, Roy P C

    2015-01-01

    Performance on working memory (WM) tasks may partially be supported by long-term memory (LTM) processing. Hence, brain activation recently being implicated in WM may actually have been driven by (incidental) LTM formation. We examined which brain regions actually support successful WM processing, rather than being confounded by LTM processes, during the maintenance and probe phase of a WM task. We administered a four-pair (faces and houses) associative delayed-match-to-sample (WM) task using event-related functional MRI (fMRI) and a subsequent associative recognition LTM task, using the same stimuli. This enabled us to analyze subsequent memory effects for both the WM and the LTM test by contrasting correctly recognized pairs with incorrect pairs for either task. Critically, with respect to the subsequent WM effect, we computed this analysis exclusively for trials that were forgotten in the subsequent LTM recognition task. Hence, brain activity associated with successful WM processing was less likely to be confounded by incidental LTM formation. The subsequent LTM effect, in contrast, was analyzed exclusively for pairs that previously had been correctly recognized in the WM task, disclosing brain regions involved in successful LTM formation after successful WM processing. Results for the subsequent WM effect showed no significantly activated brain areas for WM maintenance, possibly due to an insensitivity of fMRI to mechanisms underlying active WM maintenance. In contrast, a correct decision at WM probe was linked to activation in the "retrieval success network" (anterior and posterior midline brain structures). The subsequent LTM analyses revealed greater activation in left dorsolateral prefrontal cortex and posterior parietal cortex in the early phase of the maintenance stage. No supra-threshold activation was found during the WM probe. Together, we obtained clearer insights in which brain regions support successful WM and LTM without the potential confound of

  6. Deep-Learning-Based Drug-Target Interaction Prediction.

    Science.gov (United States)

    Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

    2017-04-07

    Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

  7. Early perception of medication benefit predicts subsequent antipsychotic response in schizophrenia: "the consumer has a point" revisited.

    Science.gov (United States)

    Ascher-Svanum, Haya; Weiden, Peter; Nyhuis, Allen W; Faries, Douglas E; Stauffer, Virginia; Kollack-Walker, Sara; Kinon, Bruce J

    2014-07-01

    An easy-to-administer tool for predicting response to antipsychotic treatment could improve the acute management of patients with schizophrenia. We assessed whether a patient's perception of medication benefit early in treatment could predict subsequent response or nonresponse to continued use of the same treatment. This post hoc analysis used data from a randomized, open-label trial of antipsychotics for treatment of schizophrenia in which attitudes about medication adherence were assessed after two weeks of antipsychotic treatment using the Rating of Medication Influences (ROMI) scale. The analysis included 439 patients who had Positive and Negative Syndrome Scale (PANSS) and ROMI scale data at Weeks 2 and 8. Scores on the ROMI subscale Perceived Medication Benefit factor were used to predict subsequent antipsychotic response at Week 8, defined as a .20% reduction from baseline on the PANSS. Logistic regression was used to identify a cut-off score for the Perceived Medication Benefit factor that could accurately identify antipsychotic responders vs. nonresponders at Week 8. A score of .2.75 (equal to a mean subscale score of .11.00) on the ROMI scale Perceived Medication Benefit factor at Week 2 predicted response at Week 8 with high specificity (72%) and negative predictive value (70%), moderate sensitivity (44%) and positive predictive value (47%), and with a 38% misclassification rate. A brief assessment of the patient's perception of medication benefit at two weeks into treatment appears to be a good predictor of subsequent response and nonresponse after eight weeks of treatment with the same antipsychotic.

  8. Within-person Changes in Individual Symptoms of Depression Predict Subsequent Depressive Episodes in Adolescents: A Prospective Study

    Science.gov (United States)

    Kouros, Chrystyna D.; Morris, Matthew C.; Garber, Judy

    2015-01-01

    The current longitudinal study examined which individual symptoms of depression uniquely predicted a subsequent Major Depressive Episode (MDE) in adolescents, and whether these relations differed by sex. Adolescents (N=240) were first interviewed in grade 6 (M=11.86 years old; SD = 0.56; 54% female; 81.5% Caucasian) and then annually through grade 12 regarding their individual symptoms of depression as well as the occurrence of MDEs. Individual symptoms of depression were assessed with the Children’s Depression Rating Scale-Revised (CDRS-R) and depressive episodes were assessed with the Longitudinal Interval Follow-up Evaluation (LIFE). Results showed that within-person changes in sleep problems and low self-esteem/excessive guilt positively predicted an increased likelihood of an MDE for both boys and girls. Significant sex differences also were found. Within-person changes in anhedonia predicted an increased likelihood of a subsequent MDE among boys, whereas irritability predicted a decreased likelihood of a future MDE among boys, and concentration difficulties predicted a decreased likelihood of an MDE in girls. These results identified individual depressive symptoms that predicted subsequent depressive episodes in male and female adolescents, and may be used to guide the early detection, treatment, and prevention of depressive disorders in youth. PMID:26105209

  9. Attendance Rates in a Workplace Predict Subsequent Outcome of Employment-Based Reinforcement of Cocaine Abstinence in Methadone Patients

    Science.gov (United States)

    Donlin, Wendy D.; Knealing, Todd W.; Needham, Mick; Wong, Conrad J.; Silverman, Kenneth

    2008-01-01

    This study assessed whether attendance rates in a workplace predicted subsequent outcome of employment-based reinforcement of cocaine abstinence. Unemployed adults in Baltimore methadone programs who used cocaine (N = 111) could work in a workplace for 4 hr every weekday and earn $10.00 per hour in vouchers for 26 weeks. During an induction…

  10. Goal-directed mechanisms that constrain retrieval predict subsequent memory for new "foil" information.

    Science.gov (United States)

    Vogelsang, David A; Bonnici, Heidi M; Bergström, Zara M; Ranganath, Charan; Simons, Jon S

    2016-08-01

    To remember a previous event, it is often helpful to use goal-directed control processes to constrain what comes to mind during retrieval. Behavioral studies have demonstrated that incidental learning of new "foil" words in a recognition test is superior if the participant is trying to remember studied items that were semantically encoded compared to items that were non-semantically encoded. Here, we applied subsequent memory analysis to fMRI data to understand the neural mechanisms underlying the "foil effect". Participants encoded information during deep semantic and shallow non-semantic tasks and were tested in a subsequent blocked memory task to examine how orienting retrieval towards different types of information influences the incidental encoding of new words presented as foils during the memory test phase. To assess memory for foils, participants performed a further surprise old/new recognition test involving foil words that were encountered during the previous memory test blocks as well as completely new words. Subsequent memory effects, distinguishing successful versus unsuccessful incidental encoding of foils, were observed in regions that included the left inferior frontal gyrus and posterior parietal cortex. The left inferior frontal gyrus exhibited disproportionately larger subsequent memory effects for semantic than non-semantic foils, and significant overlap in activity during semantic, but not non-semantic, initial encoding and foil encoding. The results suggest that orienting retrieval towards different types of foils involves re-implementing the neurocognitive processes that were involved during initial encoding. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  11. Feedback on Facebook Fails to Predict the User’s Subsequent Posting

    OpenAIRE

    Sherwin E. Balbuena; Princess Z. Balbuena

    2017-01-01

    Facebook use is a new and complex social behavior that has stimulated research interests in psychology. Due to a distinct lack of theoretical basis for this new communication phenomenon, a number of studies established the significant association between personality traits and Facebook use. This study investigated the motivational effect of friends’ feedback on the user’s subsequent Facebook posting and examined the correspondence between the user’s perceived motivation and actual...

  12. Feedback on Facebook Fails to Predict the User’s Subsequent Posting

    Directory of Open Access Journals (Sweden)

    Sherwin E. Balbuena

    2017-11-01

    Full Text Available Facebook use is a new and complex social behavior that has stimulated research interests in psychology. Due to a distinct lack of theoretical basis for this new communication phenomenon, a number of studies established the significant association between personality traits and Facebook use. This study investigated the motivational effect of friends’ feedback on the user’s subsequent Facebook posting and examined the correspondence between the user’s perceived motivation and actual motivation-behavior outcome using a new method. Results showed no significant association between the number of feedback and the number of subsequent posts, users’ perceived motivations were consistent with their actual motivation-behavior outcomes, users’ self-reports validated the new results and confirmed the previous findings that Facebook use is aimed at satisfying the individual’s needs for belongingness, self-presentation, and social information-seeking. It is suggested that the amount of feedback on Facebook is an ineffective determinant of the users’ frequency of subsequent postings.

  13. Does subsequent criminal justice involvement predict foster care and termination of parental rights for children born to incarcerated women?

    Science.gov (United States)

    Kubiak, Sheryl Pimlott; Kasiborski, Natalie; Karim, Nidal; Schmittel, Emily

    2012-01-01

    This longitudinal study of 83 incarcerated women, who gave birth during incarceration and retained their parental rights through brief sentences, examines the intersection between subsequent criminal justice involvement postrelease and child welfare outcomes. Ten years of multiple state-level administrative data sets are used to determine if arrest or conviction predict foster care and/or termination of parental rights. Findings indicate that only felony arrest is a significant predictor of foster care involvement. Additionally, 69% of mothers retained legal custody, despite subsequent criminal involvement for many, suggesting supportive parenting programs and resources need to be available to these women throughout and after incarceration.

  14. Drug-target interaction prediction: A Bayesian ranking approach.

    Science.gov (United States)

    Peska, Ladislav; Buza, Krisztian; Koller, Júlia

    2017-12-01

    In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning - finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. We propose Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Evaluation on five benchmark datasets shows that BRDTI outperforms several state-of-the-art approaches in terms of per-drug nDCG and AUC. BRDTI results w.r.t. nDCG are 0.929, 0.953, 0.948, 0.897 and 0.690 for G-Protein Coupled Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR), Enzymes (E) and Kinase (K) datasets respectively. Additionally, BRDTI significantly outperformed other methods (BLM-NII, WNN-GIP, NetLapRLS and CMF) w.r.t. nDCG in 17 out of 20 cases. Furthermore, BRDTI was also shown to be able to predict novel drug-target interactions not contained in the original datasets. The average recall at top-10 predicted targets for each drug was 0.762, 0.560, 1.000 and 0.404 for GPCR, IC, NR, and E datasets respectively. Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drug

  15. Modulation of fusiform cortex activity by cholinesterase inhibition predicts effects on subsequent memory.

    Science.gov (United States)

    Bentley, P; Driver, J; Dolan, R J

    2009-09-01

    Cholinergic influences on memory are likely to be expressed at several processing stages, including via well-recognized effects of acetylcholine on stimulus processing during encoding. Since previous studies have shown that cholinesterase inhibition enhances visual extrastriate cortex activity during stimulus encoding, especially under attention-demanding tasks, we tested whether this effect correlates with improved subsequent memory. In a within-subject physostigmine versus placebo design, we measured brain activity with functional magnetic resonance imaging while healthy and mild Alzheimer's disease subjects performed superficial and deep encoding tasks on face (and building) visual stimuli. We explored regions in which physostigmine modulation of face-selective neural responses correlated with physostigmine effects on subsequent recognition performance. In healthy subjects physostigmine led to enhanced later recognition for deep- versus superficially-encoded faces, which correlated across subjects with a physostigmine-induced enhancement of face-selective responses in right fusiform cortex during deep- versus superficial-encoding tasks. In contrast, the Alzheimer's disease group showed neither a depth of processing effect nor restoration of this with physostigmine. Instead, patients showed a task-independent improvement in confident memory with physostigmine, an effect that correlated with enhancements in face-selective (but task-independent) responses in bilateral fusiform cortices. Our results indicate that one mechanism by which cholinesterase inhibitors can improve memory is by enhancing extrastriate cortex stimulus selectivity at encoding, in a manner that for healthy people but not in Alzheimer's disease is dependent upon depth of processing.

  16. Breeding phenology and winter activity predict subsequent breeding success in a trans-global migratory seabird.

    Science.gov (United States)

    Shoji, A; Aris-Brosou, S; Culina, A; Fayet, A; Kirk, H; Padget, O; Juarez-Martinez, I; Boyle, D; Nakata, T; Perrins, C M; Guilford, T

    2015-10-01

    Inter-seasonal events are believed to connect and affect reproductive performance (RP) in animals. However, much remains unknown about such carry-over effects (COEs), in particular how behaviour patterns during highly mobile life-history stages, such as migration, affect RP. To address this question, we measured at-sea behaviour in a long-lived migratory seabird, the Manx shearwater (Puffinus puffinus) and obtained data for individual migration cycles over 5 years, by tracking with geolocator/immersion loggers, along with 6 years of RP data. We found that individual breeding and non-breeding phenology correlated with subsequent RP, with birds hyperactive during winter more likely to fail to reproduce. Furthermore, parental investment during one year influenced breeding success during the next, a COE reflecting the trade-off between current and future RP. Our results suggest that different life-history stages interact to influence RP in the next breeding season, so that behaviour patterns during winter may be important determinants of variation in subsequent fitness among individuals. © 2015 The Authors.

  17. Monitoring western spruce budworm with pheromone- baited sticky traps to predict subsequent defoliation

    Science.gov (United States)

    Christine G. Niwa; David L. Overhulser

    2015-01-01

    A detailed procedure is described for monitoring western spruce budworm with pheromone-baited sticky traps and interpreting the results to predict defoliation the following year. Information provided includes timing of the survey, how to obtain traps and baits, how many traps are needed, trap assembly, field placement of traps, and how to evaluate the catches.

  18. Attendance Rates in A Workplace Predict Subsequent Outcome of Employment-Based Reinforcement of Cocaine Abstinence in Methadone Patients

    OpenAIRE

    Donlin, Wendy D; Knealing, Todd W; Needham, Mick; Wong, Conrad J; Silverman, Kenneth

    2008-01-01

    This study assessed whether attendance rates in a workplace predicted subsequent outcome of employment-based reinforcement of cocaine abstinence. Unemployed adults in Baltimore methadone programs who used cocaine (N  =  111) could work in a workplace for 4 hr every weekday and earn $10.00 per hour in vouchers for 26 weeks. During an induction period, participants provided urine samples but could work independent of their urinalysis results. After the induction period, participants had to prov...

  19. Cardiovascular reactivity to video game predicts subsequent blood pressure increases in young men: The CARDIA study.

    Science.gov (United States)

    Markovitz, J H; Raczynski, J M; Wallace, D; Chettur, V; Chesney, M A

    1998-01-01

    This study was undertaken to determine the relationship between heightened reactivity of blood pressure (BP) during stress and 5-year changes in blood pressure and hypertensive status, using the CARDIA study. A total of 3364 participants (910 white men, 909 white women, 678 black men, and 867 black women), initially 20 to 32 years old and normotensive, were included. Cardiovascular reactivity to psychological stressors (video game and star-tracing tasks for 3 minutes, cold pressor test for 1 minute) was measured in 1987-1988. We then examined reactivity as a predictor of significant BP change (> or = 8 mm Hg, thought to represent a clinically significant increase) over the next 5 years. Logistic regression models were used to control for potential covariates. Significant BP change and the development of hypertension (BP greater than 140/90 or taking medication for hypertension) over the 5-year follow-up were examined in separate analyses. Increased systolic blood pressure (SBP) reactivity to the video game was associated with a significant 5-year SBP increase among the entire cohort, independent of resting SBP (p men but not for women. Reactivity to the star-tracing task or the cold pressor test did not predict significant BP change. Among black men only, new hypertensives (N = 36) had greater diastolic blood pressure (DBP) reactivity to the video game (p = .01). Although BP reactivity to all physical and mental stressors used in this study did not consistently predict 5-year change in BP in this young cohort, the results indicate that reactivity to a video game stressor predicts 5-year change in BP and early hypertension among young adult men. These findings are consistent with other studies showing the usefulness of stressors producing a primarily beta-adrenergic response in predicting BP change and hypertension. The results may be limited by the shortened initial rest and recovery periods used in the CARDIA protocol.

  20. Quantitative prediction of drug side effects based on drug-related features.

    Science.gov (United States)

    Niu, Yanqing; Zhang, Wen

    2017-09-01

    Unexpected side effects of drugs are great concern in the drug development, and the identification of side effects is an important task. Recently, machine learning methods are proposed to predict the presence or absence of interested side effects for drugs, but it is difficult to make the accurate prediction for all of them. In this paper, we transform side effect profiles of drugs as their quantitative scores, by summing up their side effects with weights. The quantitative scores may measure the dangers of drugs, and thus help to compare the risk of different drugs. Here, we attempt to predict quantitative scores of drugs, namely the quantitative prediction. Specifically, we explore a variety of drug-related features and evaluate their discriminative powers for the quantitative prediction. Then, we consider several feature combination strategies (direct combination, average scoring ensemble combination) to integrate three informative features: chemical substructures, targets, and treatment indications. Finally, the average scoring ensemble model which produces the better performances is used as the final quantitative prediction model. Since weights for side effects are empirical values, we randomly generate different weights in the simulation experiments. The experimental results show that the quantitative method is robust to different weights, and produces satisfying results. Although other state-of-the-art methods cannot make the quantitative prediction directly, the prediction results can be transformed as the quantitative scores. By indirect comparison, the proposed method produces much better results than benchmark methods in the quantitative prediction. In conclusion, the proposed method is promising for the quantitative prediction of side effects, which may work cooperatively with existing state-of-the-art methods to reveal dangers of drugs.

  1. Encoded exposure to tobacco use in social media predicts subsequent smoking behavior.

    Science.gov (United States)

    Depue, Jacob B; Southwell, Brian G; Betzner, Anne E; Walsh, Barbara M

    2015-01-01

    Assessing the potential link between smoking behavior and exposure to mass media depictions of smoking on social networking Web sites. A representative longitudinal panel of 200 young adults in Connecticut. Telephone surveys were conducted by using computer assisted telephone interviewing technology and electronic dialing for random digit dialing and listed samples. Connecticut residents aged 18 to 24 years. To measure encoded exposure, respondents were asked whether or not they had smoked a cigarette in the past 30 days and about how often they had seen tobacco use on television, in movies, and in social media content. Respondents were also asked about cigarette use in the past 30 days, and a series of additional questions that have been shown to be predictive of tobacco use. Logistic regression was used to test for our main prediction that reported exposure to social media tobacco depictions at time 1 would influence time 2 smoking behavior. Encoded exposure to social media tobacco depictions (B = .47, p media depictions of tobacco use predict future smoking tendency, over and above the influence of TV and movie depictions of smoking. This is the first known study to specifically assess the role of social media in informing tobacco behavior.

  2. Response to intravenous fentanyl infusion predicts subsequent response to transdermal fentanyl.

    Science.gov (United States)

    Hayashi, Norihito; Kanai, Akifumi; Suzuki, Asaha; Nagahara, Yuki; Okamoto, Hirotsugu

    2016-04-01

    Prediction of the response to transdermal fentanyl (FENtd) before its use for chronic pain is desirable. We tested the hypothesis that the response to intravenous fentanyl infusion (FENiv) can predict the response to FENtd, including the analgesic and adverse effects. The study subjects were 70 consecutive patients with chronic pain. The response to fentanyl at 0.1 mg diluted in 50 ml of physiological saline and infused over 30 min was tested. This was followed by treatment with FENtd (Durotep MT patch 2.1 mg) at a dose of 12.5 µg/h for 2 weeks. Pain intensity before and after FENiv and 2 weeks after FENtd, and the response to treatment, were assessed by the numerical rating scale (NRS), clinical global impression-improvement scale (CGI-I), satisfaction scale (SS), and adverse effects. The NRS score decreased significantly from 7 (4-9) [median (range)] at baseline to 3 (0-8) after FENiv (p 0.04, each). The analgesic and side effects after intravenous fentanyl infusion can be used to predict the response to short-term transdermal treatment with fentanyl.

  3. Gaussian interaction profile kernels for predicting drug-target interaction.

    Science.gov (United States)

    van Laarhoven, Twan; Nabuurs, Sander B; Marchiori, Elena

    2011-11-01

    The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug-target pairs in current datasets are experimentally validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. We show that a simple machine learning method that uses the drug-target network as the only source of information is capable of predicting true interaction pairs with high accuracy. Specifically, we introduce interaction profiles of drugs (and of targets) in a network, which are binary vectors specifying the presence or absence of interaction with every target (drug) in that network. We define a kernel on these profiles, called the Gaussian Interaction Profile (GIP) kernel, and use a simple classifier, (kernel) Regularized Least Squares (RLS), for prediction drug-target interactions. We test comparatively the effectiveness of RLS with the GIP kernel on four drug-target interaction networks used in previous studies. The proposed algorithm achieves area under the precision-recall curve (AUPR) up to 92.7, significantly improving over results of state-of-the-art methods. Moreover, we show that using also kernels based on chemical and genomic information further increases accuracy, with a neat improvement on small datasets. These results substantiate the relevance of the network topology (in the form of interaction profiles) as source of information for predicting drug-target interactions. Software and Supplementary Material are available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2011/. tvanlaarhoven@cs.ru.nl; elenam@cs.ru.nl. Supplementary data are available at Bioinformatics online.

  4. Modulating effect of the nootropic drug, piracetam on stress- and subsequent morphine-induced prolactin secretion in male rats.

    Science.gov (United States)

    Matton, A.; Engelborghs, S.; Bollengier, F.; Finné, E.; Vanhaeist, L.

    1996-01-01

    1. The effect of the nootropic drug, piracetam on stress- and subsequent morphine-induced prolactin (PRL) secretion was investigated in vivo in male rats, by use of a stress-free blood sampling and drug administration method by means of a permanent indwelling catheter in the right jugular vein. 2. Four doses of piracetam were tested (20, 100, 200 and 400 mg kg-1), being given intraperitoneally 1 h before blood sampling; control rats received saline instead. After a first blood sample, rats were subjected to immobilization stress and received morphine, 6 mg kg-1, 90 min later. 3. Piracetam had no effect on basal plasma PRL concentration. 4. While in the non-piracetam-treated rats, stress produced a significant rise in plasma PRL concentration, in the piracetam-pretreated rats PRL peaks were attenuated, especially in the group given 100 mg kg-1 piracetam, where plasma PRL concentration was not significantly different from basal values. The dose-response relationship showed a U-shaped curve; the smallest dose had a minor inhibitory effect and the highest dose had no further effect on the PRL rise. 5. In unrestrained rats, morphine led to a significant elevation of plasma PRL concentration. After the application of immobilization stress it lost its ability to raise plasma PRL concentration in the control rats, but not in the piracetam-treated rats. This tolerance was overcome by piracetam in a significant manner but with a reversed dose-response curve; i.e. the smaller the dose of piracetam, the higher the subsequent morphine-induced PRL peak. 6. There is no simple explanation for the mechanism by which piracetam induces these contradictory effects. Interference with the excitatory amino acid system, which is also involved in opiate action, is proposed speculatively as a possible mediator of the effects of piracetam. PMID:8821540

  5. Drug response prediction in high-risk multiple myeloma

    DEFF Research Database (Denmark)

    Vangsted, A J; Helm-Petersen, S; Cowland, J B

    2018-01-01

    from high-risk patients by GEP70 at diagnosis from Total Therapy 2 and 3A to predict the response by the DRP score of drugs used in the treatment of myeloma patients. The DRP score stratified patients further. High-risk myeloma with a predicted sensitivity to melphalan by the DRP score had a prolonged...

  6. Perceived support from a caregiver's social ties predicts subsequent care-recipient health

    Directory of Open Access Journals (Sweden)

    Dannielle E. Kelley

    2017-12-01

    Full Text Available Most social support research has examined support from an individual patient perspective and does not model the broader social context of support felt by caregivers. Understanding how social support networks may complement healthcare services is critical, considering the aging population, as social support networks may be a valuable resource to offset some of the demands placed on the healthcare system. We sought to identify how caregivers' perceived organizational and interpersonal support from their social support network influences care-recipient health.We created a dyadic dataset of care-recipient and caregivers from the first two rounds of the National Health and Aging Trends survey (2011, 2012 and the first round of the associated National Study of Caregivers survey (2011. Using structural equation modeling, we explored how caregivers' perceived social support is associated with caregiver confidence to provide care, and is associated with care-recipient health outcomes at two time points. All data were analyzed in 2016.Social engagement with members from caregivers' social support networks was positively associated with caregiver confidence, and social engagement and confidence were positively associated with care-recipient health at time 1. Social engagement positively predicted patient health at time 2 controlling for time 1. Conversely, use of organizational support negatively predicted care-recipient health at time 2.Care-recipients experience better health outcomes when caregivers are able to be more engaged with members of their social support network. Keywords: Informal caregiving, Social support, Social support network, Patient-caregiver dyads

  7. Perceived support from a caregiver's social ties predicts subsequent care-recipient health.

    Science.gov (United States)

    Kelley, Dannielle E; Lewis, Megan A; Southwell, Brian G

    2017-12-01

    Most social support research has examined support from an individual patient perspective and does not model the broader social context of support felt by caregivers. Understanding how social support networks may complement healthcare services is critical, considering the aging population, as social support networks may be a valuable resource to offset some of the demands placed on the healthcare system. We sought to identify how caregivers' perceived organizational and interpersonal support from their social support network influences care-recipient health. We created a dyadic dataset of care-recipient and caregivers from the first two rounds of the National Health and Aging Trends survey (2011, 2012) and the first round of the associated National Study of Caregivers survey (2011). Using structural equation modeling, we explored how caregivers' perceived social support is associated with caregiver confidence to provide care, and is associated with care-recipient health outcomes at two time points. All data were analyzed in 2016. Social engagement with members from caregivers' social support networks was positively associated with caregiver confidence, and social engagement and confidence were positively associated with care-recipient health at time 1. Social engagement positively predicted patient health at time 2 controlling for time 1. Conversely, use of organizational support negatively predicted care-recipient health at time 2. Care-recipients experience better health outcomes when caregivers are able to be more engaged with members of their social support network.

  8. Recalling and forgetting dreams: theta and alpha oscillations during sleep predict subsequent dream recall.

    Science.gov (United States)

    Marzano, Cristina; Ferrara, Michele; Mauro, Federica; Moroni, Fabio; Gorgoni, Maurizio; Tempesta, Daniela; Cipolli, Carlo; De Gennaro, Luigi

    2011-05-04

    Under the assumption that dream recall is a peculiar form of declarative memory, we have hypothesized that (1) the encoding of dream contents during sleep should share some electrophysiological mechanisms with the encoding of episodic memories of the awake brain and (2) recalling a dream(s) after awakening from non-rapid eye movement (NREM) and rapid eye movement (REM) sleep should be associated with different brain oscillations. Here, we report that cortical brain oscillations of human sleep are predictive of successful dream recall. In particular, after morning awakening from REM sleep, a higher frontal 5-7 Hz (theta) activity was associated with successful dream recall. This finding mirrors the increase in frontal theta activity during successful encoding of episodic memories in wakefulness. Moreover, in keeping with the different EEG background, a different predictive relationship was found after awakening from stage 2 NREM sleep. Specifically, a lower 8-12 Hz (alpha) oscillatory activity of the right temporal area was associated with a successful dream recall. These findings provide the first evidence of univocal cortical electroencephalographic correlates of dream recall, suggesting that the neurophysiological mechanisms underlying the encoding and recall of episodic memories may remain the same across different states of consciousness.

  9. Expression changes in the stroma of prostate cancer predict subsequent relapse.

    Directory of Open Access Journals (Sweden)

    Zhenyu Jia

    Full Text Available Biomarkers are needed to address overtreatment that occurs for the majority of prostate cancer patients that would not die of the disease but receive radical treatment. A possible barrier to biomarker discovery may be the polyclonal/multifocal nature of prostate tumors as well as cell-type heterogeneity between patient samples. Tumor-adjacent stroma (tumor microenvironment is less affected by genetic alteration and might therefore yield more consistent biomarkers in response to tumor aggressiveness. To this end we compared Affymetrix gene expression profiles in stroma near tumor and identified a set of 115 probe sets for which the expression levels were significantly correlated with time-to-relapse. We also compared patients that chemically relapsed shortly after prostatectomy (<1 year, and patients that did not relapse in the first four years after prostatectomy. We identified 131 differentially expressed microarray probe sets between these two categories. 19 probe sets (15 genes overlapped between the two gene lists with p<0.0001. We developed a PAM-based classifier by training on samples containing stroma near tumor: 9 rapid relapse patient samples and 9 indolent patient samples. We then tested the classifier on 47 different samples, containing 90% or more stroma. The classifier predicted the risk status of patients with an average accuracy of 87%. This is the first general tumor microenvironment-based prognostic classifier. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for predicting outcomes for patients.

  10. Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

    Science.gov (United States)

    Huang, Cai; Mezencev, Roman; McDonald, John F; Vannberg, Fredrik

    2017-01-01

    Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60). The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.

  11. Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

    Directory of Open Access Journals (Sweden)

    Cai Huang

    Full Text Available Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM algorithm combined with a standard recursive feature elimination (RFE approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60. The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.

  12. In silico modeling to predict drug-induced phospholipidosis

    International Nuclear Information System (INIS)

    Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G.; Sadrieh, Nakissa

    2013-01-01

    Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure–activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80–81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ≥ 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: • New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. • The training set data in the models is derived from the FDA's phospholipidosis database. • We find excellent predictivity values of the models based on external validation. • The models can support drug screening and regulatory decision-making on DIPL

  13. Prediction of adverse drug reactions using decision tree modeling.

    Science.gov (United States)

    Hammann, F; Gutmann, H; Vogt, N; Helma, C; Drewe, J

    2010-07-01

    Drug safety is of great importance to public health. The detrimental effects of drugs not only limit their application but also cause suffering in individual patients and evoke distrust of pharmacotherapy. For the purpose of identifying drugs that could be suspected of causing adverse reactions, we present a structure-activity relationship analysis of adverse drug reactions (ADRs) in the central nervous system (CNS), liver, and kidney, and also of allergic reactions, for a broad variety of drugs (n = 507) from the Swiss drug registry. Using decision tree induction, a machine learning method, we determined the chemical, physical, and structural properties of compounds that predispose them to causing ADRs. The models had high predictive accuracies (78.9-90.2%) for allergic, renal, CNS, and hepatic ADRs. We show the feasibility of predicting complex end-organ effects using simple models that involve no expensive computations and that can be used (i) in the selection of the compound during the drug discovery stage, (ii) to understand how drugs interact with the target organ systems, and (iii) for generating alerts in postmarketing drug surveillance and pharmacovigilance.

  14. Increase in waist circumference over 6 years predicts subsequent cardiovascular disease and total mortality in nordic women

    DEFF Research Database (Denmark)

    Klingberg, Sofia; Mehlig, Kirsten; Lanfer, Anne

    2015-01-01

    -shaped association. Associations between increase in WC and outcomes were restricted to women with normal weight at baseline and to ever-smokers. CONCLUSIONS: In contrast to changes in HC which did not predict mortality and CVD, a 6-year increase in WC is strongly predictive, particularly among initially lean women...... and cardiovascular disease (CVD) mortality in women but that gain or loss in HC was unrelated to these outcomes. This study examines whether a 6-year change in waist circumference (WC) predicts mortality and CVD in the same study sample. METHODS: Baseline WC and 6-year change in WC as predictors of mortality and CVD...... were analyzed in 2,492 women from the Danish MONICA study and the Prospective Population Study of Women in Gothenburg, Sweden. RESULTS: Increase in WC was significantly associated with increased subsequent mortality and CVD adjusting for BMI and other covariates, with some evidence of a J...

  15. Externalizing problems in childhood and adolescence predict subsequent educational achievement but for different genetic and environmental reasons.

    Science.gov (United States)

    Lewis, Gary J; Asbury, Kathryn; Plomin, Robert

    2017-03-01

    Childhood behavior problems predict subsequent educational achievement; however, little research has examined the etiology of these links using a longitudinal twin design. Moreover, it is unknown whether genetic and environmental innovations provide incremental prediction for educational achievement from childhood to adolescence. We examined genetic and environmental influences on parental ratings of behavior problems across childhood (age 4) and adolescence (ages 12 and 16) as predictors of educational achievement at age 16 using a longitudinal classical twin design. Shared-environmental influences on anxiety, conduct problems, and peer problems at age 4 predicted educational achievement at age 16. Genetic influences on the externalizing behaviors of conduct problems and hyperactivity at age 4 predicted educational achievement at age 16. Moreover, novel genetic and (to a lesser extent) nonshared-environmental influences acting on conduct problems and hyperactivity emerged at ages 12 and 16, adding to the genetic prediction from age 4. These findings demonstrate that genetic and shared-environmental factors underpinning behavior problems in early childhood predict educational achievement in midadolescence. These findings are consistent with the notion that early-childhood behavior problems reflect the initiation of a life-course persistent trajectory with concomitant implications for social attainment. However, we also find evidence that genetic and nonshared-environment innovations acting on behavior problems have implications for subsequent educational achievement, consistent with recent work arguing that adolescence represents a sensitive period for socioaffective development. © 2016 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.

  16. Leptin concentrations in response to acute stress predict subsequent intake of comfort foods

    Science.gov (United States)

    Tomiyama, A. Janet; Schamarek, Imke; Lustig, Robert H.; Kirschbaum, Clemens; Puterman, Eli; Havel, Peter J.; Epel, Elissa S.

    2012-01-01

    Both animals and humans show a tendency toward eating more “comfort food” (high fat, sweet food) after acute stress. Such stress eating may be contributing to the obesity epidemic, and it is important to understand the underlying psychobiological mechanisms. Prior investigations have studied what makes individuals eat more after stress; this study investigates what might make individuals eat less. Leptin has been shown to increase following a laboratory stressor, and is known to affect eating behavior. This study examined whether leptin reactivity accounts for individual differences in stress eating. To test this, we exposed forty women to standardized acute psychological laboratory stress (Trier Social Stress Test) while blood was sampled repeatedly for measurements of plasma leptin. We then measured food intake after the stressor in 29 of these women. Increasing leptin during the stressor predicted lower intake of comfort food. These initial findings suggest that acute changes in leptin may be one of the factors modulating down the consumption of comfort food following stress. PMID:22579988

  17. Predicted levels of HIV drug resistance

    DEFF Research Database (Denmark)

    Cambiano, Valentina; Bertagnolio, Silvia; Jordan, Michael R

    2014-01-01

    -term effects. METHODS: The previously validated HIV Synthesis model was calibrated to South Africa. Resistance was modeled at the level of single mutations, transmission potential, persistence, and effect on drug activity. RESULTS: We estimate 652 000 people (90% uncertainty range: 543 000-744 000) are living...... are maintained, in 20 years' time HIV incidence is projected to have declined by 22% (95% confidence interval, CI -23 to -21%), and the number of people carrying NNRTI resistance to be 2.9-fold higher. If enhancements in diagnosis and retention in care occur, and ART is initiated at CD4 cell count less than 500......  cells/μl, HIV incidence is projected to decline by 36% (95% CI: -37 to -36%) and the number of people with NNRTI resistance to be 4.1-fold higher than currently. Prevalence of people with viral load more than 500  copies/ml carrying NRMV is not projected to differ markedly according to future ART...

  18. Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization.

    Science.gov (United States)

    Ezzat, Ali; Zhao, Peilin; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

    2017-01-01

    Experimental determination of drug-target interactions is expensive and time-consuming. Therefore, there is a continuous demand for more accurate predictions of interactions using computational techniques. Algorithms have been devised to infer novel interactions on a global scale where the input to these algorithms is a drug-target network (i.e., a bipartite graph where edges connect pairs of drugs and targets that are known to interact). However, these algorithms had difficulty predicting interactions involving new drugs or targets for which there are no known interactions (i.e., "orphan" nodes in the network). Since data usually lie on or near to low-dimensional non-linear manifolds, we propose two matrix factorization methods that use graph regularization in order to learn such manifolds. In addition, considering that many of the non-occurring edges in the network are actually unknown or missing cases, we developed a preprocessing step to enhance predictions in the "new drug" and "new target" cases by adding edges with intermediate interaction likelihood scores. In our cross validation experiments, our methods achieved better results than three other state-of-the-art methods in most cases. Finally, we simulated some "new drug" and "new target" cases and found that GRMF predicted the left-out interactions reasonably well.

  19. Predicting Drug Recalls From Internet Search Engine Queries.

    Science.gov (United States)

    Yom-Tov, Elad

    2017-01-01

    Batches of pharmaceuticals are sometimes recalled from the market when a safety issue or a defect is detected in specific production runs of a drug. Such problems are usually detected when patients or healthcare providers report abnormalities to medical authorities. Here, we test the hypothesis that defective production lots can be detected earlier by monitoring queries to Internet search engines. We extracted queries from the USA to the Bing search engine, which mentioned one of the 5195 pharmaceutical drugs during 2015 and all recall notifications issued by the Food and Drug Administration (FDA) during that year. By using attributes that quantify the change in query volume at the state level, we attempted to predict if a recall of a specific drug will be ordered by FDA in a time horizon ranging from 1 to 40 days in future. Our results show that future drug recalls can indeed be identified with an AUC of 0.791 and a lift at 5% of approximately 6 when predicting a recall occurring one day ahead. This performance degrades as prediction is made for longer periods ahead. The most indicative attributes for prediction are sudden spikes in query volume about a specific medicine in each state. Recalls of prescription drugs and those estimated to be of medium-risk are more likely to be identified using search query data. These findings suggest that aggregated Internet search engine data can be used to facilitate in early warning of faulty batches of medicines.

  20. Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates

    DEFF Research Database (Denmark)

    Jonsdottir, Svava Osk; Jorgensen, FS; Brunak, Søren

    2005-01-01

    about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability......MOTIVATION: To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process. RESULTS: We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information...... of chemical compounds as potential drugs, as well as for predicting their physico-chemical and ADMET properties have been proposed in recent years. These methods are discussed, and some possible future directions in this rapidly developing field are described....

  1. Drug-target interaction prediction from PSSM based evolutionary information.

    Science.gov (United States)

    Mousavian, Zaynab; Khakabimamaghani, Sahand; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-01-01

    The labor-intensive and expensive experimental process of drug-target interaction prediction has motivated many researchers to focus on in silico prediction, which leads to the helpful information in supporting the experimental interaction data. Therefore, they have proposed several computational approaches for discovering new drug-target interactions. Several learning-based methods have been increasingly developed which can be categorized into two main groups: similarity-based and feature-based. In this paper, we firstly use the bi-gram features extracted from the Position Specific Scoring Matrix (PSSM) of proteins in predicting drug-target interactions. Our results demonstrate the high-confidence prediction ability of the Bigram-PSSM model in terms of several performance indicators specifically for enzymes and ion channels. Moreover, we investigate the impact of negative selection strategy on the performance of the prediction, which is not widely taken into account in the other relevant studies. This is important, as the number of non-interacting drug-target pairs are usually extremely large in comparison with the number of interacting ones in existing drug-target interaction data. An interesting observation is that different levels of performance reduction have been attained for four datasets when we change the sampling method from the random sampling to the balanced sampling. Copyright © 2015 Elsevier Inc. All rights reserved.

  2. Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.

    Science.gov (United States)

    Alaimo, Salvatore; Giugno, Rosalba; Pulvirenti, Alfredo

    2016-01-01

    The usage of computational methods in drug discovery is a common practice. More recently, by exploiting the wealth of biological knowledge bases, a novel approach called drug repositioning has raised. Several computational methods are available, and these try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter, we review drug-target interaction prediction methods based on a recommendation system. We also give some extensions which go beyond the bipartite network case.

  3. DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue

    OpenAIRE

    Wang, QuanQiu; Xu, Rong

    2015-01-01

    Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algori...

  4. Semen molecular and cellular features: these parameters can reliably predict subsequent ART outcome in a goat model

    Directory of Open Access Journals (Sweden)

    Mereu Paolo

    2009-11-01

    Full Text Available Abstract Currently, the assessment of sperm function in a raw or processed semen sample is not able to reliably predict sperm ability to withstand freezing and thawing procedures and in vivo fertility and/or assisted reproductive biotechnologies (ART outcome. The aim of the present study was to investigate which parameters among a battery of analyses could predict subsequent spermatozoa in vitro fertilization ability and hence blastocyst output in a goat model. Ejaculates were obtained by artificial vagina from 3 adult goats (Capra hircus aged 2 years (A, B and C. In order to assess the predictive value of viability, computer assisted sperm analyzer (CASA motility parameters and ATP intracellular concentration before and after thawing and of DNA integrity after thawing on subsequent embryo output after an in vitro fertility test, a logistic regression analysis was used. Individual differences in semen parameters were evident for semen viability after thawing and DNA integrity. Results of IVF test showed that spermatozoa collected from A and B lead to higher cleavage rates (0

  5. DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue.

    Science.gov (United States)

    Wang, QuanQiu; Xu, Rong

    2015-01-01

    Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algorithm to prioritize FDA-approved drugs from dengue-related diseases to treat dengue. When tested in a de-novo validation setting, DenguePredict found the only two drugs tested in clinical trials for treating dengue and ranked them highly: chloroquine ranked at top 0.96% and ivermectin at top 22.75%. We showed that drugs targeting immune systems and arachidonic acid metabolism-related apoptotic pathways might represent innovative drugs to treat dengue. In summary, DenguePredict, by combining comprehensive disease- and drug-related data and novel algorithms, may greatly facilitate drug discovery for dengue.

  6. Risk Factors at Birth Predictive of Subsequent Injury Among Japanese Preschool Children: A Nationwide 5-Year Cohort Study.

    Science.gov (United States)

    Morioka, Hisayoshi; Itani, Osamu; Jike, Maki; Nakagome, Sachi; Otsuka, Yuichiro; Ohida, Takashi

    2018-03-19

    To identify risk factors at birth that are predictive of subsequent injury among preschool children. Retrospective analysis of population-based birth cohort data from the "Longitudinal Survey of Babies Born in the 21st Century" was performed from 2001 through 2007 in Japan (n = 47,015). The cumulative incidence and the total number of hospitalizations or examinations conducted at medical facilities for injury among children from birth up to the age of 5 years were calculated. To identify risk factors at birth that are predictive of injury, multivariate analysis of data for hospitalization or admission because of injury during a 5-year period (age, 0-5 years) was performed using the total number of hospital examinations as the dependent variable. The cumulative incidence (95% confidence interval) of hospital examinations for injury over the 5-year period was 34.8% (34.2%-35.4%) for boys and 27.6% (27.0%-28.2%) for girls. The predictive risk factors at birth we identified for injury among preschool children were sex (boys), heavy birth weight, late birth order, no cohabitation with the grandfather or grandmother, father's long working hours, mother's high education level, and strong intensity of parenting anxiety. Based on the results of this study, we identified a number of predictive factors for injury in children. To reduce the risk of injury in the juvenile population as a whole, it is important to pursue a high-risk or population approach by focusing on the predictive factors we have identified.

  7. Bitterness prediction in-silico: A step towards better drugs.

    Science.gov (United States)

    Bahia, Malkeet Singh; Nissim, Ido; Niv, Masha Y

    2018-02-05

    Bitter taste is innately aversive and thought to protect against consuming poisons. Bitter taste receptors (Tas2Rs) are G-protein coupled receptors, expressed both orally and extra-orally and proposed as novel targets for several indications, including asthma. Many clinical drugs elicit bitter taste, suggesting the possibility of drugs re-purposing. On the other hand, the bitter taste of medicine presents a major compliance problem for pediatric drugs. Thus, efficient tools for predicting, measuring and masking bitterness of active pharmaceutical ingredients (APIs) are required by the pharmaceutical industry. Here we highlight the BitterDB database of bitter compounds and survey the main computational approaches to prediction of bitter taste based on compound's chemical structure. Current in silico bitterness prediction methods provide encouraging results, can be constantly improved using growing experimental data, and present a reliable and efficient addition to the APIs development toolbox. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Large-scale prediction of drug-target interactions using protein sequences and drug topological structures

    Energy Technology Data Exchange (ETDEWEB)

    Cao Dongsheng [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China); Liu Shao [Xiangya Hospital, Central South University, Changsha 410008 (China); Xu Qingsong [School of Mathematical Sciences and Computing Technology, Central South University, Changsha 410083 (China); Lu Hongmei; Huang Jianhua [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China); Hu Qiannan [Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan 430071 (China); Liang Yizeng, E-mail: yizeng_liang@263.net [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China)

    2012-11-08

    Highlights: Black-Right-Pointing-Pointer Drug-target interactions are predicted using an extended SAR methodology. Black-Right-Pointing-Pointer A drug-target interaction is regarded as an event triggered by many factors. Black-Right-Pointing-Pointer Molecular fingerprint and CTD descriptors are used to represent drugs and proteins. Black-Right-Pointing-Pointer Our approach shows compatibility between the new scheme and current SAR methodology. - Abstract: The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug-target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug-target interactions in a timely manner. In this article, we aim at extending current structure-activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug-target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug

  9. Fast prediction of cytochrome P450 mediated drug metabolism

    DEFF Research Database (Denmark)

    Rydberg, Patrik Åke Anders; Poongavanam, Vasanthanathan; Oostenbrink, Chris

    2009-01-01

    Cytochrome P450 mediated metabolism of drugs is one of the major determinants of their kinetic profile, and prediction of this metabolism is therefore highly relevant during the drug discovery and development process. A new rule-based method, based on results from density functional theory...... calculations, for predicting activation energies for aliphatic and aromatic oxidations by cytochromes P450 is developed and compared with several other methods. Although the applicability of the method is currently limited to a subset of P450 reactions, these reactions describe more than 90...

  10. Do Functional Movement Screen (FMS) composite scores predict subsequent injury? A systematic review with meta-analysis.

    Science.gov (United States)

    Moran, Robert W; Schneiders, Anthony G; Mason, Jesse; Sullivan, S John

    2017-12-01

    This paper aims to systematically review studies investigating the strength of association between FMS composite scores and subsequent risk of injury, taking into account both methodological quality and clinical and methodological diversity. Systematic review with meta-analysis. A systematic search of electronic databases was conducted for the period between their inception and 3 March 2016 using PubMed, Medline, Google Scholar, Scopus, Academic Search Complete, AMED (Allied and Complementary Medicine Database), CINAHL (Cumulative Index to Nursing and Allied Health Literature), Health Source and SPORTDiscus. Inclusion criteria: (1) English language, (2) observational prospective cohort design, (3) original and peer-reviewed data, (4) composite FMS score, used to define exposure and non-exposure groups and (5) musculoskeletal injury, reported as the outcome. (1) data reported in conference abstracts or non-peer-reviewed literature, including theses, and (2) studies employing cross-sectional or retrospective study designs. 24 studies were appraised using the Quality of Cohort Studies assessment tool. In male military personnel, there was 'strong' evidence that the strength of association between FMS composite score (cut-point ≤14/21) and subsequent injury was 'small' (pooled risk ratio=1.47, 95% CI 1.22 to 1.77, p<0.0001, I 2 =57%). There was 'moderate' evidence to recommend against the use of FMS composite score as an injury prediction test in football (soccer). For other populations (including American football, college athletes, basketball, ice hockey, running, police and firefighters), the evidence was 'limited' or 'conflicting'. The strength of association between FMS composite scores and subsequent injury does not support its use as an injury prediction tool. PROSPERO registration number CRD42015025575. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted

  11. iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.

    Science.gov (United States)

    Fan, Yue-Nong; Xiao, Xuan; Min, Jian-Liang; Chou, Kuo-Chen

    2014-03-19

    Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called "iNR-Drug" was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.

  12. Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning.

    Science.gov (United States)

    Rathore, Saima; Akbari, Hamed; Doshi, Jimit; Shukla, Gaurav; Rozycki, Martin; Bilello, Michel; Lustig, Robert; Davatzikos, Christos

    2018-04-01

    Standard surgical resection of glioblastoma, mainly guided by the enhancement on postcontrast T1-weighted magnetic resonance imaging (MRI), disregards infiltrating tumor within the peritumoral edema region (ED). Subsequent radiotherapy typically delivers uniform radiation to peritumoral FLAIR-hyperintense regions, without attempting to target areas likely to be infiltrated more heavily. Noninvasive in vivo delineation of the areas of tumor infiltration and prediction of early recurrence in peritumoral ED could assist in targeted intensification of local therapies, thereby potentially delaying recurrence and prolonging survival. This paper presents a method for estimating peritumoral edema infiltration using radiomic signatures determined via machine learning methods, and tests it on 90 patients with de novo glioblastoma. The generalizability of the proposed predictive model was evaluated via cross-validation in a discovery cohort ([Formula: see text]) and was subsequently evaluated in a replication cohort ([Formula: see text]). Spatial maps representing the likelihood of tumor infiltration and future early recurrence were compared with regions of recurrence on postresection follow-up studies with pathology confirmation. The cross-validated accuracy of our predictive infiltration model on the discovery and replication cohorts was 87.51% (odds ratio = 10.22, sensitivity = 80.65, and specificity = 87.63) and 89.54% (odds ratio = 13.66, sensitivity = 97.06, and specificity = 76.73), respectively. The radiomic signature of the recurrent tumor region revealed higher vascularity and cellularity when compared with the nonrecurrent region. The proposed model shows evidence that multiparametric pattern analysis from clinical MRI sequences can assist in in vivo estimation of the spatial extent and pattern of tumor recurrence in peritumoral edema, which may guide supratotal resection and/or intensification of postoperative radiation therapy.

  13. Predicting Drug Court Treatment Completion Using the MMPI-2-RF

    Science.gov (United States)

    Mattson, Curtis; Powers, Bradley; Halfaker, Dale; Akeson, Steven; Ben-Porath, Yossef

    2012-01-01

    We examined the ability of the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008) substantive scales to predict Drug Court treatment completion in a sample of individuals identified as being at risk for failure to complete the program. Higher scores on MMPI-2-RF scales…

  14. Psychophysiological prediction of choice: relevance to insight and drug addiction

    Science.gov (United States)

    Moeller, Scott J.; Hajcak, Greg; Parvaz, Muhammad A.; Dunning, Jonathan P.; Volkow, Nora D.

    2012-01-01

    An important goal of addiction research and treatment is to predict behavioural responses to drug-related stimuli. This goal is especially important for patients with impaired insight, which can interfere with therapeutic interventions and potentially invalidate self-report questionnaires. This research tested (i) whether event-related potentials, specifically the late positive potential, predict choice to view cocaine images in cocaine addiction; and (ii) whether such behaviour prediction differs by insight (operationalized in this study as self-awareness of image choice). Fifty-nine cocaine abusers and 32 healthy controls provided data for the following laboratory components that were completed in a fixed-sequence (to establish prediction): (i) event-related potential recordings while passively viewing pleasant, unpleasant, neutral and cocaine images, during which early (400–1000 ms) and late (1000–2000 ms) window late positive potentials were collected; (ii) self-reported arousal ratings for each picture; and (iii) two previously validated tasks: one to assess choice for viewing these same images, and the other to group cocaine abusers by insight. Results showed that pleasant-related late positive potentials and arousal ratings predicted pleasant choice (the choice to view pleasant pictures) in all subjects, validating the method. In the cocaine abusers, the predictive ability of the late positive potentials and arousal ratings depended on insight. Cocaine-related late positive potentials better predicted cocaine image choice in cocaine abusers with impaired insight. Another emotion-relevant event-related potential component (the early posterior negativity) did not show these results, indicating specificity of the late positive potential. In contrast, arousal ratings better predicted respective cocaine image choice (and actual cocaine use severity) in cocaine abusers with intact insight. Taken together, the late positive potential could serve as a biomarker

  15. iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking

    Directory of Open Access Journals (Sweden)

    Yue-Nong Fan

    2014-03-01

    Full Text Available Nuclear receptors (NRs are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.

  16. Herb-drug interactions: challenges and opportunities for improved predictions.

    Science.gov (United States)

    Brantley, Scott J; Argikar, Aneesh A; Lin, Yvonne S; Nagar, Swati; Paine, Mary F

    2014-03-01

    Supported by a usage history that predates written records and the perception that "natural" ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb-drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb-drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb-drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb-drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens.

  17. Herb–Drug Interactions: Challenges and Opportunities for Improved Predictions

    Science.gov (United States)

    Brantley, Scott J.; Argikar, Aneesh A.; Lin, Yvonne S.; Nagar, Swati

    2014-01-01

    Supported by a usage history that predates written records and the perception that “natural” ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb–drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb–drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb–drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb–drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens. PMID:24335390

  18. Prefrontal recruitment during social rejection predicts greater subsequent self-regulatory imbalance and impairment: neural and longitudinal evidence.

    Science.gov (United States)

    Chester, David S; DeWall, C Nathan

    2014-11-01

    Social rejection impairs self-regulation, yet the neural mechanisms underlying this relationship remain unknown. The right ventrolateral prefrontal cortex (rVLPFC) facilitates self-regulation and plays a robust role in regulating the distress of social rejection. However, recruiting this region's inhibitory function during social rejection may come at a self-regulatory cost. As supported by prominent theories of self-regulation, we hypothesized that greater rVLPFC recruitment during rejection would predict a subsequent self-regulatory imbalance that favored reflexive impulses (i.e., cravings), which would then impair self-regulation. Supporting our hypotheses, rVLPFC activation during social rejection was associated with greater subsequent nucleus accumbens (NAcc) activation and lesser functional connectivity between the NAcc and rVLPFC to appetitive cues. Over seven days, the effect of daily felt rejection on daily self-regulatory impairment was exacerbated among participants who showed a stronger rVLPFC response to social rejection. This interactive effect was mirrored in the effect of daily felt rejection on heightened daily alcohol cravings. Our findings suggest that social rejection likely impairs self-regulation by recruiting the rVLPFC, which then tips the regulatory balance towards reward-based impulses. Copyright © 2014 Elsevier Inc. All rights reserved.

  19. Idea density measured in late life predicts subsequent cognitive trajectories: implications for the measurement of cognitive reserve.

    Science.gov (United States)

    Farias, Sarah Tomaszewski; Chand, Vineeta; Bonnici, Lisa; Baynes, Kathleen; Harvey, Danielle; Mungas, Dan; Simon, Christa; Reed, Bruce

    2012-11-01

    The Nun Study showed that lower linguistic ability in young adulthood, measured by idea density (ID), increased the risk of dementia in late life. The present study examined whether ID measured in late life continues to predict the trajectory of cognitive change. ID was measured in 81 older adults who were followed longitudinally for an average of 4.3 years. Changes in global cognition and 4 specific neuropsychological domains (episodic memory, semantic memory, spatial abilities, and executive function) were examined as outcomes. Separate random effects models tested the effect of ID on longitudinal change in outcomes, adjusted for age and education. Lower ID was associated with greater subsequent decline in global cognition, semantic memory, episodic memory, and spatial abilities. When analysis was restricted to only participants without dementia at the time ID was collected, results were similar. Linguistic ability in young adulthood, as measured by ID, has been previously proposed as an index of neurocognitive development and/or cognitive reserve. The present study provides evidence that even when ID is measured in old age, it continues to be associated with subsequent cognitive decline and as such may continue to provide a marker of cognitive reserve.

  20. Differential effect of IP- and IV-injected nitrogen mustard on subsequently-irradiated intestinal crypts: implications for 'dose-effect factors' predicted by experimental, combined modality therapy

    International Nuclear Information System (INIS)

    Moore, J.V.

    1984-01-01

    In experimental chemotherapy-radiotherapy, cytotoxic drugs are almost invariably injected by the intraperitoneal (IP) route. This contrasts with normal clinical practice, which is to employ the intravenous (IV) route. We have used a clonogenic assay of gastrointestinal (GI) injury in mice to show that a given administered dose of nitrogen mustard (HN 2 ), injected IP, results in a much greater reduction in the subsequent radiation dose required to achieve an isoeffect, than if the drug is injected IV. At an administered dose of 3.5 mg kg -1 of HN 2 (the animal LDsub(10/30) for IP injection), the radiation dose-reduction factor for 10% survival of intestinal crypts, was 1.94 for IP HN 2 and only 1.28 for IV HN 2 . Even the grossly-equitoxic (mouse LDsub(10/30)) dose of IV HN 2 resulted in a smaller predicted radiation dose reduction for GI injury, by a factor of 1.45. The validity of using the IP route in combined chemotherapy-radiotherapy studies designed to generate quantitative estimates of toxicity is discussed. (author)

  1. Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives.

    Science.gov (United States)

    Nath, Abhigyan; Kumari, Priyanka; Chaube, Radha

    2018-01-01

    Identification of drug targets and drug target interactions are important steps in the drug-discovery pipeline. Successful computational prediction methods can reduce the cost and time demanded by the experimental methods. Knowledge of putative drug targets and their interactions can be very useful for drug repurposing. Supervised machine learning methods have been very useful in drug target prediction and in prediction of drug target interactions. Here, we describe the details for developing prediction models using supervised learning techniques for human drug target prediction and their interactions.

  2. Predictive typing of drug-induced neurological sufferings from studies of the distribution of labelled drugs

    International Nuclear Information System (INIS)

    Takasu, T.

    1980-01-01

    A drug given to an animal becomes widely distributed throughout the body, acting on the living mechanisms or structures, and is gradually excreted. Some drugs can remain in some parts of the body for a long period. For example, 14 C-chloramphenical was found to remain preferentially in the salivary gland, liver and bone marrow of mice 24 hours after its oral administration. If such a drug is given repeatedly, it could possibly accumulate gradually in these organs. Thus, when its accumulation in a particular part of the body exceeds a certain level, the living mechanism or structure may possibly be injured. The harmful effects of a drug in repeated administration are called its chronic toxicity. The author discusses whether it is possible to predict the toxicity of a drug by studying its distribution in relation to time, and, if possible, the points in time. This problem is studied especially in relation to the nervous system. (Auth.)

  3. Severe Spastic Contractures and Diabetes Mellitus Independently Predict Subsequent Minimal Trauma Fractures Among Long-Term Care Residents.

    Science.gov (United States)

    Lam, Kuen; Leung, Man Fuk; Kwan, Chi Wai; Kwan, Joseph

    2016-11-01

    The study aimed to examine the epidemiology of hypertonic contractures and its relationship with minimal trauma fracture (MTF), and to determine the incidence and predictors of (MTF) in long-term care residents. This was a longitudinal cohort study of prospectively collected data. Participants were followed from March 2007 to March 2016 or until death. A 300-bed long-term care hospital in Hong Kong. All long-term care residents who were in need of continuous medical and nursing care for their activities of daily living. Information on patients' demographic data, severe contracture defined as a decrease of 50% or more of the normal passive range of joint movement of the joint, and severe limb spasticity defined by the Modified Ashworth Scale higher than grade 3, medical comorbidities, functional status, cognitive status, nutritional status including body mass index and serum albumin, past history of fractures, were evaluated as potential risk factors for subsequent MTF. Three hundred ninety-six residents [148 males, mean ± standard deviation (SD), age = 79 ± 16 years] were included for analysis. The presence of severe contracture was highly prevalent among the study population: 91% of residents had at least 1 severe contracture, and 41% of residents had severe contractures involving all 4 limbs. Moreover, there were a significant proportion of residents who had severe limb spasticity with the elbow flexors (32.4%) and knee flexors (33.9%) being the most commonly involved muscles. Twelve residents (3%) suffered from subsequent MTF over a median follow-up of 33 (SD = 30) months. Seven out of these 12 residents died during the follow-up period, with a mean survival of 17.8 months (SD = 12.6) after the fracture event. The following 2 factors were found to independently predict subsequent MTF in a multivariate Cox regression: bilateral severe spastic knee contractures (hazard ratio = 16.5, P contractures are common morbidities in long-term care residents

  4. Predicting Drug-Target Interactions Based on Small Positive Samples.

    Science.gov (United States)

    Hu, Pengwei; Chan, Keith C C; Hu, Yanxing

    2018-01-01

    A basic task in drug discovery is to find new medication in the form of candidate compounds that act on a target protein. In other words, a drug has to interact with a target and such drug-target interaction (DTI) is not expected to be random. Significant and interesting patterns are expected to be hidden in them. If these patterns can be discovered, new drugs are expected to be more easily discoverable. Currently, a number of computational methods have been proposed to predict DTIs based on their similarity. However, such as approach does not allow biochemical features to be directly considered. As a result, some methods have been proposed to try to discover patterns in physicochemical interactions. Since the number of potential negative DTIs are very high both in absolute terms and in comparison to that of the known ones, these methods are rather computationally expensive and they can only rely on subsets, rather than the full set, of negative DTIs for training and validation. As there is always a relatively high chance for negative DTIs to be falsely identified and as only partial subset of such DTIs is considered, existing approaches can be further improved to better predict DTIs. In this paper, we present a novel approach, called ODT (one class drug target interaction prediction), for such purpose. One main task of ODT is to discover association patterns between interacting drugs and proteins from the chemical structure of the former and the protein sequence network of the latter. ODT does so in two phases. First, the DTI-network is transformed to a representation by structural properties. Second, it applies a oneclass classification algorithm to build a prediction model based only on known positive interactions. We compared the best AUROC scores of the ODT with several state-of-art approaches on Gold standard data. The prediction accuracy of the ODT is superior in comparison with all the other methods at GPCRs dataset and Ion channels dataset. Performance

  5. Outpatient Pain Predicts Subsequent One-Year Acute Health Care Utilization Among Adults With Sickle Cell Disease

    Science.gov (United States)

    Ezenwa, Miriam O.; Molokie, Robert E.; Wang, Zaijie Jim; Yao, Yingwei; Suarez, Marie L.; Angulo, Veronica; Wilkie, Diana J.

    2014-01-01

    Context Patient demographic and clinical factors have known associations with acute health care utilization (AHCU) among patients with sickle cell disease (SCD), but it is unknown if pain measured predominantly in an outpatient setting is a predictor of future AHCU in patients with SCD. Objectives To determine whether multidimensional pain scores obtained predominantly in an outpatient setting predicted subsequent one-year AHCU by 137 adults with SCD and whether the pain measured at a second visit also predicted AHCU. Methods Pain data included the Composite Pain Index (CPI), a single score representative of a multidimensional pain experience (number of pain sites, intensity, quality, and pattern). Based on the distribution of AHCU events, we divided patients into three groups: (1) zero events (Zero), (2) 1–3 events (Low), or (3) 4–23 events (High). Results The initial CPI scores differed significantly by the three groups (F(2,134)=7.38, P=0.001). Post hoc comparisons showed that the Zero group had lower CPI scores than both the Low group (Pgroup (Page, and CPI scores (at both measurement times) were statistically significant predictors of utilization events. Pain intensity scores at both measurement times were significant predictors of utilization, but other pain scores (number of pain sites, quality, and pattern) were not. Conclusion Findings support use of outpatient CPI scores or pain intensity and age to identify at-risk young adults with SCD who are likely to benefit from improved outpatient pain management plans. PMID:24636960

  6. Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization.

    Science.gov (United States)

    Yu, Hui; Mao, Kui-Tao; Shi, Jian-Yu; Huang, Hua; Chen, Zhi; Dong, Kai; Yiu, Siu-Ming

    2018-04-11

    Drug-drug interactions (DDIs) always cause unexpected and even adverse drug reactions. It is important to identify DDIs before drugs are used in the market. However, preclinical identification of DDIs requires much money and time. Computational approaches have exhibited their abilities to predict potential DDIs on a large scale by utilizing pre-market drug properties (e.g. chemical structure). Nevertheless, none of them can predict two comprehensive types of DDIs, including enhancive and degressive DDIs, which increases and decreases the behaviors of the interacting drugs respectively. There is a lack of systematic analysis on the structural relationship among known DDIs. Revealing such a relationship is very important, because it is able to help understand how DDIs occur. Both the prediction of comprehensive DDIs and the discovery of structural relationship among them play an important guidance when making a co-prescription. In this work, treating a set of comprehensive DDIs as a signed network, we design a novel model (DDINMF) for the prediction of enhancive and degressive DDIs based on semi-nonnegative matrix factorization. Inspiringly, DDINMF achieves the conventional DDI prediction (AUROC = 0.872 and AUPR = 0.605) and the comprehensive DDI prediction (AUROC = 0.796 and AUPR = 0.579). Compared with two state-of-the-art approaches, DDINMF shows it superiority. Finally, representing DDIs as a binary network and a signed network respectively, an analysis based on NMF reveals crucial knowledge hidden among DDIs. Our approach is able to predict not only conventional binary DDIs but also comprehensive DDIs. More importantly, it reveals several key points about the DDI network: (1) both binary and signed networks show fairly clear clusters, in which both drug degree and the difference between positive degree and negative degree show significant distribution; (2) the drugs having large degrees tend to have a larger difference between positive degree

  7. Does early improvement in depressive symptoms predict subsequent remission in patients with depression who are treated with duloxetine?

    Directory of Open Access Journals (Sweden)

    Sueki A

    2016-05-01

    Full Text Available Akitsugu Sueki, Eriko Suzuki, Hitoshi Takahashi, Jun Ishigooka Department of Neuropsychiatry, Tokyo Women’s Medical University, Tokyo, Japan Purpose: In this prospective study, we examined whether early reduction in depressive symptoms predicts later remission to duloxetine in the treatment of depression, as monitored using the Montgomery–Asberg Depression Rating Scale (MADRS. Patients and methods: Among the 106 patients who were enrolled in this study, 67 were included in the statistical analysis. A clinical evaluation using the MADRS was performed at weeks 0, 4, 8, 12, and 16 after commencing treatment. For each time point, the MADRS total score was separated into three components: dysphoria, retardation, and vegetative scores. Results: Remission was defined as an MADRS total score of ≤10 at end point. From our univariate logistic regression analysis, we found that improvements in both the MADRS total score and the dysphoria score at week 4 had a significant interaction with subsequent remission. Furthermore, age and sex were significant predictors of remission. There was an increase of approximately 4% in the odds of remission for each unit increase in age, and female sex had an odds of remission of 0.318 times that of male sex (remission rate for men was 73.1% [19/26] and for women 46.3% [19/41]. However, in the multivariate model using the change from baseline in the total MADRS, dysphoria, retardation, and vegetative scores at week 4, in which age and sex were included as covariates, only sex retained significance, except for an improvement in the dysphoria score. Conclusion: No significant interaction was found between early response to duloxetine and eventual remission in this study. Sex difference was found to be a predictor of subsequent remission in patients with depression who were treated with duloxetine, with the male sex having greater odds of remission. Keywords: antidepressant, early response, sex difference, serotonin

  8. In Vitro Drug Sensitivity Tests to Predict Molecular Target Drug Responses in Surgically Resected Lung Cancer.

    Directory of Open Access Journals (Sweden)

    Ryohei Miyazaki

    Full Text Available Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs and anaplastic lymphoma kinase (ALK inhibitors have dramatically changed the strategy of medical treatment of lung cancer. Patients should be screened for the presence of the EGFR mutation or echinoderm microtubule-associated protein-like 4 (EML4-ALK fusion gene prior to chemotherapy to predict their clinical response. The succinate dehydrogenase inhibition (SDI test and collagen gel droplet embedded culture drug sensitivity test (CD-DST are established in vitro drug sensitivity tests, which may predict the sensitivity of patients to cytotoxic anticancer drugs. We applied in vitro drug sensitivity tests for cyclopedic prediction of clinical responses to different molecular targeting drugs.The growth inhibitory effects of erlotinib and crizotinib were confirmed for lung cancer cell lines using SDI and CD-DST. The sensitivity of 35 cases of surgically resected lung cancer to erlotinib was examined using SDI or CD-DST, and compared with EGFR mutation status.HCC827 (Exon19: E746-A750 del and H3122 (EML4-ALK cells were inhibited by lower concentrations of erlotinib and crizotinib, respectively than A549, H460, and H1975 (L858R+T790M cells were. The viability of the surgically resected lung cancer was 60.0 ± 9.8 and 86.8 ± 13.9% in EGFR-mutants vs. wild types in the SDI (p = 0.0003. The cell viability was 33.5 ± 21.2 and 79.0 ± 18.6% in EGFR mutants vs. wild-type cases (p = 0.026 in CD-DST.In vitro drug sensitivity evaluated by either SDI or CD-DST correlated with EGFR gene status. Therefore, SDI and CD-DST may be useful predictors of potential clinical responses to the molecular anticancer drugs, cyclopedically.

  9. Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions.

    Science.gov (United States)

    La, Mary K; Sedykh, Alexander; Fourches, Denis; Muratov, Eugene; Tropsha, Alexander

    2018-06-06

    Given that adverse drug effects (ADEs) have led to post-market patient harm and subsequent drug withdrawal, failure of candidate agents in the drug development process, and other negative outcomes, it is essential to attempt to forecast ADEs and other relevant drug-target-effect relationships as early as possible. Current pharmacologic data sources, providing multiple complementary perspectives on the drug-target-effect paradigm, can be integrated to facilitate the inference of relationships between these entities. This study aims to identify both existing and unknown relationships between chemicals (C), protein targets (T), and ADEs (E) based on evidence in the literature. Cheminformatics and data mining approaches were employed to integrate and analyze publicly available clinical pharmacology data and literature assertions interrelating drugs, targets, and ADEs. Based on these assertions, a C-T-E relationship knowledge base was developed. Known pairwise relationships between chemicals, targets, and ADEs were collected from several pharmacological and biomedical data sources. These relationships were curated and integrated according to Swanson's paradigm to form C-T-E triangles. Missing C-E edges were then inferred as C-E relationships. Unreported associations between drugs, targets, and ADEs were inferred, and inferences were prioritized as testable hypotheses. Several C-E inferences, including testosterone → myocardial infarction, were identified using inferences based on the literature sources published prior to confirmatory case reports. Timestamping approaches confirmed the predictive ability of this inference strategy on a larger scale. The presented workflow, based on free-access databases and an association-based inference scheme, provided novel C-E relationships that have been validated post hoc in case reports. With refinement of prioritization schemes for the generated C-E inferences, this workflow may provide an effective computational method for

  10. Experimental and computational prediction of glass transition temperature of drugs.

    Science.gov (United States)

    Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

    2014-12-22

    Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

  11. Analysis of clinical drug-drug interaction data to predict uncharacterized interaction magnitudes between antiretroviral drugs and co-medications.

    Science.gov (United States)

    Stader, Felix; Kinvig, Hannah; Battegay, Manuel; Khoo, Saye; Owen, Andrew; Siccardi, Marco; Marzolini, Catia

    2018-04-23

    Despite their high potential for drug-drug-interactions (DDI), clinical DDI studies of antiretroviral drugs (ARVs) are often lacking, because the full range of potential interactions cannot feasibly or pragmatically be studied, with some high-risk DDI studies also ethically difficult to undertake. Thus, a robust method to screen and to predict the likelihood of DDIs is required.We developed a method to predict DDIs based on two parameters: the degree of metabolism by specific enzymes such as CYP3A and the strength of an inhibitor or inducer. These parameters were derived from existing studies utilizing paradigm substrates, inducers and inhibitors of CYP3A, to assess the predictive performance of this method by verifying predicted magnitudes of changes in drug exposure against clinical DDI studies involving ARVs.The derived parameters were consistent with the FDA classification of sensitive CYP3A substrates and the strength of CYP3A inhibitors and inducers. Characterized DDI magnitudes (n = 68) between ARVs and co-medications were successfully quantified meaning 53%, 85% and 98% of the predictions were within 1.25-fold (0.80 - 1.25), 1.5-fold (0.66 - 1.48) and 2-fold (0.66 - 1.94) of the observed clinical data. In addition, the method identifies CYP3A substrates likely to be highly or conversely minimally impacted by CYP3A inhibitors or inducers, thus categorizing the magnitude of DDIs.The developed effective and robust method has the potential to support a more rational identification of dose adjustment to overcome DDIs being particularly relevant in a HIV-setting giving the treatments complexity, high DDI risk and limited guidance on the management of DDIs. Copyright © 2018 American Society for Microbiology.

  12. Does Immediate Pain Relief After an Injection into the Sacroiliac Joint with Anesthetic and Corticosteroid Predict Subsequent Pain Relief?

    Science.gov (United States)

    Schneider, Byron J; Huynh, Lisa; Levin, Josh; Rinkaekan, Pranathip; Kordi, Ramin; Kennedy, David J

    2018-02-01

    To determine if immediate pain response following an injection with local anesthetic and corticosteroid predicts subsequent relief. Prospective observational cohort. An institutional review board-approved prospective study from a single academic medical center. Patients with clinical diagnosis of sacroiliac (SIJ) pain and referred for SIJ injection were enrolled; 1 cc of 2% lidocaine and 1 cc of triamcinolone 40 mg/mL were injected into the SIJ. Pain score on 0-10 numeric rating scale (NRS) during provocation maneuvers was recorded immediately before injection, immediately after injection, and at two and four weeks of follow-up. Oswestry Disability Index (ODI) was also recorded. Various cutoffs were identified to establish positive anesthetic response and successful outcomes at follow-up. These were used to calculated likelihood ratios. Of those with 100% anesthetic response, six of 11 (54.5%, 95% confidence interval [CI]+/-29.4%, +LR 2.6, 95% CI = 1.1-5.9) demonstrated 50% or greater pain relief at follow-up, and four of 11 (36.5%, 95% CI+/-28.4%, +LR 3.00, 95% CI = 1.4-5.1) had 100% relief at two to four weeks. Fourteen of 14 (100%, 95% CI+/-21.5%, -LR 0.0, 95% CI = 0.0-2.1) with an initial negative block failed to achieve 100% relief at follow-up. Patients who fail to achieve initial relief after SIJ injection with anesthetic and steroid are very unlikely to achieve significant pain relief at follow-up; negative likelihood ratios (LR) in this study, based on how success is defined, range between 0 and 0.9. Clinically significant positive likelihood ratios of anesthetic response to SIJ injection are more limited and less robust, but are valuable in predicting 50% relief or 100% relief at two to four weeks. © 2017 American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

  13. Personalized Cancer Medicine: Molecular Diagnostics, Predictive biomarkers, and Drug Resistance

    Science.gov (United States)

    Gonzalez de Castro, D; Clarke, P A; Al-Lazikani, B; Workman, P

    2013-01-01

    The progressive elucidation of the molecular pathogenesis of cancer has fueled the rational development of targeted drugs for patient populations stratified by genetic characteristics. Here we discuss general challenges relating to molecular diagnostics and describe predictive biomarkers for personalized cancer medicine. We also highlight resistance mechanisms for epidermal growth factor receptor (EGFR) kinase inhibitors in lung cancer. We envisage a future requiring the use of longitudinal genome sequencing and other omics technologies alongside combinatorial treatment to overcome cellular and molecular heterogeneity and prevent resistance caused by clonal evolution. PMID:23361103

  14. Scientific Prediction and Prophetic Patenting in Drug Discovery.

    Science.gov (United States)

    Curry, Stephen H; Schneiderman, Anne M

    2015-01-01

    Pharmaceutical patenting involves writing claims based on both discoveries already made, and on prophesy of future developments in an ongoing project. This is necessitated by the very different timelines involved in the drug discovery and product development process on the one hand, and successful patenting on the other. If patents are sought too early there is a risk that patent examiners will disallow claims because of lack of enablement. If patenting is delayed, claims are at risk of being denied on the basis of existence of prior art, because the body of relevant known science will have developed significantly while the project was being pursued. This review examines the role of prophetic patenting in relation to the essential predictability of many aspects of drug discovery science, promoting the concepts of discipline-related and project-related prediction. This is especially directed towards patenting activities supporting commercialization of academia-based discoveries, where long project timelines occur, and where experience, and resources to pay for patenting, are limited. The need for improved collaborative understanding among project scientists, technology transfer professionals in, for example, universities, patent attorneys, and patent examiners is emphasized.

  15. Smart Nanoparticles Undergo Phase Transition for Enhanced Cellular Uptake and Subsequent Intracellular Drug Release in a Tumor Microenvironment.

    Science.gov (United States)

    Ye, Guihua; Jiang, Yajun; Yang, Xiaoying; Hu, Hongxiang; Wang, Beibei; Sun, Lu; Yang, Victor C; Sun, Duxin; Gao, Wei

    2018-01-10

    Inefficient cellular uptake and intracellular drug release at the tumor site are two major obstacles limiting the antitumor efficacy of nanoparticle delivery systems. To overcome both problems, we designed a smart nanoparticle that undergoes phase transition in a tumor microenvironment (TME). The smart nanoparticle is generated using a lipid-polypetide hybrid nanoparticle, which comprises a PEGylated lipid monolayer shell and a pH-sensitive hydrophobic poly-l-histidine core and is loaded with the antitumor drug doxorubicin (DOX). The smart nanoparticle undergoes a two-step phase transition at two different pH values in the TME: (i) At the TME (pH e : 7.0-6.5), the smart nanoparticle swells, and its surface potential turns from negative to neutral, facilitating the cellular uptake; (ii) After internalization, at the acid endolysosome (pH endo : 6.5-4.5), the smart nanoparticle dissociates and induces endolysosome escape to release DOX into the cytoplasm. In addition, a tumor-penetrating peptide iNRG was modified on the surface of the smart nanoparticle as a tumor target moiety. The in vitro studies demonstrated that the iNGR-modified smart nanoparticles promoted cellular uptake in the acidic environment (pH 6.8). The in vivo studies showed that the iNGR-modified smart nanoparticles exerted more potent antitumor efficacy against late-stage aggressive breast carcinoma than free DOX. These data suggest that the smart nanoparticles may serve as a promising delivery system for sequential uptake and intracellular drug release of antitumor agents. The easy preparation of these smart nanoparticles may also have advantages in the future manufacture for clinical trials and clinical use.

  16. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information.

    Science.gov (United States)

    Luo, Yunan; Zhao, Xinbin; Zhou, Jingtian; Yang, Jinglin; Zhang, Yanqing; Kuang, Wenhua; Peng, Jian; Chen, Ligong; Zeng, Jianyang

    2017-09-18

    The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug-target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug-target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug-target interactions and repurpose existing drugs.Network-based data integration for drug-target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations.

  17. A unified frame of predicting side effects of drugs by using linear neighborhood similarity.

    Science.gov (United States)

    Zhang, Wen; Yue, Xiang; Liu, Feng; Chen, Yanlin; Tu, Shikui; Zhang, Xining

    2017-12-14

    Drug side effects are one of main concerns in the drug discovery, which gains wide attentions. Investigating drug side effects is of great importance, and the computational prediction can help to guide wet experiments. As far as we known, a great number of computational methods have been proposed for the side effect predictions. The assumption that similar drugs may induce same side effects is usually employed for modeling, and how to calculate the drug-drug similarity is critical in the side effect predictions. In this paper, we present a novel measure of drug-drug similarity named "linear neighborhood similarity", which is calculated in a drug feature space by exploring linear neighborhood relationship. Then, we transfer the similarity from the feature space into the side effect space, and predict drug side effects by propagating known side effect information through a similarity-based graph. Under a unified frame based on the linear neighborhood similarity, we propose method "LNSM" and its extension "LNSM-SMI" to predict side effects of new drugs, and propose the method "LNSM-MSE" to predict unobserved side effect of approved drugs. We evaluate the performances of LNSM and LNSM-SMI in predicting side effects of new drugs, and evaluate the performances of LNSM-MSE in predicting missing side effects of approved drugs. The results demonstrate that the linear neighborhood similarity can improve the performances of side effect prediction, and the linear neighborhood similarity-based methods can outperform existing side effect prediction methods. More importantly, the proposed methods can predict side effects of new drugs as well as unobserved side effects of approved drugs under a unified frame.

  18. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions

    KAUST Repository

    Abdelaziz, Ibrahim; Fokoue, Achille; Hassanzadeh, Oktie; Zhang, Ping; Sadoghi, Mohammad

    2017-01-01

    Drug-Drug Interactions (DDIs) are a major cause of preventable Adverse Drug Reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed Tiresias and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs as well as newly developed drugs.

  19. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions

    KAUST Repository

    Abdelaziz, Ibrahim

    2017-06-12

    Drug-Drug Interactions (DDIs) are a major cause of preventable Adverse Drug Reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed Tiresias and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs as well as newly developed drugs.

  20. Predicting client attendance at further treatment following drug and alcohol detoxification: Theory of Planned Behaviour and Implementation Intentions.

    Science.gov (United States)

    Kelly, Peter J; Leung, Joanne; Deane, Frank P; Lyons, Geoffrey C B

    2016-11-01

    Despite clinical recommendations that further treatment is critical for successful recovery following drug and alcohol detoxification, a large proportion of clients fail to attend treatment after detoxification. In this study, individual factors and constructs based on motivational and volitional models of health behaviour were examined as predictors of post-detoxification treatment attendance. The sample consisted of 220 substance-dependent individuals participating in short-term detoxification programs provided by The Australian Salvation Army. The Theory of Planned Behaviour and Implementation Intentions were used to predict attendance at subsequent treatment. Follow-up data were collected for 177 participants (81%), with 104 (80%) of those participants reporting that they had either attended further formal treatment (e.g. residential rehabilitation programs, outpatient counselling) or mutual support groups in the 2 weeks after leaving the detoxification program. Logistic regression examined the predictors of further treatment attendance. The full model accounted for 21% of the variance in treatment attendance, with attitude and Implementation Intentions contributing significantly to the prediction. Findings from the present study would suggest that assisting clients to develop a specific treatment plan, as well as helping clients to build positive perceptions about subsequent treatment, will promote greater attendance at further treatment following detoxification. [Kelly PJ, Leung J, Deane FP, Lyons GCB. Predicting client attendance at further treatment following drug and alcohol detoxification: Theory of Planned Behaviour and Implementation Intentions. Drug Alcohol Rev 2016;35:678-685]. © 2015 Australasian Professional Society on Alcohol and other Drugs.

  1. Presence of Eosinophils in Nasal Secretion during Acute Respiratory Tract Infection in Young Children Predicts Subsequent Wheezing within Two Months

    Directory of Open Access Journals (Sweden)

    Miwa Shinohara

    2008-01-01

    Conclusions: Our findings not only suggest that nasal eosinophil testing may serve as a convenient clinical marker for identifying young children at risk for subsequent wheezing, but also shed new light on the role of eosinophils in the onset of wheezing in young children.

  2. Scaling predictive modeling in drug development with cloud computing.

    Science.gov (United States)

    Moghadam, Behrooz Torabi; Alvarsson, Jonathan; Holm, Marcus; Eklund, Martin; Carlsson, Lars; Spjuth, Ola

    2015-01-26

    Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.

  3. Mind the gap : predicting cardiovascular risk during drug development

    NARCIS (Netherlands)

    Chain, Anne S. Y.

    2012-01-01

    Cardiovascular safety issues, specifically drug-induced QT/QTc-interval prolongation, remain a major cause of drug attrition during clinical development and is one of the main causes for post-market drug withdrawals accounting for 15-34% of all drug discontinuation. Given the potentially fatal

  4. Fasting Plasma Insulin at 5 Years of Age Predicted Subsequent Weight Increase in Early Childhood over a 5-Year Period—The Da Qing Children Cohort Study

    Science.gov (United States)

    Chen, Yan Yan; Wang, Jin Ping; Jiang, Ya Yun; Li, Hui; Hu, Ying Hua; Lee, Kok Onn; Li, Guang Wei

    2015-01-01

    Background The association between hyperinsulinemia and obesity is well known. However, it is uncertain especially in childhood obesity, if initial fasting hyperinsulinemia predicts obesity, or obesity leads to hyperinsulinemia through insulin resistance. Objective To investigate the predictive effect of fasting plasma insulin on subsequent weight change after a 5-year interval in childhood. Methods 424 Children from Da Qing city, China, were recruited at 5 years of age and followed up for 5 years. Blood pressure, anthropometric measurements, fasting plasma insulin, glucose and triglycerides were measured at baseline and 5 years later. Results Fasting plasma insulin at 5 years of age was significantly correlated with change of weight from 5 to 10 years (ΔWeight). Children in the lowest insulin quartile had ΔWeight of 13.08±0.73 kg compare to 18.39±0.86 in the highest insulin quartile (P<0.0001) in boys, and similarly 12.03±0.71 vs 15.80±0.60 kg (P<0.0001) in girls. Multivariate analysis showed that the predictive effect of insulin at 5 years of age on subsequent weight gain over 5 years remained statistically significant even after the adjustment for age, sex, birth weight, TV-viewing time and weight (or body mass index) at baseline. By contrast, the initial weight at 5 years of age did not predict subsequent changes in insulin level 5 years later. Children who had both higher fasting insulin and weight at 5 years of age showed much higher levels of systolic blood pressures, fasting plasma glucose, the homeostasis model assessment for insulin resistance (HOMA-IR) and triglycerides at 10 years of age. Conclusions Fasting plasma insulin at 5 years of age predicts weight gain and cardiovascular risk factors 5 year later in Chinese children of early childhood, but the absolute weight at 5 years of age did not predict subsequent change in fasting insulin. PMID:26047327

  5. Fasting Plasma Insulin at 5 Years of Age Predicted Subsequent Weight Increase in Early Childhood over a 5-Year Period-The Da Qing Children Cohort Study.

    Directory of Open Access Journals (Sweden)

    Yan Yan Chen

    Full Text Available The association between hyperinsulinemia and obesity is well known. However, it is uncertain especially in childhood obesity, if initial fasting hyperinsulinemia predicts obesity, or obesity leads to hyperinsulinemia through insulin resistance.To investigate the predictive effect of fasting plasma insulin on subsequent weight change after a 5-year interval in childhood.424 Children from Da Qing city, China, were recruited at 5 years of age and followed up for 5 years. Blood pressure, anthropometric measurements, fasting plasma insulin, glucose and triglycerides were measured at baseline and 5 years later.Fasting plasma insulin at 5 years of age was significantly correlated with change of weight from 5 to 10 years (ΔWeight. Children in the lowest insulin quartile had ΔWeight of 13.08±0.73 kg compare to 18.39±0.86 in the highest insulin quartile (P<0.0001 in boys, and similarly 12.03±0.71 vs 15.80±0.60 kg (P<0.0001 in girls. Multivariate analysis showed that the predictive effect of insulin at 5 years of age on subsequent weight gain over 5 years remained statistically significant even after the adjustment for age, sex, birth weight, TV-viewing time and weight (or body mass index at baseline. By contrast, the initial weight at 5 years of age did not predict subsequent changes in insulin level 5 years later. Children who had both higher fasting insulin and weight at 5 years of age showed much higher levels of systolic blood pressures, fasting plasma glucose, the homeostasis model assessment for insulin resistance (HOMA-IR and triglycerides at 10 years of age.Fasting plasma insulin at 5 years of age predicts weight gain and cardiovascular risk factors 5 year later in Chinese children of early childhood, but the absolute weight at 5 years of age did not predict subsequent change in fasting insulin.

  6. Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.

    Science.gov (United States)

    Zhang, Wen; Chen, Yanlin; Li, Dingfang

    2017-11-25

    Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.

  7. Predictability of the recent slowdown and subsequent recovery of large-scale surface warming using statistical methods

    Science.gov (United States)

    Mann, Michael E.; Steinman, Byron A.; Miller, Sonya K.; Frankcombe, Leela M.; England, Matthew H.; Cheung, Anson H.

    2016-04-01

    The temporary slowdown in large-scale surface warming during the early 2000s has been attributed to both external and internal sources of climate variability. Using semiempirical estimates of the internal low-frequency variability component in Northern Hemisphere, Atlantic, and Pacific surface temperatures in concert with statistical hindcast experiments, we investigate whether the slowdown and its recent recovery were predictable. We conclude that the internal variability of the North Pacific, which played a critical role in the slowdown, does not appear to have been predictable using statistical forecast methods. An additional minor contribution from the North Atlantic, by contrast, appears to exhibit some predictability. While our analyses focus on combining semiempirical estimates of internal climatic variability with statistical hindcast experiments, possible implications for initialized model predictions are also discussed.

  8. Encouraging prediction during production facilitates subsequent comprehension: Evidence from interleaved object naming in sentence context and sentence reading

    NARCIS (Netherlands)

    Hintz, F.; Meyer, A.S.; Hüttig, F.

    2016-01-01

    Many studies have shown that a supportive context facilitates language comprehension. A currently influential view is that language production may support prediction in language comprehension. Experimental evidence for this, however, is relatively sparse. Here we explored whether encouraging

  9. Species differences in drug glucuronidation: Humanized UDP-glucuronosyltransferase 1 mice and their application for predicting drug glucuronidation and drug-induced toxicity in humans.

    Science.gov (United States)

    Fujiwara, Ryoichi; Yoda, Emiko; Tukey, Robert H

    2018-02-01

    More than 20% of clinically used drugs are glucuronidated by a microsomal enzyme UDP-glucuronosyltransferase (UGT). Inhibition or induction of UGT can result in an increase or decrease in blood drug concentration. To avoid drug-drug interactions and adverse drug reactions in individuals, therefore, it is important to understand whether UGTs are involved in metabolism of drugs and drug candidates. While most of glucuronides are inactive metabolites, acyl-glucuronides that are formed from compounds with a carboxylic acid group can be highly toxic. Animals such as mice and rats are widely used to predict drug metabolism and drug-induced toxicity in humans. However, there are marked species differences in the expression and function of drug-metabolizing enzymes including UGTs. To overcome the species differences, mice in which certain drug-metabolizing enzymes are humanized have been recently developed. Humanized UGT1 (hUGT1) mice were created in 2010 by crossing Ugt1-null mice with human UGT1 transgenic mice in a C57BL/6 background. hUGT1 mice can be promising tools to predict human drug glucuronidation and acyl-glucuronide-associated toxicity. In this review article, studies of drug metabolism and toxicity in the hUGT1 mice are summarized. We further discuss research and strategic directions to advance the understanding of drug glucuronidation in humans. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

  10. Freshman year alcohol and marijuana use prospectively predict time to college graduation and subsequent adult roles and independence.

    Science.gov (United States)

    Wilhite, Emily R; Ashenhurst, James R; Marino, Elise N; Fromme, Kim

    2017-06-15

    This study examined how freshman year substance use prospectively predicted time to college graduation, and whether delayed graduation predicted postponed adoption of adult roles and future substance use. Participants were part of a longitudinal study that began in 2004. The first analyses focused on freshman year (N = 2,050). The second analyses corresponded to a subset of participants at age 27 (N = 575). Measures included self-reported substance use, adult role adoption, and university reported graduation dates. Results indicated that frequent binge drinking and marijuana use during freshman year predicted delayed college graduation. Those who took longer to graduate were more likely to have lower incomes and were less likely to obtain a graduate degree. Taking 5-6 years to graduate was associated with greater likelihood of alcohol-related problems. Findings support the importance of interventions during freshman year of college to decrease substance use and promote timely graduation.

  11. An analysis of abstracts presented to the College on Problems of Drug Dependence meeting and subsequent publication in peer review journals

    Science.gov (United States)

    Valderrama-Zurián, Juan Carlos; Bolaños-Pizarro, Máxima; Bueno-Cañigral, Francisco Jesús; Álvarez, F Javier; Ontalba-Ruipérez, José Antonio; Aleixandre-Benavent, Rafael

    2009-01-01

    Background Subsequent publication rate of abstracts presented at meetings is seen as an indicator of the interest and quality of the meeting. We have analyzed characteristics and rate publication in peer-reviewed journals derived from oral communications and posters presented at the 1999 College on Problems of Drug Dependence (CPDD) meeting. Methods All 689 abstracts presented at the 1999 CPDD meeting were reviewed. In order to find the existence of publications derived from abstracts presented at that meeting, a set of bibliographical searches in the database Medline was developed in July 2006. Information was gathered concerning the abstracts, articles and journals in which they were published. Results 254 out of 689 abstracts (36.9%) gave rise to at least one publication. The oral communications had a greater likelihood of being published than did the posters (OR = 2.53, 95% CI 1.80-3.55). The average time lapse to publication of an article was 672.97 days. The number of authors per work in the subsequent publications was 4.55. The articles were published in a total of 84 journals, of which eight were indexed with the subject term Substance-Related Disorders. Psychopharmacology (37 articles, 14.5%) was the journal that published the greatest number of articles subsequent to the abstracts presented at the 1999 CPDD meeting. Conclusion One out of every three abstracts presented to the 1999 CPDD meeting were later published in peer-reviewed journals indexed in Medline. The subsequent publication of the abstracts presented in the CPDD meetings should be actively encouraged, as this maximizes the dissemination of the scientific research and therefore the investment. PMID:19889211

  12. Predicting the Toxicity of Adjuvant Breast Cancer Drug Combination Therapy

    Science.gov (United States)

    2013-03-01

    Neratinib Versus Lapatinib Plus Capecitabine For ErbB2 Positive Advanced Breast Cancer Active, not recruiting No Results Available YES neratinib -9...Drug: Neratinib |Drug: Lapatinib|Drug: Capecitabine Efficacy and Safety of BMS-690514 in Combination With Letrozole to Treat Metastatic Breast Cancer

  13. Does type of first contact in depressive and bipolar disorders predict subsequent hospitalisation and risk of suicide?

    DEFF Research Database (Denmark)

    Kessing, Lars Vedel; Munk-Jørgensen, Povl

    2004-01-01

    BACKGROUND: Only a few studies have investigated how the type of first contact is associated with the risk of subsequent hospitalisation and the risk of committing suicide for patients with depressive or bipolar disorders. METHOD: All outpatients (patients in psychiatric ambulatories and community...... treatment as their first contact. Patients with depressive disorder who were admitted also had increased risk of committing suicide eventually. LIMITATIONS: The diagnoses are clinician based. CONCLUSIONS: Patients referred to inpatient treatment have a poorer long-term prognosis than patients treated...... psychiatry centres) and in-patients (patients admitted during daytime or overnight to a psychiatric hospital) with a diagnosis of depressive or bipolar disorder at first contact ever in a period from 1995 to 1999 in Denmark were identified from the Danish Psychiatric Central Research Register (DPCRR...

  14. Post processing of protein-compound docking for fragment-based drug discovery (FBDD): in-silico structure-based drug screening and ligand-binding pose prediction.

    Science.gov (United States)

    Fukunishi, Yoshifumi

    2010-01-01

    For fragment-based drug development, both hit (active) compound prediction and docking-pose (protein-ligand complex structure) prediction of the hit compound are important, since chemical modification (fragment linking, fragment evolution) subsequent to the hit discovery must be performed based on the protein-ligand complex structure. However, the naïve protein-compound docking calculation shows poor accuracy in terms of docking-pose prediction. Thus, post-processing of the protein-compound docking is necessary. Recently, several methods for the post-processing of protein-compound docking have been proposed. In FBDD, the compounds are smaller than those for conventional drug screening. This makes it difficult to perform the protein-compound docking calculation. A method to avoid this problem has been reported. Protein-ligand binding free energy estimation is useful to reduce the procedures involved in the chemical modification of the hit fragment. Several prediction methods have been proposed for high-accuracy estimation of protein-ligand binding free energy. This paper summarizes the various computational methods proposed for docking-pose prediction and their usefulness in FBDD.

  15. Early follow-up data from seizure diaries can be used to predict subsequent seizures in same cohort by borrowing strength across participants

    Science.gov (United States)

    Hall, Charles B.; Lipton, Richard B.; Tennen, Howard; Haut, Sheryl R.

    2014-01-01

    Accurate prediction of seizures in persons with epilepsy offers opportunities for both precautionary measures and preemptive treatment. Previously identified predictors of seizures include patient-reported seizure anticipation, as well as stress, anxiety, and decreased sleep. In this study, we developed three models using 30 days of nightly seizure diary data in a cohort of 71 individuals with a history of uncontrolled seizures to predict subsequent seizures in the same cohort over a 30-day follow-up period. The best model combined the individual’s seizure history with that of the remainder of the cohort, resulting in 72% sensitivity for 80% specificity, and 0.83 area under the receiver operating characteristic curve. The possibility of clinically relevant prediction should be examined through electronic data capture and more specific and more frequent sampling, and with patient training to improve prediction. PMID:19138755

  16. Use of a radioimmunoassay of plasma progesterone for predicting litter size and subsequent adaptation of feeding level in sheep

    International Nuclear Information System (INIS)

    Wiel, D.F.M. van de; Visscher, A.H.; Dekker, T.P.

    1976-01-01

    Litter sizes in ewes were predicted using the plasma progesterone concentration at 80-110 days after mating, with or without multiplication by bodyweight, as well as a priori probabilities and expected economic losses caused by incorrect classifications. Progesterone was assayed using a fluorimetric method and radioimmunoassay, and the results of both methods were compared in the Texel breed. Ewes were allotted to three feeding classes, according to the predicted litter sizes of 0-1, 2-3 and >=4 lambs. Using these classes the fluorimetric method gave 82.9% correct classifications, and the radioimmunoassay 80.0% correct. When calculated on the total of 194 ewes of the Finnish Landrace, Ile de France and Texel breeds, the fluorimetric method showed an accuracy of 65.0% correct classifications. (author)

  17. Drug target prediction and prioritization: using orthology to predict essentiality in parasite genomes

    Directory of Open Access Journals (Sweden)

    Hall Ross S

    2010-04-01

    Full Text Available Abstract Background New drug targets are urgently needed for parasites of socio-economic importance. Genes that are essential for parasite survival are highly desirable targets, but information on these genes is lacking, as gene knockouts or knockdowns are difficult to perform in many species of parasites. We examined the applicability of large-scale essentiality information from four model eukaryotes, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Saccharomyces cerevisiae, to discover essential genes in each of their genomes. Parasite genes that lack orthologues in their host are desirable as selective targets, so we also examined prediction of essential genes within this subset. Results Cross-species analyses showed that the evolutionary conservation of genes and the presence of essential orthologues are each strong predictors of essentiality in eukaryotes. Absence of paralogues was also found to be a general predictor of increased relative essentiality. By combining several orthology and essentiality criteria one can select gene sets with up to a five-fold enrichment in essential genes compared with a random selection. We show how quantitative application of such criteria can be used to predict a ranked list of potential drug targets from Ancylostoma caninum and Haemonchus contortus - two blood-feeding strongylid nematodes, for which there are presently limited sequence data but no functional genomic tools. Conclusions The present study demonstrates the utility of using orthology information from multiple, diverse eukaryotes to predict essential genes. The data also emphasize the challenge of identifying essential genes among those in a parasite that are absent from its host.

  18. Lying and the Subsequent Desire for Toothpaste: Activity in the Somatosensory Cortex Predicts Embodiment of the Moral-Purity Metaphor.

    Science.gov (United States)

    Denke, Claudia; Rotte, Michael; Heinze, Hans-Jochen; Schaefer, Michael

    2016-02-01

    It is well known from literature and religious ceremonies that there is a link between physical cleansing and moral transgressions. Only recently, psychological experiments explored this association and demonstrated that a threat to moral purity increases the demand of physical cleansing. Moreover, it has been shown that physical cleansing is actually efficacious to cope with threatened morality. This so-called Macbeth effect has been explained by an embodiment of the moral-purity metaphor. We tested this hypothesis by means of an functional magnetic resonsce imaging (fMRI) experiment. Participants were instructed to enact scenarios including either an immoral act (lying) or a moral deed (telling the truth). Subsequently, the participants were asked to rate the desirableness of various products. Results revealed that participants rated cleansing products (but not other goods) more desirable after performing an immoral than after a moral act. This Macbeth effect was accompanied by an active cortical network including sensorimotor brain areas during rating of cleansing products (but not while evaluating noncleansing goods). The results demonstrate neurobiological evidence for an embodiment of the moral-purity metaphor. Thus, abstract thoughts about morality can be grounded in sensory experiences. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  19. Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering.

    Science.gov (United States)

    Shi, Jian-Yu; Yiu, Siu-Ming; Li, Yiming; Leung, Henry C M; Chin, Francis Y L

    2015-07-15

    Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are "missing" in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a "super-target" to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. Our approach is available at http://web.hku.hk/∼liym1018/projects/drug/drug.html or http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. The Combination of GIS and Biphasic to Better Predict In Vivo Dissolution of BCS Class IIb Drugs, Ketoconazole and Raloxifene.

    Science.gov (United States)

    Tsume, Yasuhiro; Igawa, Naoto; Drelich, Adam J; Amidon, Gregory E; Amidon, Gordon L

    2018-01-01

    The formulation developments and the in vivo assessment of Biopharmaceutical Classification System (BCS) class II drugs are challenging due to their low solubility and high permeability in the human gastrointestinal (GI) tract. Since the GI environment influences the drug dissolution of BCS class II drugs, the human GI characteristics should be incorporated into the in vitro dissolution system to predict bioperformance of BCS class II drugs. An absorptive compartment may be important in dissolution apparatus for BCS class II drugs, especially for bases (BCS IIb) because of high permeability, precipitation, and supersaturation. Thus, the in vitro dissolution system with an absorptive compartment may help predicting the in vivo phenomena of BCS class II drugs better than compendial dissolution apparatuses. In this study, an absorptive compartment (a biphasic device) was introduced to a gastrointestinal simulator. This addition was evaluated if this in vitro system could improve the prediction of in vivo dissolution for BCS class IIb drugs, ketoconazole and raloxifene, and subsequent absorption. The gastrointestinal simulator is a practical in vivo predictive tool and exhibited an improved in vivo prediction utilizing the biphasic format and thus a better tool for evaluating the bioperformance of BCS class IIb drugs than compendial apparatuses. Copyright © 2018. Published by Elsevier Inc.

  1. Predicting Adolescent Drug Abuse: A Review of Issues, Methods and Correlates. Research Issues 11.

    Science.gov (United States)

    Lettieri, Dan J., Ed.

    Presented are 18 papers on predicting adolescent drug abuse. The papers have the following titles: "Current Issues in the Epidemiology of Drug Abuse as Related to Psychosocial Studies of Adolescent Drug Use"; "The Quest for Interpersonal Predictors of Marihuana Abuse in Adolescents"; "Assessing the Interpersonal Determinants of Adolescent Drug…

  2. Using human genetics to predict the effects and side-effects of drugs

    DEFF Research Database (Denmark)

    Stender, Stefan; Tybjærg-Hansen, Anne

    2016-01-01

    PURPOSE OF REVIEW: 'Genetic proxies' are increasingly being used to predict the effects of drugs. We present an up-to-date overview of the use of human genetics to predict effects and adverse effects of lipid-targeting drugs. RECENT FINDINGS: LDL cholesterol lowering variants in HMG-Coenzyme A re...

  3. Pharmacogenetic approaches to the prediction of drug response

    International Nuclear Information System (INIS)

    Vesell, E.S.

    1986-01-01

    The following review of pharmacogenetic progress and methodology is offered to stimulate and suggest analogous studies on drugs of abuse. It is readily acknowledged that formidable methodological problems are posed by adapting to drugs of abuse these pharmacogenetic approaches based on the administration of single safe doses of various prescription drugs to normal subjects under carefully controlled environmental conditions. Results of similarly designed studies on drugs of abuse in addicts might be uninterpretable because of confounding by numerous environmental perturbations, including the smoking of cigarettes and/or marijuana, nutritional variations, and intake of other drugs such as ethanol. Ethical considerations render objectionable the administration to unaddicted subjects of drugs at dosage levels usually ingested by drug abusers. Other approaches would have to be taken in such normal subjects. Possibilities include administration of tracer doses of /sup 14/C- or /sup 13/C- labeled drugs or growth of normal cells in culture to investigate their pharmacokinetic and/or pharmacodynamic responses to various drugs of abuse

  4. Predicting Hepatotoxicity of Drug Metabolites Via an Ensemble Approach Based on Support Vector Machine

    Science.gov (United States)

    Lu, Yin; Liu, Lili; Lu, Dong; Cai, Yudong; Zheng, Mingyue; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian

    2017-11-20

    Drug-induced liver injury (DILI) is a major cause of drug withdrawal. The chemical properties of the drug, especially drug metabolites, play key roles in DILI. Our goal is to construct a QSAR model to predict drug hepatotoxicity based on drug metabolites. 64 hepatotoxic drug metabolites and 3,339 non-hepatotoxic drug metabolites were gathered from MDL Metabolite Database. Considering the imbalance of the dataset, we randomly split the negative samples and combined each portion with all the positive samples to construct individually balanced datasets for constructing independent classifiers. Then, we adopted an ensemble approach to make prediction based on the results of all individual classifiers and applied the minimum Redundancy Maximum Relevance (mRMR) feature selection method to select the molecular descriptors. Eventually, for the drugs in the external test set, a Bayesian inference method was used to predict the hepatotoxicity of a drug based on its metabolites. The model showed the average balanced accuracy=78.47%, sensitivity =74.17%, and specificity=82.77%. Five molecular descriptors characterizing molecular polarity, intramolecular bonding strength, and molecular frontier orbital energy were obtained. When predicting the hepatotoxicity of a drug based on all its metabolites, the sensitivity, specificity and balanced accuracy were 60.38%, 70.00%, and 65.19%, respectively, indicating that this method is useful for identifying the hepatotoxicity of drugs. We developed an in silico model to predict hepatotoxicity of drug metabolites. Moreover, Bayesian inference was applied to predict the hepatotoxicity of a drug based on its metabolites which brought out valuable high sensitivity and specificity. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  5. Prediction of resistance development against drug combinations by collateral responses to component drugs

    DEFF Research Database (Denmark)

    Munck, Christian; Gumpert, Heidi; Nilsson Wallin, Annika

    2014-01-01

    the genomes of all evolved E. coli lineages, we identified the mutational events that drive the differences in drug resistance levels and found that the degree of resistance development against drug combinations can be understood in terms of collateral sensitivity and resistance that occurred during...... adaptation to the component drugs. Then, using engineered E. coli strains, we confirmed that drug resistance mutations that imposed collateral sensitivity were suppressed in a drug pair growth environment. These results provide a framework for rationally selecting drug combinations that limit resistance......Resistance arises quickly during chemotherapeutic selection and is particularly problematic during long-term treatment regimens such as those for tuberculosis, HIV infections, or cancer. Although drug combination therapy reduces the evolution of drug resistance, drug pairs vary in their ability...

  6. Early preschool processing abilities predict subsequent reading outcomes in bilingual Spanish-Catalan children with Specific Language Impairment (SLI).

    Science.gov (United States)

    Aguilar-Mediavilla, Eva; Buil-Legaz, Lucía; Pérez-Castelló, Josep A; Rigo-Carratalà, Eduard; Adrover-Roig, Daniel

    2014-01-01

    Children with Specific Language Impairment (SLI) have severe language difficulties without showing hearing impairments, cognitive deficits, neurological damage or socio-emotional deprivation. However, previous studies have shown that children with SLI show some cognitive and literacy problems. Our study analyses the relationship between preschool cognitive and linguistic abilities and the later development of reading abilities in Spanish-Catalan bilingual children with SLI. The sample consisted of 17 bilingual Spanish-Catalan children with SLI and 17 age-matched controls. We tested eight distinct processes related to phonological, attention, and language processing at the age of 6 years and reading at 8 years of age. Results show that bilingual Spanish-Catalan children with SLI show significantly lower scores, as compared to typically developing peers, in phonological awareness, phonological memory, and rapid automatized naming (RAN), together with a lower outcome in tasks measuring sentence repetition and verbal fluency. Regarding attentional processes, bilingual Spanish-Catalan children with SLI obtained lower scores in auditory attention, but not in visual attention. At the age of 8 years Spanish-Catalan children with SLI had lower scores than their age-matched controls in total reading score, letter identification (decoding), and in semantic task (comprehension). Regression analyses identified both phonological awareness and verbal fluency at the age of 6 years to be the best predictors of subsequent reading performance at the age of 8 years. Our data suggest that language acquisition problems and difficulties in reading acquisition in bilingual children with SLI might be related to the close interdependence between a limitation in cognitive processing and a deficit at the linguistic level. After reading this article, readers will be able to: identify their understanding of the relation between language difficulties and reading outcomes; explain how processing

  7. Pharmacogenetics and Predictive Testing of Drug Hypersensitivity Reactions.

    Science.gov (United States)

    Böhm, Ruwen; Cascorbi, Ingolf

    2016-01-01

    Adverse drug reactions adverse drug reaction (ADR) occur in approximately 17% of patients. Avoiding ADR is thus mandatory from both an ethical and an economic point of view. Whereas, pharmacogenetics changes of the pharmacokinetics may contribute to the explanation of some type A reactions, strong relationships of genetic markers has also been shown for drug hypersensitivity belonging to type B reactions. We present the classifications of ADR, discuss genetic influences and focus on delayed-onset hypersensitivity reactions, i.e., drug-induced liver injury, drug-induced agranulocytosis, and severe cutaneous ADR. A guidance how to read and interpret the contingency table is provided as well as an algorithm whether and how a test for a pharmacogenetic biomarker should be conducted.

  8. Cell-specific prediction and application of drug-induced gene expression profiles.

    Science.gov (United States)

    Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David; Dudley, Joel

    2018-01-01

    Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

  9. Prediction and Factor Extraction of Drug Function by Analyzing Medical Records in Developing Countries.

    Science.gov (United States)

    Hu, Min; Nohara, Yasunobu; Nakamura, Masafumi; Nakashima, Naoki

    2017-01-01

    The World Health Organization has declared Bangladesh one of 58 countries facing acute Human Resources for Health (HRH) crisis. Artificial intelligence in healthcare has been shown to be successful for diagnostics. Using machine learning to predict pharmaceutical prescriptions may solve HRH crises. In this study, we investigate a predictive model by analyzing prescription data of 4,543 subjects in Bangladesh. We predict the function of prescribed drugs, comparing three machine-learning approaches. The approaches compare whether a subject shall be prescribed medicine from the 21 most frequently prescribed drug functions. Receiver Operating Characteristics (ROC) were selected as a way to evaluate and assess prediction models. The results show the drug function with the best prediction performance was oral hypoglycemic drugs, which has an average AUC of 0.962. To understand how the variables affect prediction, we conducted factor analysis based on tree-based algorithms and natural language processing techniques.

  10. iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.

    Directory of Open Access Journals (Sweden)

    Xuan Xiao

    Full Text Available Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called "iGPCR-drug", was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug - target interaction networks.

  11. Do psychopathic traits assessed in mid-adolescence predict mental health, psychosocial, and antisocial, including criminal outcomes, over the subsequent 5 years?

    Science.gov (United States)

    Hemphälä, Malin; Hodgins, Sheilagh

    2014-01-01

    To determine whether psychopathic traits assessed in mid-adolescence predicted mental health, psychosocial, and antisocial (including criminal) outcomes 5 years later and would thereby provide advantages over diagnosing conduct disorder (CD). Eighty-six women and 61 men were assessed in mid-adolescence when they first contacted a clinic for substance misuse and were reassessed 5 years later. Assessments in adolescence include the Psychopathy Checklist-Youth Version (PCL-YV), and depending on their age, either the Kiddie-Schedule for Affective Disorders and Schizophrenia for School-Aged Children or the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (SCID). Assessments in early adulthood included the SCID, self-reports of psychosocial functioning, aggressive behaviour, and criminality and official criminal records. The antisocial facet score positively predicted the number of anxiety symptoms and likelihood of receiving treatment for substance use disorders (SUDs). Lifestyle and antisocial facet scores negatively predicted Global Assessment of Functioning scores. By contrast, the interpersonal score and male sex independently and positively predicted the number of months worked or studied, as did the interaction of Lifestyle × Sex indicating that among men, but not women, an increase in lifestyle facet score was associated with less time worked or studied. Interpersonal and antisocial scores positively predicted school drop-out. Antisocial facet scores predicted the number of symptoms of antisocial personality disorder, alcohol and SUDs, and violent and nonviolent criminality but much more strongly among males than females. Predictions from numbers of CD symptoms were similar. Psychopathic traits among adolescents who misuse substances predict an array of outcomes over the subsequent 5 years. Information on the levels of these traits may be useful for planning treatment.

  12. High levels of C-reactive protein in the peripheral blood during visceral leishmaniasis predict subsequent development of post kala-azar dermal leishmaniasis

    DEFF Research Database (Denmark)

    Gasim, S; Theander, T G; ElHassan, A M

    2000-01-01

    Post kala-azar dermal leishmaniasis (PKDL) is a known sequel to visceral leishmaniasis in India and East Africa, and in Sudan about 50% of the kala-azar patients develop PKDL. In this study we followed kala-azar patients from diagnosis and up to 2 years after initiation of treatment. During...... and in keratinocytes during visceral leishmaniasis predict subsequent development of PKDL. The method however requires expensive equipment and reagents. The results of the present study indicate that kala-azar patients, who have a high risk of developing PKDL after treatment can be identified by measuring plasma CRP....

  13. MARKETING PREDICTIONS IN ANTI-DRUG SOCIAL PROGRAMS: USE OF CAUSAL METHODS IN THE STUDY AND PREVENTION OF DRUG ABUSE

    Directory of Open Access Journals (Sweden)

    Serban Corina

    2010-12-01

    Full Text Available Drug use is one of the major challenges that todays society faces; its effects are felt at the level of various social, professional and age categories. Over 50 non-profit organizations are involved in the development of anti-drug social programs in Romania. Their role is to improve the degree of awareness of the target population concerning the risks associated with drug use, but also to steer consumers towards healthy areas, beneficial to their future. This paper aims to detail the issue of drug use in Romania, by making predictions based on the evolution of this phenomenon during the next five years. The obtained results have revealed the necessity to increase the number of programs preventing drug use, aswell as the need to continue social programs that have proved effective in previous years.

  14. Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction.

    Science.gov (United States)

    Liu, Yong; Wu, Min; Miao, Chunyan; Zhao, Peilin; Li, Xiao-Li

    2016-02-01

    In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches.

  15. An introduction to predictive modelling of drug concentration in anaesthesia monitors.

    Science.gov (United States)

    DeCou, J; Johnson, K

    2017-01-01

    A significant amount of anaesthetists' work involves the prediction of drug effects and interactions to produce a smooth general anaesthetic that minimises drug side effects and promotes rapid emergence. Successfully managing this process requires a basic understanding of drug effects, experience and inevitably some guesswork, since it is difficult (and in some cases impossible) to anticipate all relevant patient and surgical factors. Although data are generally available to allow calculation of plasma drug and effect site concentrations, this is often difficult to apply in complex clinical contexts, particularly when multiple drug types are used. In recent years, manufacturers have developed and incorporated into anaesthetic workstations technologies that use drug pharmacodynamic and pharmacokinetic data to predict drug effects and interactions. Such systems can predict the duration and effects of drugs during anaesthesia and assist the anaesthetist to understand complex drug interactions. With this information available, different drug types, doses and combinations may be tailored in a scientific way to maximise useful effects whilst minimising overdose and side-effects, particularly in high-risk patients. Examples are used to illustrate how such systems can be used in practice, and how drug effects and interactions can be simulated to "rehearse" an anaesthetic before any drugs are actually administered. At present only a small number of anaesthetic workstations use this technology, and as yet they are not able to manage all drugs used in anaesthetic practice. However, such systems have the potential to help anaesthetists manage the complexity of their work, and to provide information on predicted drug effects in a way that is useful and relevant to both experienced anaesthetists and trainees. © 2017 The Association of Anaesthetists of Great Britain and Ireland.

  16. DR2DI: a powerful computational tool for predicting novel drug-disease associations

    Science.gov (United States)

    Lu, Lu; Yu, Hua

    2018-05-01

    Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

  17. Design of a tripartite network for the prediction of drug targets

    Science.gov (United States)

    Kunimoto, Ryo; Bajorath, Jürgen

    2018-02-01

    Drug-target networks have aided in many target prediction studies aiming at drug repurposing or the analysis of side effects. Conventional drug-target networks are bipartite. They contain two different types of nodes representing drugs and targets, respectively, and edges indicating pairwise drug-target interactions. In this work, we introduce a tripartite network consisting of drugs, other bioactive compounds, and targets from different sources. On the basis of analog relationships captured in the network and so-called neighbor targets of drugs, new drug targets can be inferred. The tripartite network was found to have a stable structure and simulated network growth was accompanied by a steady increase in assortativity, reflecting increasing correlation between degrees of connected nodes leading to even network connectivity. Local drug environments in the tripartite network typically contained neighbor targets and revealed interesting drug-compound-target relationships for further analysis. Candidate targets were prioritized. The tripartite network design extends standard drug-target networks and provides additional opportunities for drug target prediction.

  18. DR2DI: a powerful computational tool for predicting novel drug-disease associations

    Science.gov (United States)

    Lu, Lu; Yu, Hua

    2018-04-01

    Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

  19. Predictive tools for the evaluation of microbial effects on drugs during gastrointestinal passage.

    Science.gov (United States)

    Pieper, Ines A; Bertau, Martin

    2010-06-01

    Predicting drug metabolism after oral administration is highly complex, yet indispensable. Hitherto, drug metabolism mainly focuses on hepatic processes. In the intestine, drug molecules encounter the metabolic activity of microorganisms prior to absorption through the gut wall. Drug biotransformation through the gastrointestinal microflora has the potential to evoke serious problems because the metabolites formed may cause unexpected and undesired side effects in patients. Hence, in the course of drug development, the question has to be addressed if microbially formed metabolites are physiologically active, pharmaceutically active or even toxic. In order to provide answers to these questions and to keep the number of laboratory tests needed low, predictive tools - in vivo as well as in silico - are invaluable. This review gives an outline of the current state of the art in the field of predicting the drug biotransformation through the gastrointestinal microflora on several levels of modelling. A comprehensive review of the literature with a thorough discussion on assets and drawbacks of the different modelling approaches. The impact of the gastrointestinal drug biotransformation on patients' health will grow with increasing complexity of drug entities. Predicting metabolic fates of drugs by combining in vitro and in silico models provides invaluable information which will be suitable to particularly reduce in vivo studies.

  20. An ensemble based top performing approach for NCI-DREAM drug sensitivity prediction challenge.

    Directory of Open Access Journals (Sweden)

    Qian Wan

    Full Text Available We consider the problem of predicting sensitivity of cancer cell lines to new drugs based on supervised learning on genomic profiles. The genetic and epigenetic characterization of a cell line provides observations on various aspects of regulation including DNA copy number variations, gene expression, DNA methylation and protein abundance. To extract relevant information from the various data types, we applied a random forest based approach to generate sensitivity predictions from each type of data and combined the predictions in a linear regression model to generate the final drug sensitivity prediction. Our approach when applied to the NCI-DREAM drug sensitivity prediction challenge was a top performer among 47 teams and produced high accuracy predictions. Our results show that the incorporation of multiple genomic characterizations lowered the mean and variance of the estimated bootstrap prediction error. We also applied our approach to the Cancer Cell Line Encyclopedia database for sensitivity prediction and the ability to extract the top targets of an anti-cancer drug. The results illustrate the effectiveness of our approach in predicting drug sensitivity from heterogeneous genomic datasets.

  1. DISIS: prediction of drug response through an iterative sure independence screening.

    Directory of Open Access Journals (Sweden)

    Yun Fang

    Full Text Available Prediction of drug response based on genomic alterations is an important task in the research of personalized medicine. Current elastic net model utilized a sure independence screening to select relevant genomic features with drug response, but it may neglect the combination effect of some marginally weak features. In this work, we applied an iterative sure independence screening scheme to select drug response relevant features from the Cancer Cell Line Encyclopedia (CCLE dataset. For each drug in CCLE, we selected up to 40 features including gene expressions, mutation and copy number alterations of cancer-related genes, and some of them are significantly strong features but showing weak marginal correlation with drug response vector. Lasso regression based on the selected features showed that our prediction accuracies are higher than those by elastic net regression for most drugs.

  2. A high content screening assay to predict human drug-induced liver injury during drug discovery.

    Science.gov (United States)

    Persson, Mikael; Løye, Anni F; Mow, Tomas; Hornberg, Jorrit J

    2013-01-01

    Adverse drug reactions are a major cause for failures of drug development programs, drug withdrawals and use restrictions. Early hazard identification and diligent risk avoidance strategies are therefore essential. For drug-induced liver injury (DILI), this is difficult using conventional safety testing. To reduce the risk for DILI, drug candidates with a high risk need to be identified and deselected. And, to produce drug candidates without that risk associated, risk factors need to be assessed early during drug discovery, such that lead series can be optimized on safety parameters. This requires methods that allow for medium-to-high throughput compound profiling and that generate quantitative results suitable to establish structure-activity-relationships during lead optimization programs. We present the validation of such a method, a novel high content screening assay based on six parameters (nuclei counts, nuclear area, plasma membrane integrity, lysosomal activity, mitochondrial membrane potential (MMP), and mitochondrial area) using ~100 drugs of which the clinical hepatotoxicity profile is known. We find that a 100-fold TI between the lowest toxic concentration and the therapeutic Cmax is optimal to classify compounds as hepatotoxic or non-hepatotoxic, based on the individual parameters. Most parameters have ~50% sensitivity and ~90% specificity. Drugs hitting ≥2 parameters at a concentration below 100-fold their Cmax are typically hepatotoxic, whereas non-hepatotoxic drugs typically hit based on nuclei count, MMP and human Cmax, we identified an area without a single false positive, while maintaining 45% sensitivity. Hierarchical clustering using the multi-parametric dataset roughly separates toxic from non-toxic compounds. We employ the assay in discovery projects to prioritize novel compound series during hit-to-lead, to steer away from a DILI risk during lead optimization, for risk assessment towards candidate selection and to provide guidance of safe

  3. Reward Prediction Errors in Drug Addiction and Parkinson's Disease: from Neurophysiology to Neuroimaging.

    Science.gov (United States)

    García-García, Isabel; Zeighami, Yashar; Dagher, Alain

    2017-06-01

    Surprises are important sources of learning. Cognitive scientists often refer to surprises as "reward prediction errors," a parameter that captures discrepancies between expectations and actual outcomes. Here, we integrate neurophysiological and functional magnetic resonance imaging (fMRI) results addressing the processing of reward prediction errors and how they might be altered in drug addiction and Parkinson's disease. By increasing phasic dopamine responses, drugs might accentuate prediction error signals, causing increases in fMRI activity in mesolimbic areas in response to drugs. Chronic substance dependence, by contrast, has been linked with compromised dopaminergic function, which might be associated with blunted fMRI responses to pleasant non-drug stimuli in mesocorticolimbic areas. In Parkinson's disease, dopamine replacement therapies seem to induce impairments in learning from negative outcomes. The present review provides a holistic overview of reward prediction errors across different pathologies and might inform future clinical strategies targeting impulsive/compulsive disorders.

  4. An investigation of classification algorithms for predicting HIV drug resistance without genotype resistance testing

    CSIR Research Space (South Africa)

    Brandt, P

    2014-01-01

    Full Text Available is limited in low-resource settings. In this paper we investigate machine learning techniques for drug resistance prediction from routine treatment and laboratory data to help clinicians select patients for confirmatory genotype testing. The techniques...

  5. Improving Predictive Modeling in Pediatric Drug Development: Pharmacokinetics, Pharmacodynamics, and Mechanistic Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Slikker, William; Young, John F.; Corley, Rick A.; Dorman, David C.; Conolly, Rory B.; Knudsen, Thomas; Erstad, Brian L.; Luecke, Richard H.; Faustman, Elaine M.; Timchalk, Chuck; Mattison, Donald R.

    2005-07-26

    A workshop was conducted on November 18?19, 2004, to address the issue of improving predictive models for drug delivery to developing humans. Although considerable progress has been made for adult humans, large gaps remain for predicting pharmacokinetic/pharmacodynamic (PK/PD) outcome in children because most adult models have not been tested during development. The goals of the meeting included a description of when, during development, infants/children become adultlike in handling drugs. The issue of incorporating the most recent advances into the predictive models was also addressed: both the use of imaging approaches and genomic information were considered. Disease state, as exemplified by obesity, was addressed as a modifier of drug pharmacokinetics and pharmacodynamics during development. Issues addressed in this workshop should be considered in the development of new predictive and mechanistic models of drug kinetics and dynamics in the developing human.

  6. Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome.

    Science.gov (United States)

    Mallory, Emily K; Acharya, Ambika; Rensi, Stefano E; Turnbaugh, Peter J; Bright, Roselie A; Altman, Russ B

    2018-01-01

    Bacteria in the human gut have the ability to activate, inactivate, and reactivate drugs with both intended and unintended effects. For example, the drug digoxin is reduced to the inactive metabolite dihydrodigoxin by the gut Actinobacterium E. lenta, and patients colonized with high levels of drug metabolizing strains may have limited response to the drug. Understanding the complete space of drugs that are metabolized by the human gut microbiome is critical for predicting bacteria-drug relationships and their effects on individual patient response. Discovery and validation of drug metabolism via bacterial enzymes has yielded >50 drugs after nearly a century of experimental research. However, there are limited computational tools for screening drugs for potential metabolism by the gut microbiome. We developed a pipeline for comparing and characterizing chemical transformations using continuous vector representations of molecular structure learned using unsupervised representation learning. We applied this pipeline to chemical reaction data from MetaCyc to characterize the utility of vector representations for chemical reaction transformations. After clustering molecular and reaction vectors, we performed enrichment analyses and queries to characterize the space. We detected enriched enzyme names, Gene Ontology terms, and Enzyme Consortium (EC) classes within reaction clusters. In addition, we queried reactions against drug-metabolite transformations known to be metabolized by the human gut microbiome. The top results for these known drug transformations contained similar substructure modifications to the original drug pair. This work enables high throughput screening of drugs and their resulting metabolites against chemical reactions common to gut bacteria.

  7. Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm.

    Science.gov (United States)

    Bai, Li-Yue; Dai, Hao; Xu, Qin; Junaid, Muhammad; Peng, Shao-Liang; Zhu, Xiaolei; Xiong, Yi; Wei, Dong-Qing

    2018-02-05

    Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters) were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.

  8. Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm

    Directory of Open Access Journals (Sweden)

    Li-Yue Bai

    2018-02-01

    Full Text Available Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.

  9. Statistical Analysis of a Method to Predict Drug-Polymer Miscibility

    DEFF Research Database (Denmark)

    Knopp, Matthias Manne; Olesen, Niels Erik; Huang, Yanbin

    2016-01-01

    In this study, a method proposed to predict drug-polymer miscibility from differential scanning calorimetry measurements was subjected to statistical analysis. The method is relatively fast and inexpensive and has gained popularity as a result of the increasing interest in the formulation of drug...... as provided in this study. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci....

  10. Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces.

    Science.gov (United States)

    Xia, Zheng; Wu, Ling-Yun; Zhou, Xiaobo; Wong, Stephen T C

    2010-09-13

    Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data. Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG. We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

  11. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

    Energy Technology Data Exchange (ETDEWEB)

    Hao, Ming; Wang, Yanli, E-mail: ywang@ncbi.nlm.nih.gov; Bryant, Stephen H., E-mail: bryant@ncbi.nlm.nih.gov

    2016-02-25

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  12. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

    International Nuclear Information System (INIS)

    Hao, Ming; Wang, Yanli; Bryant, Stephen H.

    2016-01-01

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  13. Novel Methods for Drug-Target Interaction Prediction using Graph Mining

    KAUST Repository

    Ba Alawi, Wail

    2016-08-31

    The problem of developing drugs that can be used to cure diseases is important and requires a careful approach. Since pursuing the wrong candidate drug for a particular disease could be very costly in terms of time and money, there is a strong interest in minimizing such risks. Drug repositioning has become a hot topic of research, as it helps reduce these risks significantly at the early stages of drug development by reusing an approved drug for the treatment of a different disease. Still, finding new usage for a drug is non-trivial, as it is necessary to find out strong supporting evidence that the proposed new uses of drugs are plausible. Many computational approaches were developed to narrow the list of possible candidate drug-target interactions (DTIs) before any experiments are done. However, many of these approaches suffer from unacceptable levels of false positives. We developed two novel methods based on graph mining networks of drugs and targets. The first method (DASPfind) finds all non-cyclic paths that connect a drug and a target, and using a function that we define, calculates a score from all the paths. This score describes our confidence that DTI is correct. We show that DASPfind significantly outperforms other state-of-the-art methods in predicting the top ranked target for each drug. We demonstrate the utility of DASPfind by predicting 15 novel DTIs over a set of ion channel proteins, and confirming 12 out of these 15 DTIs through experimental evidence reported in literature and online drug databases. The second method (DASPfind+) modifies DASPfind in order to increase the confidence and reliability of the resultant predictions. Based on the structure of the drug-target interaction (DTI) networks, we introduced an optimization scheme that incrementally alters the network structure locally for each drug to achieve more robust top 1 ranked predictions. Moreover, we explored effects of several similarity measures between the targets on the prediction

  14. A hybrid approach to advancing quantitative prediction of tissue distribution of basic drugs in human

    International Nuclear Information System (INIS)

    Poulin, Patrick; Ekins, Sean; Theil, Frank-Peter

    2011-01-01

    A general toxicity of basic drugs is related to phospholipidosis in tissues. Therefore, it is essential to predict the tissue distribution of basic drugs to facilitate an initial estimate of that toxicity. The objective of the present study was to further assess the original prediction method that consisted of using the binding to red blood cells measured in vitro for the unbound drug (RBCu) as a surrogate for tissue distribution, by correlating it to unbound tissue:plasma partition coefficients (Kpu) of several tissues, and finally to predict volume of distribution at steady-state (V ss ) in humans under in vivo conditions. This correlation method demonstrated inaccurate predictions of V ss for particular basic drugs that did not follow the original correlation principle. Therefore, the novelty of this study is to provide clarity on the actual hypotheses to identify i) the impact of pharmacological mode of action on the generic correlation of RBCu-Kpu, ii) additional mechanisms of tissue distribution for the outlier drugs, iii) molecular features and properties that differentiate compounds as outliers in the original correlation analysis in order to facilitate its applicability domain alongside the properties already used so far, and finally iv) to present a novel and refined correlation method that is superior to what has been previously published for the prediction of human V ss of basic drugs. Applying a refined correlation method after identifying outliers would facilitate the prediction of more accurate distribution parameters as key inputs used in physiologically based pharmacokinetic (PBPK) and phospholipidosis models.

  15. Incorporation of lysosomal sequestration in the mechanistic model for prediction of tissue distribution of basic drugs.

    Science.gov (United States)

    Assmus, Frauke; Houston, J Brian; Galetin, Aleksandra

    2017-11-15

    The prediction of tissue-to-plasma water partition coefficients (Kpu) from in vitro and in silico data using the tissue-composition based model (Rodgers & Rowland, J Pharm Sci. 2005, 94(6):1237-48.) is well established. However, distribution of basic drugs, in particular into lysosome-rich lung tissue, tends to be under-predicted by this approach. The aim of this study was to develop an extended mechanistic model for the prediction of Kpu which accounts for lysosomal sequestration and the contribution of different cell types in the tissue of interest. The extended model is based on compound-specific physicochemical properties and tissue composition data to describe drug ionization, distribution into tissue water and drug binding to neutral lipids, neutral phospholipids and acidic phospholipids in tissues, including lysosomes. Physiological data on the types of cells contributing to lung, kidney and liver, their lysosomal content and lysosomal pH were collated from the literature. The predictive power of the extended mechanistic model was evaluated using a dataset of 28 basic drugs (pK a ≥7.8, 17 β-blockers, 11 structurally diverse drugs) for which experimentally determined Kpu data in rat tissue have been reported. Accounting for the lysosomal sequestration in the extended mechanistic model improved the accuracy of Kpu predictions in lung compared to the original Rodgers model (56% drugs within 2-fold or 88% within 3-fold of observed values). Reduction in the extent of Kpu under-prediction was also evident in liver and kidney. However, consideration of lysosomal sequestration increased the occurrence of over-predictions, yielding overall comparable model performances for kidney and liver, with 68% and 54% of Kpu values within 2-fold error, respectively. High lysosomal concentration ratios relative to cytosol (>1000-fold) were predicted for the drugs investigated; the extent differed depending on the lysosomal pH and concentration of acidic phospholipids among

  16. A community effort to assess and improve drug sensitivity prediction algorithms.

    Science.gov (United States)

    Costello, James C; Heiser, Laura M; Georgii, Elisabeth; Gönen, Mehmet; Menden, Michael P; Wang, Nicholas J; Bansal, Mukesh; Ammad-ud-din, Muhammad; Hintsanen, Petteri; Khan, Suleiman A; Mpindi, John-Patrick; Kallioniemi, Olli; Honkela, Antti; Aittokallio, Tero; Wennerberg, Krister; Collins, James J; Gallahan, Dan; Singer, Dinah; Saez-Rodriguez, Julio; Kaski, Samuel; Gray, Joe W; Stolovitzky, Gustavo

    2014-12-01

    Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

  17. Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.

    Science.gov (United States)

    Aliper, Alexander; Plis, Sergey; Artemov, Artem; Ulloa, Alvaro; Mamoshina, Polina; Zhavoronkov, Alex

    2016-07-05

    Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

  18. Cocrystal solubilization in biorelevant media and its prediction from drug solubilization

    Science.gov (United States)

    Lipert, Maya P.; Roy, Lilly; Childs, Scott L.

    2015-01-01

    This work examines cocrystal solubility in biorelevant media, (FeSSIF, fed state simulated intestinal fluid), and develops a theoretical framework that allows for the simple and quantitative prediction of cocrystal solubilization from drug solubilization. The solubilities of four hydrophobic drugs and seven cocrystals containing these drugs were measured in FeSSIF and in acetate buffer at pH 5.00. In all cases, the cocrystal solubility (Scocrystal) was higher than the drug solubility (Sdrug) in both buffer and FeSSIF; however, the solubilization ratio of drug, SRdrug = (SFeSSIF/Sbuffer)drug, was not the same as the solubilization ratio of cocrystal, SRcocrystal = (SFeSSIF/Sbuffer)cocrystal, meaning drug and cocrystal were not solubilized to the same extent in FeSSIF. This highlights the potential risk of anticipating cocrystal behavior in biorelevant media based on solubility studies in water. Predictions of SRcocrystal from simple equations based only on SRdrug were in excellent agreement with measured values. For 1:1 cocrystals, the cocrystal solubilization ratio can be obtained from the square root of the drug solubilization ratio. For 2:1 cocrystals, SRcocrystal is found from (SRdrug)2/3. The findings in FeSSIF can be generalized to describe cocrystal behavior in other systems involving preferential solubilization of a drug such as surfactants, lipids, and other drug solubilizing media. PMID:26390213

  19. Drug-target interaction prediction via class imbalance-aware ensemble learning.

    Science.gov (United States)

    Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

    2016-12-22

    Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was

  20. ATM-deficient human fibroblast cells are resistant to low levels of DNA double-strand break induced apoptosis and subsequently undergo drug-induced premature senescence

    International Nuclear Information System (INIS)

    Park, Jun; Jo, Yong Hwa; Cho, Chang Hoon; Choe, Wonchae; Kang, Insug; Baik, Hyung Hwan; Yoon, Kyung-Sik

    2013-01-01

    Highlights: ► A-T cells were not hypersensitive to low levels of DNA DSBs. ► A-T cells have enhanced Akt but defect in activation of p53 and apoptotic proteins. ► A-T cells underwent premature senescence after DNA damage accumulated. ► Chemotherapeutic effect in cancer therapy may be associated with premature senescence. -- Abstract: DNA DSBs are induced by IR or radiomimetic drugs such as doxorubicin. It has been indicated that cells from ataxia-telangiectasia patients are highly sensitive to radiation due to defects in DNA repair, but whether they have impairment in apoptosis has not been fully elucidated. A-T cells showed increased sensitivity to high levels of DNA damage, however, they were more resistant to low doses. Normal cells treated with combination of KU55933, a specific ATM kinase inhibitor, and doxorubicin showed increased resistance as they do in a similar manner to A-T cells. A-T cells have higher viability but more DNA breaks, in addition, the activations of p53 and apoptotic proteins (Bax and caspase-3) were deficient, but Akt expression was enhanced. A-T cells subsequently underwent premature senescence after treatment with a low dose of doxorubicin, which was confirmed by G2 accumulation, senescent morphology, and SA-β-gal positive until 15 days repair incubation. Finally, A-T cells are radio-resistant at low doses due to its defectiveness in detecting DNA damage and apoptosis, but the accumulation of DNA damage leads cells to premature senescence.

  1. Different slopes for different folks: alpha and delta EEG power predict subsequent video game learning rate and improvements in cognitive control tasks.

    Science.gov (United States)

    Mathewson, Kyle E; Basak, Chandramallika; Maclin, Edward L; Low, Kathy A; Boot, Walter R; Kramer, Arthur F; Fabiani, Monica; Gratton, Gabriele

    2012-12-01

    We hypothesized that control processes, as measured using electrophysiological (EEG) variables, influence the rate of learning of complex tasks. Specifically, we measured alpha power, event-related spectral perturbations (ERSPs), and event-related brain potentials during early training of the Space Fortress task, and correlated these measures with subsequent learning rate and performance in transfer tasks. Once initial score was partialled out, the best predictors were frontal alpha power and alpha and delta ERSPs, but not P300. By combining these predictors, we could explain about 50% of the learning rate variance and 10%-20% of the variance in transfer to other tasks using only pretraining EEG measures. Thus, control processes, as indexed by alpha and delta EEG oscillations, can predict learning and skill improvements. The results are of potential use to optimize training regimes. Copyright © 2012 Society for Psychophysiological Research.

  2. Adverse trajectories of mental health problems predict subsequent burnout and work-family conflict - a longitudinal study of employed women with children followed over 18 years.

    Science.gov (United States)

    Nilsen, Wendy; Skipstein, Anni; Demerouti, Evangelia

    2016-11-08

    The long-term consequence of experiencing mental health problems may lead to several adverse outcomes. The current study aims to validate previous identified trajectories of mental health problems from 1993 to 2006 in women by examining their implications on subsequent work and family-related outcomes in 2011. Employed women (n = 439) with children were drawn from the Tracking Opportunities and Problems-Study (TOPP), a community-based longitudinal study following Norwegian families across 18 years. Previous identified latent profiles of mental health trajectories (i.e., High; Moderate; Low-rising and Low levels of mental health problems over time) measured at six time points between 1993 and 2006 were examined as predictors of burnout (e.g., exhaustion and disengagement from work) and work-family conflict in 2011 in univariate and multivariate analyses of variance adjusted for potential confounders (age, job demands, and negative emotionality). We found that having consistently High and Moderate symptoms as well as Low-Rising symptoms from 1993 to 2006 predicted higher levels of exhaustion, disengagement from work and work-family conflict in 2011. Findings remained unchanged when adjusting for several potential confounders, but when adjusting for current mental health problems only levels of exhaustion were predicted by the mental health trajectories. The study expands upon previous studies on the field by using a longer time span and by focusing on employed women with children who experience different patterns of mental health trajectories. The long-term effect of these trajectories highlight and validate the importance of early identification and prevention in women experiencing adverse patterns of mental health problems with regards to subsequent work and family-related outcomes.

  3. Prediction of Effective Drug Combinations by Chemical Interaction, Protein Interaction and Target Enrichment of KEGG Pathways

    Directory of Open Access Journals (Sweden)

    Lei Chen

    2013-01-01

    Full Text Available Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1 chemical interaction between drugs, (2 protein interactions between drugs’ targets, and (3 target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.

  4. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

    Science.gov (United States)

    Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

    2018-02-01

    For a drug, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

  5. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

    Directory of Open Access Journals (Sweden)

    Hongbin Yang

    2018-02-01

    Full Text Available During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

  6. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts.

    Science.gov (United States)

    Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

    2018-01-01

    During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

  7. Pharmacokinetics, efficacy prediction indexes and residue depletion of antibacterial drugs.

    Directory of Open Access Journals (Sweden)

    Arturo Anadón

    2016-06-01

    Full Text Available Pharmacokinetics behaviour of the antibacterial in food producing animals, provides information on the rates of absorption and elimination, half-life in plasma and tissue, elimination pathways and metabolism. The dose and the dosing interval of the antimicrobial can be justified by considering the pharmacokinetic/pharmacodynamic (PK/PD relationship, if established, as well as the severity of the disease, whereas the number of administrations should be in line with the nature of the disease. The target population for therapy should be well defined and possible to identify under field conditions. Based on in vitro susceptibility data, and target animal PK data, an analysis for the PK/PD relationship may be used to support dose regimen selection and interpretation criteria for a clinical breakpoint. Therefore, for all antibacterials with systemic activity, the MIC data collected should be compared with the concentration of the compound at the relevant biophase following administration at the assumed therapeutic dose as recorded in the pharmacokinetic studies. Currently, the most frequently used parameters to express the PK/PD relationship are Cmax/MIC (maximum serum concentration/MIC, %T > MIC (fraction of time in which concentration exceeds MIC and AUC/MIC (area under the inhibitory concentration– time curve/MIC. Furthermore, the pharmacokinetic parameters provide the first indication of the potential for persistent residues and the tissues in which they may occur. The information on residue depletion in food-producing animals, provides the data on which MRL recommendations will be based. A critical factor in the antibacterial medication of all food-producing animals is the mandatory withdrawal period, defined as the time during which drug must not be administered prior to the slaughter of the animal for consumption. The withdrawal period is an integral part of the regulatory authorities’ approval process and is designed to ensure that no

  8. Predicting relapse of Graves' disease following treatment with antithyroid drugs

    Science.gov (United States)

    LIU, LIN; LU, HONGWEN; LIU, YANG; LIU, CHANGSHAN; XUN, CHU

    2016-01-01

    The aim of the present study was to monitor long term antithyroid drug treatments and to identify prognostic factors for Graves' disease (GD). A total of 306 patients with GD who were referred to the Endocrinology Clinic at Weifang People's Hospital (Weifang, China) between August 2005 and June 2009 and treated with methimazole were included in the present study. Following treatment, patients were divided into non-remission, including recurrence and constant treatment subgroups, and remission groups. Various prognosis factors were analyzed and compared, including: Patient age, gender, size of thyroid prior to and following treatment, thyroid hormone levels, disease relapse, hypothyroidism and drug side-effects, and states of thyrotropin suppression were observed at 3, 6 and 12 months post-treatment. Sixty-five patients (21.2%) were male, and 241 patients (78.8%) were female. The mean age was 42±11 years, and the follow-up was 31.5±6.8 months. Following long-term treatment, 141 patients (46%) demonstrated remission of hyperthyroidism with a mean duration of 18.7±1.9 months. The average age at diagnosis was 45.6±10.3 years in the remission group, as compared with 36.4±8.8 years in the non-remission group (t=3.152; P=0.002). Free thyroxine (FT)3 levels were demonstrated to be 25.2±8.9 and 18.7±9.4 pmol/l in the non-remission and remission groups, respectively (t=3.326, P=0.001). The FT3/FT4 ratio and thyrotrophin receptor antibody (TRAb) levels were both significantly higher in the non-remission group (t=3.331, 3.389, P=0.001), as compared with the remission group. Logistic regression analysis demonstrated that elevated thyroid size, FT3/FT4 ratio and TRAb at diagnosis were associated with poor outcomes. The ratio of continued thyrotropin suppression in the recurrent subgroup was significantly increased, as compared with the remission group (P=0.001), as thyroid function reached euthyroid state at 3, 6 and 12 months post-treatment. Patients with GD exhibiting

  9. Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations.

    Science.gov (United States)

    Zong, Nansu; Kim, Hyeoneui; Ngo, Victoria; Harismendy, Olivier

    2017-08-01

    A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction. We propose a similarity-based drug-target prediction method that enhances existing association discovery methods by using a topology-based similarity measure. DeepWalk, a deep learning method, is adopted in this study to calculate the similarities within Linked Tripartite Network (LTN), a heterogeneous network generated from biomedical linked datasets. This proposed method shows promising results for drug-target association prediction: 98.96% AUC ROC score with a 10-fold cross-validation and 99.25% AUC ROC score with a Monte Carlo cross-validation with LTN. By utilizing DeepWalk, we demonstrate that: (i) this method outperforms other existing topology-based similarity computation methods, (ii) the performance is better for tripartite than with bipartite networks and (iii) the measure of similarity using network topology outperforms the ones derived from chemical structure (drugs) or genomic sequence (targets). Our proposed methodology proves to be capable of providing a promising solution for drug-target prediction based on topological similarity with a heterogeneous network, and may be readily re-purposed and adapted in the existing of similarity-based methodologies. The proposed method has been developed in JAVA and it is available, along with the data at the following URL: https://github.com/zongnansu1982/drug-target-prediction . nazong@ucsd.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  10. New in vitro system to predict chemotherapeutic efficacy of drug combinations in fresh tumor samples

    Directory of Open Access Journals (Sweden)

    Frank Christian Kischkel

    2017-03-01

    Full Text Available Background To find the best individual chemotherapy for cancer patients, the efficacy of different chemotherapeutic drugs can be predicted by pretesting tumor samples in vitro via the chemotherapy-resistance (CTR-Test®. Although drug combinations are widely used among cancer therapy, so far only single drugs are tested by this and other tests. However, several first line chemotherapies are combining two or more chemotherapeutics, leading to the necessity of drug combination testing methods. Methods We established a system to measure and predict the efficacy of chemotherapeutic drug combinations with the help of the Loewe additivity concept in combination with the CTR-test. A combination is measured by using half of the monotherapy’s concentration of both drugs simultaneously. With this method, the efficacy of a combination can also be calculated based on single drug measurements. Results The established system was tested on a data set of ovarian carcinoma samples using the combination carboplatin and paclitaxel and confirmed by using other tumor species and chemotherapeutics. Comparing the measured and the calculated values of the combination testings revealed a high correlation. Additionally, in 70% of the cases the measured and the calculated values lead to the same chemotherapeutic resistance category of the tumor. Conclusion Our data suggest that the best drug combination consists of the most efficient single drugs and the worst drug combination of the least efficient single drugs. Our results showed that single measurements are sufficient to predict combinations in specific cases but there are exceptions in which it is necessary to measure combinations, which is possible with the presented system.

  11. Nucleus accumbens response to food cues predicts subsequent snack consumption in women and increased body mass index in those with reduced self-control.

    Science.gov (United States)

    Lawrence, Natalia S; Hinton, Elanor C; Parkinson, John A; Lawrence, Andrew D

    2012-10-15

    Individuals have difficulty controlling their food consumption, which is due in part to the ubiquity of tempting food cues in the environment. Individual differences in the propensity to attribute incentive (motivational) salience to and act on these cues may explain why some individuals eat more than others. Using fMRI in healthy women, we found that food cue related activity in the nucleus accumbens, a key brain region for food motivation and reward, was related to subsequent snack food consumption. However, both nucleus accumbens activation and snack food consumption were unrelated to self-reported hunger, or explicit wanting and liking for the snack. In contrast, food cue reactivity in the ventromedial prefrontal cortex was associated with subjective hunger/appetite, but not with consumption. Whilst the food cue reactivity in the nucleus accumbens that predicted snack consumption was not directly related to body mass index (BMI), it was associated with increased BMI in individuals reporting low self-control. Our findings reveal a neural substrate underpinning automatic environmental influences on consumption in humans and demonstrate how self-control interacts with this response to predict BMI. Our data provide support for theoretical models that advocate a 'dual hit' of increased incentive salience attribution to food cues and poor self-control in determining vulnerability to overeating and overweight. Copyright © 2012 Elsevier Inc. All rights reserved.

  12. A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions.

    Science.gov (United States)

    Cherkaoui-Rbati, Mohammed H; Paine, Stuart W; Littlewood, Peter; Rauch, Cyril

    2017-01-01

    All pharmaceutical companies are required to assess pharmacokinetic drug-drug interactions (DDIs) of new chemical entities (NCEs) and mathematical prediction helps to select the best NCE candidate with regard to adverse effects resulting from a DDI before any costly clinical studies. Most current models assume that the liver is a homogeneous organ where the majority of the metabolism occurs. However, the circulatory system of the liver has a complex hierarchical geometry which distributes xenobiotics throughout the organ. Nevertheless, the lobule (liver unit), located at the end of each branch, is composed of many sinusoids where the blood flow can vary and therefore creates heterogeneity (e.g. drug concentration, enzyme level). A liver model was constructed by describing the geometry of a lobule, where the blood velocity increases toward the central vein, and by modeling the exchange mechanisms between the blood and hepatocytes. Moreover, the three major DDI mechanisms of metabolic enzymes; competitive inhibition, mechanism based inhibition and induction, were accounted for with an undefined number of drugs and/or enzymes. The liver model was incorporated into a physiological-based pharmacokinetic (PBPK) model and simulations produced, that in turn were compared to ten clinical results. The liver model generated a hierarchy of 5 sinusoidal levels and estimated a blood volume of 283 mL and a cell density of 193 × 106 cells/g in the liver. The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s) including spatial and temporal enzyme levels changes. The model presented herein may reduce costs and the use of laboratory animals and give the opportunity to explore different clinical scenarios, which reduce the risk of adverse events, prior to costly human clinical studies.

  13. A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions.

    Directory of Open Access Journals (Sweden)

    Mohammed H Cherkaoui-Rbati

    Full Text Available All pharmaceutical companies are required to assess pharmacokinetic drug-drug interactions (DDIs of new chemical entities (NCEs and mathematical prediction helps to select the best NCE candidate with regard to adverse effects resulting from a DDI before any costly clinical studies. Most current models assume that the liver is a homogeneous organ where the majority of the metabolism occurs. However, the circulatory system of the liver has a complex hierarchical geometry which distributes xenobiotics throughout the organ. Nevertheless, the lobule (liver unit, located at the end of each branch, is composed of many sinusoids where the blood flow can vary and therefore creates heterogeneity (e.g. drug concentration, enzyme level. A liver model was constructed by describing the geometry of a lobule, where the blood velocity increases toward the central vein, and by modeling the exchange mechanisms between the blood and hepatocytes. Moreover, the three major DDI mechanisms of metabolic enzymes; competitive inhibition, mechanism based inhibition and induction, were accounted for with an undefined number of drugs and/or enzymes. The liver model was incorporated into a physiological-based pharmacokinetic (PBPK model and simulations produced, that in turn were compared to ten clinical results. The liver model generated a hierarchy of 5 sinusoidal levels and estimated a blood volume of 283 mL and a cell density of 193 × 106 cells/g in the liver. The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s including spatial and temporal enzyme levels changes. The model presented herein may reduce costs and the use of laboratory animals and give the opportunity to explore different clinical scenarios, which reduce the risk of adverse events, prior to costly human clinical studies.

  14. BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs

    Directory of Open Access Journals (Sweden)

    Tsafnat Guy

    2011-04-01

    Full Text Available Abstract Background The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources. We have developed a statistical text-mining approach (BInary Characteristics Extractor and biomedical Properties Predictor: BICEPP to help experts screen drugs that may have important clinical characteristics of interest. Results BICEPP first retrieves MEDLINE abstracts containing drug names, then selects tokens that best predict the list of drugs which represents the characteristic of interest. Machine learning is then used to classify drugs using a document frequency-based measure. Evaluation experiments were performed to validate BICEPP's performance on 484 characteristics of 857 drugs, identified from the Australian Medicines Handbook (AMH and the PharmacoKinetic Interaction Screening (PKIS database. Stratified cross-validations revealed that BICEPP was able to classify drugs into all 20 major therapeutic classes (100% and 157 (of 197 minor drug classes (80% with areas under the receiver operating characteristic curve (AUC > 0.80. Similarly, AUC > 0.80 could be obtained in the classification of 173 (of 238 adverse events (73%, up to 12 (of 15 groups of clinically significant cytochrome P450 enzyme (CYP inducers or inhibitors (80%, and up to 11 (of 14 groups of narrow therapeutic index drugs (79%. Interestingly, it was observed that the keywords used to describe a drug characteristic were not necessarily the most predictive ones for the classification task. Conclusions BICEPP has sufficient classification power to automatically distinguish a wide range of clinical properties of drugs. This may be used in pharmacovigilance applications to assist with rapid screening of large drug databases to identify important characteristics for further evaluation.

  15. ADVERPred-Web Service for Prediction of Adverse Effects of Drugs.

    Science.gov (United States)

    Ivanov, Sergey M; Lagunin, Alexey A; Rudik, Anastasia V; Filimonov, Dmitry A; Poroikov, Vladimir V

    2018-01-22

    Application of structure-activity relationships (SARs) for the prediction of adverse effects of drugs (ADEs) has been reported in many published studies. Training sets for the creation of SAR models are usually based on drug label information which allows for the generation of data sets for many hundreds of drugs. Since many ADEs may not be related to drug consumption, one of the main problems in such studies is the quality of data on drug-ADE pairs obtained from labels. The information on ADEs may be included in three sections of the drug labels: "Boxed warning," "Warnings and Precautions," and "Adverse reactions." The first two sections, especially Boxed warning, usually contain the most frequent and severe ADEs that have either known or probable relationships to drug consumption. Using this information, we have created manually curated data sets for the five most frequent and severe ADEs: myocardial infarction, arrhythmia, cardiac failure, severe hepatotoxicity, and nephrotoxicity, with more than 850 drugs on average for each effect. The corresponding SARs were built with PASS (Prediction of Activity Spectra for Substances) software and had balanced accuracy values of 0.74, 0.7, 0.77, 0.67, and 0.75, respectively. They were implemented in a freely available ADVERPred web service ( http://www.way2drug.com/adverpred/ ), which enables a user to predict five ADEs based on the structural formula of compound. This web service can be applied for estimation of the corresponding ADEs for hits and lead compounds at the early stages of drug discovery.

  16. Some Remarks on Prediction of Drug-Target Interaction with Network Models.

    Science.gov (United States)

    Zhang, Shao-Wu; Yan, Xiao-Ying

    2017-01-01

    System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Thus, it is highly desired to develop computational methods for efficiently and effectively analyzing and detecting new drug-target interaction pairs. With the explosive growth of different types of omics data, such as genome, pharmacology, phenotypic, and other kinds of molecular networks, numerous computational approaches have been developed to predict Drug-Target Interactions (DTI). In this review, we make a survey on the recent advances in predicting drug-target interaction with network-based models from the following aspects: i) Available public data sources and benchmark datasets; ii) Drug/target similarity metrics; iii) Network construction; iv) Common network algorithms; v) Performance comparison of existing network-based DTI predictors. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  17. A mechanistic framework for in vitro-in vivo extrapolation of liver membrane transporters: prediction of drug-drug interaction between rosuvastatin and cyclosporine.

    Science.gov (United States)

    Jamei, M; Bajot, F; Neuhoff, S; Barter, Z; Yang, J; Rostami-Hodjegan, A; Rowland-Yeo, K

    2014-01-01

    The interplay between liver metabolising enzymes and transporters is a complex process involving system-related parameters such as liver blood perfusion as well as drug attributes including protein and lipid binding, ionisation, relative magnitude of passive and active permeation. Metabolism- and/or transporter-mediated drug-drug interactions (mDDIs and tDDIs) add to the complexity of this interplay. Thus, gaining meaningful insight into the impact of each element on the disposition of a drug and accurately predicting drug-drug interactions becomes very challenging. To address this, an in vitro-in vivo extrapolation (IVIVE)-linked mechanistic physiologically based pharmacokinetic (PBPK) framework for modelling liver transporters and their interplay with liver metabolising enzymes has been developed and implemented within the Simcyp Simulator(®). In this article an IVIVE technique for liver transporters is described and a full-body PBPK model is developed. Passive and active (saturable) transport at both liver sinusoidal and canalicular membranes are accounted for and the impact of binding and ionisation processes is considered. The model also accommodates tDDIs involving inhibition of multiple transporters. Integrating prior in vitro information on the metabolism and transporter kinetics of rosuvastatin (organic-anion transporting polypeptides OATP1B1, OAT1B3 and OATP2B1, sodium-dependent taurocholate co-transporting polypeptide [NTCP] and breast cancer resistance protein [BCRP]) with one clinical dataset, the PBPK model was used to simulate the drug disposition of rosuvastatin for 11 reported studies that had not been used for development of the rosuvastatin model. The simulated area under the plasma concentration-time curve (AUC), maximum concentration (C max) and the time to reach C max (t max) values of rosuvastatin over the dose range of 10-80 mg, were within 2-fold of the observed data. Subsequently, the validated model was used to investigate the impact of

  18. A systematic investigation of computation models for predicting Adverse Drug Reactions (ADRs.

    Directory of Open Access Journals (Sweden)

    Qifan Kuang

    Full Text Available Early and accurate identification of adverse drug reactions (ADRs is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs.In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper.Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms.

  19. A systematic investigation of computation models for predicting Adverse Drug Reactions (ADRs).

    Science.gov (United States)

    Kuang, Qifan; Wang, MinQi; Li, Rong; Dong, YongCheng; Li, Yizhou; Li, Menglong

    2014-01-01

    Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs. In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper. Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms.

  20. Predicting abuse potential of stimulants and other dopaminergic drugs: overview and recommendations.

    Science.gov (United States)

    Huskinson, Sally L; Naylor, Jennifer E; Rowlett, James K; Freeman, Kevin B

    2014-12-01

    Examination of a drug's abuse potential at multiple levels of analysis (molecular/cellular action, whole-organism behavior, epidemiological data) is an essential component to regulating controlled substances under the Controlled Substances Act (CSA). We reviewed studies that examined several central nervous system (CNS) stimulants, focusing on those with primarily dopaminergic actions, in drug self-administration, drug discrimination, and physical dependence. For drug self-administration and drug discrimination, we distinguished between experiments conducted with rats and nonhuman primates (NHP) to highlight the common and unique attributes of each model in the assessment of abuse potential. Our review of drug self-administration studies suggests that this procedure is important in predicting abuse potential of dopaminergic compounds, but there were many false positives. We recommended that tests to determine how reinforcing a drug is relative to a known drug of abuse may be more predictive of abuse potential than tests that yield a binary, yes-or-no classification. Several false positives also occurred with drug discrimination. With this procedure, we recommended that future research follow a standard decision-tree approach that may require examining the drug being tested for abuse potential as the training stimulus. This approach would also allow several known drugs of abuse to be tested for substitution, and this may reduce false positives. Finally, we reviewed evidence of physical dependence with stimulants and discussed the feasibility of modeling these phenomena in nonhuman animals in a rational and practical fashion. This article is part of the Special Issue entitled 'CNS Stimulants'. Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. Strategy for the Prediction of Steady-State Exposure of Digoxin to Determine Drug-Drug Interaction Potential of Digoxin With Other Drugs in Digitalization Therapy.

    Science.gov (United States)

    Srinivas, Nuggehally R

    2016-01-20

    Digoxin, a narrow therapeutic index drug, is widely used in congestive heart failure. However, the digitalization therapy involves dose titration and can exhibit drug-drug interaction. Ctrough versus area under the plasma concentration versus time curve in a dosing interval of 24 hours (AUC0-24h) and Cmax versus AUC0-24h for digoxin were established by linear regression. The predictions of digoxin AUC0-24h values were performed using published Ctrough or Cmax with appropriate regression lines. The fold difference, defined as the quotient of the observed/predicted AUC0-24h values, was evaluated. The mean square error and root mean square error, correlation coefficient (r), and goodness of the fold prediction were used to evaluate the models. Both Ctrough versus AUC0-24h (r = 0.9215) and Cmax versus AUC0-24h models for digoxin (r = 0.7781) showed strong correlations. Approximately 93.8% of the predicted digoxin AUC0-24h values were within 0.76-fold to 1.25-fold difference for Ctrough model. In sharp contrast, the Cmax model showed larger variability with only 51.6% of AUC0-24h predictions within 0.76-1.25-fold difference. The r value for observed versus predicted AUC0-24h for Ctrough (r = 0.9551; n = 177; P < 0.001) was superior to the Cmax (r = 0.6134; n = 275; P < 0.001) model. The mean square error and root mean square error (%) for the Ctrough model were 11.95% and 16.2% as compared to 67.17% and 42.3% obtained for the Cmax model. Simple linear regression models for Ctrough/Cmax versus AUC0-24h were derived for digoxin. On the basis of statistical evaluation, Ctrough was superior to Cmax model for the prediction of digoxin AUC0-24h and can be potentially used in a prospective setting for predicting drug-drug interaction or lack of it.

  2. Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.

    Science.gov (United States)

    Hao, Ming; Bryant, Stephen H; Wang, Yanli

    2018-02-06

    While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred. Published by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US.

  3. Interaction between the Basolateral Amygdala and Dorsal Hippocampus Is Critical for Cocaine Memory Reconsolidation and Subsequent Drug Context-Induced Cocaine-Seeking Behaviorin Rats

    Science.gov (United States)

    Wells, Audrey M.; Lasseter, Heather C.; Xie, Xiaohu; Cowhey, Kate E.; Reittinger, Andrew M.; Fuchs, Rita A.

    2011-01-01

    Contextual stimulus control over instrumental drug-seeking behavior relies on the reconsolidation of context-response-drug associative memories into long-term memory storage following retrieval-induced destabilization. According to previous studies, the basolateral amygdala (BLA) and dorsal hippocampus (DH) regulate cocaine-related memory…

  4. Large-scale prediction of drug–target interactions using protein sequences and drug topological structures

    International Nuclear Information System (INIS)

    Cao Dongsheng; Liu Shao; Xu Qingsong; Lu Hongmei; Huang Jianhua; Hu Qiannan; Liang Yizeng

    2012-01-01

    Highlights: ► Drug–target interactions are predicted using an extended SAR methodology. ► A drug–target interaction is regarded as an event triggered by many factors. ► Molecular fingerprint and CTD descriptors are used to represent drugs and proteins. ► Our approach shows compatibility between the new scheme and current SAR methodology. - Abstract: The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug–target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug–target interactions in a timely manner. In this article, we aim at extending current structure–activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug–target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug–target interactions, and show a general compatibility between the new scheme and current SAR

  5. Computerized techniques pave the way for drug-drug interaction prediction and interpretation

    Directory of Open Access Journals (Sweden)

    Reza Safdari

    2016-06-01

    Results: Computerized data-mining in pharmaceutical sciences and related databases provide new key transformative paradigms that can revolutionize the treatment of diseases and hence medical care. Given that various aspects of drug discovery and pharmacotherapy are closely related to the clinical and molecular/biological information, the scientifically sound databases (e.g., DDIs, ADRs can be of importance for the success of pharmacotherapy modalities. Conclusion: A better understanding of DDIs not only provides a robust means for designing more effective medicines but also grantees patient safety.

  6. Concordance and predictive value of two adverse drug event data sets.

    Science.gov (United States)

    Cami, Aurel; Reis, Ben Y

    2014-08-22

    Accurate prediction of adverse drug events (ADEs) is an important means of controlling and reducing drug-related morbidity and mortality. Since no single "gold standard" ADE data set exists, a range of different drug safety data sets are currently used for developing ADE prediction models. There is a critical need to assess the degree of concordance between these various ADE data sets and to validate ADE prediction models against multiple reference standards. We systematically evaluated the concordance of two widely used ADE data sets - Lexi-comp from 2010 and SIDER from 2012. The strength of the association between ADE (drug) counts in Lexi-comp and SIDER was assessed using Spearman rank correlation, while the differences between the two data sets were characterized in terms of drug categories, ADE categories and ADE frequencies. We also performed a comparative validation of the Predictive Pharmacosafety Networks (PPN) model using both ADE data sets. The predictive power of PPN using each of the two validation sets was assessed using the area under Receiver Operating Characteristic curve (AUROC). The correlations between the counts of ADEs and drugs in the two data sets were 0.84 (95% CI: 0.82-0.86) and 0.92 (95% CI: 0.91-0.93), respectively. Relative to an earlier snapshot of Lexi-comp from 2005, Lexi-comp 2010 and SIDER 2012 introduced a mean of 1,973 and 4,810 new drug-ADE associations per year, respectively. The difference between these two data sets was most pronounced for Nervous System and Anti-infective drugs, Gastrointestinal and Nervous System ADEs, and postmarketing ADEs. A minor difference of 1.1% was found in the AUROC of PPN when SIDER 2012 was used for validation instead of Lexi-comp 2010. In conclusion, the ADE and drug counts in Lexi-comp and SIDER data sets were highly correlated and the choice of validation set did not greatly affect the overall prediction performance of PPN. Our results also suggest that it is important to be aware of the

  7. EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to Improve Prediction Accuracy.

    Science.gov (United States)

    Zhou, Xianxiao; Wang, Minghui; Katsyv, Igor; Irie, Hanna; Zhang, Bin

    2018-04-24

    Availability of large-scale genomic, epigenetic and proteomic data in complex diseases makes it possible to objectively and comprehensively identify therapeutic targets that can lead to new therapies. The Connectivity Map has been widely used to explore novel indications of existing drugs. However, the prediction accuracy of the existing methods, such as Kolmogorov-Smirnov statistic remains low. Here we present a novel high-performance drug repositioning approach that improves over the state-of-the-art methods. We first designed an expression weighted cosine method (EWCos) to minimize the influence of the uninformative expression changes and then developed an ensemble approach termed EMUDRA (Ensemble of Multiple Drug Repositioning Approaches) to integrate EWCos and three existing state-of-the-art methods. EMUDRA significantly outperformed individual drug repositioning methods when applied to simulated and independent evaluation datasets. We predicted using EMUDRA and experimentally validated an antibiotic rifabutin as an inhibitor of cell growth in triple negative breast cancer. EMUDRA can identify drugs that more effectively target disease gene signatures and will thus be a useful tool for identifying novel therapies for complex diseases and predicting new indications for existing drugs. The EMUDRA R package is available at doi:10.7303/syn11510888. bin.zhang@mssm.edu or zhangb@hotmail.com. Supplementary data are available at Bioinformatics online.

  8. Improving the prediction of the brain disposition for orally administered drugs using BDDCS

    DEFF Research Database (Denmark)

    Broccatelli, Fabio; Larregieu, Caroline A.; Cruciani, Gabriele

    2012-01-01

    outcome. Passive permeability and P-glycoprotein (Pgp, ABCB1) efflux have been successfully recognized to impact xenobiotic extrusion from the brain, as Pgp is known to play a role in limiting the BBB penetration of oral drugs in humans. However, these two properties alone fail to explain the BBB...... penetration for a significant number of marketed central nervous system (CNS) agents. The Biopharmaceutics Drug Disposition Classification System (BDDCS) has proved useful in predicting drug disposition in the human body, particularly in the liver and intestine. Here we discuss the value of using BDDCS...

  9. Trunk-to-Peripheral Fat Ratio Predicts Subsequent Blood Pressure Levels in Pubertal Children With Relatively Low Body Fat - Three-Year Follow-up Study.

    Science.gov (United States)

    Kouda, Katsuyasu; Ohara, Kumiko; Fujita, Yuki; Nakamura, Harunobu; Iki, Masayuki

    2016-07-25

    Only a few studies have examined the relationship between fat distribution measured by dual-energy X-ray absorptiometry (DXA) and blood pressure (BP), and no cohort study has targeted a pediatric population. The source population comprised all students registered as fifth graders in the 2 elementary schools in Hamamatsu, Japan. Of these, 258 children participated in both baseline (at age 11) and follow-up (at age 14) surveys. Body fat distribution was assessed using trunk-to-appendicular fat ratio (TAR) and trunk-to-leg fat ratio (TLR) measured by DXA. Relationships between BP levels and fat distribution (TAR or TLR) were examined after stratification by tertiles of whole-body fat.Systolic BP at follow-up was significantly (Pfat. Moreover, adjusted means of systolic and diastolic BPs in girls showed a significant increase from the lowest to highest tertile of TAR within the lowest tertile of whole-body fat. Body fat distribution in childhood could predict subsequent BP levels in adolescence. Children with a relatively low body fat that is more centrally distributed tended to show relatively high BP later on. (Circ J 2016; 80: 1838-1845).

  10. A Copula Based Approach for Design of Multivariate Random Forests for Drug Sensitivity Prediction.

    Science.gov (United States)

    Haider, Saad; Rahman, Raziur; Ghosh, Souparno; Pal, Ranadip

    2015-01-01

    Modeling sensitivity to drugs based on genetic characterizations is a significant challenge in the area of systems medicine. Ensemble based approaches such as Random Forests have been shown to perform well in both individual sensitivity prediction studies and team science based prediction challenges. However, Random Forests generate a deterministic predictive model for each drug based on the genetic characterization of the cell lines and ignores the relationship between different drug sensitivities during model generation. This application motivates the need for generation of multivariate ensemble learning techniques that can increase prediction accuracy and improve variable importance ranking by incorporating the relationships between different output responses. In this article, we propose a novel cost criterion that captures the dissimilarity in the output response structure between the training data and node samples as the difference in the two empirical copulas. We illustrate that copulas are suitable for capturing the multivariate structure of output responses independent of the marginal distributions and the copula based multivariate random forest framework can provide higher accuracy prediction and improved variable selection. The proposed framework has been validated on genomics of drug sensitivity for cancer and cancer cell line encyclopedia database.

  11. Accelerating early anti-tuberculosis drug discovery by creating mycobacterial indicator strains that predict mode of action

    KAUST Repository

    Boot, Maikel

    2018-04-13

    Due to the rise of drug resistant forms of tuberculosis there is an urgent need for novel antibiotics to effectively combat these cases and shorten treatment regimens. Recently, drug screens using whole cell analyses have been shown to be successful. However, current high-throughput screens focus mostly on stricto sensu life-death screening that give little qualitative information. In doing so, promising compound scaffolds or non-optimized compounds that fail to reach inhibitory concentrations are missed. To accelerate early TB drug discovery, we performed RNA sequencing on Mycobacterium tuberculosis and Mycobacterium marinum to map the stress responses that follow upon exposure to sub-inhibitory concentrations of antibiotics with known targets: ciprofloxacin, ethambutol, isoniazid, streptomycin and rifampicin. The resulting dataset comprises the first overview of transcriptional stress responses of mycobacteria to different antibiotics. We show that antibiotics can be distinguished based on their specific transcriptional stress fingerprint. Notably, this fingerprint was more distinctive in M. marinum. We decided to use this to our advantage and continue with this model organism. A selection of diverse antibiotic stress genes was used to construct stress reporters. In total, three functional reporters were constructed to respond to DNA damage, cell wall damage and ribosomal inhibition. Subsequently, these reporter strains were used to screen a small anti-TB compound library to predict the mode of action. In doing so, we could identify the putative mode of action for three novel compounds, which confirms our approach.

  12. An integrated approach to improved toxicity prediction for the safety assessment during preclinical drug development using Hep G2 cells

    International Nuclear Information System (INIS)

    Noor, Fozia; Niklas, Jens; Mueller-Vieira, Ursula; Heinzle, Elmar

    2009-01-01

    Efficient and accurate safety assessment of compounds is extremely important in the preclinical development of drugs especially when hepatotoxicty is in question. Multiparameter and time resolved assays are expected to greatly improve the prediction of toxicity by assessing complex mechanisms of toxicity. An integrated approach is presented in which Hep G2 cells and primary rat hepatocytes are compared in frequently used cytotoxicity assays for parent compound toxicity. The interassay variability was determined. The cytotoxicity assays were also compared with a reliable alternative time resolved respirometric assay. The set of training compounds consisted of well known hepatotoxins; amiodarone, carbamazepine, clozapine, diclofenac, tacrine, troglitazone and verapamil. The sensitivity of both cell systems in each tested assay was determined. Results show that careful selection of assay parameters and inclusion of a kinetic time resolved assay improves prediction for non-metabolism mediated toxicity using Hep G2 cells as indicated by a sensitivity ratio of 1. The drugs with EC 50 values 100 μM or lower were considered toxic. The difference in the sensitivity of the two cell systems to carbamazepine which causes toxicity via reactive metabolites emphasizes the importance of human cell based in-vitro assays. Using the described system, primary rat hepatocytes do not offer advantage over the Hep G2 cells in parent compound toxicity evaluation. Moreover, respiration method is non invasive, highly sensitive and allows following the time course of toxicity. Respiration assay could serve as early indicator of changes that subsequently lead to toxicity.

  13. In Silico Modeling of Gastrointestinal Drug Absorption: Predictive Performance of Three Physiologically Based Absorption Models.

    Science.gov (United States)

    Sjögren, Erik; Thörn, Helena; Tannergren, Christer

    2016-06-06

    Gastrointestinal (GI) drug absorption is a complex process determined by formulation, physicochemical and biopharmaceutical factors, and GI physiology. Physiologically based in silico absorption models have emerged as a widely used and promising supplement to traditional in vitro assays and preclinical in vivo studies. However, there remains a lack of comparative studies between different models. The aim of this study was to explore the strengths and limitations of the in silico absorption models Simcyp 13.1, GastroPlus 8.0, and GI-Sim 4.1, with respect to their performance in predicting human intestinal drug absorption. This was achieved by adopting an a priori modeling approach and using well-defined input data for 12 drugs associated with incomplete GI absorption and related challenges in predicting the extent of absorption. This approach better mimics the real situation during formulation development where predictive in silico models would be beneficial. Plasma concentration-time profiles for 44 oral drug administrations were calculated by convolution of model-predicted absorption-time profiles and reported pharmacokinetic parameters. Model performance was evaluated by comparing the predicted plasma concentration-time profiles, Cmax, tmax, and exposure (AUC) with observations from clinical studies. The overall prediction accuracies for AUC, given as the absolute average fold error (AAFE) values, were 2.2, 1.6, and 1.3 for Simcyp, GastroPlus, and GI-Sim, respectively. The corresponding AAFE values for Cmax were 2.2, 1.6, and 1.3, respectively, and those for tmax were 1.7, 1.5, and 1.4, respectively. Simcyp was associated with underprediction of AUC and Cmax; the accuracy decreased with decreasing predicted fabs. A tendency for underprediction was also observed for GastroPlus, but there was no correlation with predicted fabs. There were no obvious trends for over- or underprediction for GI-Sim. The models performed similarly in capturing dependencies on dose and

  14. Prediction of drug synergy in cancer using ensemble-based machine learning techniques

    Science.gov (United States)

    Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

    2018-04-01

    Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

  15. Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A.

    Science.gov (United States)

    Yadav, Jaydeep; Korzekwa, Ken; Nagar, Swati

    2018-05-07

    Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro-in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000 146 min -1 , the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000 32 min -1 . These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach.

  16. A unified algorithm for predicting partition coefficients for PBPK modeling of drugs and environmental chemicals

    International Nuclear Information System (INIS)

    Peyret, Thomas; Poulin, Patrick; Krishnan, Kannan

    2010-01-01

    The algorithms in the literature focusing to predict tissue:blood PC (P tb ) for environmental chemicals and tissue:plasma PC based on total (K p ) or unbound concentration (K pu ) for drugs differ in their consideration of binding to hemoglobin, plasma proteins and charged phospholipids. The objective of the present study was to develop a unified algorithm such that P tb , K p and K pu for both drugs and environmental chemicals could be predicted. The development of the unified algorithm was accomplished by integrating all mechanistic algorithms previously published to compute the PCs. Furthermore, the algorithm was structured in such a way as to facilitate predictions of the distribution of organic compounds at the macro (i.e. whole tissue) and micro (i.e. cells and fluids) levels. The resulting unified algorithm was applied to compute the rat P tb , K p or K pu of muscle (n = 174), liver (n = 139) and adipose tissue (n = 141) for acidic, neutral, zwitterionic and basic drugs as well as ketones, acetate esters, alcohols, aliphatic hydrocarbons, aromatic hydrocarbons and ethers. The unified algorithm reproduced adequately the values predicted previously by the published algorithms for a total of 142 drugs and chemicals. The sensitivity analysis demonstrated the relative importance of the various compound properties reflective of specific mechanistic determinants relevant to prediction of PC values of drugs and environmental chemicals. Overall, the present unified algorithm uniquely facilitates the computation of macro and micro level PCs for developing organ and cellular-level PBPK models for both chemicals and drugs.

  17. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

    Science.gov (United States)

    Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

  18. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

    Science.gov (United States)

    Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

  19. Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms.

    Science.gov (United States)

    Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

    2018-01-01

    Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention. In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds. The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of the applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898, respectively. This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  20. Prediction of solubility and permeability class membership: provisional BCS classification of the world's top oral drugs.

    Science.gov (United States)

    Dahan, Arik; Miller, Jonathan M; Amidon, Gordon L

    2009-12-01

    The Biopharmaceutics Classification System (BCS) categorizes drugs into one of four biopharmaceutical classes according to their water solubility and membrane permeability characteristics and broadly allows the prediction of the rate-limiting step in the intestinal absorption process following oral administration. Since its introduction in 1995, the BCS has generated remarkable impact on the global pharmaceutical sciences arena, in drug discovery, development, and regulation, and extensive validation/discussion/extension of the BCS is continuously published in the literature. The BCS has been effectively implanted by drug regulatory agencies around the world in setting bioavailability/bioequivalence standards for immediate-release (IR) oral drug product approval. In this review, we describe the BCS scientific framework and impact on regulatory practice of oral drug products and review the provisional BCS classification of the top drugs on the global market. The Biopharmaceutical Drug Disposition Classification System and its association with the BCS are discussed as well. One notable finding of the provisional BCS classification is that the clinical performance of the majority of approved IR oral drug products essential for human health can be assured with an in vitro dissolution test, rather than empirical in vivo human studies.

  1. Application of physiologically based pharmacokinetic modeling in predicting drug–drug interactions for sarpogrelate hydrochloride in humans

    Directory of Open Access Journals (Sweden)

    Min JS

    2016-09-01

    Full Text Available Jee Sun Min,1 Doyun Kim,1 Jung Bae Park,1 Hyunjin Heo,1 Soo Hyeon Bae,2 Jae Hong Seo,1 Euichaul Oh,1 Soo Kyung Bae1 1Integrated Research Institute of Pharmaceutical Sciences, College of Pharmacy, The Catholic University of Korea, Bucheon, 2Department of Pharmacology, College of Medicine, The Catholic University of Korea, Seocho-gu, Seoul, South Korea Background: Evaluating the potential risk of metabolic drug–drug interactions (DDIs is clinically important. Objective: To develop a physiologically based pharmacokinetic (PBPK model for sarpogrelate hydrochloride and its active metabolite, (R,S-1-{2-[2-(3-methoxyphenylethyl]-phenoxy}-3-(dimethylamino-2-propanol (M-1, in order to predict DDIs between sarpogrelate and the clinically relevant cytochrome P450 (CYP 2D6 substrates, metoprolol, desipramine, dextromethorphan, imipramine, and tolterodine. Methods: The PBPK model was developed, incorporating the physicochemical and pharmacokinetic properties of sarpogrelate hydrochloride, and M-1 based on the findings from in vitro and in vivo studies. Subsequently, the model was verified by comparing the predicted concentration-time profiles and pharmacokinetic parameters of sarpogrelate and M-1 to the observed clinical data. Finally, the verified model was used to simulate clinical DDIs between sarpogrelate hydrochloride and sensitive CYP2D6 substrates. The predictive performance of the model was assessed by comparing predicted results to observed data after coadministering sarpogrelate hydrochloride and metoprolol. Results: The developed PBPK model accurately predicted sarpogrelate and M-1 plasma concentration profiles after single or multiple doses of sarpogrelate hydrochloride. The simulated ratios of area under the curve and maximum plasma concentration of metoprolol in the presence of sarpogrelate hydrochloride to baseline were in good agreement with the observed ratios. The predicted fold-increases in the area under the curve ratios of metoprolol

  2. CRISPR-Cas9-mediated saturated mutagenesis screen predicts clinical drug resistance with improved accuracy.

    Science.gov (United States)

    Ma, Leyuan; Boucher, Jeffrey I; Paulsen, Janet; Matuszewski, Sebastian; Eide, Christopher A; Ou, Jianhong; Eickelberg, Garrett; Press, Richard D; Zhu, Lihua Julie; Druker, Brian J; Branford, Susan; Wolfe, Scot A; Jensen, Jeffrey D; Schiffer, Celia A; Green, Michael R; Bolon, Daniel N

    2017-10-31

    Developing tools to accurately predict the clinical prevalence of drug-resistant mutations is a key step toward generating more effective therapeutics. Here we describe a high-throughput CRISPR-Cas9-based saturated mutagenesis approach to generate comprehensive libraries of point mutations at a defined genomic location and systematically study their effect on cell growth. As proof of concept, we mutagenized a selected region within the leukemic oncogene BCR-ABL1 Using bulk competitions with a deep-sequencing readout, we analyzed hundreds of mutations under multiple drug conditions and found that the effects of mutations on growth in the presence or absence of drug were critical for predicting clinically relevant resistant mutations, many of which were cancer adaptive in the absence of drug pressure. Using this approach, we identified all clinically isolated BCR-ABL1 mutations and achieved a prediction score that correlated highly with their clinical prevalence. The strategy described here can be broadly applied to a variety of oncogenes to predict patient mutations and evaluate resistance susceptibility in the development of new therapeutics. Published under the PNAS license.

  3. Adverse drug reactions in older patients during hospitalisation: are they predictable?

    LENUS (Irish Health Repository)

    O'Connor, Marie N

    2012-11-01

    adverse drug reactions (ADRs) are a major cause of morbidity and healthcare utilisation in older people. The GerontoNet ADR risk score aims to identify older people at risk of ADRs during hospitalisation. We aimed to assess the clinical applicability of this score and identify other variables that predict ADRs in hospitalised older people.

  4. A Semi-Supervised Approach for Refining Transcriptional Signatures of Drug Response and Repositioning Predictions.

    Directory of Open Access Journals (Sweden)

    Francesco Iorio

    Full Text Available We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed compound. This signature can be derived from data in existing databases, such as the connectivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the principal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising agent as seed compound. Our method predicts that glipizide and splitomicin perturb microtubule function in human cells: a result that could not be obtained through standard signature matching methods. In agreement, we find that glipizide and splitomicin reduce interphase microtubule growth rates and transiently increase the percentage of mitotic cells-consistent with our prediction. Finally, we validated the refined signatures of paclitaxel response by mining a large drug screening dataset, showing that human cancer cell lines whose basal transcriptional profile is anti-correlated to them are significantly more sensitive to paclitaxel and docetaxel.

  5. Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis

    Directory of Open Access Journals (Sweden)

    Huthmacher Carola

    2010-08-01

    Full Text Available Abstract Background Despite enormous efforts to combat malaria the disease still afflicts up to half a billion people each year of which more than one million die. Currently no approved vaccine is available and resistances to antimalarials are widely spread. Hence, new antimalarial drugs are urgently needed. Results Here, we present a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. We assembled a compartmentalized metabolic model and predicted life cycle stage specific metabolism with the help of a flux balance approach that integrates gene expression data. Predicted metabolite exchanges between parasite and host were found to be in good accordance with experimental findings when the parasite's metabolic network was embedded into that of its host (erythrocyte. Knock-out simulations identified 307 indispensable metabolic reactions within the parasite. 35 out of 57 experimentally demonstrated essential enzymes were recovered and another 16 enzymes, if additionally the assumption was made that nutrient uptake from the host cell is limited and all reactions catalyzed by the inhibited enzyme are blocked. This predicted set of putative drug targets, shown to be enriched with true targets by a factor of at least 2.75, was further analyzed with respect to homology to human enzymes, functional similarity to therapeutic targets in other organisms and their predicted potency for prophylaxis and disease treatment. Conclusions The results suggest that the set of essential enzymes predicted by our flux balance approach represents a promising starting point for further drug development.

  6. The persuasion network is modulated by drug-use risk and predicts anti-drug message effectiveness

    Science.gov (United States)

    Mangus, J Michael; Turner, Benjamin O

    2017-01-01

    Abstract While a persuasion network has been proposed, little is known about how network connections between brain regions contribute to attitude change. Two possible mechanisms have been advanced. One hypothesis predicts that attitude change results from increased connectivity between structures implicated in affective and executive processing in response to increases in argument strength. A second functional perspective suggests that highly arousing messages reduce connectivity between structures implicated in the encoding of sensory information, which disrupts message processing and thereby inhibits attitude change. However, persuasion is a multi-determined construct that results from both message features and audience characteristics. Therefore, persuasive messages should lead to specific functional connectivity patterns among a priori defined structures within the persuasion network. The present study exposed 28 subjects to anti-drug public service announcements where arousal, argument strength, and subject drug-use risk were systematically varied. Psychophysiological interaction analyses provide support for the affective-executive hypothesis but not for the encoding-disruption hypothesis. Secondary analyses show that video-level connectivity patterns among structures within the persuasion network predict audience responses in independent samples (one college-aged, one nationally representative). We propose that persuasion neuroscience research is best advanced by considering network-level effects while accounting for interactions between message features and target audience characteristics. PMID:29140500

  7. The persuasion network is modulated by drug-use risk and predicts anti-drug message effectiveness.

    Science.gov (United States)

    Huskey, Richard; Mangus, J Michael; Turner, Benjamin O; Weber, René

    2017-12-01

    While a persuasion network has been proposed, little is known about how network connections between brain regions contribute to attitude change. Two possible mechanisms have been advanced. One hypothesis predicts that attitude change results from increased connectivity between structures implicated in affective and executive processing in response to increases in argument strength. A second functional perspective suggests that highly arousing messages reduce connectivity between structures implicated in the encoding of sensory information, which disrupts message processing and thereby inhibits attitude change. However, persuasion is a multi-determined construct that results from both message features and audience characteristics. Therefore, persuasive messages should lead to specific functional connectivity patterns among a priori defined structures within the persuasion network. The present study exposed 28 subjects to anti-drug public service announcements where arousal, argument strength, and subject drug-use risk were systematically varied. Psychophysiological interaction analyses provide support for the affective-executive hypothesis but not for the encoding-disruption hypothesis. Secondary analyses show that video-level connectivity patterns among structures within the persuasion network predict audience responses in independent samples (one college-aged, one nationally representative). We propose that persuasion neuroscience research is best advanced by considering network-level effects while accounting for interactions between message features and target audience characteristics. © The Author (2017). Published by Oxford University Press.

  8. Analysis of the Risks and Benefits of New Chemical Entities Approved by the US Food and Drug Administration (FDA) and Subsequently Withdrawn From the US Market.

    Science.gov (United States)

    Patriarca, Peter A; Van Auken, R Michael; Kebschull, Scott A

    2018-01-01

    Benefit-risk evaluations of drugs have been conducted since the introduction of modern regulatory systems more than 50 years ago. Such judgments are typically made on the basis of qualitative or semiquantitative approaches, often without the aid of quantitative assessment methods, the latter having often been applied asymmetrically to place emphasis on benefit more so than harm. In an effort to preliminarily evaluate the utility of lives lost or saved, or quality-adjusted life-years (QALY) lost and gained as a means of quantitatively assessing the potential benefits and risks of a new chemical entity, we focused our attention on the unique scenario in which a drug was initially approved based on one set of data, but later withdrawn from the market based on a second set of data. In this analysis, a dimensionless risk to benefit ratio was calculated in each instance, based on the risk and benefit quantified in similar units. The results indicated that FDA decisions to approve the drug corresponded to risk to benefit ratios less than or equal to 0.136, and that decisions to withdraw the drug from the US market corresponded to risk to benefit ratios greater than or equal to 0.092. The probability of FDA approval was then estimated using logistic regression analysis. The results of this analysis indicated that there was a 50% probability of FDA approval if the risk to benefit ratio was 0.121, and that the probability approaches 100% for values much less than 0.121, and the probability approaches 0% for values much greater than 0.121. The large uncertainty in these estimates due to the small sample size and overlapping data may be addressed in the future by applying the methodology to other drugs.

  9. Prediction of Human Pharmacokinetic Profile After Transdermal Drug Application Using Excised Human Skin.

    Science.gov (United States)

    Yamamoto, Syunsuke; Karashima, Masatoshi; Arai, Yuta; Tohyama, Kimio; Amano, Nobuyuki

    2017-09-01

    Although several mathematical models have been reported for the estimation of human plasma concentration profiles of drug substances after dermal application, the successful cases that can predict human pharmacokinetic profiles are limited. Therefore, the aim of this study is to investigate the prediction of human plasma concentrations after dermal application using in vitro permeation parameters obtained from excised human skin. The in vitro skin permeability of 7 marketed drug products was evaluated. The plasma concentration-time profiles of the drug substances in humans after their dermal application were simulated using compartment models and the clinical pharmacokinetic parameters. The transdermal process was simulated using the in vitro skin permeation rate and lag time assuming a zero-order absorption. These simulated plasma concentration profiles were compared with the clinical data. The result revealed that the steady-state plasma concentration of diclofenac and the maximum concentrations of nicotine, bisoprolol, rivastigmine, and lidocaine after topical application were within 2-fold of the clinical data. Furthermore, the simulated concentration profiles of bisoprolol, nicotine, and rivastigmine reproduced the decrease in absorption due to drug depletion from the formulation. In conclusion, this simple compartment model using in vitro human skin permeation parameters as zero-order absorption predicted the human plasma concentrations accurately. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  10. PockDrug: A Model for Predicting Pocket Druggability That Overcomes Pocket Estimation Uncertainties.

    Science.gov (United States)

    Borrel, Alexandre; Regad, Leslie; Xhaard, Henri; Petitjean, Michel; Camproux, Anne-Claude

    2015-04-27

    Predicting protein druggability is a key interest in the target identification phase of drug discovery. Here, we assess the pocket estimation methods' influence on druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 Å to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5-10%) than previous studies that used an identical apo set. In conclusion, this study confirms the influence of pocket estimation on pocket druggability prediction and proposes PockDrug as a new model that overcomes pocket estimation variability.

  11. How good are publicly available web services that predict bioactivity profiles for drug repurposing?

    Science.gov (United States)

    Murtazalieva, K A; Druzhilovskiy, D S; Goel, R K; Sastry, G N; Poroikov, V V

    2017-10-01

    Drug repurposing provides a non-laborious and less expensive way for finding new human medicines. Computational assessment of bioactivity profiles shed light on the hidden pharmacological potential of the launched drugs. Currently, several freely available computational tools are available via the Internet, which predict multitarget profiles of drug-like compounds. They are based on chemical similarity assessment (ChemProt, SuperPred, SEA, SwissTargetPrediction and TargetHunter) or machine learning methods (ChemProt and PASS). To compare their performance, this study has created two evaluation sets, consisting of (1) 50 well-known repositioned drugs and (2) 12 drugs recently patented for new indications. In the first set, sensitivity values varied from 0.64 (TarPred) to 1.00 (PASS Online) for the initial indications and from 0.64 (TarPred) to 0.98 (PASS Online) for the repurposed indications. In the second set, sensitivity values varied from 0.08 (SuperPred) to 1.00 (PASS Online) for the initial indications and from 0.00 (SuperPred) to 1.00 (PASS Online) for the repurposed indications. Thus, this analysis demonstrated that the performance of machine learning methods surpassed those of chemical similarity assessments, particularly in the case of novel repurposed indications.

  12. Predicting and detecting adverse drug reactions in old age: challenges and opportunities.

    Science.gov (United States)

    Mangoni, Arduino A

    2012-05-01

    Increased, often inappropriate, drug exposure, pharmacokinetic and pharmacodynamic changes, reduced homeostatic reserve and frailty increase the risk of adverse drug reactions (ADRs) in the older population, thereby imposing a significant public health burden. Predicting and diagnosing ADRs in old age presents significant challenges for the clinician, even when specific risk scoring systems are available. The picture is further compounded by the potential adverse impact of several drugs on more 'global' health indicators, for example, physical function and independence, and the fragmentation of care (e.g., increased number of treating doctors and care transitions) experienced by older patients during their clinical journey. The current knowledge of drug safety in old age is also curtailed by the lack of efficacy and safety data from pre-marketing studies. Moreover, little consideration is given to individual patients' experiences and reporting of specific ADRs, particularly in the presence of cognitive impairment. Pending additional data on these issues, the close review and monitoring of individual patients' drug prescribing, clinical status and biochemical parameters remain essential to predict and detect ADRs in old age. Recently developed strategies, for example, medication reconciliation and trigger tool methodology, have the potential for ADRs risk mitigation in this population. However, more information is required on their efficacy and applicability in different healthcare settings.

  13. Mathematical Model to Predict Skin Concentration after Topical Application of Drugs

    Directory of Open Access Journals (Sweden)

    Hiroaki Todo

    2013-12-01

    Full Text Available Skin permeation experiments have been broadly done since 1970s to 1980s as an evaluation method for transdermal drug delivery systems. In topically applied drug and cosmetic formulations, skin concentration of chemical compounds is more important than their skin permeations, because primary target site of the chemical compounds is skin surface or skin tissues. Furthermore, the direct pharmacological reaction of a metabolically stable drug that binds with specific receptors of known expression levels in an organ can be determined by Hill’s equation. Nevertheless, little investigation was carried out on the test method of skin concentration after topically application of chemical compounds. Recently we investigated an estimating method of skin concentration of the chemical compounds from their skin permeation profiles. In the study, we took care of “3Rs” issues for animal experiments. We have proposed an equation which was capable to estimate animal skin concentration from permeation profile through the artificial membrane (silicone membrane and animal skin. This new approach may allow the skin concentration of a drug to be predicted using Fick’s second law of diffusion. The silicone membrane was found to be useful as an alternative membrane to animal skin for predicting skin concentration of chemical compounds, because an extremely excellent extrapolation to animal skin concentration was attained by calculation using the silicone membrane permeation data. In this chapter, we aimed to establish an accurate and convenient method for predicting the concentration profiles of drugs in the skin based on the skin permeation parameters of topically active drugs derived from steady-state skin permeation experiments.

  14. A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy.

    Science.gov (United States)

    Ji, Zhiwei; Wang, Bing; Yan, Ke; Dong, Ligang; Meng, Guanmin; Shi, Lei

    2017-12-21

    In recent years, the integration of 'omics' technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc. In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds' treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound's efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound's treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds. In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

  15. Predictive modeling of structured electronic health records for adverse drug event detection.

    Science.gov (United States)

    Zhao, Jing; Henriksson, Aron; Asker, Lars; Boström, Henrik

    2015-01-01

    The digitization of healthcare data, resulting from the increasingly widespread adoption of electronic health records, has greatly facilitated its analysis by computational methods and thereby enabled large-scale secondary use thereof. This can be exploited to support public health activities such as pharmacovigilance, wherein the safety of drugs is monitored to inform regulatory decisions about sustained use. To that end, electronic health records have emerged as a potentially valuable data source, providing access to longitudinal observations of patient treatment and drug use. A nascent line of research concerns predictive modeling of healthcare data for the automatic detection of adverse drug events, which presents its own set of challenges: it is not yet clear how to represent the heterogeneous data types in a manner conducive to learning high-performing machine learning models. Datasets from an electronic health record database are used for learning predictive models with the purpose of detecting adverse drug events. The use and representation of two data types, as well as their combination, are studied: clinical codes, describing prescribed drugs and assigned diagnoses, and measurements. Feature selection is conducted on the various types of data to reduce dimensionality and sparsity, while allowing for an in-depth feature analysis of the usefulness of each data type and representation. Within each data type, combining multiple representations yields better predictive performance compared to using any single representation. The use of clinical codes for adverse drug event detection significantly outperforms the use of measurements; however, there is no significant difference over datasets between using only clinical codes and their combination with measurements. For certain adverse drug events, the combination does, however, outperform using only clinical codes. Feature selection leads to increased predictive performance for both data types, in isolation and

  16. Flexing the PECs: Predicting environmental concentrations of veterinary drugs in Canadian agricultural soils.

    Science.gov (United States)

    Kullik, Sigrun A; Belknap, Andrew M

    2017-03-01

    Veterinary drugs administered to food animals primarily enter ecosystems through the application of livestock waste to agricultural land. Although veterinary drugs are essential for protecting animal health, their entry into the environment may pose a risk for nontarget organisms. A means to predict environmental concentrations of new veterinary drug ingredients in soil is required to assess their environmental fate, distribution, and potential effects. The Canadian predicted environmental concentrations in soil (PECsoil) for new veterinary drug ingredients for use in intensively reared animals is based on the approach currently used by the European Medicines Agency for VICH Phase I environmental assessments. The calculation for the European Medicines Agency PECsoil can be adapted to account for regional animal husbandry and land use practices. Canadian agricultural practices for intensively reared cattle, pigs, and poultry differ substantially from those in the European Union. The development of PECsoil default values and livestock categories representative of typical Canadian animal production methods and nutrient management practices culminates several years of research and an extensive survey and analysis of the scientific literature, Canadian agricultural statistics, national and provincial management recommendations, veterinary product databases, and producers. A PECsoil can be used to rapidly identify new veterinary drugs intended for intensive livestock production that should undergo targeted ecotoxicity and fate testing. The Canadian PECsoil model is readily available, transparent, and requires minimal inputs to generate a screening level environmental assessment for veterinary drugs that can be refined if additional data are available. PECsoil values for a hypothetical veterinary drug dosage regimen are presented and discussed in an international context. Integr Environ Assess Manag 2017;13:331-341. © 2016 Her Majesty the Queen in Right of Canada

  17. PBPK Modeling - A Predictive, Eco-Friendly, Bio-Waiver Tool for Drug Research.

    Science.gov (United States)

    De, Baishakhi; Bhandari, Koushik; Mukherjee, Ranjan; Katakam, Prakash; Adiki, Shanta K; Gundamaraju, Rohit; Mitra, Analava

    2017-01-01

    The world has witnessed growing complexities in disease scenario influenced by the drastic changes in host-pathogen- environment triadic relation. Pharmaceutical R&Ds are in constant search of novel therapeutic entities to hasten transition of drug molecules from lab bench to patient bedside. Extensive animal studies and human pharmacokinetics are still the "gold standard" in investigational new drug research and bio-equivalency studies. Apart from cost, time and ethical issues on animal experimentation, burning questions arise relating to ecological disturbances, environmental hazards and biodiversity issues. Grave concerns arises when the adverse outcomes of continued studies on one particular disease on environment gives rise to several other pathogenic agents finally complicating the total scenario. Thus Pharma R&Ds face a challenge to develop bio-waiver protocols. Lead optimization, drug candidate selection with favorable pharmacokinetics and pharmacodynamics, toxicity assessment are vital steps in drug development. Simulation tools like Gastro Plus™, PK Sim®, SimCyp find applications for the purpose. Advanced technologies like organ-on-a chip or human-on-a chip where a 3D representation of human organs and systems can mimic the related processes and activities, thereby linking them to major features of human biology can be successfully incorporated in the drug development tool box. PBPK provides the State of Art to serve as an optional of animal experimentation. PBPK models can successfully bypass bio-equivalency studies, predict bioavailability, drug interactions and on hyphenation with in vitro-in vivo correlation can be extrapolated to humans thus serving as bio-waiver. PBPK can serve as an eco-friendly bio-waiver predictive tool in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  18. Robust prediction of anti-cancer drug sensitivity and sensitivity-specific biomarker.

    Directory of Open Access Journals (Sweden)

    Heewon Park

    Full Text Available The personal genomics era has attracted a large amount of attention for anti-cancer therapy by patient-specific analysis. Patient-specific analysis enables discovery of individual genomic characteristics for each patient, and thus we can effectively predict individual genetic risk of disease and perform personalized anti-cancer therapy. Although the existing methods for patient-specific analysis have successfully uncovered crucial biomarkers, their performance takes a sudden turn for the worst in the presence of outliers, since the methods are based on non-robust manners. In practice, clinical and genomic alterations datasets usually contain outliers from various sources (e.g., experiment error, coding error, etc. and the outliers may significantly affect the result of patient-specific analysis. We propose a robust methodology for patient-specific analysis in line with the NetwrokProfiler. In the proposed method, outliers in high dimensional gene expression levels and drug response datasets are simultaneously controlled by robust Mahalanobis distance in robust principal component space. Thus, we can effectively perform for predicting anti-cancer drug sensitivity and identifying sensitivity-specific biomarkers for individual patients. We observe through Monte Carlo simulations that the proposed robust method produces outstanding performances for predicting response variable in the presence of outliers. We also apply the proposed methodology to the Sanger dataset in order to uncover cancer biomarkers and predict anti-cancer drug sensitivity, and show the effectiveness of our method.

  19. A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations

    NARCIS (Netherlands)

    Yamamoto, Yumi; Valitalo, Pyry A.; van den Berg, Dirk-Jan; Hartman, Robin; van den Brink, Willem; Wong, Yin Cheong; Huntjens, Dymphy R.; Proost, Johannes H.; Vermeulen, An; Krauwinkel, Walter; Bakshi, Suruchi; Aranzana-Climent, Vincent; Marchand, Sandrine; Dahyot-Fizelier, Claire; Couet, William; Danhof, Meindert; van Hasselt, Johan G. C.; de lange, Elizabeth C. M.

    Purpose Predicting target site drug concentration in the brain is of key importance for the successful development of drugs acting on the central nervous system. We propose a generic mathematical model to describe the pharmacokinetics in brain compartments, and apply this model to predict human

  20. Profiling persistent tubercule bacilli from patient sputa during therapy predicts early drug efficacy.

    Science.gov (United States)

    Honeyborne, Isobella; McHugh, Timothy D; Kuittinen, Iitu; Cichonska, Anna; Evangelopoulos, Dimitrios; Ronacher, Katharina; van Helden, Paul D; Gillespie, Stephen H; Fernandez-Reyes, Delmiro; Walzl, Gerhard; Rousu, Juho; Butcher, Philip D; Waddell, Simon J

    2016-04-07

    New treatment options are needed to maintain and improve therapy for tuberculosis, which caused the death of 1.5 million people in 2013 despite potential for an 86 % treatment success rate. A greater understanding of Mycobacterium tuberculosis (M.tb) bacilli that persist through drug therapy will aid drug development programs. Predictive biomarkers for treatment efficacy are also a research priority. Genome-wide transcriptional profiling was used to map the mRNA signatures of M.tb from the sputa of 15 patients before and 3, 7 and 14 days after the start of standard regimen drug treatment. The mRNA profiles of bacilli through the first 2 weeks of therapy reflected drug activity at 3 days with transcriptional signatures at days 7 and 14 consistent with reduced M.tb metabolic activity similar to the profile of pre-chemotherapy bacilli. These results suggest that a pre-existing drug-tolerant M.tb population dominates sputum before and after early drug treatment, and that the mRNA signature at day 3 marks the killing of a drug-sensitive sub-population of bacilli. Modelling patient indices of disease severity with bacterial gene expression patterns demonstrated that both microbiological and clinical parameters were reflected in the divergent M.tb responses and provided evidence that factors such as bacterial load and disease pathology influence the host-pathogen interplay and the phenotypic state of bacilli. Transcriptional signatures were also defined that predicted measures of early treatment success (rate of decline in bacterial load over 3 days, TB test positivity at 2 months, and bacterial load at 2 months). This study defines the transcriptional signature of M.tb bacilli that have been expectorated in sputum after two weeks of drug therapy, characterizing the phenotypic state of bacilli that persist through treatment. We demonstrate that variability in clinical manifestations of disease are detectable in bacterial sputa signatures, and that the changing M.tb m

  1. Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds

    Directory of Open Access Journals (Sweden)

    Mengxuan Gao

    2017-02-01

    Full Text Available Various biological factors have been implicated in convulsive seizures, involving side effects of drugs. For the preclinical safety assessment of drug development, it is difficult to predict seizure-inducing side effects. Here, we introduced a machine learning-based in vitro system designed to detect seizure-inducing side effects. We recorded local field potentials from the CA1 alveus in acute mouse neocortico-hippocampal slices, while 14 drugs were bath-perfused at 5 different concentrations each. For each experimental condition, we collected seizure-like neuronal activity and merged their waveforms as one graphic image, which was further converted into a feature vector using Caffe, an open framework for deep learning. In the space of the first two principal components, the support vector machine completely separated the vectors (i.e., doses of individual drugs that induced seizure-like events and identified diphenhydramine, enoxacin, strychnine and theophylline as “seizure-inducing” drugs, which indeed were reported to induce seizures in clinical situations. Thus, this artificial intelligence-based classification may provide a new platform to detect the seizure-inducing side effects of preclinical drugs.

  2. In Silico Identification of Proteins Associated with Drug-induced Liver Injury Based on the Prediction of Drug-target Interactions.

    Science.gov (United States)

    Ivanov, Sergey; Semin, Maxim; Lagunin, Alexey; Filimonov, Dmitry; Poroikov, Vladimir

    2017-07-01

    Drug-induced liver injury (DILI) is the leading cause of acute liver failure as well as one of the major reasons for drug withdrawal from clinical trials and the market. Elucidation of molecular interactions associated with DILI may help to detect potentially hazardous pharmacological agents at the early stages of drug development. The purpose of our study is to investigate which interactions with specific human protein targets may cause DILI. Prediction of interactions with 1534 human proteins was performed for the dataset with information about 699 drugs, which were divided into three categories of DILI: severe (178 drugs), moderate (310 drugs) and without DILI (211 drugs). Based on the comparison of drug-target interactions predicted for different drugs' categories and interpretation of those results using clustering, Gene Ontology, pathway and gene expression analysis, we identified 61 protein targets associated with DILI. Most of the revealed proteins were linked with hepatocytes' death caused by disruption of vital cellular processes, as well as the emergence of inflammation in the liver. It was found that interaction of a drug with the identified targets is the essential molecular mechanism of the severe DILI for the most of the considered pharmaceuticals. Thus, pharmaceutical agents interacting with many of the identified targets may be considered as candidates for filtering out at the early stages of drug research. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Predicting changes in cardiac myocyte contractility during early drug discovery with in vitro assays

    Energy Technology Data Exchange (ETDEWEB)

    Morton, M.J., E-mail: michael.morton@astrazeneca.com [Discovery Sciences, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Armstrong, D.; Abi Gerges, N. [Drug Safety and Metabolism, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Bridgland-Taylor, M. [Discovery Sciences, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Pollard, C.E.; Bowes, J.; Valentin, J.-P. [Drug Safety and Metabolism, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom)

    2014-09-01

    Cardiovascular-related adverse drug effects are a major concern for the pharmaceutical industry. Activity of an investigational drug at the L-type calcium channel could manifest in a number of ways, including changes in cardiac contractility. The aim of this study was to define which of the two assay technologies – radioligand-binding or automated electrophysiology – was most predictive of contractility effects in an in vitro myocyte contractility assay. The activity of reference and proprietary compounds at the L-type calcium channel was measured by radioligand-binding assays, conventional patch-clamp, automated electrophysiology, and by measurement of contractility in canine isolated cardiac myocytes. Activity in the radioligand-binding assay at the L-type Ca channel phenylalkylamine binding site was most predictive of an inotropic effect in the canine cardiac myocyte assay. The sensitivity was 73%, specificity 83% and predictivity 78%. The radioligand-binding assay may be run at a single test concentration and potency estimated. The least predictive assay was automated electrophysiology which showed a significant bias when compared with other assay formats. Given the importance of the L-type calcium channel, not just in cardiac function, but also in other organ systems, a screening strategy emerges whereby single concentration ligand-binding can be performed early in the discovery process with sufficient predictivity, throughput and turnaround time to influence chemical design and address a significant safety-related liability, at relatively low cost. - Highlights: • The L-type calcium channel is a significant safety liability during drug discovery. • Radioligand-binding to the L-type calcium channel can be measured in vitro. • The assay can be run at a single test concentration as part of a screening cascade. • This measurement is highly predictive of changes in cardiac myocyte contractility.

  4. In silico modeling predicts drug sensitivity of patient-derived cancer cells.

    Science.gov (United States)

    Pingle, Sandeep C; Sultana, Zeba; Pastorino, Sandra; Jiang, Pengfei; Mukthavaram, Rajesh; Chao, Ying; Bharati, Ila Sri; Nomura, Natsuko; Makale, Milan; Abbasi, Taher; Kapoor, Shweta; Kumar, Ansu; Usmani, Shahabuddin; Agrawal, Ashish; Vali, Shireen; Kesari, Santosh

    2014-05-21

    Glioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling ("omics") data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach. Here, we present an in silico model, where we simulate GBM tumor cells using genomic profiling data. We use this in silico tumor model to predict responses of cancer cells to targeted drugs. Initially, we probed the results from a recent hypothesis-independent, empirical study by Garnett and co-workers that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. We then used the tumor model to predict sensitivity of patient-derived GBM cell lines to different targeted therapeutic agents. Among the drug-mutation associations reported in the Garnett study, our in silico model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines, we compared our simulation predictions with experimental data using the same cells in vitro. This analysis yielded a ~75% agreement of in silico drug sensitivity with in vitro experimental findings. These results demonstrate a strong predictability of our simulation approach using the in silico tumor model presented here. Our ultimate goal is to use this model to stratify patients for clinical trials. By accurately predicting responses of cancer cells to targeted agents a priori, this in silico tumor model provides an innovative approach to personalizing therapy and promises to improve clinical management of cancer.

  5. Predicting changes in cardiac myocyte contractility during early drug discovery with in vitro assays

    International Nuclear Information System (INIS)

    Morton, M.J.; Armstrong, D.; Abi Gerges, N.; Bridgland-Taylor, M.; Pollard, C.E.; Bowes, J.; Valentin, J.-P.

    2014-01-01

    Cardiovascular-related adverse drug effects are a major concern for the pharmaceutical industry. Activity of an investigational drug at the L-type calcium channel could manifest in a number of ways, including changes in cardiac contractility. The aim of this study was to define which of the two assay technologies – radioligand-binding or automated electrophysiology – was most predictive of contractility effects in an in vitro myocyte contractility assay. The activity of reference and proprietary compounds at the L-type calcium channel was measured by radioligand-binding assays, conventional patch-clamp, automated electrophysiology, and by measurement of contractility in canine isolated cardiac myocytes. Activity in the radioligand-binding assay at the L-type Ca channel phenylalkylamine binding site was most predictive of an inotropic effect in the canine cardiac myocyte assay. The sensitivity was 73%, specificity 83% and predictivity 78%. The radioligand-binding assay may be run at a single test concentration and potency estimated. The least predictive assay was automated electrophysiology which showed a significant bias when compared with other assay formats. Given the importance of the L-type calcium channel, not just in cardiac function, but also in other organ systems, a screening strategy emerges whereby single concentration ligand-binding can be performed early in the discovery process with sufficient predictivity, throughput and turnaround time to influence chemical design and address a significant safety-related liability, at relatively low cost. - Highlights: • The L-type calcium channel is a significant safety liability during drug discovery. • Radioligand-binding to the L-type calcium channel can be measured in vitro. • The assay can be run at a single test concentration as part of a screening cascade. • This measurement is highly predictive of changes in cardiac myocyte contractility

  6. Mining predicted essential genes of Brugia malayi for nematode drug targets.

    Directory of Open Access Journals (Sweden)

    Sanjay Kumar

    Full Text Available We report results from the first genome-wide application of a rational drug target selection methodology to a metazoan pathogen genome, the completed draft sequence of Brugia malayi, a parasitic nematode responsible for human lymphatic filariasis. More than 1.5 billion people worldwide are at risk of contracting lymphatic filariasis and onchocerciasis, a related filarial disease. Drug treatments for filariasis have not changed significantly in over 20 years, and with the risk of resistance rising, there is an urgent need for the development of new anti-filarial drug therapies. The recent publication of the draft genomic sequence for B. malayi enables a genome-wide search for new drug targets. However, there is no functional genomics data in B. malayi to guide the selection of potential drug targets. To circumvent this problem, we have utilized the free-living model nematode Caenorhabditis elegans as a surrogate for B. malayi. Sequence comparisons between the two genomes allow us to map C. elegans orthologs to B. malayi genes. Using these orthology mappings and by incorporating the extensive genomic and functional genomic data, including genome-wide RNAi screens, that already exist for C. elegans, we identify potentially essential genes in B. malayi. Further incorporation of human host genome sequence data and a custom algorithm for prioritization enables us to collect and rank nearly 600 drug target candidates. Previously identified potential drug targets cluster near the top of our prioritized list, lending credibility to our methodology. Over-represented Gene Ontology terms, predicted InterPro domains, and RNAi phenotypes of C. elegans orthologs associated with the potential target pool are identified. By virtue of the selection procedure, the potential B. malayi drug targets highlight components of key processes in nematode biology such as central metabolism, molting and regulation of gene expression.

  7. A multi-scale modeling framework for individualized, spatiotemporal prediction of drug effects and toxicological risk

    Directory of Open Access Journals (Sweden)

    Juan Guillermo eDiaz Ochoa

    2013-01-01

    Full Text Available In this study, we focus on a novel multi-scale modeling approach for spatiotemporal prediction of the distribution of substances and resulting hepatotoxicity by combining cellular models, a 2D liver model, and whole-body model. As a case study, we focused on predicting human hepatotoxicity upon treatment with acetaminophen based on in vitro toxicity data and potential inter-individual variability in gene expression and enzyme activities. By aggregating mechanistic, genome-based in silico cells to a novel 2D liver model and eventually to a whole body model, we predicted pharmacokinetic properties, metabolism, and the onset of hepatotoxicity in an in silico patient. Depending on the concentration of acetaminophen in the liver and the accumulation of toxic metabolites, cell integrity in the liver as a function of space and time as well as changes in the elimination rate of substances were estimated. We show that the variations in elimination rates also influence the distribution of acetaminophen and its metabolites in the whole body. Our results are in agreement with experimental results. What is more, the integrated model also predicted variations in drug toxicity depending on alterations of metabolic enzyme activities. Variations in enzyme activity, in turn, reflect genetic characteristics or diseases of individuals. In conclusion, this framework presents an important basis for efficiently integrating inter-individual variability data into models, paving the way for personalized or stratified predictions of drug toxicity and efficacy.

  8. Pharmacokinetics in Drug Discovery: An Exposure-Centred Approach to Optimising and Predicting Drug Efficacy and Safety.

    Science.gov (United States)

    Reichel, Andreas; Lienau, Philip

    2016-01-01

    The role of pharmacokinetics (PK) in drug discovery is to support the optimisation of the absorption, distribution, metabolism and excretion (ADME) properties of lead compounds with the ultimate goal to attain a clinical candidate which achieves a concentration-time profile in the body that is adequate for the desired efficacy and safety profile. A thorough characterisation of the lead compounds aiming at the identification of the inherent PK liabilities also includes an early generation of PK/PD relationships linking in vitro potency and target exposure/engagement with expression of pharmacological activity (mode-of-action) and efficacy in animal studies. The chapter describes an exposure-centred approach to lead generation, lead optimisation and candidate selection and profiling that focuses on a stepwise generation of an understanding between PK/exposure and PD/efficacy relationships by capturing target exposure or surrogates thereof and cellular mode-of-action readouts in vivo. Once robust PK/PD relationship in animal PD models has been constructed, it is translated to anticipate the pharmacologically active plasma concentrations in patients and the human therapeutic dose and dosing schedule which is also based on the prediction of the PK behaviour in human as described herein. The chapter outlines how the level of confidence in the predictions increases with the level of understanding of both the PK and the PK/PD of the new chemical entities (NCE) in relation to the disease hypothesis and the ability to propose safe and efficacious doses and dosing schedules in responsive patient populations. A sound identification of potential drug metabolism and pharmacokinetics (DMPK)-related development risks allows proposing of an effective de-risking strategy for the progression of the project that is able to reduce uncertainties and to increase the probability of success during preclinical and clinical development.

  9. Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis

    OpenAIRE

    Huthmacher, Carola; Hoppe, Andreas; Bulik, Sascha; Holzh?tter, Hermann-Georg

    2010-01-01

    Abstract Background Despite enormous efforts to combat malaria the disease still afflicts up to half a billion people each year of which more than one million die. Currently no approved vaccine is available and resistances to antimalarials are widely spread. Hence, new antimalarial drugs are urgently needed. Results Here, we present a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. We assembled a compartmentalized metabolic model and predicte...

  10. Blinded prospective evaluation of computer-based mechanistic schizophrenia disease model for predicting drug response.

    Directory of Open Access Journals (Sweden)

    Hugo Geerts

    Full Text Available The tremendous advances in understanding the neurobiological circuits involved in schizophrenia have not translated into more effective treatments. An alternative strategy is to use a recently published 'Quantitative Systems Pharmacology' computer-based mechanistic disease model of cortical/subcortical and striatal circuits based upon preclinical physiology, human pathology and pharmacology. The physiology of 27 relevant dopamine, serotonin, acetylcholine, norepinephrine, gamma-aminobutyric acid (GABA and glutamate-mediated targets is calibrated using retrospective clinical data on 24 different antipsychotics. The model was challenged to predict quantitatively the clinical outcome in a blinded fashion of two experimental antipsychotic drugs; JNJ37822681, a highly selective low-affinity dopamine D(2 antagonist and ocaperidone, a very high affinity dopamine D(2 antagonist, using only pharmacology and human positron emission tomography (PET imaging data. The model correctly predicted the lower performance of JNJ37822681 on the positive and negative syndrome scale (PANSS total score and the higher extra-pyramidal symptom (EPS liability compared to olanzapine and the relative performance of ocaperidone against olanzapine, but did not predict the absolute PANSS total score outcome and EPS liability for ocaperidone, possibly due to placebo responses and EPS assessment methods. Because of its virtual nature, this modeling approach can support central nervous system research and development by accounting for unique human drug properties, such as human metabolites, exposure, genotypes and off-target effects and can be a helpful tool for drug discovery and development.

  11. Designing Predictive Models for Beta-Lactam Allergy Using the Drug Allergy and Hypersensitivity Database.

    Science.gov (United States)

    Chiriac, Anca Mirela; Wang, Youna; Schrijvers, Rik; Bousquet, Philippe Jean; Mura, Thibault; Molinari, Nicolas; Demoly, Pascal

    Beta-lactam antibiotics represent the main cause of allergic reactions to drugs, inducing both immediate and nonimmediate allergies. The diagnosis is well established, usually based on skin tests and drug provocation tests, but cumbersome. To design predictive models for the diagnosis of beta-lactam allergy, based on the clinical history of patients with suspicions of allergic reactions to beta-lactams. The study included a retrospective phase, in which records of patients explored for a suspicion of beta-lactam allergy (in the Allergy Unit of the University Hospital of Montpellier between September 1996 and September 2012) were used to construct predictive models based on a logistic regression and decision tree method; a prospective phase, in which we performed an external validation of the chosen models in patients with suspicion of beta-lactam allergy recruited from 3 allergy centers (Montpellier, Nîmes, Narbonne) between March and November 2013. Data related to clinical history and allergy evaluation results were retrieved and analyzed. The retrospective and prospective phases included 1991 and 200 patients, respectively, with a different prevalence of confirmed beta-lactam allergy (23.6% vs 31%, P = .02). For the logistic regression method, performances of the models were similar in both samples: sensitivity was 51% (vs 60%), specificity 75% (vs 80%), positive predictive value 40% (vs 57%), and negative predictive value 83% (vs 82%). The decision tree method reached a sensitivity of 29.5% (vs 43.5%), specificity of 96.4% (vs 94.9%), positive predictive value of 71.6% (vs 79.4%), and negative predictive value of 81.6% (vs 81.3%). Two different independent methods using clinical history predictors were unable to accurately predict beta-lactam allergy and replace a conventional allergy evaluation for suspected beta-lactam allergy. Copyright © 2017 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

  12. Adverse trajectories of mental health problems predict subsequent burnout and work-family conflict: A longitudinal study of employed women with children followed over 18 years

    NARCIS (Netherlands)

    Nilsen, W.; Skipstein, A.; Demerouti, E.

    2016-01-01

    Background The long-term consequence of experiencing mental health problems may lead to several adverse outcomes. The current study aims to validate previous identified trajectories of mental health problems from 1993 to 2006 in women by examining their implications on subsequent work and

  13. Application of optical action potentials in human induced pluripotent stem cells-derived cardiomyocytes to predict drug-induced cardiac arrhythmias.

    Science.gov (United States)

    Lu, H R; Hortigon-Vinagre, M P; Zamora, V; Kopljar, I; De Bondt, A; Gallacher, D J; Smith, G

    2017-09-01

    Human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) are emerging as new and human-relevant source in vitro model for cardiac safety assessment that allow us to investigate a set of 20 reference drugs for predicting cardiac arrhythmogenic liability using optical action potential (oAP) assay. Here, we describe our examination of the oAP measurement using a voltage sensitive dye (Di-4-ANEPPS) to predict adverse compound effects using hiPS-CMs and 20 cardioactive reference compounds. Fluorescence signals were digitized at 10kHz and the records subsequently analyzed off-line. Cells were exposed to 30min incubation to vehicle or compound (n=5/dose, 4 doses/compound) that were blinded to the investigating laboratory. Action potential parameters were measured, including rise time (T rise ) of the optical action potential duration (oAPD). Significant effects on oAPD were sensitively detected with 11 QT-prolonging drugs, while oAPD shortening was observed with I Ca -antagonists, I Kr -activator or ATP-sensitive K + channel (K ATP )-opener. Additionally, the assay detected varied effects induced by 6 different sodium channel blockers. The detection threshold for these drug effects was at or below the published values of free effective therapeutic plasma levels or effective concentrations by other studies. The results of this blinded study indicate that OAP is a sensitive method to accurately detect drug-induced effects (i.e., duration/QT-prolongation, shortening, beat rate, and incidence of early after depolarizations) in hiPS-CMs; therefore, this technique will potentially be useful in predicting drug-induced arrhythmogenic liabilities in early de-risking within the drug discovery phase. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Individualized prediction of seizure relapse and outcomes following antiepileptic drug withdrawal after pediatric epilepsy surgery.

    Science.gov (United States)

    Lamberink, Herm J; Boshuisen, Kim; Otte, Willem M; Geleijns, Karin; Braun, Kees P J

    2018-03-01

    The objective of this study was to create a clinically useful tool for individualized prediction of seizure outcomes following antiepileptic drug withdrawal after pediatric epilepsy surgery. We used data from the European retrospective TimeToStop study, which included 766 children from 15 centers, to perform a proportional hazard regression analysis. The 2 outcome measures were seizure recurrence and seizure freedom in the last year of follow-up. Prognostic factors were identified through systematic review of the literature. The strongest predictors for each outcome were selected through backward selection, after which nomograms were created. The final models included 3 to 5 factors per model. Discrimination in terms of adjusted concordance statistic was 0.68 (95% confidence interval [CI] 0.67-0.69) for predicting seizure recurrence and 0.73 (95% CI 0.72-0.75) for predicting eventual seizure freedom. An online prediction tool is provided on www.epilepsypredictiontools.info/ttswithdrawal. The presented models can improve counseling of patients and parents regarding postoperative antiepileptic drug policies, by estimating individualized risks of seizure recurrence and eventual outcome. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

  15. A Network-Based Model of Oncogenic Collaboration for Prediction of Drug Sensitivity

    Directory of Open Access Journals (Sweden)

    Ted G Laderas

    2015-12-01

    Full Text Available Tumorigenesis is a multi-step process, involving the acquisition of multiple oncogenic mutations that transform cells, resulting in systemic dysregulation that enables proliferation, among other cancer hallmarks. High throughput omics techniques are used in precision medicine, allowing identification of these mutations with the goal of identifying treatments that target them. However, the multiplicity of oncogenes required for transformation, known as oncogenic collaboration, makes assigning effective treatments difficult. Motivated by this observation, we propose a new type of oncogenic collaboration where mutations in genes that interact with an oncogene may contribute to its dysregulation, a new genomic feature that we term surrogate oncogenes. By mapping mutations to a protein/protein interaction network, we can determine significance of the observed distribution using permutation-based methods. For a panel of 38 breast cancer cell lines, we identified significant surrogate oncogenes in oncogenes such as BRCA1 and ESR1. In addition, using Random Forest Classifiers, we show that these significant surrogate oncogenes predict drug sensitivity for 74 drugs in the breast cancer cell lines with a mean error rate of 30.9%. Additionally, we show that surrogate oncogenes are predictive of survival in patients. The surrogate oncogene framework incorporates unique or rare mutations on an individual level. Our model has the potential for integrating patient-unique mutations in predicting drug-sensitivity, suggesting a potential new direction in precision medicine, as well as a new approach for drug development. Additionally, we show the prevalence of significant surrogate oncogenes in multiple cancers within the Cancer Genome Atlas, suggesting that surrogate oncogenes may be a useful genomic feature for guiding pancancer analyses and assigning therapies across many tissue types.

  16. Design of Probabilistic Random Forests with Applications to Anticancer Drug Sensitivity Prediction.

    Science.gov (United States)

    Rahman, Raziur; Haider, Saad; Ghosh, Souparno; Pal, Ranadip

    2015-01-01

    Random forests consisting of an ensemble of regression trees with equal weights are frequently used for design of predictive models. In this article, we consider an extension of the methodology by representing the regression trees in the form of probabilistic trees and analyzing the nature of heteroscedasticity. The probabilistic tree representation allows for analytical computation of confidence intervals (CIs), and the tree weight optimization is expected to provide stricter CIs with comparable performance in mean error. We approached the ensemble of probabilistic trees' prediction from the perspectives of a mixture distribution and as a weighted sum of correlated random variables. We applied our methodology to the drug sensitivity prediction problem on synthetic and cancer cell line encyclopedia dataset and illustrated that tree weights can be selected to reduce the average length of the CI without increase in mean error.

  17. [Predictive factors of clinically significant drug-drug interactions among regimens based on protease inhibitors, non-nucleoside reverse transcriptase inhibitors and raltegravir].

    Science.gov (United States)

    Cervero, Miguel; Torres, Rafael; Jusdado, Juan José; Pastor, Susana; Agud, Jose Luis

    2016-04-15

    To determine the prevalence and types of clinically significant drug-drug interactions (CSDI) in the drug regimens of HIV-infected patients receiving antiretroviral treatment. retrospective review of database. Centre: Hospital Universitario Severo Ochoa, Infectious Unit. one hundred and forty-two participants followed by one of the authors were selected from January 1985 to December 2014. from their outpatient medical records we reviewed information from the last available visit of the participants, in relation to HIV infection, comorbidities, demographics and the drugs that they were receiving; both antiretroviral drugs and drugs not related to HIV infection. We defined CSDI from the information sheet and/or database on antiretroviral drug interactions of the University of Liverpool (http://www.hiv-druginteractions.org) and we developed a diagnostic tool to predict the possibility of CSDI. By multivariate logistic regression analysis and by estimating the diagnostic performance curve obtained, we identified a quick tool to predict the existence of drug interactions. Of 142 patients, 39 (29.11%) had some type of CSDI and in 11.2% 2 or more interactions were detected. In only one patient the combination of drugs was contraindicated (this patient was receiving darunavir/r and quetiapine). In multivariate analyses, predictors of CSDI were regimen type (PI or NNRTI) and the use of 3 or more non-antiretroviral drugs (AUC 0.886, 95% CI 0.828 to 0.944; P=.0001). The risk was 18.55 times in those receiving NNRTI and 27,95 times in those receiving IP compared to those taking raltegravir. Drug interactions, including those defined as clinically significant, are common in HIV-infected patients treated with antiretroviral drugs, and the risk is greater in IP-based regimens. Raltegravir-based prescribing, especially in patients who receive at least 3 non-HIV drugs could avoid interactions. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

  18. Multiple model predictive control for optimal drug administration of mixed immunotherapy and chemotherapy of tumours.

    Science.gov (United States)

    Sharifi, N; Ozgoli, S; Ramezani, A

    2017-06-01

    Mixed immunotherapy and chemotherapy of tumours is one of the most efficient ways to improve cancer treatment strategies. However, it is important to 'design' an effective treatment programme which can optimize the ways of combining immunotherapy and chemotherapy to diminish their imminent side effects. Control engineering techniques could be used for this. The method of multiple model predictive controller (MMPC) is applied to the modified Stepanova model to induce the best combination of drugs scheduling under a better health criteria profile. The proposed MMPC is a feedback scheme that can perform global optimization for both tumour volume and immune competent cell density by performing multiple constraints. Although current studies usually assume that immunotherapy has no side effect, this paper presents a new method of mixed drug administration by employing MMPC, which implements several constraints for chemotherapy and immunotherapy by considering both drug toxicity and autoimmune. With designed controller we need maximum 57% and 28% of full dosage of drugs for chemotherapy and immunotherapy in some instances, respectively. Therefore, through the proposed controller less dosage of drugs are needed, which contribute to suitable results with a perceptible reduction in medicine side effects. It is observed that in the presence of MMPC, the amount of required drugs is minimized, while the tumour volume is reduced. The efficiency of the presented method has been illustrated through simulations, as the system from an initial condition in the malignant region of the state space (macroscopic tumour volume) transfers into the benign region (microscopic tumour volume) in which the immune system can control tumour growth. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Mathematical modeling of antibody drug conjugates with the target and tubulin dynamics to predict AUC.

    Science.gov (United States)

    Byun, Jong Hyuk; Jung, Il Hyo

    2018-04-14

    Antibody drug conjugates (ADCs)are one of the most recently developed chemotherapeutics to treat some types of tumor cells. They consist of monoclonal antibodies (mAbs), linkers, and potent cytotoxic drugs. Unlike common chemotherapies, ADCs combine selectively with a target at the surface of the tumor cell, and a potent cytotoxic drug (payload) effectively prevents microtubule polymerization. In this work, we construct an ADC model that considers both the target of antibodies and the receptor (tubulin) of the cytotoxic payloads. The model is simulated with brentuximab vedotin, one of ADCs, and used to investigate the pharmacokinetic (PK) characteristics of ADCs in vivo. It also predicts area under the curve (AUC) of ADCs and the payloads by identifying the half-life. The results show that dynamical behaviors fairly coincide with the observed data and half-life and capture AUC. Thus, the model can be used for estimating some parameters, fitting experimental observations, predicting AUC, and exploring various dynamical behaviors of the target and the receptor. Copyright © 2018 Elsevier Ltd. All rights reserved.

  20. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.

    Science.gov (United States)

    Barretina, Jordi; Caponigro, Giordano; Stransky, Nicolas; Venkatesan, Kavitha; Margolin, Adam A; Kim, Sungjoon; Wilson, Christopher J; Lehár, Joseph; Kryukov, Gregory V; Sonkin, Dmitriy; Reddy, Anupama; Liu, Manway; Murray, Lauren; Berger, Michael F; Monahan, John E; Morais, Paula; Meltzer, Jodi; Korejwa, Adam; Jané-Valbuena, Judit; Mapa, Felipa A; Thibault, Joseph; Bric-Furlong, Eva; Raman, Pichai; Shipway, Aaron; Engels, Ingo H; Cheng, Jill; Yu, Guoying K; Yu, Jianjun; Aspesi, Peter; de Silva, Melanie; Jagtap, Kalpana; Jones, Michael D; Wang, Li; Hatton, Charles; Palescandolo, Emanuele; Gupta, Supriya; Mahan, Scott; Sougnez, Carrie; Onofrio, Robert C; Liefeld, Ted; MacConaill, Laura; Winckler, Wendy; Reich, Michael; Li, Nanxin; Mesirov, Jill P; Gabriel, Stacey B; Getz, Gad; Ardlie, Kristin; Chan, Vivien; Myer, Vic E; Weber, Barbara L; Porter, Jeff; Warmuth, Markus; Finan, Peter; Harris, Jennifer L; Meyerson, Matthew; Golub, Todd R; Morrissey, Michael P; Sellers, William R; Schlegel, Robert; Garraway, Levi A

    2012-03-28

    The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.

  1. The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity

    Science.gov (United States)

    Barretina, Jordi; Caponigro, Giordano; Stransky, Nicolas; Venkatesan, Kavitha; Margolin, Adam A.; Kim, Sungjoon; Wilson, Christopher J.; Lehár, Joseph; Kryukov, Gregory V.; Sonkin, Dmitriy; Reddy, Anupama; Liu, Manway; Murray, Lauren; Berger, Michael F.; Monahan, John E.; Morais, Paula; Meltzer, Jodi; Korejwa, Adam; Jané-Valbuena, Judit; Mapa, Felipa A.; Thibault, Joseph; Bric-Furlong, Eva; Raman, Pichai; Shipway, Aaron; Engels, Ingo H.; Cheng, Jill; Yu, Guoying K.; Yu, Jianjun; Aspesi, Peter; de Silva, Melanie; Jagtap, Kalpana; Jones, Michael D.; Wang, Li; Hatton, Charles; Palescandolo, Emanuele; Gupta, Supriya; Mahan, Scott; Sougnez, Carrie; Onofrio, Robert C.; Liefeld, Ted; MacConaill, Laura; Winckler, Wendy; Reich, Michael; Li, Nanxin; Mesirov, Jill P.; Gabriel, Stacey B.; Getz, Gad; Ardlie, Kristin; Chan, Vivien; Myer, Vic E.; Weber, Barbara L.; Porter, Jeff; Warmuth, Markus; Finan, Peter; Harris, Jennifer L.; Meyerson, Matthew; Golub, Todd R.; Morrissey, Michael P.; Sellers, William R.; Schlegel, Robert; Garraway, Levi A.

    2012-01-01

    The systematic translation of cancer genomic data into knowledge of tumor biology and therapeutic avenues remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacologic annotation is available1. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacologic profiles for 24 anticancer drugs across 479 of the lines, this collection allowed identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Altogether, our results suggest that large, annotated cell line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of “personalized” therapeutic regimens2. PMID:22460905

  2. A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method.

    Science.gov (United States)

    Zhou, Shu; Li, Guo-Bo; Huang, Lu-Yi; Xie, Huan-Zhang; Zhao, Ying-Lan; Chen, Yu-Zong; Li, Lin-Li; Yang, Sheng-Yong

    2014-08-01

    Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

  3. Improvement of the Prediction of Drugs Demand Using Spatial Data Mining Tools.

    Science.gov (United States)

    Ramos, M Isabel; Cubillas, Juan José; Feito, Francisco R

    2016-01-01

    The continued availability of products at any store is the major issue in order to provide good customer service. If the store is a drugstore this matter reaches a greater importance, as out of stock of a drug when there is high demand causes problems and tensions in the healthcare system. There are numerous studies of the impact this issue has on patients. The lack of any drug in a pharmacy in certain seasons is very common, especially when some external factors proliferate favoring the occurrence of certain diseases. This study focuses on a particular drug consumed in the city of Jaen, southern Andalucia, Spain. Our goal is to determine in advance the Salbutamol demand. Advanced data mining techniques have been used with spatial variables. These last have a key role to generate an effective model. In this research we have used the attributes that are associated with Salbutamol demand and it has been generated a very accurate prediction model of 5.78% of mean absolute error. This is a very encouraging data considering that the consumption of this drug in Jaen varies 500% from one period to another.

  4. Appropriate experimental approaches for predicting abuse potential and addictive qualities in preclinical drug discovery.

    Science.gov (United States)

    Mead, Andy N

    2014-11-01

    Drug abuse is an increasing social and public health issue, putting the onus on drug developers and regulatory agencies to ensure that the abuse potential of novel drugs is adequately assessed prior to product launch. This review summarizes the core preclinical data that frequently contribute to building an understanding of abuse potential for a new molecular entity, in addition to highlighting models that can provide increased resolution regarding the level of risk. Second, an important distinction between abuse potential and addiction potential is drawn, with comments on how preclinical models can inform on each. While the currently adopted preclinical models possess strong predictive validity, there are areas for future refinement and research. These areas include a more refined use of self-administration models to assess relative reinforcement; and the need for open innovation in pursuing improvements. There is also the need for careful scientifically driven application of models rather than a standardization of methodologies, and the need to explore the opportunities that may exist for enhancing the value of physical dependence and withdrawal studies by focusing on withdrawal-induced drug seeking, rather than broad symptomology.

  5. Comparison of Ahlstrom grade 226, Munktell TFN, and Whatman 903 filter papers for dried blood spot specimen collection and subsequent HIV-1 load and drug resistance genotyping analysis.

    Science.gov (United States)

    Rottinghaus, Erin; Bile, Ebi; Modukanele, Mosetsanagape; Maruping, Maruping; Mine, Madisa; Nkengasong, John; Yang, Chunfu

    2013-01-01

    Dried blood spots (DBS) collected onto filter paper have eased the difficulty of blood collection in resource-limited settings. Currently, Whatman 903 (W-903) filter paper is the only filter paper that has been used for HIV load and HIV drug resistance (HIVDR) testing. We therefore evaluated two additional commercially available filter papers, Ahlstrom grade 226 (A-226) and Munktell TFN (M-TFN), for viral load (VL) testing and HIVDR genotyping using W-903 filter paper as a comparison group. DBS specimens were generated from 344 adult patients on antiretroviral therapy (ART) in Botswana. The VL was measured with NucliSENS EasyQ HIV-1 v2.0, and genotyping was performed for those specimens with a detectable VL (≥ 2.90 log(10) copies/ml) using an in-house method. Bland-Altman analysis revealed a strong concordance in quantitative VL analysis between W-903 and A-226 (bias = -0.034 ± 0.246 log(10) copies/ml [mean difference ± standard deviation]) and W-903 and M-TFN (bias = -0.028 ± 0.186 log(10) copies/ml) filter papers, while qualitative VL analysis for virological failure determination, defined as a VL of ≥ 3.00 log(10) copies/ml, showed low sensitivities for A-266 (71.54%) and M-TFN (65.71%) filter papers compared to W-903 filter paper. DBS collected on M-TFN filter paper had the highest genotyping efficiency (100%) compared to W-903 and A-226 filter papers (91.7%) and appeared more sensitive in detecting major HIVDR mutations. DBS collected on A-226 and M-TFN filter papers performed similarly to DBS collected on W-903 filter paper for quantitative VL analysis and HIVDR detection. Together, the encouraging genotyping results and the variability observed in determining virological failure from this small pilot study warrant further investigation of A-226 and M-TFN filter papers as specimen collection devices for HIVDR monitoring surveys.

  6. Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.

    Science.gov (United States)

    Simm, Jaak; Klambauer, Günter; Arany, Adam; Steijaert, Marvin; Wegner, Jörg Kurt; Gustin, Emmanuel; Chupakhin, Vladimir; Chong, Yolanda T; Vialard, Jorge; Buijnsters, Peter; Velter, Ingrid; Vapirev, Alexander; Singh, Shantanu; Carpenter, Anne E; Wuyts, Roel; Hochreiter, Sepp; Moreau, Yves; Ceulemans, Hugo

    2018-05-17

    In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays. Copyright © 2018 Elsevier Ltd. All rights reserved.

  7. Some uses of predictive probability of success in clinical drug development

    Directory of Open Access Journals (Sweden)

    Mauro Gasparini

    2013-03-01

    Full Text Available Predictive probability of success is a (subjective Bayesian evaluation of the prob- ability of a future successful event in a given state of information. In the context of pharmaceutical clinical drug development, successful events relate to the accrual of positive evidence on the therapy which is being developed, like demonstration of su- perior efficacy or ascertainment of safety. Positive evidence will usually be obtained via standard frequentist tools, according to the regulations imposed in the world of pharmaceutical development.Within a single trial, predictive probability of success can be identified with expected power, i.e. the evaluation of the success probability of the trial. Success means, for example, obtaining a significant result of a standard superiority test.Across trials, predictive probability of success can be the probability of a successful completion of an entire part of clinical development, for example a successful phase III development in the presence of phase II data.Calculations of predictive probability of success in the presence of normal data with known variance will be illustrated, both for within-trial and across-trial predictions.

  8. Clinical Relevance and Predictive Value of Damage Biomarkers of Drug-Induced Kidney Injury.

    Science.gov (United States)

    Kane-Gill, Sandra L; Smithburger, Pamela L; Kashani, Kianoush; Kellum, John A; Frazee, Erin

    2017-11-01

    Nephrotoxin exposure accounts for up to one-fourth of acute kidney injury episodes in hospitalized patients, and the associated consequences are as severe as acute kidney injury due to other etiologies. As the use of nephrotoxic agents represents one of the few modifiable risk factors for acute kidney injury, clinicians must be able to identify patients at high risk for drug-induced kidney injury rapidly. Recently, significant advancements have been made in the field of biomarker utilization for the prediction and detection of acute kidney injury. Such biomarkers may have a role both for detection of drug-induced kidney disease and implementation of preventative and therapeutic strategies designed to mitigate injury. In this article, basic principles of renal biomarker use in practice are summarized, and the existing evidence for six markers specifically used to detect drug-induced kidney injury are outlined, including liver-type fatty acid binding protein, neutrophil gelatinase-associated lipocalin, tissue inhibitor of metalloproteinase-2 times insulin-like growth factor-binding protein 7 ([TIMP-2]·[IGFBP7]), kidney injury molecule-1 and N-acetyl-β-D-glucosaminidase. The results of the literature search for these six kidney damage biomarkers identified 29 unique articles with none detected for liver-type fatty acid binding protein and [TIMP-2]·[IGFBP7]. For three biomarkers, kidney injury molecule-1, neutrophil gelatinase-associated lipocalin and N-acetyl-β-D-glucosaminidase, the majority of the studies suggest utility in clinical practice. While many questions need to be answered to clearly articulate the use of biomarkers to predict drug-induced kidney disease, current data are promising.

  9. The biowaiver extension for BCS class III drugs: the effect of dissolution rate on the bioequivalence of BCS class III immediate-release drugs predicted by computer simulation.

    Science.gov (United States)

    Tsume, Yasuhiro; Amidon, Gordon L

    2010-08-02

    The Biopharmaceutical Classification System (BCS) guidance issued by the FDA allows waivers for in vivo bioavailability and bioequivalence studies for immediate-release (IR) solid oral dosage forms only for BCS class I drugs. However, a number of drugs within BCS class III have been proposed to be eligible for biowaivers. The World Health Organization (WHO) has shortened the requisite dissolution time of BCS class III drugs on their Essential Medicine List (EML) from 30 to 15 min for extended biowaivers; however, the impact of the shorter dissolution time on AUC(0-inf) and C(max) is unknown. The objectives of this investigation were to assess the ability of gastrointestinal simulation software to predict the oral absorption of the BCS class I drugs propranolol and metoprolol and the BCS class III drugs cimetidine, atenolol, and amoxicillin, and to perform in silico bioequivalence studies to assess the feasibility of extending biowaivers to BCS class III drugs. The drug absorption from the gastrointestinal tract was predicted using physicochemical and pharmacokinetic properties of test drugs provided by GastroPlus (version 6.0). Virtual trials with a 200 mL dose volume at different drug release rates (T(85%) = 15 to 180 min) were performed to predict the oral absorption (C(max) and AUC(0-inf)) of the above drugs. Both BCS class I drugs satisfied bioequivalence with regard to the release rates up to 120 min. The results with BCS class III drugs demonstrated bioequivalence using the prolonged release rate, T(85%) = 45 or 60 min, indicating that the dissolution standard for bioequivalence is dependent on the intestinal membrane permeability and permeability profile throughout the gastrointestinal tract. The results of GastroPlus simulations indicate that the dissolution rate of BCS class III drugs could be prolonged to the point where dissolution, rather than permeability, would control the overall absorption. For BCS class III drugs with intestinal absorption patterns

  10. Prediction of phenotypic susceptibility to antiretroviral drugs using physiochemical properties of the primary enzymatic structure combined with artificial neural networks

    DEFF Research Database (Denmark)

    Kjaer, J; Høj, L; Fox, Z

    2008-01-01

    OBJECTIVES: Genotypic interpretation systems extrapolate observed associations in datasets to predict viral susceptibility to antiretroviral drugs (ARVs) for given isolates. We aimed to develop and validate an approach using artificial neural networks (ANNs) that employ descriptors...

  11. Predicting the Risk of Recurrence Before the Start of Antithyroid Drug Therapy in Patients With Graves' Hyperthyroidism

    NARCIS (Netherlands)

    Vos, Xander G.; Endert, Erik; Zwinderman, A. H.; Tijssen, Jan G. P.; Wiersinga, Wilmar M.

    2016-01-01

    Genotyping increases the accuracy of a clinical score (based on pretreatment age, goiter size, FT4, TBII) for predicting recurrence of Graves' hyperthyroidism after a course of antithyroid drugs: a prospective study

  12. Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

    Energy Technology Data Exchange (ETDEWEB)

    Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov [Science and Research Staff, Office of Pharmaceutical Science, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993–0002 (United States); Cross, Kevin P. [Leadscope, Inc., 1393 Dublin Road, Columbus, OH, 43215–1084 (United States)

    2012-05-01

    Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describe the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive

  13. Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

    International Nuclear Information System (INIS)

    Valerio, Luis G.; Cross, Kevin P.

    2012-01-01

    Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describe the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive performance.

  14. The prediction of drug dependence from expectancy for hostility while intoxicated.

    Science.gov (United States)

    Walter, D; Nagoshi, C; Muntaner, C; Haertzen, C A

    1990-10-01

    Three hundred seventy-one male substance-abusing volunteers for drug studies were administered the Buss-Durkee Hostility Inventory (B-D). One hundred nineteen of these subjects were readministered the B-D with the instruction to answer the items in terms of their behavior while drinking alcohol, with 67 of these subjects also completing a heroin use condition. Expectancies for hostility under alcohol or heroin were generally uncorrelated with other measures of personality, psychopathology, antisocial personality, impulsiveness, or criminality; but expectancies for hostility under alcohol were predictive of diagnoses of alcohol, opioid, and marijuana abuse and dependence over and above the influence of these other measures.

  15. Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

    Science.gov (United States)

    Chen, Lei; Zeng, Wei-Ming; Cai, Yu-Dong; Feng, Kai-Yan; Chou, Kuo-Chen

    2012-01-01

    The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels. PMID:22514724

  16. Identification of drug metabolites in human plasma or serum integrating metabolite prediction, LC-HRMS and untargeted data processing

    NARCIS (Netherlands)

    Jacobs, P.L.; Ridder, L.; Ruijken, M.; Rosing, H.; Jager, N.G.L.; Beijnen, J.H.; Bas, R.R.; Dongen, W.D. van

    2013-01-01

    Background: Comprehensive identification of human drug metabolites in first-in-man studies is crucial to avoid delays in later stages of drug development. We developed an efficient workflow for systematic identification of human metabolites in plasma or serum that combines metabolite prediction,

  17. Prediction of Central Nervous System Side Effects Through Drug Permeability to Blood-Brain Barrier and Recommendation Algorithm.

    Science.gov (United States)

    Fan, Jun; Yang, Jing; Jiang, Zhenran

    2018-04-01

    Drug side effects are one of the public health concerns. Using powerful machine-learning methods to predict potential side effects before the drugs reach the clinical stages is of great importance to reduce time consumption and protect the security of patients. Recently, researchers have proved that the central nervous system (CNS) side effects of a drug are closely related to its permeability to the blood-brain barrier (BBB). Inspired by this, we proposed an extended neighborhood-based recommendation method to predict CNS side effects using drug permeability to the BBB and other known features of drug. To the best of our knowledge, this is the first attempt to predict CNS side effects considering drug permeability to the BBB. Computational experiments demonstrated that drug permeability to the BBB is an important factor in CNS side effects prediction. Moreover, we built an ensemble recommendation model and obtained higher AUC score (area under the receiver operating characteristic curve) and AUPR score (area under the precision-recall curve) on the data set of CNS side effects by integrating various features of drug.

  18. Prediction of Smoking, Alcohol, Drugs, and Psychoactive Drugs Abuse Based on Emotional Dysregulation and Child Abuse Experience in People with Borderline Personality Traits

    Directory of Open Access Journals (Sweden)

    M GannadiFarnood

    2014-12-01

    Full Text Available Objective: This research was an attempt to predict the tendency of people having borderline personality traits to smoking, drinking alcohol, and taking psychoactive drugs based on emotional dysregulation and child abuse. Method: This study employed a correlation method which is categorized in descriptive category. A sample including 600 male and female bachelor students of Tabriz University was selected by cluster sampling. Then, high risk behaviors scale, Emotional dysregulation Scale, Child abuse scale, and borderline personality scale (STB were distributed among this group. Findings: Stepwise multiple regression analysis suggested that emotional dysregulation and child abuse significantly predicted varying degrees of smoking, drug, and alcohol usage. Conclusion: The research findings suggest the basic role of initial biological vulnerability in terms of emotional regulation (dysregulation and invalidating family environment (child abuse in the prediction of catching the disorder of borderline personality traits and producing high riskbehaviorssuch as alcohol drink and drug usage.

  19. Potential for Drug Abuse: the Predictive Role of Parenting Styles, Stress and Type D Personality

    Directory of Open Access Journals (Sweden)

    mahin soheili

    2015-06-01

    Full Text Available Objective: This study was an attempt to predict potential for drug abuse on the basis of three predictors of parenting style, stress and type D personality. Method: In this descriptive-correlational study, 200 students (100 males and 100 females of Islamic Azad University of Karaj were selected by convenience sampling. For data collection, perceived parenting styles questionnaire, perceived stress scale, type D personality scale, and addiction potential scale were used. Results: The results showed that rejecting/neglecting parenting style and emotional warmth were positively and negatively correlated with addiction potential, respectively. Conclusion: The child-parent relationship and also the relationship between stress and type D personality can be considered as predictive factors in addiction potential.

  20. Detection of Adverse Reaction to Drugs in Elderly Patients through Predictive Modeling

    Directory of Open Access Journals (Sweden)

    Rafael San-Miguel Carrasco

    2016-03-01

    Full Text Available Geriatrics Medicine constitutes a clinical research field in which data analytics, particularly predictive modeling, can deliver compelling, reliable and long-lasting benefits, as well as non-intuitive clinical insights and net new knowledge. The research work described in this paper leverages predictive modeling to uncover new insights related to adverse reaction to drugs in elderly patients. The differentiation factor that sets this research exercise apart from traditional clinical research is the fact that it was not designed by formulating a particular hypothesis to be validated. Instead, it was data-centric, with data being mined to discover relationships or correlations among variables. Regression techniques were systematically applied to data through multiple iterations and under different configurations. The obtained results after the process was completed are explained and discussed next.

  1. Using multicriteria decision analysis during drug development to predict reimbursement decisions.

    Science.gov (United States)

    Williams, Paul; Mauskopf, Josephine; Lebiecki, Jake; Kilburg, Anne

    2014-01-01

    Pharmaceutical companies design clinical development programs to generate the data that they believe will support reimbursement for the experimental compound. The objective of the study was to present a process for using multicriteria decision analysis (MCDA) by a pharmaceutical company to estimate the probability of a positive recommendation for reimbursement for a new drug given drug and environmental attributes. The MCDA process included 1) selection of decisions makers who were representative of those making reimbursement decisions in a specific country; 2) two pre-workshop questionnaires to identify the most important attributes and their relative importance for a positive recommendation for a new drug; 3) a 1-day workshop during which participants undertook three tasks: i) they agreed on a final list of decision attributes and their importance weights, ii) they developed level descriptions for these attributes and mapped each attribute level to a value function, and iii) they developed profiles for hypothetical products 'just likely to be reimbursed'; and 4) use of the data from the workshop to develop a prediction algorithm based on a logistic regression analysis. The MCDA process is illustrated using case studies for three countries, the United Kingdom, Germany, and Spain. The extent to which the prediction algorithms for each country captured the decision processes for the workshop participants in our case studies was tested using a post-meeting questionnaire that asked the participants to make recommendations for a set of hypothetical products. The data collected in the case study workshops resulted in a prediction algorithm: 1) for the United Kingdom, the probability of a positive recommendation for different ranges of cost-effectiveness ratios; 2) for Spain, the probability of a positive recommendation at the national and regional levels; and 3) for Germany, the probability of a determination of clinical benefit. The results from the post

  2. Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

    Science.gov (United States)

    Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

    2016-02-19

    Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

  3. ABC gene-ranking for prediction of drug-induced cholestasis in rats

    Directory of Open Access Journals (Sweden)

    Yauheniya Cherkas

    drugs that behaved very differently, and were distinct from both non-cholestatic and cholestatic drugs (ketoconazole, dipyridamole, cyproheptadine and aniline, and many postulated human cholestatic drugs that in rat showed no evidence of cholestasis (chlorpromazine, erythromycin, niacin, captopril, dapsone, rifampicin, glibenclamide, simvastatin, furosemide, tamoxifen, and sulfamethoxazole. Most of these latter drugs were noted previously by other groups as showing cholestasis only in humans. The results of this work suggest that the ABC procedure and similar statistical approaches can be instrumental in combining data to compare toxicants across toxicogenomics databases, extract similarities among responses and reduce unexplained data varation. Keywords: Cluster analysis, Cholestasis, Gene signature, Microarray, Prediction, Toxicogenomics

  4. Predicting the effect of cytochrome P450 inhibitors on substrate drugs: analysis of physiologically based pharmacokinetic modeling submissions to the US Food and Drug Administration.

    Science.gov (United States)

    Wagner, Christian; Pan, Yuzhuo; Hsu, Vicky; Grillo, Joseph A; Zhang, Lei; Reynolds, Kellie S; Sinha, Vikram; Zhao, Ping

    2015-01-01

    The US Food and Drug Administration (FDA) has seen a recent increase in the application of physiologically based pharmacokinetic (PBPK) modeling towards assessing the potential of drug-drug interactions (DDI) in clinically relevant scenarios. To continue our assessment of such approaches, we evaluated the predictive performance of PBPK modeling in predicting cytochrome P450 (CYP)-mediated DDI. This evaluation was based on 15 substrate PBPK models submitted by nine sponsors between 2009 and 2013. For these 15 models, a total of 26 DDI studies (cases) with various CYP inhibitors were available. Sponsors developed the PBPK models, reportedly without considering clinical DDI data. Inhibitor models were either developed by sponsors or provided by PBPK software developers and applied with minimal or no modification. The metric for assessing predictive performance of the sponsors' PBPK approach was the R predicted/observed value (R predicted/observed = [predicted mean exposure ratio]/[observed mean exposure ratio], with the exposure ratio defined as [C max (maximum plasma concentration) or AUC (area under the plasma concentration-time curve) in the presence of CYP inhibition]/[C max or AUC in the absence of CYP inhibition]). In 81 % (21/26) and 77 % (20/26) of cases, respectively, the R predicted/observed values for AUC and C max ratios were within a pre-defined threshold of 1.25-fold of the observed data. For all cases, the R predicted/observed values for AUC and C max were within a 2-fold range. These results suggest that, based on the submissions to the FDA to date, there is a high degree of concordance between PBPK-predicted and observed effects of CYP inhibition, especially CYP3A-based, on the exposure of drug substrates.

  5. DemQSAR: predicting human volume of distribution and clearance of drugs.

    Science.gov (United States)

    Demir-Kavuk, Ozgur; Bentzien, Jörg; Muegge, Ingo; Knapp, Ernst-Walter

    2011-12-01

    In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure-activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VD(ss)) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VD(ss) and CL is

  6. Predicting Drug Safety and Communicating Risk: Benefits of a Bayesian Approach.

    Science.gov (United States)

    Lazic, Stanley E; Edmunds, Nicholas; Pollard, Christopher E

    2018-03-01

    Drug toxicity is a major source of attrition in drug discovery and development. Pharmaceutical companies routinely use preclinical data to predict clinical outcomes and continue to invest in new assays to improve predictions. However, there are many open questions about how to make the best use of available data, combine diverse data, quantify risk, and communicate risk and uncertainty to enable good decisions. The costs of suboptimal decisions are clear: resources are wasted and patients may be put at risk. We argue that Bayesian methods provide answers to all of these problems and use hERG-mediated QT prolongation as a case study. Benefits of Bayesian machine learning models include intuitive probabilistic statements of risk that incorporate all sources of uncertainty, the option to include diverse data and external information, and visualizations that have a clear link between the output from a statistical model and what this means for risk. Furthermore, Bayesian methods are easy to use with modern software, making their adoption for safety screening straightforward. We include R and Python code to encourage the adoption of these methods.

  7. Binding Mode and Induced Fit Predictions for Prospective Computational Drug Design.

    Science.gov (United States)

    Grebner, Christoph; Iegre, Jessica; Ulander, Johan; Edman, Karl; Hogner, Anders; Tyrchan, Christian

    2016-04-25

    Computer-aided drug design plays an important role in medicinal chemistry to obtain insights into molecular mechanisms and to prioritize design strategies. Although significant improvement has been made in structure based design, it still remains a key challenge to accurately model and predict induced fit mechanisms. Most of the current available techniques either do not provide sufficient protein conformational sampling or are too computationally demanding to fit an industrial setting. The current study presents a systematic and exhaustive investigation of predicting binding modes for a range of systems using PELE (Protein Energy Landscape Exploration), an efficient and fast protein-ligand sampling algorithm. The systems analyzed (cytochrome P, kinase, protease, and nuclear hormone receptor) exhibit different complexities of ligand induced fit mechanisms and protein dynamics. The results are compared with results from classical molecular dynamics simulations and (induced fit) docking. This study shows that ligand induced side chain rearrangements and smaller to medium backbone movements are captured well in PELE. Large secondary structure rearrangements, however, remain challenging for all employed techniques. Relevant binding modes (ligand heavy atom RMSD PELE method within a few hours of simulation, positioning PELE as a tool applicable for rapid drug design cycles.

  8. Bigger Data, Collaborative Tools and the Future of Predictive Drug Discovery

    Science.gov (United States)

    Clark, Alex M.; Swamidass, S. Joshua; Litterman, Nadia; Williams, Antony J.

    2014-01-01

    Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service (SaaS) commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas. PMID:24943138

  9. Prediction of phospholipidosis-inducing potential of drugs by in vitro biochemical and physicochemical assays followed by multivariate analysis.

    Science.gov (United States)

    Kuroda, Yukihiro; Saito, Madoka

    2010-03-01

    An in vitro method to predict phospholipidosis-inducing potential of cationic amphiphilic drugs (CADs) was developed using biochemical and physicochemical assays. The following parameters were applied to principal component analysis, as well as physicochemical parameters: pK(a) and clogP; dissociation constant of CADs from phospholipid, inhibition of enzymatic phospholipid degradation, and metabolic stability of CADs. In the score plot, phospholipidosis-inducing drugs (amiodarone, propranolol, imipramine, chloroquine) were plotted locally forming the subspace for positive CADs; while non-inducing drugs (chlorpromazine, chloramphenicol, disopyramide, lidocaine) were placed scattering out of the subspace, allowing a clear discrimination between both classes of CADs. CADs that often produce false results by conventional physicochemical or cell-based assay methods were accurately determined by our method. Basic and lipophilic disopyramide could be accurately predicted as a nonphospholipidogenic drug. Moreover, chlorpromazine, which is often falsely predicted as a phospholipidosis-inducing drug by in vitro methods, could be accurately determined. Because this method uses the pharmacokinetic parameters pK(a), clogP, and metabolic stability, which are usually obtained in the early stages of drug development, the method newly requires only the two parameters, binding to phospholipid, and inhibition of lipid degradation enzyme. Therefore, this method provides a cost-effective approach to predict phospholipidosis-inducing potential of a drug. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

  10. Maintenance treatment with azathioprine in ulcerative colitis: outcome and predictive factors after drug withdrawal.

    Science.gov (United States)

    Cassinotti, Andrea; Actis, Giovanni C; Duca, Piergiorgio; Massari, Alessandro; Colombo, Elisabetta; Gai, Elisa; Annese, Vito; D'Albasio, Giuseppe; Manes, Gianpiero; Travis, Simon; Porro, Gabriele Bianchi; Ardizzone, Sandro

    2009-11-01

    Whether the duration of maintenance treatment with azathioprine (AZA) affects the outcome of ulcerative colitis (UC) is unclear. We investigated clinical outcomes and any predictive factors after withdrawal of AZA in UC. In this multicenter observational retrospective study, 127 Italian UC patients, who were in steroid-free remission at the time of withdrawal of AZA, were followed-up for a median of 55 months or until relapse. The frequency of clinical relapse or colectomy after AZA withdrawal was analyzed according to demographic, clinical, and endoscopic variables. After drug withdrawal, a third of the patients relapsed within 12 months, half within 2 years and two-thirds within 5 years. After multivariable analysis, predictors of relapse after drug withdrawal were lack of sustained remission during AZA maintenance (hazard ratio, HR 2.350, confidence interval, CI 95% 1.434-3.852; P=0.001), extensive colitis (HR 1.793, CI 95% 1.064-3.023, P=0.028 vs. left-sided colitis; HR 2.024, CI 95% 1.103-3.717, P=0.023 vs. distal colitis), and treatment duration, with short treatments (3-6 months) more disadvantaged than >48-month treatments (HR 2.783, CI 95% 1.267-6.114, P=0.008). Concomitant aminosalicylates were the only predictors of sustained remission during AZA therapy (P=0.009). The overall colectomy rate was 10%. Predictors of colectomy were drug-related toxicity as the cause of AZA withdrawal (P=0.041), no post-AZA drug therapy (P=0.031), and treatment duration (P<0.0005). Discontinuation of AZA while UC is in remission is associated with a high relapse rate. Disease extent, lack of sustained remission during AZA, and discontinuation due to toxicity could stratify relapse risk. Concomitant aminosalicylates were advantageous. Prospective randomized controlled trials are needed to confirm whether treatment duration is inversely associated with outcome.

  11. High levels of plasma IL-10 and expression of IL-10 by keratinocytes during visceral leishmaniasis predict subsequent development of post-kala-azar dermal leishmaniasis

    DEFF Research Database (Denmark)

    Gasim, S; Elhassan, A M; Khalil, E A

    1998-01-01

    Some patients develop post-kala-azar dermal leishmaniasis (PKDL) after they have been treated for the systemic infection kala-azar (visceral leishmaniasis). It has been an enigma why the parasites cause skin symptoms after the patients have been successfully treated for the systemic disease. We...... report here that PKDL development can be predicted before treatment of visceral leishmaniasis, and that IL-10 is involved in the pathogenesis. Before treatment of visceral leishmaniasis, Leishmania parasites were present in skin which appeared normal on all patients. However, IL-10 was detected...

  12. In silico prediction of harmful effects triggered by drugs and chemicals

    International Nuclear Information System (INIS)

    Vedani, Angelo; Dobler, Max; Lill, Markus A.

    2005-01-01

    used in the training set as well as to classify harmless compounds as being nontoxic. This suggests that our approach may be used for the prediction of adverse effects of drug molecules and chemicals. It is the aim to provide cost-covering access to this technology-particularly to universities, hospitals and regulatory bodies-as it bears a significant potential to recognize hazardous compounds early in the development process and hence improve resource and waste management as well as reduce animal testing. The Biographics Laboratory 3R is a non-profit-oriented organization aimed at reducing animal experimentation in the biomedical sciences by computational approaches (cf. http://www.biograf.ch)

  13. Is the spatial distribution of brain lesions associated with closed-head injury in children predictive of subsequent development of posttraumatic stress disorder?

    Science.gov (United States)

    Herskovits, Edward H.; Gerring, Joan P.; Davatzikos, Christos; Bryan, R. Nick

    2002-01-01

    PURPOSE: To determine whether there is an association between the spatial distributions of lesions detected at magnetic resonance (MR) imaging of the brain in children, adolescents, and young adults after closed-head injury (CHI) and development of the reexperiencing symptoms of posttraumatic stress disorder (PTSD). MATERIALS AND METHODS: Data obtained in 94 subjects without a history of PTSD as determined by parental interview were analyzed. MR images were obtained 3 months after CHI. Lesions were manually delineated and registered to the Talairach coordinate system. Mann-Whitney analysis of lesion distribution and PTSD status at 1 year (again, as determined by parental interview) was performed, consisting of an analysis of lesion distribution versus the major symptoms of PTSD: reexperiencing, hyperarousal, and avoidance. RESULTS: Of the 94 subjects, 41 met the PTSD reexperiencing criterion and nine met all three PTSD criteria. Subjects who met the reexperiencing criterion had fewer lesions in limbic system structures (eg, the cingulum) on the right than did subjects who did not meet this criterion (Mann-Whitney, P =.003). CONCLUSION: Lesions induced by CHI in the limbic system on the right may inhibit subsequent manifestation of PTSD reexperiencing symptoms in children, adolescents, and young adults. Copyright RSNA, 2002.

  14. Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity

    Directory of Open Access Journals (Sweden)

    Elisa Passini

    2017-09-01

    Full Text Available Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC50/Hill coefficient. Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca2+-transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs. Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca2+/late Na+ currents and Na+/Ca2+-exchanger, reduced Na+/K+-pump are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density

  15. Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity.

    Science.gov (United States)

    Passini, Elisa; Britton, Oliver J; Lu, Hua Rong; Rohrbacher, Jutta; Hermans, An N; Gallacher, David J; Greig, Robert J H; Bueno-Orovio, Alfonso; Rodriguez, Blanca

    2017-01-01

    Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC 50 /Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca 2+ -transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca 2+ /late Na + currents and Na + /Ca 2+ -exchanger, reduced Na + /K + -pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density

  16. Kidney-on-a-Chip: a New Technology for Predicting Drug Efficacy, Interactions, and Drug-induced Nephrotoxicity.

    Science.gov (United States)

    Lee, Jeonghwan; Kim, Sejoong

    2018-03-08

    The kidneys play a pivotal role in most drug-removal processes and are important when evaluating drug safety. Kidney dysfunction resulting from various drugs is an important issue in clinical practice and during the drug development process. Traditional in vivo animal experiments are limited with respect to evaluating drug efficacy and nephrotoxicity due to discrepancies in drug pharmacokinetics and pharmacodynamics between humans and animals, and static cell culture experiments cannot fully reflect the actual microphysiological environment in humans. A kidney-on-a-chip is a microfluidic device that allows the culture of living renal cells in 3-dimensional channels and mimics the human microphysiological environment, thus simulating the actual drug filtering, absorption, and secretion process.. In this review, we discuss recent developments in microfluidic culturing technique and describe current and future kidney-on-a-chip applications. We focus on pharmacological interactions and drug-induced nephrotoxicity, and additionally discuss the development of multi-organ chips and their possible applications. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  17. Identifying predictive features in drug response using machine learning: opportunities and challenges.

    Science.gov (United States)

    Vidyasagar, Mathukumalli

    2015-01-01

    This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

  18. Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines.

    Science.gov (United States)

    LaBute, Montiago X; Zhang, Xiaohua; Lenderman, Jason; Bennion, Brian J; Wong, Sergio E; Lightstone, Felice C

    2014-01-01

    Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number

  19. Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines.

    Directory of Open Access Journals (Sweden)

    Montiago X LaBute

    Full Text Available Late-stage or post-market identification of adverse drug reactions (ADRs is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409 of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively. Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with

  20. Prediction of Drug-Drug Interactions with Bupropion and Its Metabolites as CYP2D6 Inhibitors Using a Physiologically-Based Pharmacokinetic Model.

    Science.gov (United States)

    Xue, Caifu; Zhang, Xunjie; Cai, Weimin

    2017-12-21

    The potential of inhibitory metabolites of perpetrator drugs to contribute to drug-drug interactions (DDIs) is uncommon and underestimated. However, the occurrence of unexpected DDI suggests the potential contribution of metabolites to the observed DDI. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model for bupropion and its three primary metabolites-hydroxybupropion, threohydrobupropion and erythrohydrobupropion-based on a mixed "bottom-up" and "top-down" approach and to contribute to the understanding of the involvement and impact of inhibitory metabolites for DDIs observed in the clinic. PK profiles from clinical researches of different dosages were used to verify the bupropion model. Reasonable PK profiles of bupropion and its metabolites were captured in the PBPK model. Confidence in the DDI prediction involving bupropion and co-administered CYP2D6 substrates could be maximized. The predicted maximum concentration (C max ) area under the concentration-time curve (AUC) values and C max and AUC ratios were consistent with clinically observed data. The addition of the inhibitory metabolites into the PBPK model resulted in a more accurate prediction of DDIs (AUC and C max ratio) than that which only considered parent drug (bupropion) P450 inhibition. The simulation suggests that bupropion and its metabolites contribute to the DDI between bupropion and CYP2D6 substrates. The inhibitory potency from strong to weak is hydroxybupropion, threohydrobupropion, erythrohydrobupropion, and bupropion, respectively. The present bupropion PBPK model can be useful for predicting inhibition from bupropion in other clinical studies. This study highlights the need for caution and dosage adjustment when combining bupropion with medications metabolized by CYP2D6. It also demonstrates the feasibility of applying the PBPK approach to predict the DDI potential of drugs undergoing complex metabolism, especially in the DDI involving inhibitory

  1. Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression.

    Science.gov (United States)

    Zhang, Xinyan; Li, Bingzong; Han, Huiying; Song, Sha; Xu, Hongxia; Hong, Yating; Yi, Nengjun; Zhuang, Wenzhuo

    2018-05-10

    Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients' response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.

  2. Global Optimization of Ventricular Myocyte Model to Multi-Variable Objective Improves Predictions of Drug-Induced Torsades de Pointes

    Directory of Open Access Journals (Sweden)

    Trine Krogh-Madsen

    2017-12-01

    Full Text Available In silico cardiac myocyte models present powerful tools for drug safety testing and for predicting phenotypical consequences of ion channel mutations, but their accuracy is sometimes limited. For example, several models describing human ventricular electrophysiology perform poorly when simulating effects of long QT mutations. Model optimization represents one way of obtaining models with stronger predictive power. Using a recent human ventricular myocyte model, we demonstrate that model optimization to clinical long QT data, in conjunction with physiologically-based bounds on intracellular calcium and sodium concentrations, better constrains model parameters. To determine if the model optimized to congenital long QT data better predicts risk of drug-induced long QT arrhythmogenesis, in particular Torsades de Pointes risk, we tested the optimized model against a database of known arrhythmogenic and non-arrhythmogenic ion channel blockers. When doing so, the optimized model provided an improved risk assessment. In particular, we demonstrate an elimination of false-positive outcomes generated by the baseline model, in which simulations of non-torsadogenic drugs, in particular verapamil, predict action potential prolongation. Our results underscore the importance of currents beyond those directly impacted by a drug block in determining torsadogenic risk. Our study also highlights the need for rich data in cardiac myocyte model optimization and substantiates such optimization as a method to generate models with higher accuracy of predictions of drug-induced cardiotoxicity.

  3. A predictive ligand-based Bayesian model for human drug-induced liver injury.

    Science.gov (United States)

    Ekins, Sean; Williams, Antony J; Xu, Jinghai J

    2010-12-01

    Drug-induced liver injury (DILI) is one of the most important reasons for drug development failure at both preapproval and postapproval stages. There has been increased interest in developing predictive in vivo, in vitro, and in silico models to identify compounds that cause idiosyncratic hepatotoxicity. In the current study, we applied machine learning, a Bayesian modeling method with extended connectivity fingerprints and other interpretable descriptors. The model that was developed and internally validated (using a training set of 295 compounds) was then applied to a large test set relative to the training set (237 compounds) for external validation. The resulting concordance of 60%, sensitivity of 56%, and specificity of 67% were comparable to results for internal validation. The Bayesian model with extended connectivity functional class fingerprints of maximum diameter 6 (ECFC_6) and interpretable descriptors suggested several substructures that are chemically reactive and may also be important for DILI-causing compounds, e.g., ketones, diols, and α-methyl styrene type structures. Using Smiles Arbitrary Target Specification (SMARTS) filters published by several pharmaceutical companies, we evaluated whether such reactive substructures could be readily detected by any of the published filters. It was apparent that the most stringent filters used in this study, such as the Abbott alerts, which captures thiol traps and other compounds, may be of use in identifying DILI-causing compounds (sensitivity 67%). A significant outcome of the present study is that we provide predictions for many compounds that cause DILI by using the knowledge we have available from previous studies. These computational models may represent cost-effective selection criteria before in vitro or in vivo experimental studies.

  4. Predicting biopharmaceutical performance of oral drug candidates - Extending the volume to dissolve applied dose concept.

    Science.gov (United States)

    Muenster, Uwe; Mueck, Wolfgang; van der Mey, Dorina; Schlemmer, Karl-Heinz; Greschat-Schade, Susanne; Haerter, Michael; Pelzetter, Christian; Pruemper, Christian; Verlage, Joerg; Göller, Andreas H; Ohm, Andreas

    2016-05-01

    The purpose of the study was to experimentally deduce pH-dependent critical volumes to dissolve applied dose (VDAD) that determine whether a drug candidate can be developed as immediate release (IR) tablet containing crystalline API, or if solubilization technology is needed to allow for sufficient oral bioavailability. pH-dependent VDADs of 22 and 83 compounds were plotted vs. the relative oral bioavailability (AUC solid vs. AUC solution formulation, Frel) in humans and rats, respectively. Furthermore, in order to investigate to what extent Frel rat may predict issues with solubility limited absorption in human, Frel rat was plotted vs. Frel human. Additionally, the impact of bile salts and lecithin on in vitro dissolution of poorly soluble compounds was tested and data compared to Frel rat and human. Respective in vitro - in vivo and in vivo - in vivo correlations were generated and used to build developability criteria. As a result, based on pH-dependent VDAD, Frel rat and in vitro dissolution in simulated intestinal fluid the IR formulation strategy within Pharmaceutical Research and Development organizations can be already set at late stage of drug discovery. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. In silico and in vitro prediction of gastrointestinal absorption from potential drug eremantholide C.

    Science.gov (United States)

    Caldeira, Tamires G; Saúde-Guimarães, Dênia A; Dezani, André B; Serra, Cristina Helena Dos Reis; de Souza, Jacqueline

    2017-11-01

    Analysis of the biopharmaceutical properties of eremantholide C, sesquiterpene lactone with proven pharmacological activity and low toxicity, is required to evaluate its potential to become a drug. Preliminary analysis of the physicochemical characteristics of eremantholide C was performed in silico. Equilibrium solubility was evaluated using the shake-flask method, at 37.0 °C, 100 rpm during 72 h in biorelevant media. The permeability was analysed using parallel artificial membrane permeability assay, at 37.0 °C, 50 rpm for 5 h. The donor compartment was composed of an eremantholide C solution in intestinal fluid simulated without enzymes, while the acceptor compartment consisted of phosphate buffer. Physicochemical characteristics predicted in silico indicated that eremantholide C has a low solubility and high permeability. In-vitro data of eremantholide C showed low solubility, with values for the dose/solubility ratio (ml): 9448.82, 10 389.61 e 15 000.00 for buffers acetate (pH 4.5), intestinal fluid simulated without enzymes (pH 6.8) and phosphate (pH 7.4), respectively. Also, it showed high permeability, with effective permeability of 30.4 × 10 -6 cm/s, a higher result compared with propranolol hydrochloride (9.23 × 10 -6 cm/s). The high permeability combined with its solubility, pharmacological activity and low toxicity demonstrate the importance of eremantholide C as a potential drug candidate. © 2017 Royal Pharmaceutical Society.

  6. Prediction of drug-related cardiac adverse effects in humans--B: use of QSAR programs for early detection of drug-induced cardiac toxicities.

    Science.gov (United States)

    Frid, Anna A; Matthews, Edwin J

    2010-04-01

    This report describes the use of three quantitative structure-activity relationship (QSAR) programs to predict drug-related cardiac adverse effects (AEs), BioEpisteme, MC4PC, and Leadscope Predictive Data Miner. QSAR models were constructed for 9 cardiac AE clusters affecting Purkinje nerve fibers (arrhythmia, bradycardia, conduction disorder, electrocardiogram, palpitations, QT prolongation, rate rhythm composite, tachycardia, and Torsades de pointes) and 5 clusters affecting the heart muscle (coronary artery disorders, heart failure, myocardial disorders, myocardial infarction, and valve disorders). The models were based on a database of post-marketing AEs linked to 1632 chemical structures, and identical training data sets were configured for three QSAR programs. Model performance was optimized and shown to be affected by the ratio of the number of active to inactive drugs. Results revealed that the three programs were complementary and predictive performances using any single positive, consensus two positives, or consensus three positives were as follows, respectively: 70.7%, 91.7%, and 98.0% specificity; 74.7%, 47.2%, and 21.0% sensitivity; and 138.2, 206.3, and 144.2 chi(2). In addition, a prospective study using AE data from the U.S. Food and Drug Administration's (FDA's) MedWatch Program showed 82.4% specificity and 94.3% sensitivity. Furthermore, an external validation study of 18 drugs with serious cardiotoxicity not considered in the models had 88.9% sensitivity. Published by Elsevier Inc.

  7. Matching Subsequences in Trees

    DEFF Research Database (Denmark)

    Bille, Philip; Gørtz, Inge Li

    2009-01-01

    Given two rooted, labeled trees P and T the tree path subsequence problem is to determine which paths in P are subsequences of which paths in T. Here a path begins at the root and ends at a leaf. In this paper we propose this problem as a useful query primitive for XML data, and provide new...

  8. Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors.

    Science.gov (United States)

    Mitra, A K; Mukherjee, U K; Harding, T; Jang, J S; Stessman, H; Li, Y; Abyzov, A; Jen, J; Kumar, S; Rajkumar, V; Van Ness, B

    2016-05-01

    Multiple myeloma (MM) is characterized by significant genetic diversity at subclonal levels that have a defining role in the heterogeneity of tumor progression, clinical aggressiveness and drug sensitivity. Although genome profiling studies have demonstrated heterogeneity in subclonal architecture that may ultimately lead to relapse, a gene expression-based prediction program that can identify, distinguish and quantify drug response in sub-populations within a bulk population of myeloma cells is lacking. In this study, we performed targeted transcriptome analysis on 528 pre-treatment single cells from 11 myeloma cell lines and 418 single cells from 8 drug-naïve MM patients, followed by intensive bioinformatics and statistical analysis for prediction of proteasome inhibitor sensitivity in individual cells. Using our previously reported drug response gene expression profile signature at the single-cell level, we developed an R Statistical analysis package available at https://github.com/bvnlabSCATTome, SCATTome (single-cell analysis of targeted transcriptome), that restructures the data obtained from Fluidigm single-cell quantitative real-time-PCR analysis run, filters missing data, performs scaling of filtered data, builds classification models and predicts drug response of individual cells based on targeted transcriptome using an assortment of machine learning methods. Application of SCATT should contribute to clinically relevant analysis of intratumor heterogeneity, and better inform drug choices based on subclonal cellular responses.

  9. Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees.

    Science.gov (United States)

    Amirabadizadeh, Alireza; Nezami, Hossein; Vaughn, Michael G; Nakhaee, Samaneh; Mehrpour, Omid

    2018-05-12

    Substance abuse exacts considerable social and health care burdens throughout the world. The aim of this study was to create a prediction model to better identify risk factors for drug use. A prospective cross-sectional study was conducted in South Khorasan Province, Iran. Of the total of 678 eligible subjects, 70% (n: 474) were randomly selected to provide a training set for constructing decision tree and multiple logistic regression (MLR) models. The remaining 30% (n: 204) were employed in a holdout sample to test the performance of the decision tree and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the testing set. Independent variables were selected from demographic characteristics and history of drug use. For the decision tree model, the sensitivity and specificity for identifying people at risk for drug abuse were 66% and 75%, respectively, while the MLR model was somewhat less effective at 60% and 73%. Key independent variables in the analyses included first substance experience, age at first drug use, age, place of residence, history of cigarette use, and occupational and marital status. While study findings are exploratory and lack generalizability they do suggest that the decision tree model holds promise as an effective classification approach for identifying risk factors for drug use. Convergent with prior research in Western contexts is that age of drug use initiation was a critical factor predicting a substance use disorder.

  10. Predicting pharmacy syringe sales to people who inject drugs: Policy, practice and perceptions.

    Science.gov (United States)

    Meyerson, Beth E; Davis, Alissa; Agley, Jon D; Shannon, David J; Lawrence, Carrie A; Ryder, Priscilla T; Ritchie, Karleen; Gassman, Ruth

    2018-06-01

    Pharmacies have much to contribute to the health of people who inject drugs (PWID) and to community efforts in HIV and hepatitis C (HCV) prevention through syringe access. However, little is known about what predicts pharmacy syringe sales without a prescription. To identify factors predicting pharmacy syringes sales to PWID. A hybrid staggered online survey of 298 Indiana community pharmacists occurred from July-September 2016 measuring pharmacy policy, practice, and pharmacist perceptions about syringe sales to PWID. Separate bivariate logistical regressions were followed by multivariable logistic regression to predict pharmacy syringe sales and pharmacist comfort dispensing syringes to PWID. Half (50.5%) of Indiana pharmacies sold syringes without a prescription to PWID. Pharmacy syringe sales was strongly associated with pharmacist supportive beliefs about syringe access by PWID and their comfort level selling syringes to PWID. Notably, pharmacies located in communities with high rates of opioid overdose mortality were 56% less likely to sell syringes without a prescription than those in communities with lower rates. Pharmacist comfort dispensing syringes was associated with being male, working at a pharmacy that sold syringes to PWID and one that stocked naloxone, having been asked about syringe access by medical providers, and agreement that PWID should be able to buy syringes without a prescription. As communities with high rates of opioid overdose mortality were less likely to have pharmacies that dispensed syringes to PWID, a concerted effort with these communities and their pharmacies should be made to understand opportunities to increase syringe access. Future studies should explore nuances between theoretical support for syringe access by PWID without a prescription and actual dispensing behaviors. Addressing potential policy conflicts and offering continuing education on non-prescription syringe distribution for pharmacists may improve comfort

  11. Integrating genomics and proteomics data to predict drug effects using binary linear programming.

    Science.gov (United States)

    Ji, Zhiwei; Su, Jing; Liu, Chenglin; Wang, Hongyan; Huang, Deshuang; Zhou, Xiaobo

    2014-01-01

    The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be

  12. The Brain Activity in Brodmann Area 17: A Potential Bio-Marker to Predict Patient Responses to Antiepileptic Drugs.

    Directory of Open Access Journals (Sweden)

    Yida Hu

    Full Text Available In this study, we aimed to predict newly diagnosed patient responses to antiepileptic drugs (AEDs using resting-state functional magnetic resonance imaging tools to explore changes in spontaneous brain activity. We recruited 21 newly diagnosed epileptic patients, 8 drug-resistant (DR patients, 11 well-healed (WH patients, and 13 healthy controls. After a 12-month follow-up, 11 newly diagnosed epileptic patients who showed a poor response to AEDs were placed into the seizures uncontrolled (SUC group, while 10 patients were enrolled in the seizure-controlled (SC group. By calculating the amplitude of fractional low-frequency fluctuations (fALFF of blood oxygen level-dependent signals to measure brain activity during rest, we found that the SUC patients showed increased activity in the bilateral occipital lobe, particularly in the cuneus and lingual gyrus compared with the SC group and healthy controls. Interestingly, DR patients also showed increased activity in the identical cuneus and lingual gyrus regions, which comprise Brodmann's area 17 (BA17, compared with the SUC patients; however, these abnormalities were not observed in SC and WH patients. The receiver operating characteristic (ROC curves indicated that the fALFF value of BA17 could differentiate SUC patients from SC patients and healthy controls with sufficient sensitivity and specificity prior to the administration of medication. Functional connectivity analysis was subsequently performed to evaluate the difference in connectivity between BA17 and other brain regions in the SUC, SC and control groups. Regions nearby the cuneus and lingual gyrus were found positive connectivity increased changes or positive connectivity changes with BA17 in the SUC patients, while remarkably negative connectivity increased changes or positive connectivity decreased changes were found in the SC patients. Additionally, default mode network (DMN regions showed negative connectivity increased changes or

  13. Development of a Unified Dissolution and Precipitation Model and Its Use for the Prediction of Oral Drug Absorption.

    Science.gov (United States)

    Jakubiak, Paulina; Wagner, Björn; Grimm, Hans Peter; Petrig-Schaffland, Jeannine; Schuler, Franz; Alvarez-Sánchez, Rubén

    2016-02-01

    Drug absorption is a complex process involving dissolution and precipitation, along with other kinetic processes. The purpose of this work was to (1) establish an in vitro methodology to study dissolution and precipitation in early stages of drug development where low compound consumption and high throughput are necessary, (2) develop a mathematical model for a mechanistic explanation of generated in vitro dissolution and precipitation data, and (3) extrapolate in vitro data to in vivo situations using physiologically based models to predict oral drug absorption. Small-scale pH-shift studies were performed in biorelevant media to monitor the precipitation of a set of poorly soluble weak bases. After developing a dissolution-precipitation model from this data, it was integrated into a simplified, physiologically based absorption model to predict clinical pharmacokinetic profiles. The model helped explain the consequences of supersaturation behavior of compounds. The predicted human pharmacokinetic profiles closely aligned with the observed clinical data. In summary, we describe a novel approach combining experimental dissolution/precipitation methodology with a mechanistic model for the prediction of human drug absorption kinetics. The approach unifies the dissolution and precipitation theories and enables accurate predictions of in vivo oral absorption by means of physiologically based modeling.

  14. The value of {sup 18}F-FDG PET before and after induction chemotherapy for the early prediction of a poor pathologic response to subsequent preoperative chemoradiotherapy in oesophageal adenocarcinoma

    Energy Technology Data Exchange (ETDEWEB)

    Rossum, Peter S.N. van [The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, Houston, TX (United States); University Medical Center Utrecht, Department of Radiation Oncology, Utrecht (Netherlands); Fried, David V.; Zhang, Lifei; Court, Laurence E. [The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX (United States); Hofstetter, Wayne L. [The University of Texas MD Anderson Cancer Center, Department of Thoracic and Cardiovascular Surgery, Houston, TX (United States); Ho, Linus [The University of Texas MD Anderson Cancer Center, Department of Gastrointestinal Medical Oncology, Houston, TX (United States); Meijer, Gert J. [University Medical Center Utrecht, Department of Radiation Oncology, Utrecht (Netherlands); Carter, Brett W. [The University of Texas MD Anderson Cancer Center, Department of Diagnostic Radiology, Houston, TX (United States); Lin, Steven H. [The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, Houston, TX (United States)

    2017-01-15

    The purpose of our study was to determine the value of {sup 18}F-FDG PET before and after induction chemotherapy in patients with oesophageal adenocarcinoma for the early prediction of a poor pathologic response to subsequent preoperative chemoradiotherapy (CRT). In 70 consecutive patients receiving a three-step treatment strategy of induction chemotherapy and preoperative chemoradiotherapy for oesophageal adenocarcinoma, {sup 18}F-FDG PET scans were performed before and after induction chemotherapy (before preoperative CRT). SUV{sub max}, SUV{sub mean}, metabolic tumour volume (MTV), and total lesion glycolysis (TLG) were determined at these two time points. The predictive potential of (the change in) these parameters for a poor pathologic response, progression-free survival (PFS) and overall survival (OS) was assessed. A poor pathologic response after induction chemotherapy and preoperative CRT was found in 27 patients (39 %). Patients with a poor pathologic response experienced less of a reduction in TLG after induction chemotherapy (p < 0.01). The change in TLG was predictive for a poor pathologic response at a threshold of -26 % (sensitivity 67 %, specificity 84 %, accuracy 77 %, PPV 72 %, NPV 80 %), yielding an area-under-the-curve of 0.74 in ROC analysis. Also, patients with a decrease in TLG lower than 26 % had a significantly worse PFS (p = 0.02), but not OS (p = 0.18). {sup 18}F-FDG PET appears useful to predict a poor pathologic response as well as PFS early after induction chemotherapy in patients with oesophageal adenocarcinoma undergoing a three-step treatment strategy. As such, the early {sup 18}F-FDG PET response after induction chemotherapy could aid in individualizing treatment by modification or withdrawal of subsequent preoperative CRT in poor responders. (orig.)

  15. The value of 18F-FDG PET before and after induction chemotherapy for the early prediction of a poor pathologic response to subsequent preoperative chemoradiotherapy in oesophageal adenocarcinoma

    International Nuclear Information System (INIS)

    Rossum, Peter S.N. van; Fried, David V.; Zhang, Lifei; Court, Laurence E.; Hofstetter, Wayne L.; Ho, Linus; Meijer, Gert J.; Carter, Brett W.; Lin, Steven H.

    2017-01-01

    The purpose of our study was to determine the value of 18 F-FDG PET before and after induction chemotherapy in patients with oesophageal adenocarcinoma for the early prediction of a poor pathologic response to subsequent preoperative chemoradiotherapy (CRT). In 70 consecutive patients receiving a three-step treatment strategy of induction chemotherapy and preoperative chemoradiotherapy for oesophageal adenocarcinoma, 18 F-FDG PET scans were performed before and after induction chemotherapy (before preoperative CRT). SUV max , SUV mean , metabolic tumour volume (MTV), and total lesion glycolysis (TLG) were determined at these two time points. The predictive potential of (the change in) these parameters for a poor pathologic response, progression-free survival (PFS) and overall survival (OS) was assessed. A poor pathologic response after induction chemotherapy and preoperative CRT was found in 27 patients (39 %). Patients with a poor pathologic response experienced less of a reduction in TLG after induction chemotherapy (p < 0.01). The change in TLG was predictive for a poor pathologic response at a threshold of -26 % (sensitivity 67 %, specificity 84 %, accuracy 77 %, PPV 72 %, NPV 80 %), yielding an area-under-the-curve of 0.74 in ROC analysis. Also, patients with a decrease in TLG lower than 26 % had a significantly worse PFS (p = 0.02), but not OS (p = 0.18). 18 F-FDG PET appears useful to predict a poor pathologic response as well as PFS early after induction chemotherapy in patients with oesophageal adenocarcinoma undergoing a three-step treatment strategy. As such, the early 18 F-FDG PET response after induction chemotherapy could aid in individualizing treatment by modification or withdrawal of subsequent preoperative CRT in poor responders. (orig.)

  16. In Vitro Dissolution of Fluconazole and Dipyridamole in Gastrointestinal Simulator (GIS), Predicting in Vivo Dissolution and Drug-Drug Interaction Caused by Acid-Reducing Agents.

    Science.gov (United States)

    Matsui, Kazuki; Tsume, Yasuhiro; Amidon, Gregory E; Amidon, Gordon L

    2015-07-06

    Weakly basic drugs typically exhibit pH-dependent solubility in the physiological pH range, displaying supersaturation or precipitation along the gastrointestinal tract. Additionally, their oral bioavailabilities may be affected by coadministration of acid-reducing agents that elevate gastric pH. The purpose of this study was to assess the feasibility of a multicompartmental in vitro dissolution apparatus, Gastrointestinal Simulator (GIS), in predicting in vivo dissolution of certain oral medications. In vitro dissolution studies of fluconazole, a BCS class I, and dipyridamole, a BCS class II weak bases (class IIb), were performed in the GIS as well as United States Pharmacopeia (USP) apparatus II and compared with the results of clinical drug-drug interaction (DDI) studies. In both USP apparatus II and GIS, fluconazole completely dissolved within 60 min regardless of pH, reflecting no DDI between fluconazole and acid-reducing agents in a clinical study. On the other hand, seven-fold and 15-fold higher concentrations of dipyridamole than saturation solubility were observed in the intestinal compartments in GIS with gastric pH 2.0. Precipitation of dipyridamole was also observed in the GIS, and the percentage of dipyridamole in solution was 45.2 ± 7.0%. In GIS with gastric pH 6.0, mimicking the coadministration of acid-reducing agents, the concentration of dipyridamole was equal to its saturation solubility, and the percentage of drug in solution was 9.3 ± 2.7%. These results are consistent with the clinical DDI study of dipyridamole with famotidine, which significantly reduced the Cmax and area under the curve. An In situ mouse infusion study combined with GIS revealed that high concentration of dipyridamole in the GIS enhanced oral drug absorption, which confirmed the supersaturation of dipyridamole. In conclusion, GIS was shown to be a useful apparatus to predict in vivo dissolution for BCS class IIb drugs.

  17. Fluency of pharmaceutical drug names predicts perceived hazardousness, assumed side effects and willingness to buy.

    Science.gov (United States)

    Dohle, Simone; Siegrist, Michael

    2014-10-01

    The impact of pharmaceutical drug names on people's evaluations and behavioural intentions is still uncertain. According to the representativeness heuristic, evaluations should be more positive for complex drug names; in contrast, fluency theory suggests that evaluations should be more positive for simple drug names. Results of three experimental studies showed that complex drug names were perceived as more hazardous than simple drug names and negatively influenced willingness to buy. The results are of particular importance given the fact that there is a worldwide trend to make more drugs available for self-medication. © The Author(s) 2013.

  18. PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins.

    Science.gov (United States)

    Hussein, Hiba Abi; Borrel, Alexandre; Geneix, Colette; Petitjean, Michel; Regad, Leslie; Camproux, Anne-Claude

    2015-07-01

    Predicting protein pocket's ability to bind drug-like molecules with high affinity, i.e. druggability, is of major interest in the target identification phase of drug discovery. Therefore, pocket druggability investigations represent a key step of compound clinical progression projects. Currently computational druggability prediction models are attached to one unique pocket estimation method despite pocket estimation uncertainties. In this paper, we propose 'PockDrug-Server' to predict pocket druggability, efficient on both (i) estimated pockets guided by the ligand proximity (extracted by proximity to a ligand from a holo protein structure) and (ii) estimated pockets based solely on protein structure information (based on amino atoms that form the surface of potential binding cavities). PockDrug-Server provides consistent druggability results using different pocket estimation methods. It is robust with respect to pocket boundary and estimation uncertainties, thus efficient using apo pockets that are challenging to estimate. It clearly distinguishes druggable from less druggable pockets using different estimation methods and outperformed recent druggability models for apo pockets. It can be carried out from one or a set of apo/holo proteins using different pocket estimation methods proposed by our web server or from any pocket previously estimated by the user. PockDrug-Server is publicly available at: http://pockdrug.rpbs.univ-paris-diderot.fr. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  19. Prediction of interindividual variation in drug plasma levels in vivo from individual enzyme kinetic data and physiologically based pharmacokinetic modeling

    NARCIS (Netherlands)

    Bogaards, J.J.P.; Hissink, E.M.; Briggs, M.; Weaver, R.; Jochemsen, R.; Jackson, P.; Bertrand, M.; Bladeren, P. van

    2000-01-01

    A strategy is presented to predict interindividual variation in drug plasma levels in vivo by the use of physiologically based pharmacokinetic modeling and human in vitro metabolic parameters, obtained through the combined use of microsomes containing single cytochrome P450 enzymes and a human liver

  20. Modelled in vivo HIV fitness under drug selective pressure and estimated genetic barrier towards resistance are predictive for virological response

    DEFF Research Database (Denmark)

    Deforche, Koen; Cozzi-Lepri, Alessandro; Theys, Kristof

    2008-01-01

    landscapes (nelfinavir [NFV] and zidovudine [AZT] plus lamivudine [3TC]) to predict week 12 viral load (VL) change for 176 treatment change episodes (TCEs) and probability of week 48 virological failure for 90 TCEs, in treatment experienced patients starting these drugs in combination. RESULTS: A higher...

  1. Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.

    Science.gov (United States)

    Ali, Mehreen; Khan, Suleiman A; Wennerberg, Krister; Aittokallio, Tero

    2018-04-15

    Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. Processed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps. mehreen

  2. The role of personality in predicting drug and alcohol use among sexual minorities.

    Science.gov (United States)

    Livingston, Nicholas A; Oost, Kathryn M; Heck, Nicholas C; Cochran, Bryan N

    2015-06-01

    Research consistently demonstrates that sexual minority status is associated with increased risk of problematic substance use. Existing literature in this area has focused on group-specific minority stress factors (e.g., victimization and internalized heterosexism). However, no known research has tested the incremental validity of personality traits as predictors of substance use beyond identified group-specific risk factors. A sample of 704 sexual minority adults was recruited nationally from lesbian, gay, bisexual, transgender, queer, and questioning community organizations and social networking Web sites and asked to complete an online survey containing measures of personality, sexual minority stress, and substance use. Hierarchical regression models were constructed to test the incremental predictive validity of five-factor model personality traits over and above known sexual minority risk factors. Consistent with hypotheses, extraversion and conscientiousness were associated with drug and alcohol use after accounting for minority stress factors, and all factors except agreeableness were associated with substance use at the bivariate level of analysis. Future research should seek to better understand the role of normal personality structures and processes conferring risk for substance use among sexual minorities. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

  3. Prediction of overall in vitro microsomal stability of drug candidates based on molecular modeling and support vector machines. Case study of novel arylpiperazines derivatives.

    Directory of Open Access Journals (Sweden)

    Szymon Ulenberg

    Full Text Available Other than efficacy of interaction with the molecular target, metabolic stability is the primary factor responsible for the failure or success of a compound in the drug development pipeline. The ideal drug candidate should be stable enough to reach its therapeutic site of action. Despite many recent excellent achievements in the field of computational methods supporting drug metabolism studies, a well-recognized procedure to model and predict metabolic stability quantitatively is still lacking. This study proposes a workflow for developing quantitative metabolic stability-structure relationships, taking a set of 30 arylpiperazine derivatives as an example. The metabolic stability of the compounds was assessed in in vitro incubations in the presence of human liver microsomes and NADPH and subsequently quantified by liquid chromatography-mass spectrometry (LC-MS. Density functional theory (DFT calculations were used to obtain 30 models of the molecules, and Dragon software served as a source of structure-based molecular descriptors. For modeling structure-metabolic stability relationships, Support Vector Machines (SVM, a non-linear machine learning technique, were found to be more effective than a regression technique, based on the validation parameters obtained. Moreover, for the first time, general sites of metabolism for arylpiperazines bearing the 4-aryl-2H-pyrido[1,2-c]pyrimidine-1,3-dione system were defined by analysis of Q-TOF-MS/MS spectra. The results indicated that the application of one of the most advanced chemometric techniques combined with a simple and quick in vitro procedure and LC-MS analysis provides a novel and valuable tool for predicting metabolic half-life values. Given the reduced time and simplicity of analysis, together with the accuracy of the predictions obtained, this is a valid approach for predicting metabolic stability using structural data. The approach presented provides a novel, comprehensive and reliable tool

  4. Application of receiver operating characteristic analysis to refine the prediction of potential digoxin drug interactionss

    NARCIS (Netherlands)

    Ellens, H.; Deng, S.; Coleman, J.; Bentz, J.; Taub, M.E.; Ragueneau-Majlessi, I.; Chung, S.P.; Herédi-Szabó, K.; Neuhoff, S.; Palm, J.; Balimane, P.; Zhang, L.; Jamei, M.; Hanna, I.; O'connor, M.; Bednarczyk, D.; Forsgard, M.; Chu, X.; Funk, C.; Guo, A.; Hillgren, K.M.; Li, L.; Pak, A.Y.; Perloff, E.S.; Rajaraman, G.; Salphati, L.; Taur, J.-S.; Weitz, D.; Wortelboer, H.M.; Xia, C.Q.; Xiao, G.; Yamagata, T.; Lee, C.A.

    2013-01-01

    In the 2012 Food and Drug Administration (FDA) draft guidance on drug-drug interactions (DDIs), a new molecular entity that inhibits Pglycoprotein (P-gp) may need a clinical DDI study with a P-gp substrate such as digoxin when themaximumconcentration of inhibitor at steady state divided by IC50

  5. FDA-approved drugs that are spermatotoxic in animals and the utility of animal testing for human risk prediction.

    Science.gov (United States)

    Rayburn, Elizabeth R; Gao, Liang; Ding, Jiayi; Ding, Hongxia; Shao, Jun; Li, Haibo

    2018-02-01

    This study reviews FDA-approved drugs that negatively impact spermatozoa in animals, as well as how these findings reflect on observations in human male gametes. The FDA drug warning labels included in the DailyMed database and the peer-reviewed literature in the PubMed database were searched for information to identify single-ingredient, FDA-approved prescription drugs with spermatotoxic effects. A total of 235 unique, single-ingredient, FDA-approved drugs reported to be spermatotoxic in animals were identified in the drug labels. Forty-nine of these had documented negative effects on humans in either the drug label or literature, while 31 had no effect or a positive impact on human sperm. For the other 155 drugs that were spermatotoxic in animals, no human data was available. The current animal models are not very effective for predicting human spermatotoxicity, and there is limited information available about the impact of many drugs on human spermatozoa. New approaches should be designed that more accurately reflect the findings in men, including more studies on human sperm in vitro and studies using other systems (ex vivo tissue culture, xenograft models, in silico studies, etc.). In addition, the present data is often incomplete or reported in a manner that prevents interpretation of their clinical relevance. Changes should be made to the requirements for pre-clinical testing, drug surveillance, and the warning labels of drugs to ensure that the potential risks to human fertility are clearly indicated.

  6. Using pharmacokinetics to predict the effects of pregnancy and maternal-infant transfer of drugs during lactation.

    Science.gov (United States)

    Anderson, Gail D

    2006-12-01

    Knowledge of pharmacokinetics and the use of a mechanistic-based approach can improve our ability to predict the effects of pregnancy for medications when data are limited. Despite the many physiological changes that occur during pregnancy that could theoretically affect absorption, bioavailability does not appear to be altered. Decreased albumin and alpha(1)-acid glycoprotein concentrations during pregnancy will result in decreased protein binding for highly bound drugs. For drugs metabolised by the liver, this can result in misinterpretation of total plasma concentrations of low extraction ratio drugs and overdosing of high extraction ratio drugs administered by non-oral routes. Renal clearance and the activity of the CYP isozymes, CYP3A4, 2D6 and 2C9, and uridine 5'-diphosphate glucuronosyltransferase are increased during pregnancy. In contrast, CYP1A2 and 2C19 activity is decreased. The dose of a drug an infant receives during breastfeeding is dependent on the amount excreted into the breast milk, the daily volume of milk ingested and the average plasma concentration of the mother. The lipophilicity, protein binding and ionisation properties of a drug will determine how much is excreted into the breast milk. The milk to plasma concentration ratio has large inter- and intrasubject variability and is often not known. In contrast, protein binding is usually known. An extensive literature review was done to identify case reports including infant concentrations from breast-fed infants exposed to maternal drugs. For drugs that were at least 85% protein bound, measurable concentrations of drug in the infant did not occur if there was no placental exposure immediately prior to or during delivery. Knowledge of the protein binding properties of a drug can provide a quick and easy tool to estimate exposure of an infant to medication from breastfeeding.

  7. Current status of prediction of drug disposition and toxicity in humans using chimeric mice with humanized liver.

    Science.gov (United States)

    Kitamura, Shigeyuki; Sugihara, Kazumi

    2014-01-01

    1. Human-chimeric mice with humanized liver have been constructed by transplantation of human hepatocytes into several types of mice having genetic modifications that injure endogenous liver cells. Here, we focus on liver urokinase-type plasminogen activator-transgenic severe combined immunodeficiency (uPA/SCID) mice, which are the most widely used human-chimeric mice. Studies so far indicate that drug metabolism, drug transport, pharmacological effects and toxicological action in these mice are broadly similar to those in humans. 2. Expression of various drug-metabolizing enzymes is known to be different between humans and rodents. However, the expression pattern of cytochrome P450, aldehyde oxidase and phase II enzymes in the liver of human-chimeric mice resembles that in humans, not that in the host mice. 3. Metabolism of various drugs, including S-warfarin, zaleplon, ibuprofen, naproxen, coumarin, troglitazone and midazolam, in human-chimeric mice is mediated by human drug-metabolizing enzymes, not by host mouse enzymes, and thus resembles that in humans. 4. Pharmacological and toxicological effects of various drugs in human-chimeric mice are also similar to those in humans. 5. The current consensus is that chimeric mice with humanized liver are useful to predict drug metabolism catalyzed by cytochrome P450, aldehyde oxidase and phase II enzymes in humans in vivo and in vitro. Some remaining issues are discussed in this review.

  8. Usefulness of Two-Compartment Model-Assisted and Static Overall Inhibitory-Activity Method for Prediction of Drug-Drug Interaction.

    Science.gov (United States)

    Iga, Katsumi; Kiriyama, Akiko

    2017-12-01

    Our study of drug-drug interaction (DDI) started with the clarification of unusually large DDI observed between ramelteon (RAM) and fluvoxamine (FLV). The main cause of this DDI was shown to be the extremely small hepatic availability of RAM (vF h ). Traditional DDI prediction assuming the well-stirred hepatic extraction kinetic ignores the relative increase of vF h by DDI, while we could solve this problem by use of the tube model. Ultimately, we completed a simple and useful method for prediction of DDI. Currently, DDI prediction becomes more complex and difficult when examining issues such as dynamic changes in perpetrator level, inhibitory metabolites, etc. The regulatory agents recommend DDI prediction by use of some sophisticated methods. However, they seem problematic in requiring plural in vitro data that reduce the flexibility and accuracy of the simulation. In contrast, our method is based on the static and two-compartment models. The two-compartment model has advantages in that it uses common pharmacokinetics (PK) parameters determined from the actual clinical data, guaranteeing the simulation of the reference standard in DDI. Our studies confirmed that dynamic changes in perpetrator level do not make a difference between static and dynamic methods. DDIs perpetrated by FLV and itraconazole were successfully predicted by use of the present method where two DDI predictors [perpetrator-specific inhibitory activities toward CYP isoforms (pA i, CYP s) and victim-specific fractional CYP-isoform contributions to the clearance (vf m, CYP s)] are determined successively as shown in the graphical abstract. Accordingly, this approach will accelerate DDI prediction over the traditional methods.

  9. The Role of Early Maladaptive Schemas in Prediction of Dysfunctional Attitudes toward Drug Abuse among Students of university

    Directory of Open Access Journals (Sweden)

    NedaNaeemi

    2016-07-01

    Full Text Available Drug addiction as the most serious social issue of the world has different sociological, psychological, legal, and political aspects. In this regard, the purpose of this study is to determine the role of early maladaptive schemas in prediction of dysfunctional attitudes toward drug abuse among students of Islamic Azad Universities in Tehran Province, Iran. Statistical population of this study includes all students of Islamic Azad Universities in Tehran Province during 2013 and sample size is equal to 300 members that are randomly chosen. First, the name of university branches in Tehran Province were determined then three branches were randomly chosen out of them and then 300 members were chosen from those branches using random sampling method. All sample members filled out Young Schema Questionnaire Short Form and Dysfunctional Attitude Scale (DAS toward drug. Data were analyzed through regression correlation method and SPSS22 software. The obtained findings indicated a significant relation (P<0/05 between early maladaptive schemas and dysfunctional attitude toward drug abuse among students. Early maladaptive schemas can predict dysfunctional attitudes toward drug among students.

  10. The persuasion network is modulated by drug-use risk and predicts anti-drug message effectiveness

    OpenAIRE

    Huskey, Richard; Mangus, J Michael; Turner, Benjamin O; Weber, René

    2017-01-01

    Abstract While a persuasion network has been proposed, little is known about how network connections between brain regions contribute to attitude change. Two possible mechanisms have been advanced. One hypothesis predicts that attitude change results from increased connectivity between structures implicated in affective and executive processing in response to increases in argument strength. A second functional perspective suggests that highly arousing messages reduce connectivity between stru...

  11. Prediction of clinical response to drugs in ovarian cancer using the chemotherapy resistance test (CTR-test).

    Science.gov (United States)

    Kischkel, Frank Christian; Meyer, Carina; Eich, Julia; Nassir, Mani; Mentze, Monika; Braicu, Ioana; Kopp-Schneider, Annette; Sehouli, Jalid

    2017-10-27

    In order to validate if the test result of the Chemotherapy Resistance Test (CTR-Test) is able to predict the resistances or sensitivities of tumors in ovarian cancer patients to drugs, the CTR-Test result and the corresponding clinical response of individual patients were correlated retrospectively. Results were compared to previous recorded correlations. The CTR-Test was performed on tumor samples from 52 ovarian cancer patients for specific chemotherapeutic drugs. Patients were treated with monotherapies or drug combinations. Resistances were classified as extreme (ER), medium (MR) or slight (SR) resistance in the CTR-Test. Combination treatment resistances were transformed by a scoring system into these classifications. Accurate sensitivity prediction was accomplished in 79% of the cases and accurate prediction of resistance in 100% of the cases in the total data set. The data set of single agent treatment and drug combination treatment were analyzed individually. Single agent treatment lead to an accurate sensitivity in 44% of the cases and the drug combination to 95% accuracy. The detection of resistances was in both cases to 100% correct. ROC curve analysis indicates that the CTR-Test result correlates with the clinical response, at least for the combination chemotherapy. Those values are similar or better than the values from a publication from 1990. Chemotherapy resistance testing in vitro via the CTR-Test is able to accurately detect resistances in ovarian cancer patients. These numbers confirm and even exceed results published in 1990. Better sensitivity detection might be caused by a higher percentage of drug combinations tested in 2012 compared to 1990. Our study confirms the functionality of the CTR-Test to plan an efficient chemotherapeutic treatment for ovarian cancer patients.

  12. Prediction of Relative In Vivo Metabolite Exposure from In Vitro Data Using Two Model Drugs: Dextromethorphan and Omeprazole

    Science.gov (United States)

    Lutz, Justin D.

    2012-01-01

    Metabolites can have pharmacological or toxicological effects, inhibit metabolic enzymes, and be used as probes of drug-drug interactions or specific cytochrome P450 (P450) phenotypes. Thus, better understanding and prediction methods are needed to characterize metabolite exposures in vivo. This study aimed to test whether in vitro data could be used to predict and rationalize in vivo metabolite exposures using two model drugs and P450 probes: dextromethorphan and omeprazole with their primary metabolites dextrorphan, 5-hydroxyomeprazole (5OH-omeprazole), and omeprazole sulfone. Relative metabolite exposures were predicted using metabolite formation and elimination clearances. For dextrorphan, the formation clearances of dextrorphan glucuronide and 3-hydroxymorphinan from dextrorphan in human liver microsomes were used to predict metabolite (dextrorphan) clearance. For 5OH-omeprazole and omeprazole sulfone, the depletion rates of the metabolites in human hepatocytes were used to predict metabolite clearance. Dextrorphan/dextromethorphan in vivo metabolite/parent area under the plasma concentration versus time curve ratio (AUCm/AUCp) was overpredicted by 2.1-fold, whereas 5OH-omeprazole/omeprazole and omeprazole sulfone/omeprazole were predicted within 0.75- and 1.1-fold, respectively. The effect of inhibition or induction of the metabolite's formation and elimination on the AUCm/AUCp ratio was simulated. The simulations showed that unless metabolite clearance pathways are characterized, interpretation of the metabolic ratios is exceedingly difficult. This study shows that relative in vivo metabolite exposure can be predicted from in vitro data and characterization of secondary metabolism of probe metabolites is critical for interpretation of phenotypic data. PMID:22010218

  13. Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics.

    Science.gov (United States)

    de Lange, Elizabeth C M; van den Brink, Willem; Yamamoto, Yumi; de Witte, Wilhelmus E A; Wong, Yin Cheong

    2017-12-01

    CNS drug development has been hampered by inadequate consideration of CNS pharmacokinetic (PK), pharmacodynamics (PD) and disease complexity (reductionist approach). Improvement is required via integrative model-based approaches. Areas covered: The authors summarize factors that have played a role in the high attrition rate of CNS compounds. Recent advances in CNS research and drug discovery are presented, especially with regard to assessment of relevant neuro-PK parameters. Suggestions for further improvements are also discussed. Expert opinion: Understanding time- and condition dependent interrelationships between neuro-PK and neuro-PD processes is key to predictions in different conditions. As a first screen, it is suggested to use in silico/in vitro derived molecular properties of candidate compounds and predict concentration-time profiles of compounds in multiple compartments of the human CNS, using time-course based physiology-based (PB) PK models. Then, for selected compounds, one can include in vitro drug-target binding kinetics to predict target occupancy (TO)-time profiles in humans. This will improve neuro-PD prediction. Furthermore, a pharmaco-omics approach is suggested, providing multilevel and paralleled data on systems processes from individuals in a systems-wide manner. Thus, clinical trials will be better informed, using fewer animals, while also, needing fewer individuals and samples per individual for proof of concept in humans.

  14. Performance of Machine Learning Algorithms for Qualitative and Quantitative Prediction Drug Blockade of hERG1 channel.

    Science.gov (United States)

    Wacker, Soren; Noskov, Sergei Yu

    2018-05-01

    Drug-induced abnormal heart rhythm known as Torsades de Pointes (TdP) is a potential lethal ventricular tachycardia found in many patients. Even newly released anti-arrhythmic drugs, like ivabradine with HCN channel as a primary target, block the hERG potassium current in overlapping concentration interval. Promiscuous drug block to hERG channel may potentially lead to perturbation of the action potential duration (APD) and TdP, especially when with combined with polypharmacy and/or electrolyte disturbances. The example of novel anti-arrhythmic ivabradine illustrates clinically important and ongoing deficit in drug design and warrants for better screening methods. There is an urgent need to develop new approaches for rapid and accurate assessment of how drugs with complex interactions and multiple subcellular targets can predispose or protect from drug-induced TdP. One of the unexpected outcomes of compulsory hERG screening implemented in USA and European Union resulted in large datasets of IC 50 values for various molecules entering the market. The abundant data allows now to construct predictive machine-learning (ML) models. Novel ML algorithms and techniques promise better accuracy in determining IC 50 values of hERG blockade that is comparable or surpassing that of the earlier QSAR or molecular modeling technique. To test the performance of modern ML techniques, we have developed a computational platform integrating various workflows for quantitative structure activity relationship (QSAR) models using data from the ChEMBL database. To establish predictive powers of ML-based algorithms we computed IC 50 values for large dataset of molecules and compared it to automated patch clamp system for a large dataset of hERG blocking and non-blocking drugs, an industry gold standard in studies of cardiotoxicity. The optimal protocol with high sensitivity and predictive power is based on the novel eXtreme gradient boosting (XGBoost) algorithm. The ML-platform with XGBoost

  15. Validation of the cephalosporin intradermal skin test for predicting immediate hypersensitivity: a prospective study with drug challenge.

    Science.gov (United States)

    Yoon, S-Y; Park, S Y; Kim, S; Lee, T; Lee, Y S; Kwon, H-S; Cho, Y S; Moon, H-B; Kim, T-B

    2013-07-01

    Cephalosporin is a major offending agent in terms of drug hypersensitivity along with penicillin. Cephalosporin intradermal skin tests (IDTs) have been widely used; however, their validity for predicting immediate hypersensitivity has not been studied. This study aimed to determine the predictive value of cephalosporin intradermal skin testing before administration of the drug. We prospectively conducted IDTs with four cephalosporins, one each of selected first-, second-, third-, or fourth-generation cephalosporins: ceftezol; cefotetan or cefamandole; ceftriaxone or cefotaxime; and flomoxef, respectively, as well as with penicillin G. After the skin test, whatever the result, one of the tested cephalosporins was administered intravenously and the patient was carefully observed. We recruited 1421 patients who required preoperative cephalosporins. Seventy-four patients (74/1421, 5.2%) were positive to at least one cephalosporin. However, none of responders had immediate hypersensitivity reactions after a challenge dose of the same or different cephalosporin, which were positive in the skin test. Four patients who suffered generalized urticaria and itching after challenge gave negative skin tests for the corresponding drug. The IDT for cephalosporin had a sensitivity of 0%, a specificity of 97.5%, a negative predictive value of 99.7%, and a positive predictive value (PPV) of 0%, when challenged with the same drugs that were positive in the skin test. Routine skin testing with a cephalosporin before its administration is not useful for predicting immediate hypersensitivity because of the extremely low sensitivity and PPV of the skin test (CRIS registration no. KCT0000455). © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  16. In Silico Predictions of hERG Channel Blockers in Drug Discovery

    DEFF Research Database (Denmark)

    Taboureau, Olivier; Sørensen, Flemming Steen

    2011-01-01

    The risk for cardiotoxic side effects represents a major problem in clinical studies of drug candidates and regulatory agencies have explicitly recommended that all new drug candidates should be tested for blockage of the human Ether-a-go-go Related-Gene (hERG) potassium channel. Indeed, several ...

  17. Prediction of the overall renal tubular secretion and hepatic clearance of anionic drugs and a renal drug-drug interaction involving organic anion transporter 3 in humans by in vitro uptake experiments.

    Science.gov (United States)

    Watanabe, Takao; Kusuhara, Hiroyuki; Watanabe, Tomoko; Debori, Yasuyuki; Maeda, Kazuya; Kondo, Tsunenori; Nakayama, Hideki; Horita, Shigeru; Ogilvie, Brian W; Parkinson, Andrew; Hu, Zhuohan; Sugiyama, Yuichi

    2011-06-01

    The present study investigated prediction of the overall renal tubular secretion and hepatic clearances of anionic drugs based on in vitro transport studies. The saturable uptake of eight drugs, most of which were OAT3 substrates (rosuvastatin, pravastatin, pitavastatin, valsartan, olmesartan, trichlormethiazide, p-aminohippurate, and benzylpenicillin) by freshly prepared human kidney slices underestimated the overall intrinsic clearance of the tubular secretion; therefore, a scaling factor of 10 was required for in vitro-in vivo extrapolation. We examined the effect of gemfibrozil and its metabolites, gemfibrozil glucuronide and the carboxylic metabolite, gemfibrozil M3, on pravastatin uptake by human kidney slices. The inhibition study using human kidney slices suggests that OAT3 plays a predominant role in the renal uptake of pravastatin. Comparison of unbound concentrations and K(i) values (1.5, 9.1, and 4.0 μM, for gemfibrozil, gemfibrozil glucuronide, and gemfibrozil M3, respectively) suggests that the mechanism of the interaction is due mainly to inhibition by gemfibrozil and gemfibrozil glucuronide. Furthermore, extrapolation of saturable uptake by cryopreserved human hepatocytes predicts clearance comparable with the observed hepatic clearance although fluvastatin and rosuvastatin required a scaling factor of 11 and 6.9, respectively. This study suggests that in vitro uptake assays using human kidney slices and hepatocytes provide a good prediction of the overall tubular secretion and hepatic clearances of anionic drugs and renal drug-drug interactions. It is also recommended that in vitro-in vivo extrapolation be performed in animals to obtain more reliable prediction.

  18. Are predictions of cancer response to targeted drugs, based on effects in unrelated tissues, the 'Black Swan' events?

    Science.gov (United States)

    Kurbel, Beatrica; Golem, Ante Zvonimir; Kurbel, Sven

    2015-01-01

    Adverse effects of targeted drugs on normal tissues can predict the cancer response. Rash correlates with efficacy of erlotinib, cetuximab and gefitinib and onset of arterial hypertension with response to bevacizumab, sunitinib, axitinib and sorafenib, possible examples of 'Black Swan' events, unexpected scientific observations, as described by Karl Popper in 1935. The proposition is that our patients have individual intrinsic variants of cell growth control, important for tumor response and adverse effects on tumor-unrelated tissue. This means that the lack of predictive side effects in healthy tissue is linked with poor results of tumor therapy when tumor resistance is caused by mechanisms that protect all cells of that patient from the targeted drug effects.

  19. Personalizing oncology treatments by predicting drug efficacy, side-effects, and improved therapy: mathematics, statistics, and their integration.

    Science.gov (United States)

    Agur, Zvia; Elishmereni, Moran; Kheifetz, Yuri

    2014-01-01

    Despite its great promise, personalized oncology still faces many hurdles, and it is increasingly clear that targeted drugs and molecular biomarkers alone yield only modest clinical benefit. One reason is the complex relationships between biomarkers and the patient's response to drugs, obscuring the true weight of the biomarkers in the overall patient's response. This complexity can be disentangled by computational models that integrate the effects of personal biomarkers into a simulator of drug-patient dynamic interactions, for predicting the clinical outcomes. Several computational tools have been developed for personalized oncology, notably evidence-based tools for simulating pharmacokinetics, Bayesian-estimated tools for predicting survival, etc. We describe representative statistical and mathematical tools, and discuss their merits, shortcomings and preliminary clinical validation attesting to their potential. Yet, the individualization power of mathematical models alone, or statistical models alone, is limited. More accurate and versatile personalization tools can be constructed by a new application of the statistical/mathematical nonlinear mixed effects modeling (NLMEM) approach, which until recently has been used only in drug development. Using these advanced tools, clinical data from patient populations can be integrated with mechanistic models of disease and physiology, for generating personal mathematical models. Upon a more substantial validation in the clinic, this approach will hopefully be applied in personalized clinical trials, P-trials, hence aiding the establishment of personalized medicine within the main stream of clinical oncology. © 2014 Wiley Periodicals, Inc.

  20. Computational Analysis of Epidermal Growth Factor Receptor Mutations Predicts Differential Drug Sensitivity Profiles toward Kinase Inhibitors.

    Science.gov (United States)

    Akula, Sravani; Kamasani, Swapna; Sivan, Sree Kanth; Manga, Vijjulatha; Vudem, Dashavantha Reddy; Kancha, Rama Krishna

    2018-05-01

    A significant proportion of patients with lung cancer carry mutations in the EGFR kinase domain. The presence of a deletion mutation in exon 19 or L858R point mutation in the EGFR kinase domain has been shown to cause enhanced efficacy of inhibitor treatment in patients with NSCLC. Several less frequent (uncommon) mutations in the EGFR kinase domain with potential implications in treatment response have also been reported. The role of a limited number of uncommon mutations in drug sensitivity was experimentally verified. However, a huge number of these mutations remain uncharacterized for inhibitor sensitivity or resistance. A large-scale computational analysis of clinically reported 298 point mutants of EGFR kinase domain has been performed, and drug sensitivity profiles for each mutant toward seven kinase inhibitors has been determined by molecular docking. In addition, the relative inhibitor binding affinity toward each drug as compared with that of adenosine triphosphate was calculated for each mutant. The inhibitor sensitivity profiles predicted in this study for a set of previously characterized mutants correlated well with the published clinical, experimental, and computational data. Both the single and compound mutations displayed differential inhibitor sensitivity toward first- and next-generation kinase inhibitors. The present study provides predicted drug sensitivity profiles for a large panel of uncommon EGFR mutations toward multiple inhibitors, which may help clinicians in deciding mutant-specific treatment strategies. Copyright © 2018 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

  1. ADME evaluation in drug discovery. 1. Applications of genetic algorithms to the prediction of blood-brain partitioning of a large set of drugs.

    Science.gov (United States)

    Hou, Tingjun; Xu, Xiaojie

    2002-12-01

    In this study, the relationships between the brain-blood concentration ratio of 96 structurally diverse compounds with a large number of structurally derived descriptors were investigated. The linear models were based on molecular descriptors that can be calculated for any compound simply from a knowledge of its molecular structure. The linear correlation coefficients of the models were optimized by genetic algorithms (GAs), and the descriptors used in the linear models were automatically selected from 27 structurally derived descriptors. The GA optimizations resulted in a group of linear models with three or four molecular descriptors with good statistical significance. The change of descriptor use as the evolution proceeds demonstrates that the octane/water partition coefficient and the partial negative solvent-accessible surface area multiplied by the negative charge are crucial to brain-blood barrier permeability. Moreover, we found that the predictions using multiple QSPR models from GA optimization gave quite good results in spite of the diversity of structures, which was better than the predictions using the best single model. The predictions for the two external sets with 37 diverse compounds using multiple QSPR models indicate that the best linear models with four descriptors are sufficiently effective for predictive use. Considering the ease of computation of the descriptors, the linear models may be used as general utilities to screen the blood-brain barrier partitioning of drugs in a high-throughput fashion.

  2. A strategy for early-risk predictions of clinical drug-drug interactions involving the GastroPlusTM DDI module for time-dependent CYP inhibitors.

    Science.gov (United States)

    Sohlenius-Sternbeck, Anna-Karin; Meyerson, Gabrielle; Hagbjörk, Ann-Louise; Juric, Sanja; Terelius, Ylva

    2018-04-01

    1. A set of reference compounds for time-dependent inhibition (TDI) of cytochrome P450 with available literature data for k inact and K I was used to predict clinical implications using the GastroPlus TM software. Comparisons were made to in vivo literature interaction data. 2. The predicted AUC ratios (AUC +inhibitor /AUC control ) could be compared with the observed ratios from literature for all compounds with detailed information about in vivo administration, pharmacokinetics and in vivo interactions (N = 21). For this dataset, the difference between predicted and observed AUC ratios for interactions with midazolam was within twofold for all compounds except one (telaprevir, for which non-CYP-mediated metabolism likely plays a role after multiple dosing). 3. The sensitivity, specificity and accuracy of the GastroPlus TM predictions using a binary classification as no-to-weak interaction versus moderate-to-strong interaction for all compounds with available in vivo interaction data, were 80%, 82% and 81%, respectively (N = 31). 4. As a result of our evaluations of the DDI module in GastroPlus TM , we have implemented an early TDI risk assessment decision tree for our drug discovery projects involving in vitro screening and early GastroPlus TM predictions. Shifted IC 50 values are determined and k inact /K I estimated (by using a regression line established with in house-shifted IC 50 values and literature k inact /K I ratios), followed by GastroPlus TM predictions.

  3. Role of laboratory biomarkers in monitoring and prediction of the effectiveness of treatment of rheumatic diseases using genetically engineered drugs

    Directory of Open Access Journals (Sweden)

    Elena Nikolayevna Aleksandrova

    2014-03-01

    Full Text Available Significant progress in treating immunoinflammatory rheumatic diseases (RD is related to the design of a novel family of drugs, genetically engineered (GE drugs. Molecular and cellular biomarkers (antibodies, indicators of acute inflammation, cytokines, chemokines, growth factors, endothelial activation markers, immunoglobulins, cryoglobulins, T- and B-cell subpopulations, products of bone and cartilage metabolism, genetic and metabolic markers that allow one to conduct immunological monitoring and prediction of the effectiveness of RD therapy using tumor necrosis factor α inhibitors (infliximab, adalimumab, golimumab, etanercept, anti-B-cell drugs (rituximab, belimumab, interleukin-6 receptor antagonist (tocilizumab, and T-cell costimulation blocker (abatacept have been detected in blood, synovial fluid, urine, and bioptates of the affected tissues. In addition to the conventional uniplex immunodiagnostics techniques, multiplex analysis of marker, which is based on genetic, transcriptomic and proteomic technologies using DNA and protein microarrays, polymerase chain reaction, and flow cytometry, is becoming increasingly widespread. The search for and validation of immunological predictors of the effective response to GE drug therapy make it possible to optimize and reduce the cost of therapy using these drugs in future.

  4. Role of laboratory biomarkers in monitoring and prediction of the effectiveness of treatment of rheumatic diseases using genetically engineered drugs

    Directory of Open Access Journals (Sweden)

    Elena Nikolayevna Aleksandrova

    2014-01-01

    Full Text Available Significant progress in treating immunoinflammatory rheumatic diseases (RD is related to the design of a novel family of drugs, genetically engineered (GE drugs. Molecular and cellular biomarkers (antibodies, indicators of acute inflammation, cytokines, chemokines, growth factors, endothelial activation markers, immunoglobulins, cryoglobulins, T- and B-cell subpopulations, products of bone and cartilage metabolism, genetic and metabolic markers that allow one to conduct immunological monitoring and prediction of the effectiveness of RD therapy using tumor necrosis factor α inhibitors (infliximab, adalimumab, golimumab, etanercept, anti-B-cell drugs (rituximab, belimumab, interleukin-6 receptor antagonist (tocilizumab, and T-cell costimulation blocker (abatacept have been detected in blood, synovial fluid, urine, and bioptates of the affected tissues. In addition to the conventional uniplex immunodiagnostics techniques, multiplex analysis of marker, which is based on genetic, transcriptomic and proteomic technologies using DNA and protein microarrays, polymerase chain reaction, and flow cytometry, is becoming increasingly widespread. The search for and validation of immunological predictors of the effective response to GE drug therapy make it possible to optimize and reduce the cost of therapy using these drugs in future.

  5. Chick embryo chorioallantoic membrane (CAM): an alternative predictive model in acute toxicological studies for anti-cancer drugs.

    Science.gov (United States)

    Kue, Chin Siang; Tan, Kae Yi; Lam, May Lynn; Lee, Hong Boon

    2015-01-01

    The chick embryo chorioallantoic membrane (CAM) is a preclinical model widely used for vascular and anti-vascular effects of therapeutic agents in vivo. In this study, we examine the suitability of CAM as a predictive model for acute toxicology studies of drugs by comparing it to conventional mouse and rat models for 10 FDA-approved anticancer drugs (paclitaxel, carmustine, camptothecin, cyclophosphamide, vincristine, cisplatin, aloin, mitomycin C, actinomycin-D, melphalan). Suitable formulations for intravenous administration were determined before the average of median lethal dose (LD50) and median survival dose (SD(50)) in the CAM were measured and calculated for these drugs. The resultant ideal LD(50) values were correlated to those reported in the literature using Pearson's correlation test for both intravenous and intraperitoneal routes of injection in rodents. Our results showed moderate correlations (r(2)=0.42 - 0.68, PLD(50) values obtained using the CAM model with LD(50) values from mice and rats models for both intravenous and intraperitoneal administrations, suggesting that the chick embryo may be a suitable alternative model for acute drug toxicity screening before embarking on full toxicological investigations in rodents in development of anticancer drugs.

  6. Novel Methods for Drug-Target Interaction Prediction using Graph Mining

    KAUST Repository

    Ba Alawi, Wail

    2016-01-01

    -target interactions (DTIs) before any experiments are done. However, many of these approaches suffer from unacceptable levels of false positives. We developed two novel methods based on graph mining networks of drugs and targets. The first method (DASPfind) finds all

  7. Prediction of the permeability of neutral drugs inferred from their solvation properties

    KAUST Repository

    Milanetti, Edoardo

    2015-12-10

    Motivation: Determination of drug absorption is an important component of the drug discovery and development process in that it plays a key role in the decision to promote drug candidates to clinical trials. We have developed a method that, on the basis of an analysis of the dynamic distribution of water molecules around a compound obtained by molecular dynamics simulations, can compute a parameter-free value that correlates very well with the compound permeability measured using the human colon adenocarcinoma (Caco-2) cell line assay. Results: The method has been tested on twenty-three neutral drugs for which a consistent set of experimental data is available. We show here that our method reproduces the experimental data better than other existing tools. Furthermore it provides a detailed view of the relationship between the hydration and the permeability properties of molecules.

  8. Drug use Discrimination Predicts Formation of High-Risk Social Networks: Examining Social Pathways of Discrimination.

    Science.gov (United States)

    Crawford, Natalie D; Ford, Chandra; Rudolph, Abby; Kim, BoRin; Lewis, Crystal M

    2017-09-01

    Experiences of discrimination, or social marginalization and ostracism, may lead to the formation of social networks characterized by inequality. For example, those who experience discrimination may be more likely to develop drug use and sexual partnerships with others who are at increased risk for HIV compared to those without experiences of discrimination. This is critical as engaging in risk behaviors with others who are more likely to be HIV positive can increase one's risk of HIV. We used log-binomial regression models to examine the relationship between drug use, racial and incarceration discrimination with changes in the composition of one's risk network among 502 persons who use drugs. We examined both absolute and proportional changes with respect to sex partners, drug use partners, and injecting partners, after accounting for individual risk behaviors. At baseline, participants were predominately male (70%), black or Latino (91%), un-married (85%), and used crack (64%). Among those followed-up (67%), having experienced discrimination due to drug use was significantly related to increases in the absolute number of sex networks and drug networks over time. No types of discrimination were related to changes in the proportion of high-risk network members. Discrimination may increase one's risk of HIV acquisition by leading them to preferentially form risk relationships with higher-risk individuals, thereby perpetuating racial and ethnic inequities in HIV. Future social network studies and behavioral interventions should consider whether social discrimination plays a role in HIV transmission.

  9. A prediction model-based algorithm for computer-assisted database screening of adverse drug reactions in the Netherlands.

    Science.gov (United States)

    Scholl, Joep H G; van Hunsel, Florence P A M; Hak, Eelko; van Puijenbroek, Eugène P

    2018-02-01

    The statistical screening of pharmacovigilance databases containing spontaneously reported adverse drug reactions (ADRs) is mainly based on disproportionality analysis. The aim of this study was to improve the efficiency of full database screening using a prediction model-based approach. A logistic regression-based prediction model containing 5 candidate predictors was developed and internally validated using the Summary of Product Characteristics as the gold standard for the outcome. All drug-ADR associations, with the exception of those related to vaccines, with a minimum of 3 reports formed the training data for the model. Performance was based on the area under the receiver operating characteristic curve (AUC). Results were compared with the current method of database screening based on the number of previously analyzed associations. A total of 25 026 unique drug-ADR associations formed the training data for the model. The final model contained all 5 candidate predictors (number of reports, disproportionality, reports from healthcare professionals, reports from marketing authorization holders, Naranjo score). The AUC for the full model was 0.740 (95% CI; 0.734-0.747). The internal validity was good based on the calibration curve and bootstrapping analysis (AUC after bootstrapping = 0.739). Compared with the old method, the AUC increased from 0.649 to 0.740, and the proportion of potential signals increased by approximately 50% (from 12.3% to 19.4%). A prediction model-based approach can be a useful tool to create priority-based listings for signal detection in databases consisting of spontaneous ADRs. © 2017 The Authors. Pharmacoepidemiology & Drug Safety Published by John Wiley & Sons Ltd.

  10. Whole genome transcript profiling of drug induced steatosis in rats reveals a gene signature predictive of outcome.

    Directory of Open Access Journals (Sweden)

    Nishika Sahini

    Full Text Available Drug induced steatosis (DIS is characterised by excess triglyceride accumulation in the form of lipid droplets (LD in liver cells. To explore mechanisms underlying DIS we interrogated the publically available microarray data from the Japanese Toxicogenomics Project (TGP to study comprehensively whole genome gene expression changes in the liver of treated rats. For this purpose a total of 17 and 12 drugs which are diverse in molecular structure and mode of action were considered based on their ability to cause either steatosis or phospholipidosis, respectively, while 7 drugs served as negative controls. In our efforts we focused on 200 genes which are considered to be mechanistically relevant in the process of lipid droplet biogenesis in hepatocytes as recently published (Sahini and Borlak, 2014. Based on mechanistic considerations we identified 19 genes which displayed dose dependent responses while 10 genes showed time dependency. Importantly, the present study defined 9 genes (ANGPTL4, FABP7, FADS1, FGF21, GOT1, LDLR, GK, STAT3, and PKLR as signature genes to predict DIS. Moreover, cross tabulation revealed 9 genes to be regulated ≥10 times amongst the various conditions and included genes linked to glucose metabolism, lipid transport and lipogenesis as well as signalling events. Additionally, a comparison between drugs causing phospholipidosis and/or steatosis revealed 26 genes to be regulated in common including 4 signature genes to predict DIS (PKLR, GK, FABP7 and FADS1. Furthermore, a comparison between in vivo single dose (3, 6, 9 and 24 h and findings from rat hepatocyte studies (2 h, 8 h, 24 h identified 10 genes which are regulated in common and contained 2 DIS signature genes (FABP7, FGF21. Altogether, our studies provide comprehensive information on mechanistically linked gene expression changes of a range of drugs causing steatosis and phospholipidosis and encourage the screening of DIS signature genes at the preclinical stage.

  11. Prediction of drug-packaging interactions via molecular dynamics (MD) simulations.

    Science.gov (United States)

    Feenstra, Peter; Brunsteiner, Michael; Khinast, Johannes

    2012-07-15

    The interaction between packaging materials and drug products is an important issue for the pharmaceutical industry, since during manufacturing, processing and storage a drug product is continuously exposed to various packaging materials. The experimental investigation of a great variety of different packaging material-drug product combinations in terms of efficacy and safety can be a costly and time-consuming task. In our work we used molecular dynamics (MD) simulations in order to evaluate the applicability of such methods to pre-screening of the packaging material-solute compatibility. The solvation free energy and the free energy of adsorption of diverse solute/solvent/solid systems were estimated. The results of our simulations agree with experimental values previously published in the literature, which indicates that the methods in question can be used to semi-quantitatively reproduce the solid-liquid interactions of the investigated systems. Copyright © 2012 Elsevier B.V. All rights reserved.

  12. Patient-rated health status predicts prognosis following percutaneous coronary intervention with drug-eluting stenting

    DEFF Research Database (Denmark)

    Pedersen, Susanne S.; Versteeg, Henneke; Denollet, Johan

    2011-01-01

    In patients treated with percutaneous coronary intervention (PCI) with the paclitaxel-eluting stent, we examined whether patient-rated health status predicts adverse clinical events.......In patients treated with percutaneous coronary intervention (PCI) with the paclitaxel-eluting stent, we examined whether patient-rated health status predicts adverse clinical events....

  13. Integration of in vitro biorelevant dissolution and in silico PBPK model of carvedilol to predict bioequivalence of oral drug products.

    Science.gov (United States)

    Ibarra, Manuel; Valiante, Cristian; Sopeña, Patricia; Schiavo, Alejandra; Lorier, Marianela; Vázquez, Marta; Fagiolino, Pietro

    2018-06-15

    Bioequivalence implementation in developing countries where a high proportion of similar drug products are being marketed has found several obstacles, impeding regulatory agencies to move forward with this policy. Biopharmaceutical quality of these products, several of which are massively prescribed, remains unknown. In this context, an in vitro-in silico-in vivo approach is proposed as a mean to screen product performance and target specific formulations for bioequivalence assessment. By coupling in vitro biorelevant dissolution testing in USP-4 Apparatus (flow-through cell) with physiologically-based pharmacokinetic (PBPK) modeling in PK-Sim® software (Bayer, Germany), the performance of seven similar products of carvedilol tablets containing 25 mg available in the Uruguayan market were compared with the brand-name drug Dilatrend®. In silico simulations for Dilatrend® were compared with published results of bioequivalence studies performed in fasting conditions allowing model development through a learning and confirming process. Single-dose pharmacokinetic profiles were then simulated for the brand-name drug and two similar drug products selected according to in vitro observations, in a virtual Caucasian population of 1000 subjects (50% male, aged between 18 and 50 years with standard body-weights). Population bioequivalence ratios were estimated revealing that in vitro differences in drug release would have a major impact in carvedilol maximum plasma concentration, leading to a non-bioequivalence outcome. Predictions support the need to perform in vivo bioequivalence for these products of extensive use. Application of the in vitro-in silico-in vivo approach stands as an interesting alternative to tackle and reduce drug product variability in biopharmaceutical quality. Copyright © 2018 Elsevier B.V. All rights reserved.

  14. Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods.

    Science.gov (United States)

    Li, Mengshan; Zhang, Huaijing; Chen, Bingsheng; Wu, Yan; Guan, Lixin

    2018-03-05

    The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.

  15. Prediction of the permeability of neutral drugs inferred from their solvation properties

    KAUST Repository

    Milanetti, Edoardo; Raimondo, Domenico; Tramontano, Anna

    2015-01-01

    Results: The method has been tested on twenty-three neutral drugs for which a consistent set of experimental data is available. We show here that our method reproduces the experimental data better than other existing tools. Furthermore it provides a detailed view of the relationship between the hydration and the permeability properties of molecules.

  16. Predicting the usefulness of therapeutic drug monitoring of mycophenolic acid: a computer simulation

    NARCIS (Netherlands)

    van Hest, Reinier; Mathot, Ron; Vulto, Arnold; Weimar, Willem; van Gelder, Teun

    2005-01-01

    The usefulness of therapeutic drug monitoring (TDM) of mycophenolate mofetil (MMF) was investigated with a computer simulation model. For a fixed-dose (FD) and a concentration-controlled (CC) MMF dosing regimen exposure to mycophenolic acid (MPA) was compared. A nonlinear mixed-effects model

  17. Application of PK/PD Modeling in Veterinary Field: Dose Optimization and Drug Resistance Prediction

    Directory of Open Access Journals (Sweden)

    Ijaz Ahmad

    2016-01-01

    Full Text Available Among veterinary drugs, antibiotics are frequently used. The true mean of antibiotic treatment is to administer dose of drug that will have enough high possibility of attaining the preferred curative effect, with adequately low chance of concentration associated toxicity. Rising of antibacterial resistance and lack of novel antibiotic is a global crisis; therefore there is an urgent need to overcome this problem. Inappropriate antibiotic selection, group treatment, and suboptimal dosing are mostly responsible for the mentioned problem. One approach to minimizing the antibacterial resistance is to optimize the dosage regimen. PK/PD model is important realm to be used for that purpose from several years. PK/PD model describes the relationship between drug potency, microorganism exposed to drug, and the effect observed. Proper use of the most modern PK/PD modeling approaches in veterinary medicine can optimize the dosage for patient, which in turn reduce toxicity and reduce the emergence of resistance. The aim of this review is to look at the existing state and application of PK/PD in veterinary medicine based on in vitro, in vivo, healthy, and disease model.

  18. Characteristics Predicting Dyslipidemia in Drug-naïve Type 2 Diabetes Patients

    Directory of Open Access Journals (Sweden)

    Shi-Dou Lin

    2006-09-01

    Conclusion: We recommend a more intensive monitoring of lipid levels in drug-naïve diabetic patients who possess the characteristics of alcohol consumption or older age (men, long duration of diabetes (women, and higher BMI or WHR (both genders.

  19. A Physiologically Based Pharmacokinetic Model to Predict the Pharmacokinetics of Highly Protein-Bound Drugs and Impact of Errors in Plasma Protein Binding

    Science.gov (United States)

    Ye, Min; Nagar, Swati; Korzekwa, Ken

    2015-01-01

    Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data was often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding, and blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for terminal elimination half-life (t1/2, 100% of drugs), peak plasma concentration (Cmax, 100%), area under the plasma concentration-time curve (AUC0–t, 95.4%), clearance (CLh, 95.4%), mean retention time (MRT, 95.4%), and steady state volume (Vss, 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. PMID:26531057

  20. A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding.

    Science.gov (United States)

    Ye, Min; Nagar, Swati; Korzekwa, Ken

    2016-04-01

    Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for the terminal elimination half-life (t1/2 , 100% of drugs), peak plasma concentration (Cmax , 100%), area under the plasma concentration-time curve (AUC0-t , 95.4%), clearance (CLh , 95.4%), mean residence time (MRT, 95.4%) and steady state volume (Vss , 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  1. Predicting targeted drug combinations based on Pareto optimal patterns of coexpression network connectivity.

    Science.gov (United States)

    Penrod, Nadia M; Greene, Casey S; Moore, Jason H

    2014-01-01

    Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets. We have developed a framework to mine high-throughput transcriptomic data, based on differential coexpression and Pareto optimization, to investigate drug-induced tumor adaptation. We use this approach to identify tumor-essential genes as druggable candidates. We apply our method to a set of ER(+) breast tumor samples, collected before (n = 58) and after (n = 60) neoadjuvant treatment with the aromatase inhibitor letrozole, to prioritize genes as targets for combination therapy with letrozole treatment. We validate letrozole-induced tumor adaptation through coexpression and pathway analyses in an independent data set (n = 18). We find pervasive differential coexpression between the untreated and letrozole-treated tumor samples as evidence of letrozole-induced tumor adaptation. Based on patterns of coexpression, we identify ten genes as potential candidates for combination therapy with letrozole including EPCAM, a letrozole-induced essential gene and a target to which drugs have already been developed as cancer therapeutics. Through replication, we validate six letrozole-induced coexpression relationships and confirm the epithelial-to-mesenchymal transition as a process that is upregulated in the residual tumor samples following letrozole treatment. To derive the greatest benefit from molecularly targeted drugs it is critical to design combination

  2. Comparative Analysis and Predictors of 10-year Tumor Necrosis Factor Inhibitors Drug Survival in Patients with Spondyloarthritis: First-year Response Predicts Longterm Drug Persistence.

    Science.gov (United States)

    Flouri, Irini D; Markatseli, Theodora E; Boki, Kyriaki A; Papadopoulos, Ioannis; Skopouli, Fotini N; Voulgari, Paraskevi V; Settas, Loukas; Zisopoulos, Dimitrios; Iliopoulos, Alexios; Geborek, Pierre; Drosos, Alexandros A; Boumpas, Dimitrios T; Sidiropoulos, Prodromos

    2018-04-01

    To evaluate the 10-year drug survival of the first tumor necrosis factor inhibitor (TNFi) administered to patients with spondyloarthritis (SpA) overall and comparatively between SpA subsets, and to identify predictors of drug retention. Patients with SpA in the Hellenic Registry of Biologic Therapies, a prospective multicenter observational cohort, starting their first TNFi between 2004-2014 were analyzed. Kaplan-Meier curves and Cox regression models were used. Overall, 404 out of 1077 patients (37.5%) discontinued treatment (followup: 4288 patient-yrs). Ten-year drug survival was 49%. In the unadjusted analyses, higher TNFi survival was observed in patients with ankylosing spondylitis (AS) compared to undifferentiated SpA and psoriatic arthritis [PsA; significant beyond the first 2.5 (p = 0.003) years and 7 years (p < 0.001), respectively], and in patients treated for isolated axial versus peripheral arthritis (p = 0.001). In all multivariable analyses, male sex was a predictor for longer TNFi survival. Use of methotrexate (MTX) was a predictor in PsA and in patients with peripheral arthritis. Absence of peripheral arthritis and use of a monoclonal antibody (as opposed to non-antibody TNFi) independently predicted longer TNFi survival in axial disease because of lower rates of inefficacy. Achievement of major responses during the first year in either axial or peripheral arthritis was the strongest predictor of longer therapy retention (HR 0.33, 95% CI 0.26-0.41 for Ankylosing Spondylitis Disease Activity Score inactive disease, and HR 0.35, 95% CI 0.24-0.50 for 28-joint Disease Activity Score remission). The longterm retention of the first TNFi administered to patients with SpA is high, especially for males with axial disease. The strongest predictor of longterm TNFi survival is a major response within the first year of treatment.

  3. In silico prediction of drug dissolution and absorption with variation in intestinal pH for BCS class II weak acid drugs: ibuprofen and ketoprofen.

    Science.gov (United States)

    Tsume, Yasuhiro; Langguth, Peter; Garcia-Arieta, Alfredo; Amidon, Gordon L

    2012-10-01

    The FDA Biopharmaceutical Classification System guidance allows waivers for in vivo bioavailability and bioequivalence studies for immediate-release solid oral dosage forms only for BCS class I. Extensions of the in vivo biowaiver for a number of drugs in BCS class III and BCS class II have been proposed, in particular, BCS class II weak acids. However, a discrepancy between the in vivo BE results and in vitro dissolution results for BCS class II acids was recently observed. The objectives of this study were to determine the oral absorption of BCS class II weak acids via simulation software and to determine if the in vitro dissolution test with various dissolution media could be sufficient for in vitro bioequivalence studies of ibuprofen and ketoprofen as models of carboxylic acid drugs. The oral absorption of these BCS class II acids from the gastrointestinal tract was predicted by GastroPlus™. Ibuprofen did not satisfy the bioequivalence criteria at lower settings of intestinal pH of 6.0. Further the experimental dissolution of ibuprofen tablets in a low concentration phosphate buffer at pH 6.0 (the average buffer capacity 2.2 mmol l (-1) /pH) was dramatically reduced compared with the dissolution in SIF (the average buffer capacity 12.6 mmol l (-1) /pH). Thus these predictions for the oral absorption of BCS class II acids indicate that the absorption patterns depend largely on the intestinal pH and buffer strength and must be considered carefully for a bioequivalence test. Simulation software may be a very useful tool to aid the selection of dissolution media that may be useful in setting an in vitro bioequivalence dissolution standard. Copyright © 2012 John Wiley & Sons, Ltd.

  4. In Silico Prediction of Drug Dissolution and Absorption with variation in Intestinal pH for BCS Class II Weak Acid Drugs: Ibuprofen and Ketoprofen§

    Science.gov (United States)

    Tsume, Yasuhiro; Langguth, Peter; Garcia-Arieta, Alfredo; Amidon, Gordon L.

    2012-01-01

    The FDA Biopharmaceutical Classification System guidance allows waivers for in vivo bioavailability and bioequivalence studies for immediate-release solid oral dosage forms only for BCS class I. Extensions of the in vivo biowaiver for a number of drugs in BCS Class III and BCS class II have been proposed, particularly, BCS class II weak acids. However, a discrepancy between the in vivo- BE results and in vitro- dissolution results for a BCS class II acids was recently observed. The objectives of this study were to determine the oral absorption of BCS class II weak acids via simulation software and to determine if the in vitro dissolution test with various dissolution media could be sufficient for in vitro bioequivalence studies of ibuprofen and ketoprofen as models of carboxylic acid drugs. The oral absorption of these BCS class II acids from the gastrointestinal tract was predicted by GastroPlus™. Ibuprofen did not satisfy the bioequivalence criteria at lower settings of intestinal pH=6.0. Further the experimental dissolution of ibuprofen tablets in the low concentration phosphate buffer at pH 6.0 (the average buffer capacity 2.2 mmol L-1/pH) was dramatically reduced compared to the dissolution in SIF (the average buffer capacity 12.6 mmol L -1/pH). Thus these predictions for oral absorption of BCS class II acids indicate that the absorption patterns largely depend on the intestinal pH and buffer strength and must be carefully considered for a bioequivalence test. Simulation software may be very useful tool to aid the selection of dissolution media that may be useful in setting an in vitro bioequivalence dissolution standard. PMID:22815122

  5. Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach.

    Science.gov (United States)

    Lin, Frank P Y; Pokorny, Adrian; Teng, Christina; Dear, Rachel; Epstein, Richard J

    2016-12-01

    Multidisciplinary team (MDT) meetings are used to optimise expert decision-making about treatment options, but such expertise is not digitally transferable between centres. To help standardise medical decision-making, we developed a machine learning model designed to predict MDT decisions about adjuvant breast cancer treatments. We analysed MDT decisions regarding adjuvant systemic therapy for 1065 breast cancer cases over eight years. Machine learning classifiers with and without bootstrap aggregation were correlated with MDT decisions (recommended, not recommended, or discussable) regarding adjuvant cytotoxic, endocrine and biologic/targeted therapies, then tested for predictability using stratified ten-fold cross-validations. The predictions so derived were duly compared with those based on published (ESMO and NCCN) cancer guidelines. Machine learning more accurately predicted adjuvant chemotherapy MDT decisions than did simple application of guidelines. No differences were found between MDT- vs. ESMO/NCCN- based decisions to prescribe either adjuvant endocrine (97%, p = 0.44/0.74) or biologic/targeted therapies (98%, p = 0.82/0.59). In contrast, significant discrepancies were evident between MDT- and guideline-based decisions to prescribe chemotherapy (87%, p machine learning models. A machine learning approach based on clinicopathologic characteristics can predict MDT decisions about adjuvant breast cancer drug therapies. The discrepancy between MDT- and guideline-based decisions regarding adjuvant chemotherapy implies that certain non-clincopathologic criteria, such as patient preference and resource availability, are factored into clinical decision-making by local experts but not captured by guidelines.

  6. The eTOX Data-Sharing Project to Advance in Silico Drug-Induced Toxicity Prediction

    Directory of Open Access Journals (Sweden)

    Montserrat Cases

    2014-11-01

    Full Text Available The high-quality in vivo preclinical safety data produced by the pharmaceutical industry during drug development, which follows numerous strict guidelines, are mostly not available in the public domain. These safety data are sometimes published as a condensed summary for the few compounds that reach the market, but the majority of studies are never made public and are often difficult to access in an automated way, even sometimes within the owning company itself. It is evident from many academic and industrial examples, that useful data mining and model development requires large and representative data sets and careful curation of the collected data. In 2010, under the auspices of the Innovative Medicines Initiative, the eTOX project started with the objective of extracting and sharing preclinical study data from paper or pdf archives of toxicology departments of the 13 participating pharmaceutical companies and using such data for establishing a detailed, well-curated database, which could then serve as source for read-across approaches (early assessment of the potential toxicity of a drug candidate by comparison of similar structure and/or effects and training of predictive models. The paper describes the efforts undertaken to allow effective data sharing intellectual property (IP protection and set up of adequate controlled vocabularies and to establish the database (currently with over 4000 studies contributed by the pharma companies corresponding to more than 1400 compounds. In addition, the status of predictive models building and some specific features of the eTOX predictive system (eTOXsys are presented as decision support knowledge-based tools for drug development process at an early stage.

  7. Predictive performance of three practical approaches for grapefruit juice-induced 2-fold or greater increases in AUC of concomitantly administered drugs.

    Science.gov (United States)

    Takahashi, M; Onozawa, S; Ogawa, R; Uesawa, Y; Echizen, H

    2015-02-01

    Clinical pharmacists have a challenging task when answering patients' question about whether they can take specific drugs with grapefruit juice (GFJ) without risk of drug interaction. To identify the most practicable method for predicting clinically relevant changes in plasma concentrations of orally administered drugs caused by the ingestion of GFJ, we compared the predictive performance of three methods using data obtained from the literature. We undertook a systematic search of drug interactions associated with GFJ using MEDLINE and the Metabolism & Transport Drug Interaction Database (DIDB version 4.0). We considered an elevation of the area under the plasma concentration-time curve (AUC) of 2 or greater relative to the control value [AUC ratio (AUCR) ≥ 2.0] as a clinically significant interaction. The data from 74 drugs (194 data sets) were analysed. When the reported information of CYP3A involvement in the metabolism of a drug of interest was adopted as a predictive criterion for GFJ-drug interaction, the performance assessed by positive predictive value (PPV) was low (0.26), but that assessed by negative predictive value (NPV) and sensitivity was high (1.00 for both). When the reported oral bioavailability of ≤ 0.1 was used as a criterion, the PPV improved to 0.50 with an acceptable NPV of 0.81, but sensitivity was reduced to 0.21. When the reported AUCR was ≥ 10 after co-administration of a typical CYP3A inhibitor, the corresponding values were 0.64, 0.79 and 0.19, respectively. We consider that an oral bioavailability of ≤ 0.1 or an AUCR of ≥ 10 caused by a CYP3A inhibitor of a drug of interest may be a practical prediction criterion for avoiding significant interactions with GFJ. Information about the involvement of CYP3A in their metabolism should also be taken into account for drugs with narrow therapeutic ranges. © 2014 John Wiley & Sons Ltd.

  8. Improving the prediction of in-sewer transformation of illicit drug biomarkers by identifying a new modelling framework

    DEFF Research Database (Denmark)

    Ramin, Pedram; Brock, Andreas Libonati; Polesel, Fabio

    -3-β-D-glucuronide; codeine and its metabolite norcodeine; methadone and its metabolite 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP); mephedrone; and tetrahydrocannabinol (THC) and its metabolites 11-hydroxy-Δ9-THC (THCOH), and 11-nor-9-carboxy-Δ9-THC (THCCOOH). All the transformation....... Furthermore, abiotic transformation was found to be the main transformation mechanism for THC (aerobic conditions); mephedrone, methadone, cocaine, ecgonine methyl ester, cocaethylene, THCOH and THCCOOH (anaerobic conditions). By use of the proposed model the uncertainty of predicting illicit drug...

  9. Does brain slices from pentylenetetrazole-kindled mice provide a more predictive screening model for antiepileptic drugs?

    DEFF Research Database (Denmark)

    Hansen, Suzanne L.; Sterjev, Zoran; Werngreen, Marie

    2012-01-01

    The cortical wedge is a commonly applied model for in vitro screening of new antiepileptic drugs (AEDs) and has been extensively used in characterization of well-known AEDs. However, the predictive validity of this model as a screening model has been questioned as, e.g., carbamazepine has been...... screening model for AEDs. To this end, we compared the in vitro and in vivo pharmacological profile of several selected AEDs (phenobarbital, phenytoin, tiagabine, fosphenytoin, valproate, and carbamazepine) along with citalopram using the PTZ-kindled model and brain slices from naïve, saline...

  10. Evaluation of microwave oven heating for prediction of drug-excipient compatibilities and accelerated stability studies

    DEFF Research Database (Denmark)

    Schou-Pedersen, Anne Marie V; Østergaard, Jesper; Cornett, Claus

    2015-01-01

    , if a microwave oven is applicable for accelerated drug stability testing. Chemical interactions were investigated in three selected model formulations of drug and excipients regarding the formation of ester and amide reaction products. The accelerated stability studies performed in the microwave oven using...... a design of experiments (DoE) approach in order to be able to rank excipients regarding reactivity: Study A: cetirizine with PEG 400, sorbitol, glycerol and propylene glycol. Study B: 6-aminocaproic acid with citrate, acetate, tartrate and gluconate. Study C: atenolol with citric, tartaric, malic, glutaric......, and sorbic acid. The model formulations were representative for oral solutions (co-solvents), parenteral solutions (buffer species) and solid dosage forms (organic acids applicable for solubility enhancement). The DoE studies showed overall that the same impurities were generated by microwave oven heating...

  11. Evaluation of microwave oven heating for prediction of drug-excipient compatibilities and accelerated stability studies.

    Science.gov (United States)

    Schou-Pedersen, Anne Marie V; Østergaard, Jesper; Cornett, Claus; Hansen, Steen Honoré

    2015-05-15

    Microwave ovens have been used extensively in organic synthesis in order to accelerate reaction rates. Here, a set up comprising a microwave oven combined with silicon carbide (SiC) plates for the controlled microwave heating of model formulations has been applied in order to investigate, if a microwave oven is applicable for accelerated drug stability testing. Chemical interactions were investigated in three selected model formulations of drug and excipients regarding the formation of ester and amide reaction products. In the accelerated stability studies, a design of experiments (DoE) approach was applied in order to be able to rank excipients regarding reactivity: Study A: cetirizine with PEG 400, sorbitol, glycerol and propylene glycol. Study B: 6-aminocaproic acid with citrate, acetate, tartrate and gluconate. Study C: atenolol with citric, tartaric, malic, glutaric, and sorbic acid. The model formulations were representative for oral solutions (co-solvents), parenteral solutions (buffer species) and solid dosage forms (organic acids applicable for solubility enhancement). The DoE studies showed overall that the same impurities were generated by microwave oven heating leading to temperatures between 150°C and 180°C as compared to accelerated stability studies performed at 40°C and 80°C using a conventional oven. Ranking of the reactivity of the excipients could be made in the DoE studies performed at 150-180°C, which was representative for the ranking obtained after storage at 40°C and 80°C. It was possible to reduce the time needed for drug-excipient compatibility testing of the three model formulations from weeks to less than an hour in the three case studies. The microwave oven is therefore considered to be an interesting alternative to conventional thermal techniques for the investigation of drug-excipient interactions during preformulation. Copyright © 2015 Elsevier B.V. All rights reserved.

  12. Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.

    Science.gov (United States)

    Lampa, Samuel; Alvarsson, Jonathan; Spjuth, Ola

    2016-01-01

    Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.Graphical abstract.

  13. Evaluation of limited sampling models for prediction of oral midazolam AUC for CYP3A phenotyping and drug interaction studies.

    Science.gov (United States)

    Mueller, Silke C; Drewelow, Bernd

    2013-05-01

    The area under the concentration-time curve (AUC) after oral midazolam administration is commonly used for cytochrome P450 (CYP) 3A phenotyping studies. The aim of this investigation was to evaluate a limited sampling strategy for the prediction of AUC with oral midazolam. A total of 288 concentration-time profiles from 123 healthy volunteers who participated in four previously performed drug interaction studies with intense sampling after a single oral dose of 7.5 mg midazolam were available for evaluation. Of these, 45 profiles served for model building, which was performed by stepwise multiple linear regression, and the remaining 243 datasets served for validation. Mean prediction error (MPE), mean absolute error (MAE) and root mean squared error (RMSE) were calculated to determine bias and precision The one- to four-sampling point models with the best coefficient of correlation were the one-sampling point model (8 h; r (2) = 0.84), the two-sampling point model (0.5 and 8 h; r (2) = 0.93), the three-sampling point model (0.5, 2, and 8 h; r (2) = 0.96), and the four-sampling point model (0.5,1, 2, and 8 h; r (2) = 0.97). However, the one- and two-sampling point models were unable to predict the midazolam AUC due to unacceptable bias and precision. Only the four-sampling point model predicted the very low and very high midazolam AUC of the validation dataset with acceptable precision and bias. The four-sampling point model was also able to predict the geometric mean ratio of the treatment phase over the baseline (with 90 % confidence interval) results of three drug interaction studies in the categories of strong, moderate, and mild induction, as well as no interaction. A four-sampling point limited sampling strategy to predict the oral midazolam AUC for CYP3A phenotyping is proposed. The one-, two- and three-sampling point models were not able to predict midazolam AUC accurately.

  14. New active drugs against liver stages of Plasmodium predicted by molecular topology.

    NARCIS (Netherlands)

    Mahmoudi, N.; Garcia-Domenech, R.; Galvez, J.; Farhati, K.; Franetich, J.F.; Sauerwein, R.W.; Hannoun, L.; Derouin, F.; Danis, M.; Mazier, D.

    2008-01-01

    We conducted a quantitative structure-activity relationship (QSAR) study based on a database of 127 compounds previously tested against the liver stage of Plasmodium yoelii in order to develop a model capable of predicting the in vitro antimalarial activities of new compounds. Topological indices

  15. Predictive Value of Gene Polymorphisms on Recurrence after the Withdrawal of Antithyroid Drugs in Patients with Graves’ Disease

    Directory of Open Access Journals (Sweden)

    Jia Liu

    2017-09-01

    Full Text Available Graves’ disease (GD is one of the most common endocrine diseases. Antithyroid drugs (ATDs treatment is frequently used as the first-choice therapy for GD patients in most countries due to the superiority in safety and tolerance. However, GD patients treated with ATD have a relatively high recurrence rate after drug withdrawal, which is a main limitation for ATD treatment. It is of great importance to identify some predictors of the higher recurrence risk for GD patients, which may facilitate an appropriate therapeutic approach for a given patient at the time of GD diagnosis. The genetic factor was widely believed to be an important pathogenesis for GD. Increasing studies were conducted to investigate the relationship between gene polymorphisms and the recurrence risk in GD patients. In this article, we updated the current literatures to highlight the predictive value of gene polymorphisms on recurrence risk in GD patients after ATD withdrawal. Some gene polymorphisms, such as CTLA4 rs231775, human leukocyte antigen polymorphisms (DRB1*03, DQA1*05, and DQB1*02 might be associated with the high recurrence risk in GD patients. Further prospective studies on patients of different ethnicities, especially studies with large sample sizes, and long-term follow-up, should be conducted to confirm the predictive roles of gene polymorphism.

  16. Predicting Inpatient Detoxification Outcome of Alcohol and Drug Dependent Patients: The Influence of Sociodemographic Environment, Motivation, Impulsivity, and Medical Comorbidities

    Directory of Open Access Journals (Sweden)

    Yvonne Sofin

    2017-01-01

    Full Text Available Aims. This prospective study aims to identify patient characteristics as predictors for treatment outcome during inpatient detoxification treatment for drug and alcohol dependent patients. Methods. A mixed gender sample of 832 consecutively admitted drug and alcohol dependent patients were interviewed by an experienced physician. The impact of a variety of factors concerning social environment, therapy motivation, impulsivity related variables, medical history, and addiction severity on treatment outcome was examined. Results. 525 (63.1% of the patients completed detoxification treatment whereas 307 (36.9% dropped out prematurely. Being female, living in a partnership, having children, being employed, and having good education were predictive for a positive outcome. Family, health, the fear of losing the job, prosecution, and emergency admission were significant motivational predictors for treatment outcome. Being younger, history of imprisonment, and the number of previous drop-outs were predictive for a negative outcome. Conclusions. Variables concerning social environment and the number of previous drop-outs have been identified as best predictors for treatment outcome. Socially stable patients benefit from the current treatment setting and treatment shall be adapted for patients with negative predictors. Treatment may consequently be tailored with respect to intervention type, duration, and intensity to improve the outcome for those patients that fulfil criteria with negative impact on treatment retention.

  17. Chimeric mice transplanted with human hepatocytes as a model for prediction of human drug metabolism and pharmacokinetics.

    Science.gov (United States)

    Sanoh, Seigo; Ohta, Shigeru

    2014-03-01

    Preclinical studies in animal models are used routinely during drug development, but species differences of pharmacokinetics (PK) between animals and humans have to be taken into account in interpreting the results. Human hepatocytes are also widely used to examine metabolic activities mediated by cytochrome P450 (P450) and other enzymes, but such in vitro metabolic studies also have limitations. Recently, chimeric mice with humanized liver (h-chimeric mice), generated by transplantation of human donor hepatocytes, have been developed as a model for the prediction of metabolism and PK in humans, using both in vitro and in vivo approaches. The expression of human-specific metabolic enzymes and metabolic activities was confirmed in humanized liver of h-chimeric mice with high replacement ratios, and several reports indicate that the profiles of P450 and non-P450 metabolism in these mice adequately reflect those in humans. Further, the combined use of h-chimeric mice and r-chimeric mice, in which endogenous hepatocytes are replaced with rat hepatocytes, is a promising approach for evaluation of species differences in drug metabolism. Recent work has shown that data obtained in h-chimeric mice enable the semi-quantitative prediction of not only metabolites, but also PK parameters, such as hepatic clearance, of drug candidates in humans, although some limitations remain because of differences in the metabolic activities, hepatic blood flow and liver structure between humans and mice. In addition, fresh h-hepatocytes can be isolated reproducibly from h-chimeric mice for metabolic studies. Copyright © 2013 John Wiley & Sons, Ltd.

  18. Predicting changes in drug use and treatment entry for local programs: a case study.

    Science.gov (United States)

    Flaherty, E W; Olsen, K; Bencivengo, M

    1980-01-01

    Recent sharp decline in treatment admissions by opiate abusers stimulated the conduct of a study designed to provide timely data to treatment system administrators for the next cycle of program and budgetary planning. The process of designing the study involved definition of required study characteristics, review of four categories of drug abuse research, and generation of seven locally relevant hypotheses. Interviews were conducted with 335 heroin adicts: 196 new admissions to treatment and 139 "street" addicts not currently in treatment. Major findings were a marked reduction in the quality, availability, and price of heroin; very negative perceptions of methadone maintenance, especially by female respondents; decline in heroin popularity and increase in reported use of alcohol, amphetamines, and barbiturates; and differing perceptions of treatment by sex of respondent. Response patterns suggest that users who are not entering treatment are less "strung-out than those entering treatment because of decline in availability and quality of heroin and consequent increased mixing of drugs. The emphasis in the report is on the conduct of a study which can be timely, feasible, and useful to local planners. The study weaknesses and recommended remedies are discussed, as well as the characteristics which made the findings immediately useful to administrators and planners.

  19. Using PEGylated magnetic nanoparticles to describe the EPR effect in tumor for predicting therapeutic efficacy of micelle drugs.

    Science.gov (United States)

    Chen, Ling; Zang, Fengchao; Wu, Haoan; Li, Jianzhong; Xie, Jun; Ma, Ming; Gu, Ning; Zhang, Yu

    2018-01-25

    Micelle drugs based on a polymeric platform offer great advantages over liposomal drugs for tumor treatment. Although nearly all of the nanomedicines approved in the clinical use can passively target to the tumor tissues on the basis of an enhanced permeability and retention (EPR) effect, the nanodrugs have shown heterogenous responses in the patients. This phenomenon may be traced back to the EPR effect of tumor, which is extremely variable in the individuals from extensive studies. Nevertheless, there is a lack of experimental data describing the EPR effect and predicting its impact on therapeutic efficacy of nanoagents. Herein, we developed 32 nm magnetic iron oxide nanoparticles (MION) as a T 2 -weighted contrast agent to describe the EPR effect of each tumor by in vivo magnetic resonance imaging (MRI). The MION were synthesized by a thermal decomposition method and modified with DSPE-PEG2000 for biological applications. The PEGylated MION (Fe 3 O 4 @PEG) exhibited high r 2 of 571 mM -1 s -1 and saturation magnetization (M s ) of 94 emu g -1 Fe as well as long stability and favorable biocompatibility through the in vitro studies. The enhancement intensities of the tumor tissue from the MR images were quantitatively measured as TNR (Tumor/Normal tissue signal Ratio) values, which were correlated with the delay of tumor growth after intravenous administration of the PLA-PEG/PTX micelle drug. The results demonstrated that the group with the smallest TNR values (TNR EPR effect in patients for accurate medication guidance of micelle drugs in the future treatment of tumors.

  20. Hepatobiliary Clearance Prediction: Species Scaling From Monkey, Dog, and Rat, and In Vitro-In Vivo Extrapolation of Sandwich-Cultured Human Hepatocytes Using 17 Drugs.

    Science.gov (United States)

    Kimoto, Emi; Bi, Yi-An; Kosa, Rachel E; Tremaine, Larry M; Varma, Manthena V S

    2017-09-01

    Hepatobiliary elimination can be a major clearance pathway dictating the pharmacokinetics of drugs. Here, we first compared the dose eliminated in bile in preclinical species (monkey, dog, and rat) with that in human and further evaluated single-species scaling (SSS) to predict human hepatobiliary clearance. Six compounds dosed in bile duct-cannulated (BDC) monkeys showed biliary excretion comparable to human; and the SSS of hepatobiliary clearance with plasma fraction unbound correction yielded reasonable predictions (within 3-fold). Although dog SSS also showed reasonable predictions, rat overpredicted hepatobiliary clearance for 13 of 24 compounds. Second, we evaluated the translatability of in vitro sandwich-cultured human hepatocytes (SCHHs) to predict human hepatobiliary clearance for 17 drugs. For drugs with no significant active uptake in SCHH studies (i.e., with or without rifamycin SV), measured intrinsic biliary clearance was directly scalable with good predictability (absolute average fold error [AAFE] = 1.6). Drugs showing significant active uptake in SCHH, however, showed improved predictability when scaled based on extended clearance term (AAFE = 2.0), which incorporated sinusoidal uptake along with a global scaling factor for active uptake and the canalicular efflux clearance. In conclusion, SCHH is a useful tool to predict human hepatobiliary clearance, whereas BDC monkey model may provide further confidence in the prospective predictions. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  1. In vitro dissolution methodology, mini-Gastrointestinal Simulator (mGIS), predicts better in vivo dissolution of a weak base drug, dasatinib.

    Science.gov (United States)

    Tsume, Yasuhiro; Takeuchi, Susumu; Matsui, Kazuki; Amidon, Gregory E; Amidon, Gordon L

    2015-08-30

    USP apparatus I and II are gold standard methodologies for determining the in vitro dissolution profiles of test drugs. However, it is difficult to use in vitro dissolution results to predict in vivo dissolution, particularly the pH-dependent solubility of weak acid and base drugs, because the USP apparatus contains one vessel with a fixed pH for the test drug, limiting insight into in vivo drug dissolution of weak acid and weak base drugs. This discrepancy underscores the need to develop new in vitro dissolution methodology that better predicts in vivo response to assure the therapeutic efficacy and safety of oral drug products. Thus, the development of the in vivo predictive dissolution (IPD) methodology is necessitated. The major goals of in vitro dissolution are to ensure the performance of oral drug products and the support of drug formulation design, including bioequivalence (BE). Orally administered anticancer drugs, such as dasatinib and erlotinib (tyrosine kinase inhibitors), are used to treat various types of cancer. These drugs are weak bases that exhibit pH-dependent and high solubility in the acidic stomach and low solubility in the small intestine (>pH 6.0). Therefore, these drugs supersaturate and/or precipitate when they move from the stomach to the small intestine. Also of importance, gastric acidity for cancer patients may be altered with aging (reduction of gastric fluid secretion) and/or co-administration of acid-reducing agents. These may result in changes to the dissolution profiles of weak base and the reduction of drug absorption and efficacy. In vitro dissolution methodologies that assess the impact of these physiological changes in the GI condition are expected to better predict in vivo dissolution of oral medications for patients and, hence, better assess efficacy, toxicity and safety concerns. The objective of this present study is to determine the initial conditions for a mini-Gastrointestinal Simulator (mGIS) to assess in vivo

  2. Predicting the activity of drugs for a group of imidazopyridine anticoccidial compounds.

    Science.gov (United States)

    Si, Hongzong; Lian, Ning; Yuan, Shuping; Fu, Aiping; Duan, Yun-Bo; Zhang, Kejun; Yao, Xiaojun

    2009-10-01

    Gene expression programming (GEP) is a novel machine learning technique. The GEP is used to build nonlinear quantitative structure-activity relationship model for the prediction of the IC(50) for the imidazopyridine anticoccidial compounds. This model is based on descriptors which are calculated from the molecular structure. Four descriptors are selected from the descriptors' pool by heuristic method (HM) to build multivariable linear model. The GEP method produced a nonlinear quantitative model with a correlation coefficient and a mean error of 0.96 and 0.24 for the training set, 0.91 and 0.52 for the test set, respectively. It is shown that the GEP predicted results are in good agreement with experimental ones.

  3. The cytochrome P450 isoenzyme and some new opportunities for the prediction of negative drug interaction in vivo

    Directory of Open Access Journals (Sweden)

    Sychev DA

    2018-05-01

    Full Text Available Dmitrij A Sychev,1 Ghulam Md Ashraf,2 Andrey A Svistunov,3 Maksim L Maksimov,4 Vadim V Tarasov,3 Vladimir N Chubarev,3 Vitalij A Otdelenov,1 Natal’ja P Denisenko,1 George E Barreto,5,6 Gjumrakch Aliev7–9 1Russian Medical Academy of Postgraduate Education Studies, Moscow, Russia; 2King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia; 3Sechenov First Moscow State Medical University, Moscow, Russia; 4Branch Campus of the Federal State Budgetary Educational Institution of Further Professional Education «Russian Medical Academy of Continuous Professional Education» of the Ministry of Healthcare of the Russian Federation, Kazan State Medical Academy, Volga Region, Kazan, Russia; 5Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá D.C., Colombia; 6Instituto de Ciencias Biomédicas, Universidad Autónoma de Chile, Santiago, Chile; 7GALLY International Biomedical Research Consulting LLC, San Antonio, TX, USA; 8School of Health Science and Healthcare Administration, University of Atlanta, Johns Creek, GA, USA; 9Institute of Physiologically Active Compounds Russian Academy of Sciences, Chernogolovka, Russia Abstract: Cytochrome (CYP 450 isoenzymes are the basic enzymes involved in Phase I biotransformation. The most important role in biotransformation belongs to CYP3A4, CYP2D6, CYP2C9, CYP2C19 and CYP1A2. Inhibition and induction of CYP isoenzymes caused by drugs are important and clinically relevant pharmacokinetic mechanisms of drug interaction. Investigation of the activity of CYP isoenzymes by using phenotyping methods (such as the determination of the concentration of specific substrates and metabolites in biological fluids during drug administration provides the prediction of negative side effects caused by drug interaction. In clinical practice, the process of phenotyping of CYP isoenzymes and some endogenous substrates in the ratio of cortisol to 6

  4. Oral administration of drugs with hypersensitivity potential induces germinal center hyperplasia in secondary lymphoid organ/tissue in Brown Norway rats, and this histological lesion is a promising candidate as a predictive biomarker for drug hypersensitivity occurrence in humans

    International Nuclear Information System (INIS)

    Tamura, Akitoshi; Miyawaki, Izuru; Yamada, Toru; Kimura, Juki; Funabashi, Hitoshi

    2013-01-01

    It is important to evaluate the potential of drug hypersensitivity as well as other adverse effects during the preclinical stage of the drug development process, but validated methods are not available yet. In the present study we examined whether it would be possible to develop a new predictive model of drug hypersensitivity using Brown Norway (BN) rats. As representative drugs with hypersensitivity potential in humans, phenytoin (PHT), carbamazepine (CBZ), amoxicillin (AMX), and sulfamethoxazole (SMX) were orally administered to BN rats for 28 days to investigate their effects on these animals by examinations including observation of clinical signs, hematology, determination of serum IgE levels, histology, and flow cytometric analysis. Skin rashes were not observed in any animals treated with these drugs. Increases in the number of circulating inflammatory cells and serum IgE level did not necessarily occur in the animals treated with these drugs. However, histological examination revealed that germinal center hyperplasia was commonly induced in secondary lymphoid organs/tissues in the animals treated with these drugs. In cytometric analysis, changes in proportions of lymphocyte subsets were noted in the spleen of the animals treated with PHT or CBZ during the early period of administration. The results indicated that the potential of drug hypersensitivity was identified in BN rat by performing histological examination of secondary lymphoid organs/tissues. Data obtained herein suggested that drugs with hypersensitivity potential in humans gained immune reactivity in BN rat, and the germinal center hyperplasia induced by administration of these drugs may serve as a predictive biomarker for drug hypersensitivity occurrence. - Highlights: • We tested Brown Norway rats as a candidate model for predicting drug hypersensitivity. • The allergic drugs did not induce skin rash, whereas D-penicillamine did so in the rats. • Some of allergic drugs increased

  5. Oral administration of drugs with hypersensitivity potential induces germinal center hyperplasia in secondary lymphoid organ/tissue in Brown Norway rats, and this histological lesion is a promising candidate as a predictive biomarker for drug hypersensitivity occurrence in humans

    Energy Technology Data Exchange (ETDEWEB)

    Tamura, Akitoshi, E-mail: akitoshi-tamura@ds-pharma.co.jp; Miyawaki, Izuru; Yamada, Toru; Kimura, Juki; Funabashi, Hitoshi

    2013-08-15

    It is important to evaluate the potential of drug hypersensitivity as well as other adverse effects during the preclinical stage of the drug development process, but validated methods are not available yet. In the present study we examined whether it would be possible to develop a new predictive model of drug hypersensitivity using Brown Norway (BN) rats. As representative drugs with hypersensitivity potential in humans, phenytoin (PHT), carbamazepine (CBZ), amoxicillin (AMX), and sulfamethoxazole (SMX) were orally administered to BN rats for 28 days to investigate their effects on these animals by examinations including observation of clinical signs, hematology, determination of serum IgE levels, histology, and flow cytometric analysis. Skin rashes were not observed in any animals treated with these drugs. Increases in the number of circulating inflammatory cells and serum IgE level did not necessarily occur in the animals treated with these drugs. However, histological examination revealed that germinal center hyperplasia was commonly induced in secondary lymphoid organs/tissues in the animals treated with these drugs. In cytometric analysis, changes in proportions of lymphocyte subsets were noted in the spleen of the animals treated with PHT or CBZ during the early period of administration. The results indicated that the potential of drug hypersensitivity was identified in BN rat by performing histological examination of secondary lymphoid organs/tissues. Data obtained herein suggested that drugs with hypersensitivity potential in humans gained immune reactivity in BN rat, and the germinal center hyperplasia induced by administration of these drugs may serve as a predictive biomarker for drug hypersensitivity occurrence. - Highlights: • We tested Brown Norway rats as a candidate model for predicting drug hypersensitivity. • The allergic drugs did not induce skin rash, whereas D-penicillamine did so in the rats. • Some of allergic drugs increased

  6. The predictive power of family history measures of alcohol and drug problems and internalizing disorders in a college population.

    Science.gov (United States)

    Kendler, Kenneth S; Edwards, Alexis; Myers, John; Cho, Seung Bin; Adkins, Amy; Dick, Danielle

    2015-07-01

    A family history (FH) of psychiatric and substance use problems is a potent risk factor for common internalizing and externalizing disorders. In a large web-based assessment of mental health in college students, we developed a brief set of screening questions for a FH of alcohol problems (AP), drug problems (DP) and depression-anxiety in four classes of relatives (father, mother, aunts/uncles/grandparents, and siblings) as reported by the student. Positive reports of a history of AP, DP, and depression-anxiety were substantially correlated within relatives. These FH measures predicted in the student, in an expected pattern, dimensions of personality and impulsivity, alcohol consumption and problems, smoking and nicotine dependence, use of illicit drugs, and symptoms of depression and anxiety. Using the mean score from the four classes of relatives was more predictive than using a familial/sporadic dichotomy. Interactions were seen between the FH of AP, DP, and depression-anxiety and peer deviance in predicting symptoms of alcohol and tobacco dependence. As the students aged, the FH of AP became a stronger predictor of alcohol problems. While we cannot directly assess the validity of these FH reports, the pattern of findings suggest that our brief screening items were able to assess, with some accuracy, the FH of substance misuse and internalizing psychiatric disorders in relatives. If correct, these measures can play an important role in the creation of developmental etiologic models for substance and internalizing psychiatric disorders which constitute one of the central goals of the overall project. © 2015 Wiley Periodicals, Inc.

  7. High-Throughput Gene Expression Profiles to Define Drug Similarity and Predict Compound Activity.

    Science.gov (United States)

    De Wolf, Hans; Cougnaud, Laure; Van Hoorde, Kirsten; De Bondt, An; Wegner, Joerg K; Ceulemans, Hugo; Göhlmann, Hinrich

    2018-04-01

    By adding biological information, beyond the chemical properties and desired effect of a compound, uncharted compound areas and connections can be explored. In this study, we add transcriptional information for 31K compounds of Janssen's primary screening deck, using the HT L1000 platform and assess (a) the transcriptional connection score for generating compound similarities, (b) machine learning algorithms for generating target activity predictions, and (c) the scaffold hopping potential of the resulting hits. We demonstrate that the transcriptional connection score is best computed from the significant genes only and should be interpreted within its confidence interval for which we provide the stats. These guidelines help to reduce noise, increase reproducibility, and enable the separation of specific and promiscuous compounds. The added value of machine learning is demonstrated for the NR3C1 and HSP90 targets. Support Vector Machine models yielded balanced accuracy values ≥80% when the expression values from DDIT4 & SERPINE1 and TMEM97 & SPR were used to predict the NR3C1 and HSP90 activity, respectively. Combining both models resulted in 22 new and confirmed HSP90-independent NR3C1 inhibitors, providing two scaffolds (i.e., pyrimidine and pyrazolo-pyrimidine), which could potentially be of interest in the treatment of depression (i.e., inhibiting the glucocorticoid receptor (i.e., NR3C1), while leaving its chaperone, HSP90, unaffected). As such, the initial hit rate increased by a factor 300, as less, but more specific chemistry could be screened, based on the upfront computed activity predictions.

  8. Comparative Study of Different Methods for the Prediction of Drug-Polymer Solubility

    DEFF Research Database (Denmark)

    Knopp, Matthias Manne; Tajber, Lidia; Tian, Yiwei

    2015-01-01

    monomer weight ratios. The drug–polymer solubility at 25 °C was predicted using the Flory–Huggins model, from data obtained at elevated temperature using thermal analysis methods based on the recrystallization of a supersaturated amorphous solid dispersion and two variations of the melting point......, which suggests that this method can be used as an initial screening tool if a liquid analogue is available. The learnings of this important comparative study provided general guidance for the selection of the most suitable method(s) for the screening of drug–polymer solubility....

  9. Cerebrospinal Fluid Indices in Acute Drug Intoxication; Do They Predict the Patients’ Outcome?

    Directory of Open Access Journals (Sweden)

    Mohammadreza Farsinejad

    2012-08-01

    Full Text Available Introduction: In some intoxicated patients, cerebrospinal fluid (CSF is examined due to the prolonged loss of consciousness, focal neurologic findings, and fever of unknown origin. We aimed to evaluate the probable relationship between the different toxicity causes and the CSF indices in poisoned patients and determine if they could predict the patients’ outcome. Methods: All patients who had been admitted to the toxicology intensive care unit of Loghman-Hakim hospital between March 2006 and March 2011 and had undergone lumbar puncture (LP were included into this retrospective study. The patients’ demographic data and results of CSF evaluation (level of glucose, lactate dehydrogenase, protein, and white blood cells in CSF fluid were evaluated. The data was analyzed using SPSS software version 17. Results: A total of 111 patients were evaluated. Mean age of the patients was 37±15 years. Thirteen (11.7% had deceased. No relation was found between the cause of poisoning (medication involved and the changes in CSF indices. A statistically significant difference was found between the survivors and non-survivors in terms of CSF protein, LDH, and WBC. However, such a difference was not detected between these two groups regarding CSF glucose. Conclusion: In intoxicated patients with prolonged decreased level of consciousness or prolonged fever, early evaluation of CSF can help early diagnosis of complications such as meningitis and prompt treatment. Also, high level of protein, LDH, and WBC in the CSF can predict higher mortality rates in these patients.

  10. The predictive value of drug-induced sleep endoscopy for CPAP titration in OSA patients.

    Science.gov (United States)

    Lan, Ming-Chin; Hsu, Yen-Bin; Lan, Ming-Ying; Huang, Yun-Chen; Kao, Ming-Chang; Huang, Tung-Tsun; Chiu, Tsan-Jen; Yang, Mei-Chen

    2017-12-15

    The aim of this study was to identify possible upper airway obstructions causing a higher continuous positive airway pressure (CPAP) titration level, utilizing drug-induced sleep endoscopy (DISE). A total of 76 patients with obstructive sleep apnea (OSA) underwent CPAP titration and DISE. DISE findings were recorded using the VOTE classification system. Polysomnographic (PSG) data, anthropometric variables, and patterns of airway collapse during DISE were analyzed with CPAP titration levels. A significant association was found between the CPAP titration level and BMI, oxygen desaturation index (ODI), apnea-hypopnea index (AHI), and neck circumference (NC) (P CPAP titration level (P CPAP titration level and any other collapse at the tongue base or epiglottis. By analyzing PSG data, anthropometric variables, and DISE results with CPAP titration levels, we can better understand possible mechanisms resulting in a higher CPAP titration level. We believe that the role of DISE can be expanded as a tool to identify the possible anatomical structures that may be corrected by oral appliance therapy or surgical intervention to improve CPAP compliance.

  11. Development and validation of a general approach to predict and quantify the synergism of anti-cancer drugs using experimental design and artificial neural networks.

    Science.gov (United States)

    Pivetta, Tiziana; Isaia, Francesco; Trudu, Federica; Pani, Alessandra; Manca, Matteo; Perra, Daniela; Amato, Filippo; Havel, Josef

    2013-10-15

    The combination of two or more drugs using multidrug mixtures is a trend in the treatment of cancer. The goal is to search for a synergistic effect and thereby reduce the required dose and inhibit the development of resistance. An advanced model-free approach for data exploration and analysis, based on artificial neural networks (ANN) and experimental design is proposed to predict and quantify the synergism of drugs. The proposed method non-linearly correlates the concentrations of drugs with the cytotoxicity of the mixture, providing the possibility of choosing the optimal drug combination that gives the maximum synergism. The use of ANN allows for the prediction of the cytotoxicity of each combination of drugs in the chosen concentration interval. The method was validated by preparing and experimentally testing the combinations with the predicted highest synergistic effect. In all cases, the data predicted by the network were experimentally confirmed. The method was applied to several binary mixtures of cisplatin and [Cu(1,10-orthophenanthroline)2(H2O)](ClO4)2, Cu(1,10-orthophenanthroline)(H2O)2(ClO4)2 or [Cu(1,10-orthophenanthroline)2(imidazolidine-2-thione)](ClO4)2. The cytotoxicity of the two drugs, alone and in combination, was determined against human acute T-lymphoblastic leukemia cells (CCRF-CEM). For all systems, a synergistic effect was found for selected combinations. © 2013 Elsevier B.V. All rights reserved.

  12. Usefulness of Measuring Thyroid Stimulating Antibody at the Time of Antithyroid Drug Withdrawal for Predicting Relapse of Graves Disease

    Directory of Open Access Journals (Sweden)

    Hyemi Kwon

    2016-06-01

    Full Text Available BackgroundHyperthyroidism relapse in Graves disease after antithyroid drug (ATD withdrawal is common; however, measuring the thyrotropin receptor antibody (TRAb at ATD withdrawal in order to predict outcomes is controversial. This study compared measurement of thyroid stimulatory antibody (TSAb and thyrotropin-binding inhibitory immunoglobulin (TBII at ATD withdrawal to predict relapse.MethodsThis retrospective study enrolled patients with Graves disease who were treated with ATDs and whose serum thyroid-stimulating hormone levels were normal after receiving low-dose ATDs. ATD therapy was stopped irrespective of TRAb positivity after an additional 6 months of receiving the minimum dose of ATD therapy. Patients were followed using thyroid function tests and TSAb (TSAb group; n=35 or TBII (TBII group; n=39 every 3 to 6 months for 2 years after ATD withdrawal.ResultsTwenty-eight patients (38% relapsed for a median follow-up of 21 months, and there were no differences in baseline clinical characteristics between groups. In the TSAb group, relapse was more common in patients with positive TSAb at ATD withdrawal (67% than patients with negative TSAb (17%; P=0.007. Relapse-free survival was shorter in TSAb-positive patients. In the TBII group, there were no differences in the relapse rate and relapse-free survivals according to TBII positivity. For predicting Graves disease relapse, the sensitivity and specificity of TSAb were 63% and 83%, respectively, whereas those of TBII were 28% and 65%.ConclusionTSAb at ATD withdrawal can predict the relapse of Graves hyperthyroidism, but TBII cannot. Measuring TSAb at ATD withdrawal can assist with clinical decisions making for patients with Graves disease.

  13. Usefulness of Measuring Thyroid Stimulating Antibody at the Time of Antithyroid Drug Withdrawal for Predicting Relapse of Graves Disease

    Science.gov (United States)

    Kwon, Hyemi; Jang, Eun Kyung; Kim, Mijin; Park, Suyeon; Jeon, Min Ji; Kim, Tae Yong; Ryu, Jin-Sook; Shong, Young Kee; Kim, Won Bae

    2016-01-01

    Background Hyperthyroidism relapse in Graves disease after antithyroid drug (ATD) withdrawal is common; however, measuring the thyrotropin receptor antibody (TRAb) at ATD withdrawal in order to predict outcomes is controversial. This study compared measurement of thyroid stimulatory antibody (TSAb) and thyrotropin-binding inhibitory immunoglobulin (TBII) at ATD withdrawal to predict relapse. Methods This retrospective study enrolled patients with Graves disease who were treated with ATDs and whose serum thyroid-stimulating hormone levels were normal after receiving low-dose ATDs. ATD therapy was stopped irrespective of TRAb positivity after an additional 6 months of receiving the minimum dose of ATD therapy. Patients were followed using thyroid function tests and TSAb (TSAb group; n=35) or TBII (TBII group; n=39) every 3 to 6 months for 2 years after ATD withdrawal. Results Twenty-eight patients (38%) relapsed for a median follow-up of 21 months, and there were no differences in baseline clinical characteristics between groups. In the TSAb group, relapse was more common in patients with positive TSAb at ATD withdrawal (67%) than patients with negative TSAb (17%; P=0.007). Relapse-free survival was shorter in TSAb-positive patients. In the TBII group, there were no differences in the relapse rate and relapse-free survivals according to TBII positivity. For predicting Graves disease relapse, the sensitivity and specificity of TSAb were 63% and 83%, respectively, whereas those of TBII were 28% and 65%. Conclusion TSAb at ATD withdrawal can predict the relapse of Graves hyperthyroidism, but TBII cannot. Measuring TSAb at ATD withdrawal can assist with clinical decisions making for patients with Graves disease. PMID:27118279

  14. Major Source of Error in QSPR Prediction of Intrinsic Thermodynamic Solubility of Drugs: Solid vs Nonsolid State Contributions?

    Science.gov (United States)

    Abramov, Yuriy A

    2015-06-01

    The main purpose of this study is to define the major limiting factor in the accuracy of the quantitative structure-property relationship (QSPR) models of the thermodynamic intrinsic aqueous solubility of the drug-like compounds. For doing this, the thermodynamic intrinsic aqueous solubility property was suggested to be indirectly "measured" from the contributions of solid state, ΔGfus, and nonsolid state, ΔGmix, properties, which are estimated by the corresponding QSPR models. The QSPR models of ΔGfus and ΔGmix properties were built based on a set of drug-like compounds with available accurate measurements of fusion and thermodynamic solubility properties. For consistency ΔGfus and ΔGmix models were developed using similar algorithms and descriptor sets, and validated against the similar test compounds. Analysis of the relative performances of these two QSPR models clearly demonstrates that it is the solid state contribution which is the limiting factor in the accuracy and predictive power of the QSPR models of the thermodynamic intrinsic solubility. The performed analysis outlines a necessity of development of new descriptor sets for an accurate description of the long-range order (periodicity) phenomenon in the crystalline state. The proposed approach to the analysis of limitations and suggestions for improvement of QSPR-type models may be generalized to other applications in the pharmaceutical industry.

  15. A modified physiological BCS for prediction of intestinal absorption in drug discovery.

    Science.gov (United States)

    Zaki, Noha M; Artursson, Per; Bergström, Christel A S

    2010-10-04

    In this study, the influence of physiologically relevant media on the compound position in a biopharmaceutical classification system (BCS) which resembled the intestinal absorption was investigated. Both solubility and permeability limited compounds (n = 22) were included to analyze the importance of each of these on the final absorption. Solubility was determined in three different dissolution media, phosphate buffer pH 6.5 (PhB 6.5), fasted state simulated intestinal fluid (FaSSIF), and fed state simulated intestinal fluid (FeSSIF) at 37 °C, and permeability values were determined using the 2/4/A1 cell line. The solubility data and membrane permeability values were used for sorting the compounds into a BCS modified to reflect the fasted and fed state. Three of the seven compounds sorted as BCS II in PhB 6.5 (high permeability, low solubility) changed their position to BCS I when dissolved in FaSSIF and/or FeSSIF (high permeability, high solubility). These were low dosed (20 mg or less) lipophilic molecules displaying solvation limited solubility. In contrast, compounds having solid-state limited solubility had a minor increase in solubility when dissolved in FaSSIF and/or FeSSIF. Although further studies are needed to enable general cutoff values, our study indicates that low dosed BCS Class II compounds which have solubility normally restricted by poor solvation may behave as BCS Class I compounds in vivo. The large series of compounds investigated herein reveals the importance of investigating solubility and dissolution under physiologically relevant conditions in all stages of the drug discovery process to push suitable compounds forward, to select proper formulations, and to reduce the risk of food effects.

  16. Potential Impact of a Free Online HIV Treatment Response Prediction System for Reducing Virological Failures and Drug Costs after Antiretroviral Therapy Failure in a Resource-Limited Setting

    Directory of Open Access Journals (Sweden)

    Andrew D. Revell

    2013-01-01

    Full Text Available Objective. Antiretroviral drug selection in resource-limited settings is often dictated by strict protocols as part of a public health strategy. The objective of this retrospective study was to examine if the HIV-TRePS online treatment prediction tool could help reduce treatment failure and drug costs in such settings. Methods. The HIV-TRePS computational models were used to predict the probability of response to therapy for 206 cases of treatment change following failure in India. The models were used to identify alternative locally available 3-drug regimens, which were predicted to be effective. The costs of these regimens were compared to those actually used in the clinic. Results. The models predicted the responses to treatment of the cases with an accuracy of 0.64. The models identified alternative drug regimens that were predicted to result in improved virological response and lower costs than those used in the clinic in 85% of the cases. The average annual cost saving was $364 USD per year (41%. Conclusions. Computational models that do not require a genotype can predict and potentially avoid treatment failure and may reduce therapy costs. The use of such a system to guide therapeutic decision-making could confer health economic benefits in resource-limited settings.

  17. Changes in psychiatric symptoms among persons with methamphetamine dependence predicts changes in severity of drug problems but not frequency of use.

    Science.gov (United States)

    Polcin, Douglas L; Korcha, Rachael; Bond, Jason; Galloway, Gantt; Nayak, Madhabika

    2016-01-01

    Few studies have examined how changes in psychiatric symptoms over time are associated with changes in drug use and severity of drug problems. No studies have examined these relationships among methamphetamine (MA)-dependent persons receiving motivational interviewing within the context of standard outpatient treatment. Two hundred seventeen individuals with MA dependence were randomly assigned to a standard single session of motivational interviewing (MI) or an intensive 9-session model of MI. Both groups received standard outpatient group treatment. The Addiction Severity Index (ASI) and timeline follow-back (TLFB) for MA use were administered at treatment entry and 2-, 4-, and 6-month follow-ups. Changes in ASI psychiatric severity between baseline and 2 months predicted changes in ASI drug severity during the same time period, but not changes on measures of MA use. Item analysis of the ASI drug scale showed that psychiatric severity predicted how troubled or bothered participants were by their drug us, how important they felt it was for them to get treatment, and the number of days they experienced drug problems. However, it did not predict the number days they used drugs in the past 30 days. These associations did not differ between study conditions, and they persisted when psychiatric severity and outcomes were compared across 4- and 6-month time periods. Results are among the first to track how changes in psychiatric severity over time are associated with changes in MA use and severity of drug problems. Treatment efforts targeting reduction of psychiatric symptoms among MA-dependent persons might be helpful in reducing the level of distress and problems associated with MA use but not how often it is used. There is a need for additional research describing the circumstances under which the experiences and perceptions of drug-related problems diverge from frequency of consumption.

  18. Diabetes mellitus, preexisting coronary heart disease, and the risk of subsequent coronary heart disease events in patients infected with human immunodeficiency virus: the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D Study)

    DEFF Research Database (Denmark)

    Worm, Signe W; De Wit, Stephane; Weber, Rainer

    2009-01-01

    of DM and preexisting CHD on the development of a new CHD episode among 33,347 HIV-infected individuals in the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D Study). METHODS AND RESULTS: Over 159,971 person-years, 698 CHD events occurred. After adjustment for gender, age, cohort, HIV...... transmission, ethnicity, family history of CHD, smoking, and calendar year, the rate of a CHD episode was 7.52 times higher (Poisson regression, 95% CI 6.02 to 9.39, P=0.0001) in those with preexisting CHD than in those without preexisting CHD, but it was only 2.41 times higher (95% CI 1.91 to 3.05, P=0...

  19. Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach.

    Science.gov (United States)

    Chen, Shangying; Zhang, Peng; Liu, Xin; Qin, Chu; Tao, Lin; Zhang, Cheng; Yang, Sheng Yong; Chen, Yu Zong; Chui, Wai Keung

    2016-06-01

    The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates. Copyright © 2016. Published by Elsevier Inc.

  20. Non-genetic risk factors and predicting efficacy for docetaxel--drug-induced liver injury among metastatic breast cancer patients.

    Science.gov (United States)

    Wang, Zheng; Liang, Xu; Yu, Jing; Zheng, Xiaohui; Zhu, Yulin; Yan, Ying; Dong, Ningning; Di, Lijun; Song, Guohong; Zhou, Xinna; Wang, Xiaoli; Yang, Huabing; Ren, Jun; Lyerly, Herbert Kim

    2012-08-01

    Docetaxel has been chosen as one of the most popular anticancer drugs in the treatment of breast cancer for more than a decade. There is increasingly awareness for the occurrence of docetaxel and/or docetaxel-drug-induced liver injury (DILI), although the underlying mechanism of occurrence and its risk factors remain unclear. We conducted a retrospective cohort study to identify non-genetic risk factors for docetaxel-DILI among 647 metastasis breast cancer patients treated with docetaxel-containing regimens. Sixty-seven (10.36%) patients were diagnosed as docetaxel-DILI. By logistic regression analysis, premenopausal status (odds ratio [OR][95% confidence interval {CI}] = 2.24 [1.30-3.87]), past hepatitis B virus (HBV) infections (OR [95% CI] = 4.23 [1.57-11.42]), liver metastasis (OR [95% CI] = 3.70 [2.16-6.34]). The predominant occurrence of DILI was seen in groups with docetaxel combination regimens. (OR [95% CI] = 2.66 [1.59-4.55]). The potential increasing occurrence of docetaxel-DILI was associated with multiple risk factors in an exposure-response manner (P < 0.001), and patients with more than three risk factors would be exposed to a 36.61-fold risk of DILI (95% CI = 10.18-131.62). Further analysis by the risk score and area under the receiver-operator characteristic curve (AUC) showed that those four factors contributed to an AUC of 0.7536 (95% CI = 0.70-0.81), with a predictive sensitivity of 74.63% and specificity of 65.17%. Docetaxel-DILI with a relatively higher incidence should be addressed among metastatic breast cancer patients. Four predominant risk factors, including premenopausal status, past HBV infection, liver metastasis, and docetaxel combination regimens, were potential predicators for DILI. © 2012 Journal of Gastroenterology and Hepatology Foundation and Blackwell Publishing Asia Pty Ltd.

  1. Implications of central immune signaling caused by drugs of abuse: mechanisms, mediators and new therapeutic approaches for prediction and treatment of drug dependence.

    Science.gov (United States)

    Coller, Janet K; Hutchinson, Mark R

    2012-05-01

    In the past two decades a trickle of manuscripts examining the non-neuronal central nervous system immune consequences of the drugs of abuse has now swollen to a significant body of work. Initially, these studies reported associative evidence of central nervous system proinflammation resulting from exposure to the drugs of abuse demonstrating key implications for neurotoxicity and disease progression associated with, for example, HIV infection. However, more recently this drug-induced activation of central immune signaling is now understood to contribute substantially to the pharmacodynamic actions of the drugs of abuse, by enhancing the engagement of classical mesolimbic dopamine reward pathways and withdrawal centers. This review will highlight the key in vivo animal, human, biological and molecular evidence of these central immune signaling actions of opioids, alcohol, cocaine, methamphetamine, and 3,4-methylenedioxymethamphetamine (MDMA). Excitingly, this new appreciation of central immune signaling activity of drugs of abuse provides novel therapeutic interventions and opportunities to identify 'at risk' individuals through the use of immunogenetics. Discussion will also cover the evidence of modulation of this signaling by existing clinical and pre-clinical drug candidates, and novel pharmacological targets. Finally, following examination of the breadth of central immune signaling actions of the drugs of abuse highlighted here, the current known common immune signaling components will be outlined and their impact on established addiction neurocircuitry discussed, thereby synthesizing a common neuroimmune hypothesis of addiction. Copyright © 2012 Elsevier Inc. All rights reserved.

  2. Subsequence Automata with Default Transitions

    DEFF Research Database (Denmark)

    Bille, Philip; Gørtz, Inge Li; Skjoldjensen, Frederik Rye

    2016-01-01

    of states and transitions) of the subsequence automaton is O(nσ) and that this bound is asymptotically optimal. In this paper, we consider subsequence automata with default transitions, that is, special transitions to be taken only if none of the regular transitions match the current character, and which do...... not consume the current character. We show that with default transitions, much smaller subsequence automata are possible, and provide a full trade-off between the size of the automaton and the delay, i.e., the maximum number of consecutive default transitions followed before consuming a character......(nσ) and delay O(1), thus matching the bound for the standard subsequence automaton construction. The key component of our result is a novel hierarchical automata construction of independent interest....

  3. Subsequence automata with default transitions

    DEFF Research Database (Denmark)

    Bille, Philip; Gørtz, Inge Li; Skjoldjensen, Frederik Rye

    2017-01-01

    of states and transitions) of the subsequence automaton is O(nσ) and that this bound is asymptotically optimal. In this paper, we consider subsequence automata with default transitions, that is, special transitions to be taken only if none of the regular transitions match the current character, and which do...... not consume the current character. We show that with default transitions, much smaller subsequence automata are possible, and provide a full trade-off between the size of the automaton and the delay, i.e., the maximum number of consecutive default transitions followed before consuming a character......(1), thus matching the bound for the standard subsequence automaton construction. Finally, we generalize the result to multiple strings. The key component of our result is a novel hierarchical automata construction of independent interest....

  4. In Vitro-In Vivo Predictive Dissolution-Permeation-Absorption Dynamics of Highly Permeable Drug Extended-Release Tablets via Drug Dissolution/Absorption Simulating System and pH Alteration.

    Science.gov (United States)

    Li, Zi-Qiang; Tian, Shuang; Gu, Hui; Wu, Zeng-Guang; Nyagblordzro, Makafui; Feng, Guo; He, Xin

    2018-05-01

    Each of dissolution and permeation may be a rate-limiting factor in the absorption of oral drug delivery. But the current dissolution test rarely took into consideration of the permeation property. Drug dissolution/absorption simulating system (DDASS) valuably gave an insight into the combination of drug dissolution and permeation processes happening in human gastrointestinal tract. The simulated gastric/intestinal fluid of DDASS was improved in this study to realize the influence of dynamic pH change on the complete oral dosage form. To assess the effectiveness of DDASS, six high-permeability drugs were chosen as model drugs, including theophylline (pK a1  = 3.50, pK a2  = 8.60), diclofenac (pK a  = 4.15), isosorbide 5-mononitrate (pK a  = 7.00), sinomenine (pK a  = 7.98), alfuzosin (pK a  = 8.13), and metoprolol (pK a  = 9.70). A general elution and permeation relationship of their commercially available extended-release tablets was assessed as well as the relationship between the cumulative permeation and the apparent permeability. The correlations between DDASS elution and USP apparatus 2 (USP2) dissolution and also between DDASS permeation and beagle dog absorption were developed to estimate the predictability of DDASS. As a result, the common elution-dissolution relationship was established regardless of some variance in the characteristic behavior between DDASS and USP2 for drugs dependent on the pH for dissolution. Level A in vitro-in vivo correlation between DDASS permeation and dog absorption was developed for drugs with different pKa. The improved DDASS will be a promising tool to provide a screening method on the predictive dissolution-permeation-absorption dynamics of solid drug dosage forms in the early-phase formulation development.

  5. Prediction of drug-related cardiac adverse effects in humans--A: creation of a database of effects and identification of factors affecting their occurrence.

    Science.gov (United States)

    Matthews, Edwin J; Frid, Anna A

    2010-04-01

    This is the first of two reports that describes the compilation of a database of drug-related cardiac adverse effects (AEs) that was used to construct quantitative structure-activity relationship (QSAR) models to predict these AEs, to identify properties of pharmaceuticals correlated with the AEs, and to identify plausible mechanisms of action (MOAs) causing the AEs. This database of 396,985 cardiac AE reports was linked to 1632 approved drugs and their chemical structures, 1851 clinical indications (CIs), 997 therapeutic targets (TTs), 432 pharmacological MOAs, and 21,180 affinity coefficients (ACs) for the MOA receptors. AEs were obtained from the Food and Drug Administration's (FDA's) Spontaneous Reporting System (SRS) and Adverse Event Reporting System (AERS) and publicly available medical literature. Drug TTs were obtained from Integrity; drug MOAs and ACs were predicted by BioEpisteme. Significant cardiac AEs and patient exposures were estimated based on the proportional reporting ratios (PRRs) for each drug and each AE endpoint as a percentage of the total AEs. Cardiac AE endpoints were bundled based on toxicological mechanism and concordance of drug-related findings. Results revealed that significant cardiac AEs formed 9 clusters affecting Purkinje nerve fibers (arrhythmia, bradycardia, conduction disorder, electrocardiogram, palpitations, QT prolongation, rate rhythm composite, tachycardia, and Torsades de pointes), and 5 clusters affecting the heart muscle (coronary artery disorders, heart failure, myocardial disorders, myocardial infarction, and valve disorders). Based on the observation that each drug had one TT and up to 9 off-target MOAs, cardiac AEs were highly correlated with drugs affecting cardiovascular and cardioneurological functions and certain MOAs (e.g., alpha- and beta-adeno, dopamine, and hydroxytryptomine receptors). Copyright 2010. Published by Elsevier Inc.

  6. FUNCTIONAL SUBCLONE PROFILING FOR PREDICTION OF TREATMENT-INDUCED INTRA-TUMOR POPULATION SHIFTS AND DISCOVERY OF RATIONAL DRUG COMBINATIONS IN HUMAN GLIOBLASTOMA

    Science.gov (United States)

    Reinartz, Roman; Wang, Shanshan; Kebir, Sied; Silver, Daniel J.; Wieland, Anja; Zheng, Tong; Küpper, Marius; Rauschenbach, Laurèl; Fimmers, Rolf; Shepherd, Timothy M.; Trageser, Daniel; Till, Andreas; Schäfer, Niklas; Glas, Martin; Hillmer, Axel M.; Cichon, Sven; Smith, Amy A.; Pietsch, Torsten; Liu, Ying; Reynolds, Brent A.; Yachnis, Anthony; Pincus, David W.; Simon, Matthias; Brüstle, Oliver; Steindler, Dennis A.; Scheffler, Björn

    2016-01-01

    Purpose Investigation of clonal heterogeneity may be key to understanding mechanisms of therapeutic failure in human cancer. However, little is known on the consequences of therapeutic intervention on the clonal composition of solid tumors. Experimental Design Here, we used 33 single cell-derived subclones generated from five clinical glioblastoma specimens for exploring intra- and inter-individual spectra of drug resistance profiles in vitro. In a personalized setting, we explored whether differences in pharmacological sensitivity among subclones could be employed to predict drug-dependent changes to the clonal composition of tumors. Results Subclones from individual tumors exhibited a remarkable heterogeneity of drug resistance to a library of potential anti-glioblastoma compounds. A more comprehensive intra-tumoral analysis revealed that stable genetic and phenotypic characteristics of co-existing subclones could be correlated with distinct drug sensitivity profiles. The data obtained from differential drug response analysis could be employed to predict clonal population shifts within the naïve parental tumor in vitro and in orthotopic xenografts. Furthermore, the value of pharmacological profiles could be shown for establishing rational strategies for individualized secondary lines of treatment. Conclusions Our data provide a previously unrecognized strategy for revealing functional consequences of intra-tumor heterogeneity by enabling predictive modeling of treatment-related subclone dynamics in human glioblastoma. PMID:27521447

  7. The Contribution of Africentric Values and Racial Identity to the Prediction of Drug Knowledge, Attitudes, and Use among African American Youth.

    Science.gov (United States)

    Belgrave, Faye Z.; Brome, Deborah Ridley; Hampton, Carl

    2000-01-01

    Investigated the contribution of cultural variables, particularly Africentric values and racial identity, to the prediction of drug use, knowledge, and attitudes among African American youths, highlighting individual, peer, and family variables. Data from upper elementary students who participated in a prevention program indicated that Africentric…

  8. Evaluation of the use of Göttingen minipigs to predict food effects on the oral absorption of drugs in humans

    DEFF Research Database (Denmark)

    Christiansen, Martin Lau; Müllertz, Anette; Garmer, Mats

    2015-01-01

    This study investigated the oral absorption of drugs in minipigs to predict food effects in man. The protocol was based on a previously described model in dogs and further investigated the food source (i.e., US FDA breakfast or a nutritional drink) and food quantities. Two poorly soluble compounds...

  9. Integrated Theory of Planned Behavior with Extrinsic Motivation to Predict Intention Not to Use Illicit Drugs by Fifth-Grade Students in Taiwan

    Science.gov (United States)

    Liao, Jung-Yu; Chang, Li-Chun; Hsu, Hsiao-Pei; Huang, Chiu-Mieh; Huang, Su-Fei; Guo, Jong-Long

    2017-01-01

    This study assessed the effects of a model that integrated the theory of planned behavior (TPB) with extrinsic motivation (EM) in predicting the intentions of fifth-grade students to not use illicit drugs. A cluster-sampling design was adopted in a cross-sectional survey (N = 571). The structural equation modeling results showed that the model…

  10. Transmission of HIV drug resistance and the predicted effect on current first-line regimens in Europe

    NARCIS (Netherlands)

    Hofstra, L. Marije; Sauvageot, Nicolas; Albert, Jan; Alexiev, Ivailo; Garcia, Federico; Struck, Daniel; Van De Vijver, David A M C; Åsjö, Birgitta; Beshkov, Danail; Coughlan, Suzie; Descamps, Diane; Griskevicius, Algirdas; Hamouda, Osamah; Horban, Andrzej; Van Kasteren, Marjo; Kolupajeva, Tatjana; Kostrikis, Leontios G.; Liitsola, Kirsi; Linka, Marek; Mor, Orna; Nielsen, Claus; Otelea, Dan; Paraskevis, Dimitrios; Paredes, Roger; Poljak, Mario; Puchhammer-Stöckl, Elisabeth; Sönnerborg, Anders; Staneková, Danica; Stanojevic, Maja; Van Laethem, Kristel; Zazzi, Maurizio; Lepej, Snjezana Zidovec; Boucher, Charles A B; Schmit, Jean Claude; Wensing, Annemarie M J; Puchhammer-Stockl, E.; Sarcletti, M.; Schmied, B.; Geit, M.; Balluch, G.; Vandamme, A. M.; Vercauteren, J.; Derdelinckx, I.; Sasse, A.; Bogaert, M.; Ceunen, H.; De Roo, A.; De Wit, S.; Echahidi, F.; Fransen, K.; Goffard, J. C.; Goubau, P.; Goudeseune, E.; Yombi, J. C.; Lacor, P.; Liesnard, C.; Moutschen, M.; Pierard, D.; Rens, R.; Schrooten, Y.; Vaira, D.; Vandekerckhove, L. P R; Van Den Heuvel, A.; Van Der Gucht, B.; Van Ranst, M.; Van Wijngaerden, E.; Vandercam, B.; Vekemans, M.; Verhofstede, C.; Clumeck, N.; Van Laethem, K.; Beshkov, D.; Alexiev, I.; Lepej, S. Zidovec; Begovac, J.; Kostrikis, Leontios G.; Demetriades, I.; Kousiappa, I.; Demetriou, V.; Hezka, J.; Linka, M.; Maly, M.; Machala, L.; Nielsen, C.; Jørgensen, L. B.; Gerstoft, J.; Mathiesen, L.; Pedersen, C.; Nielsen, H.; Laursen, A.; Kvinesdal, B.; Liitsola, K.; Ristola, M.; Suni, J.; Sutinen, J.; Descamps, D.; Assoumou, L.; Castor, G.; Grude, M.; Flandre, P.; Storto, A.; Hamouda, O.; Kücherer, C.; Berg, T.; Braun, P.; Poggensee, G.; Däumer, M.; Eberle, J.; Heiken, H.; Kaiser, R.; Knechten, H.; Korn, K.; Müller, H.; Neifer, S.; Schmidt, B.; Walter, H.; Gunsenheimer-Bartmeyer, B.; Harrer, T.; Paraskevis, D.; Hatzakis, A.; Zavitsanou, A.; Vassilakis, A.; Lazanas, M.; Chini, M.; Lioni, A.; Sakka, V.; Kourkounti, S.; Paparizos, V.; Antoniadou, A.; Papadopoulos, A.; Poulakou, G.; Katsarolis, I.; Protopapas, K.; Chryssos, G.; Drimis, S.; Gargalianos, P.; Xylomenos, G.; Lourida, G.; Psichogiou, M.; Daikos, G. L.; Sipsas, N. V.; Kontos, A.; Gamaletsou, M. N.; Koratzanis, G.; Sambatakou, E.; Mariolis, H.; Skoutelis, A.; Papastamopoulos, V.; Georgiou, O.; Panagopoulos, P.; Maltezos, E.; Coughlan, S.; De Gascun, C.; Byrne, C.; Duffy, M.; Bergin, C.; Reidy, D.; Farrell, G.; Lambert, J.; O'Connor, E.; Rochford, A.; Low, J.; Coakely, P.; O'Dea, S.; Hall, W.; Mor, O.; Levi, I.; Chemtob, D.; Grossman, Z.; Zazzi, M.; De Luca, A.; Balotta, C.; Riva, C.; Mussini, C.; Caramma, I.; Capetti, A.; Colombo, M. C.; Rossi, C.; Prati, F.; Tramuto, F.; Vitale, F.; Ciccozzi, M.; Angarano, G.; Rezza, G.; Kolupajeva, T.; Kolupajeva, T.; Vasins, O.; Griskevicius, A.; Lipnickiene, V.; Schmit, J. C.; Struck, D.; Sauvageot, N.; Hemmer, R.; Arendt, V.; Michaux, C.; Staub, T.; Sequin-Devaux, C.; Wensing, A. M J; Boucher, C. A B; Van Kessel, A.; Van Bentum, P. H M; Brinkman, K.; Connell, B. J.; Van Der Ende, M. E.; Hoepelman, I. M.; Van Kasteren, M.; Kuipers, M.; Langebeek, N.; Richter, C.; Santegoets, R. M W J; Schrijnders-Gudde, L.; Schuurman, R.; Van De Ven, B. J M; Åsjö, B.; Kran, A. M Bakken; Ormaasen, V.; Aavitsland, P.; Horban, A.; Stanczak, J. J.; Stanczak, G. P.; Firlag-Burkacka, E.; Wiercinska-Drapalo, A.; Jablonowska, E.; Maolepsza, E.; Leszczyszyn-Pynka, M.; Szata, W.; Camacho, R.; Palma, C.; Borges, F.; Paixão, T.; Duque, V.; Araújo, F.; Otelea, D.; Paraschiv, S.; Tudor, A. M.; Cernat, R.; Chiriac, C.; Dumitrescu, F.; Prisecariu, L. J.; Stanojevic, M.; Jevtovic, Dj; Salemovic, D.; Stanekova, D.; Habekova, M.; Chabadová, Z.; Drobkova, T.; Bukovinova, P.; Shunnar, A.; Truska, P.; Poljak, M.; Lunar, M.; Babic, D.; Tomazic, J.; Vidmar, L.; Vovko, T.; Karner, P.; Garcia, F.; Paredes, R.; Monge, S.; Moreno, S.; Del Amo, J.; Asensi, V.; Sirvent, J. L.; De Mendoza, C.; Delgado, R.; Gutiérrez, F.; Berenguer, J.; Garcia-Bujalance, S.; Stella, N.; De Los Santos, I.; Blanco, J. R.; Dalmau, D.; Rivero, M.; Segura, F.; Elías, M. J Pérez; Alvarez, M.; Chueca, N.; Rodríguez-Martín, C.; Vidal, C.; Palomares, J. C.; Viciana, I.; Viciana, P.; Cordoba, J.; Aguilera, A.; Domingo, P.; Galindo, M. J.; Miralles, C.; Del Pozo, M. A.; Ribera, E.; Iribarren, J. A.; Ruiz, L.; De La Torre, J.; Vidal, F.; Clotet, B.; Albert, J.; Heidarian, A.; Aperia-Peipke, K.; Axelsson, M.; Mild, M.; Karlsson, A.; Sönnerborg, A.; Thalme, A.; Navér, L.; Bratt, G.; Karlsson, A.; Blaxhult, A.; Gisslén, M.; Svennerholm, B.; Bergbrant, I.; Björkman, P.; Säll, C.; Lindholm, A.; Kuylenstierna, N.; Montelius, R.; Azimi, F.; Johansson, B.; Carlsson, M.; Johansson, E.; Ljungberg, B.; Ekvall, H.; Strand, A.; Mäkitalo, S.; Öberg, S.; Holmblad, P.; Höfer, M.; Holmberg, H.; Josefson, P.; Ryding, U.

    2016-01-01

    Background. Numerous studies have shown that baseline drug resistance patterns may influence the outcome of antiretroviral therapy. Therefore, guidelines recommend drug resistance testing to guide the choice of initial regimen. In addition to optimizing individual patient management, these baseline

  11. Signaling Network Assessment of Mutations and Copy Number Variations Predict Breast Cancer Subtype-Specific Drug Targets

    Directory of Open Access Journals (Sweden)

    Naif Zaman

    2013-10-01

    Full Text Available Individual cancer cells carry a bewildering number of distinct genomic alterations (e.g., copy number variations and mutations, making it a challenge to uncover genomic-driven mechanisms governing tumorigenesis. Here, we performed exome sequencing on several breast cancer cell lines that represent two subtypes, luminal and basal. We integrated these sequencing data and functional RNAi screening data (for the identification of genes that are essential for cell proliferation and survival onto a human signaling network. Two subtype-specific networks that potentially represent core-signaling mechanisms underlying tumorigenesis were identified. Within both networks, we found that genes were differentially affected in different cell lines; i.e., in some cell lines a gene was identified through RNAi screening, whereas in others it was genomically altered. Interestingly, we found that highly connected network genes could be used to correctly classify breast tumors into subtypes on the basis of genomic alterations. Further, the networks effectively predicted subtype-specific drug targets, which were experimentally validated.

  12. The measurement of mixture homogeneity and dissolution to predict the degree of drug agglomerate breakdown achieved through powder mixing.

    Science.gov (United States)

    de Villiers, M M; Van der Watt, J G

    1994-11-01

    Interactive mixing of agglomerates of small, cohesive particles with coarse carrier particles facilitate the deaggregation of agglomerates. In this study dispersion of agglomerates of microfine furosemide particles by such a mixing process was followed by measuring changes in the content uniformity and area under the dissolution curve. Interactive mixtures between agglomerates of different sized furosemide particles and coarse sodium chloride particles were prepared using different mixers, mixing times and mixer speeds. The dissolution rate of the drug from and content uniformity of the mixtures were measured, and degrees of dispersion were calculated. These degrees of dispersion were compared to the dispersion values obtained from the decrease in agglomerate size after mixing. An increase in mixing time led to an increase in dispersion. An initial fast deagglomeration, indicated by an increase in dissolution, increase in content uniformity and a decrease in particle size, was followed by substantially slower deaggregation of remaining agglomerates and smaller aggregates. For all mixtures studied the degree of dispersion estimated from dissolution measurements, when compared to equivalent content uniformity measurements, agreed closely with the degree of dispersion as indicated by the decrease in particle size. The use of the area under the dissolution curve to predict agglomerate breakdown proved useful and may find application in situations where it is impossible to follow directly deagglomeration through particle size measurements.

  13. Denial of pain medication by health care providers predicts in-hospital illicit drug use among individuals who use illicit drugs.

    Science.gov (United States)

    Ti, Lianping; Voon, Pauline; Dobrer, Sabina; Montaner, Julio; Wood, Evan; Kerr, Thomas

    2015-01-01

    Undertreated pain is common among people who use illicit drugs (PWUD), and can often reflect the reluctance of health care providers to provide pain medication to individuals with substance use disorders. To investigate the relationship between having ever been denied pain medication by a health care provider and having ever reported using illicit drugs in hospital. Data were derived from participants enrolled in two Canadian prospective cohort studies between December 2012 and May 2013. Using bivariable and multivariable logistic regression analyses, the relationship between having ever been denied pain medication by a health care provider and having ever reported using illicit drugs in hospital was examined. Among 1053 PWUD who had experienced ≥ 1 hospitalization, 452 (44%) reported having ever used illicit drugs while in hospital and 491(48%) reported having ever been denied pain medication. In a multivariable model adjusted for confounders, having been denied pain medication was positively associated with having used illicit drugs in hospital (adjusted OR 1.46 [95% CI 1.14 to 1.88]). The results of the present study suggest that the denial of pain medication is associated with the use of illicit drugs while hospitalized. These findings raise questions about how to appropriately manage addiction and pain among PWUD and indicate the potential role that harm reduction programs may play in hospital settings.

  14. Predictivity of dog co-culture model, primary human hepatocytes and HepG2 cells for the detection of hepatotoxic drugs in humans

    International Nuclear Information System (INIS)

    Atienzar, Franck A.; Novik, Eric I.; Gerets, Helga H.; Parekh, Amit; Delatour, Claude; Cardenas, Alvaro; MacDonald, James; Yarmush, Martin L.; Dhalluin, Stéphane

    2014-01-01

    Drug Induced Liver Injury (DILI) is a major cause of attrition during early and late stage drug development. Consequently, there is a need to develop better in vitro primary hepatocyte models from different species for predicting hepatotoxicity in both animals and humans early in drug development. Dog is often chosen as the non-rodent species for toxicology studies. Unfortunately, dog in vitro models allowing long term cultures are not available. The objective of the present manuscript is to describe the development of a co-culture dog model for predicting hepatotoxic drugs in humans and to compare the predictivity of the canine model along with primary human hepatocytes and HepG2 cells. After rigorous optimization, the dog co-culture model displayed metabolic capacities that were maintained up to 2 weeks which indicates that such model could be also used for long term metabolism studies. Most of the human hepatotoxic drugs were detected with a sensitivity of approximately 80% (n = 40) for the three cellular models. Nevertheless, the specificity was low approximately 40% for the HepG2 cells and hepatocytes compared to 72.7% for the canine model (n = 11). Furthermore, the dog co-culture model showed a higher superiority for the classification of 5 pairs of close structural analogs with different DILI concerns in comparison to both human cellular models. Finally, the reproducibility of the canine system was also satisfactory with a coefficient of correlation of 75.2% (n = 14). Overall, the present manuscript indicates that the dog co-culture model may represent a relevant tool to perform chronic hepatotoxicity and metabolism studies. - Highlights: • Importance of species differences in drug development. • Relevance of dog co-culture model for metabolism and toxicology studies. • Hepatotoxicity: higher predictivity of dog co-culture vs HepG2 and human hepatocytes

  15. Predictivity of dog co-culture model, primary human hepatocytes and HepG2 cells for the detection of hepatotoxic drugs in humans

    Energy Technology Data Exchange (ETDEWEB)

    Atienzar, Franck A., E-mail: franck.atienzar@ucb.com [UCB Pharma SA, Non-Clinical Development, Chemin du Foriest, 1420 Braine-l' Alleud (Belgium); Novik, Eric I. [H mu rel Corporation, 675 U.S. Highway 1, North Brunswick, NJ 08902 (United States); Gerets, Helga H. [UCB Pharma SA, Non-Clinical Development, Chemin du Foriest, 1420 Braine-l' Alleud (Belgium); Parekh, Amit [H mu rel Corporation, 675 U.S. Highway 1, North Brunswick, NJ 08902 (United States); Delatour, Claude; Cardenas, Alvaro [UCB Pharma SA, Non-Clinical Development, Chemin du Foriest, 1420 Braine-l' Alleud (Belgium); MacDonald, James [Chrysalis Pharma Consulting, LLC, 385 Route 24, Suite 1G, Chester, NJ 07930 (United States); Yarmush, Martin L. [Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854 (United States); Dhalluin, Stéphane [UCB Pharma SA, Non-Clinical Development, Chemin du Foriest, 1420 Braine-l' Alleud (Belgium)

    2014-02-15

    Drug Induced Liver Injury (DILI) is a major cause of attrition during early and late stage drug development. Consequently, there is a need to develop better in vitro primary hepatocyte models from different species for predicting hepatotoxicity in both animals and humans early in drug development. Dog is often chosen as the non-rodent species for toxicology studies. Unfortunately, dog in vitro models allowing long term cultures are not available. The objective of the present manuscript is to describe the development of a co-culture dog model for predicting hepatotoxic drugs in humans and to compare the predictivity of the canine model along with primary human hepatocytes and HepG2 cells. After rigorous optimization, the dog co-culture model displayed metabolic capacities that were maintained up to 2 weeks which indicates that such model could be also used for long term metabolism studies. Most of the human hepatotoxic drugs were detected with a sensitivity of approximately 80% (n = 40) for the three cellular models. Nevertheless, the specificity was low approximately 40% for the HepG2 cells and hepatocytes compared to 72.7% for the canine model (n = 11). Furthermore, the dog co-culture model showed a higher superiority for the classification of 5 pairs of close structural analogs with different DILI concerns in comparison to both human cellular models. Finally, the reproducibility of the canine system was also satisfactory with a coefficient of correlation of 75.2% (n = 14). Overall, the present manuscript indicates that the dog co-culture model may represent a relevant tool to perform chronic hepatotoxicity and metabolism studies. - Highlights: • Importance of species differences in drug development. • Relevance of dog co-culture model for metabolism and toxicology studies. • Hepatotoxicity: higher predictivity of dog co-culture vs HepG2 and human hepatocytes.

  16. Predicting the Metabolic Sites by Flavin-Containing Monooxygenase on Drug Molecules Using SVM Classification on Computed Quantum Mechanics and Circular Fingerprints Molecular Descriptors.

    Directory of Open Access Journals (Sweden)

    Chien-Wei Fu

    Full Text Available As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum mechanics features such as the condensed Fukui function and attributes from circular fingerprints (called Molprint2D are computed and classified using the support vector machine (SVM for predicting some potential SOMs on a series of drugs that can be metabolized by FMO enzymes. The condensed Fukui function fA- representing the nucleophilicity of central atom A and the attributes from circular fingerprints accounting the influence of neighbors on the central atom. The total number of FMO substrates and non-substrates collected in the study is 85 and they are equally divided into the training and test sets with each carrying roughly the same number of potential SOMs. However, only N-oxidation and S-oxidation features were considered in the prediction since the available C-oxidation data was scarce. In the training process, the LibSVM package of WEKA package and the option of 10-fold cross validation are employed. The prediction performance on the test set evaluated by accuracy, Matthews correlation coefficient and area under ROC curve computed are 0.829, 0.659, and 0.877 respectively. This work reveals that the SVM model built can accurately predict the potential SOMs for drug molecules that are metabolizable by the FMO enzymes.

  17. A long-term three dimensional liver co-culture system for improved prediction of clinically relevant drug-induced hepatotoxicity

    International Nuclear Information System (INIS)

    Kostadinova, Radina; Boess, Franziska; Applegate, Dawn; Suter, Laura; Weiser, Thomas; Singer, Thomas; Naughton, Brian; Roth, Adrian

    2013-01-01

    Drug-induced liver injury (DILI) is the major cause for liver failure and post-marketing drug withdrawals. Due to species-specific differences in hepatocellular function, animal experiments to assess potential liabilities of drug candidates can predict hepatotoxicity in humans only to a certain extent. In addition to animal experimentation, primary hepatocytes from rat or human are widely used for pre-clinical safety assessment. However, as many toxic responses in vivo are mediated by a complex interplay among different cell types and often require chronic drug exposures, the predictive performance of hepatocytes is very limited. Here, we established and characterized human and rat in vitro three-dimensional (3D) liver co-culture systems containing primary parenchymal and non-parenchymal hepatic cells. Our data demonstrate that cells cultured on a 3D scaffold have a preserved composition of hepatocytes, stellate, Kupffer and endothelial cells and maintain liver function for up to 3 months, as measured by the production of albumin, fibrinogen, transferrin and urea. Additionally, 3D liver co-cultures maintain cytochrome P450 inducibility, form bile canaliculi-like structures and respond to inflammatory stimuli. Upon incubation with selected hepatotoxicants including drugs which have been shown to induce idiosyncratic toxicity, we demonstrated that this model better detected in vivo drug-induced toxicity, including species-specific drug effects, when compared to monolayer hepatocyte cultures. In conclusion, our results underline the importance of more complex and long lasting in vitro cell culture models that contain all liver cell types and allow repeated drug-treatments for detection of in vivo-relevant adverse drug effects. - Highlights: ► 3D liver co-cultures maintain liver specific functions for up to three months. ► Activities of Cytochrome P450s remain drug- inducible accross three months. ► 3D liver co-cultures recapitulate drug-induced liver toxicity

  18. A long-term three dimensional liver co-culture system for improved prediction of clinically relevant drug-induced hepatotoxicity

    Energy Technology Data Exchange (ETDEWEB)

    Kostadinova, Radina; Boess, Franziska [Non-Clinical Safety, Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Building 73 / Room 117b, 4070 Basel (Switzerland); Applegate, Dawn [RegeneMed, 9855 Towne Centre Drive Suite 200, San Diego, CA 92121 (United States); Suter, Laura; Weiser, Thomas; Singer, Thomas [Non-Clinical Safety, Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Building 73 / Room 117b, 4070 Basel (Switzerland); Naughton, Brian [RegeneMed, 9855 Towne Centre Drive Suite 200, San Diego, CA 92121 (United States); Roth, Adrian, E-mail: adrian_b.roth@roche.com [Non-Clinical Safety, Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Building 73 / Room 117b, 4070 Basel (Switzerland)

    2013-04-01

    Drug-induced liver injury (DILI) is the major cause for liver failure and post-marketing drug withdrawals. Due to species-specific differences in hepatocellular function, animal experiments to assess potential liabilities of drug candidates can predict hepatotoxicity in humans only to a certain extent. In addition to animal experimentation, primary hepatocytes from rat or human are widely used for pre-clinical safety assessment. However, as many toxic responses in vivo are mediated by a complex interplay among different cell types and often require chronic drug exposures, the predictive performance of hepatocytes is very limited. Here, we established and characterized human and rat in vitro three-dimensional (3D) liver co-culture systems containing primary parenchymal and non-parenchymal hepatic cells. Our data demonstrate that cells cultured on a 3D scaffold have a preserved composition of hepatocytes, stellate, Kupffer and endothelial cells and maintain liver function for up to 3 months, as measured by the production of albumin, fibrinogen, transferrin and urea. Additionally, 3D liver co-cultures maintain cytochrome P450 inducibility, form bile canaliculi-like structures and respond to inflammatory stimuli. Upon incubation with selected hepatotoxicants including drugs which have been shown to induce idiosyncratic toxicity, we demonstrated that this model better detected in vivo drug-induced toxicity, including species-specific drug effects, when compared to monolayer hepatocyte cultures. In conclusion, our results underline the importance of more complex and long lasting in vitro cell culture models that contain all liver cell types and allow repeated drug-treatments for detection of in vivo-relevant adverse drug effects. - Highlights: ► 3D liver co-cultures maintain liver specific functions for up to three months. ► Activities of Cytochrome P450s remain drug- inducible accross three months. ► 3D liver co-cultures recapitulate drug-induced liver toxicity

  19. Drug repurposing based on drug-drug interaction.

    Science.gov (United States)

    Zhou, Bin; Wang, Rong; Wu, Ping; Kong, De-Xin

    2015-02-01

    Given the high risk and lengthy procedure of traditional drug development, drug repurposing is gaining more and more attention. Although many types of drug information have been used to repurpose drugs, drug-drug interaction data, which imply possible physiological effects or targets of drugs, remain unexploited. In this work, similarity of drug interaction was employed to infer similarity of the physiological effects or targets for the drugs. We collected 10,835 drug-drug interactions concerning 1074 drugs, and for 700 of them, drug similarity scores based on drug interaction profiles were computed and rendered using a drug association network with 589 nodes (drugs) and 2375 edges (drug similarity scores). The 589 drugs were clustered into 98 groups with Markov Clustering Algorithm, most of which were significantly correlated with certain drug functions. This indicates that the network can be used to infer the physiological effects of drugs. Furthermore, we evaluated the ability of this drug association network to predict drug targets. The results show that the method is effective for 317 of 561 drugs that have known targets. Comparison of this method with the structure-based approach shows that they are complementary. In summary, this study demonstrates the feasibility of drug repurposing based on drug-drug interaction data. © 2014 John Wiley & Sons A/S.

  20. A proposal for a pharmacokinetic interaction significance classification system (PISCS) based on predicted drug exposure changes and its potential application to alert classifications in product labelling.

    Science.gov (United States)

    Hisaka, Akihiro; Kusama, Makiko; Ohno, Yoshiyuki; Sugiyama, Yuichi; Suzuki, Hiroshi

    2009-01-01

    Pharmacokinetic drug-drug interactions (DDIs) are one of the major causes of adverse events in pharmacotherapy, and systematic prediction of the clinical relevance of DDIs is an issue of significant clinical importance. In a previous study, total exposure changes of many substrate drugs of cytochrome P450 (CYP) 3A4 caused by coadministration of inhibitor drugs were successfully predicted by using in vivo information. In order to exploit these predictions in daily pharmacotherapy, the clinical significance of the pharmacokinetic changes needs to be carefully evaluated. The aim of the present study was to construct a pharmacokinetic interaction significance classification system (PISCS) in which the clinical significance of DDIs was considered with pharmacokinetic changes in a systematic manner. Furthermore, the classifications proposed by PISCS were compared in a detailed manner with current alert classifications in the product labelling or the summary of product characteristics used in Japan, the US and the UK. A matrix table was composed by stratifying two basic parameters of the prediction: the contribution ratio of CYP3A4 to the oral clearance of substrates (CR), and the inhibition ratio of inhibitors (IR). The total exposure increase was estimated for each cell in the table by associating CR and IR values, and the cells were categorized into nine zones according to the magnitude of the exposure increase. Then, correspondences between the DDI significance and the zones were determined for each drug group considering the observed exposure changes and the current classification in the product labelling. Substrate drugs of CYP3A4 selected from three therapeutic groups, i.e. HMG-CoA reductase inhibitors (statins), calcium-channel antagonists/blockers (CCBs) and benzodiazepines (BZPs), were analysed as representative examples. The product labelling descriptions of drugs in Japan, US and UK were obtained from the websites of each regulatory body. Among 220

  1. Prediction and validation of diffusion coefficients in a model drug delivery system using microsecond atomistic molecular dynamics simulation and vapour sorption analysis.

    Science.gov (United States)

    Forrey, Christopher; Saylor, David M; Silverstein, Joshua S; Douglas, Jack F; Davis, Eric M; Elabd, Yossef A

    2014-10-14

    Diffusion of small to medium sized molecules in polymeric medical device materials underlies a broad range of public health concerns related to unintended leaching from or uptake into implantable medical devices. However, obtaining accurate diffusion coefficients for such systems at physiological temperature represents a formidable challenge, both experimentally and computationally. While molecular dynamics simulation has been used to accurately predict the diffusion coefficients, D, of a handful of gases in various polymers, this success has not been extended to molecules larger than gases, e.g., condensable vapours, liquids, and drugs. We present atomistic molecular dynamics simulation predictions of diffusion in a model drug eluting system that represent a dramatic improvement in accuracy compared to previous simulation predictions for comparable systems. We find that, for simulations of insufficient duration, sub-diffusive dynamics can lead to dramatic over-prediction of D. We present useful metrics for monitoring the extent of sub-diffusive dynamics and explore how these metrics correlate to error in D. We also identify a relationship between diffusion and fast dynamics in our system, which may serve as a means to more rapidly predict diffusion in slowly diffusing systems. Our work provides important precedent and essential insights for utilizing atomistic molecular dynamics simulations to predict diffusion coefficients of small to medium sized molecules in condensed soft matter systems.

  2. Study Drugs and Academic Integrity: The Role of Beliefs about an Academic Honor Code in the Prediction of Nonmedical Prescription Drug Use for Academic Enhancement

    Science.gov (United States)

    Reisinger, Kelsy B.; Rutledge, Patricia C.; Conklin, Sarah M.

    2016-01-01

    The role of beliefs about academic integrity in college students' decisions to use nonmedical prescription drugs (NMPDs) in academic settings was examined. In Spring 2012 the authors obtained survey data from 645 participants at a small, undergraduate, private liberal arts institution in the Northeastern United States. A broadcast e-mail message…

  3. The impact of supersaturation level for oral absorption of BCS class IIb drugs, dipyridamole and ketoconazole, using in vivo predictive dissolution system: Gastrointestinal Simulator (GIS).

    Science.gov (United States)

    Tsume, Yasuhiro; Matsui, Kazuki; Searls, Amanda L; Takeuchi, Susumu; Amidon, Gregory E; Sun, Duxin; Amidon, Gordon L

    2017-05-01

    The development of formulations and the assessment of oral drug absorption for Biopharmaceutical Classification System (BCS) class IIb drugs is often a difficult issue due to the potential for supersaturation and precipitation in the gastrointestinal (GI) tract. The physiological environment in the GI tract largely influences in vivo drug dissolution rates of those drugs. Thus, those physiological factors should be incorporated into the in vitro system to better assess in vivo performance of BCS class IIb drugs. In order to predict oral bioperformance, an in vitro dissolution system with multiple compartments incorporating physiologically relevant factors would be expected to more accurately predict in vivo phenomena than a one-compartment dissolution system like USP Apparatus 2 because, for example, the pH change occurring in the human GI tract can be better replicated in a multi-compartmental platform. The Gastrointestinal Simulator (GIS) consists of three compartments, the gastric, duodenal and jejunal chambers, and is a practical in vitro dissolution apparatus to predict in vivo dissolution for oral dosage forms. This system can demonstrate supersaturation and precipitation and, therefore, has the potential to predict in vivo bioperformance of oral dosage forms where this phenomenon may occur. In this report, in vitro studies were performed with dipyridamole and ketoconazole to evaluate the precipitation rates and the relationship between the supersaturation levels and oral absorption of BCS class II weak base drugs. To evaluate the impact of observed supersaturation levels on oral absorption, a study utilizing the GIS in combination with mouse intestinal infusion was conducted. Supersaturation levels observed in the GIS enhanced dipyridamole and ketoconazole absorption in mouse, and a good correlation between their supersaturation levels and their concentration in plasma was observed. The GIS, therefore, appears to represent in vivo dissolution phenomena and

  4. Is the spatial distribution of brain lesions associated with closed-head injury predictive of subsequent development of attention-deficit/hyperactivity disorder? Analysis with brain-image database

    Science.gov (United States)

    Herskovits, E. H.; Megalooikonomou, V.; Davatzikos, C.; Chen, A.; Bryan, R. N.; Gerring, J. P.

    1999-01-01

    PURPOSE: To determine whether there is an association between the spatial distribution of lesions detected at magnetic resonance (MR) imaging of the brain in children after closed-head injury and the development of secondary attention-deficit/hyperactivity disorder (ADHD). MATERIALS AND METHODS: Data obtained from 76 children without prior history of ADHD were analyzed. MR images were obtained 3 months after closed-head injury. After manual delineation of lesions, images were registered to the Talairach coordinate system. For each subject, registered images and secondary ADHD status were integrated into a brain-image database, which contains depiction (visualization) and statistical analysis software. Using this database, we assessed visually the spatial distributions of lesions and performed statistical analysis of image and clinical variables. RESULTS: Of the 76 children, 15 developed secondary ADHD. Depiction of the data suggested that children who developed secondary ADHD had more lesions in the right putamen than children who did not develop secondary ADHD; this impression was confirmed statistically. After Bonferroni correction, we could not demonstrate significant differences between secondary ADHD status and lesion burdens for the right caudate nucleus or the right globus pallidus. CONCLUSION: Closed-head injury-induced lesions in the right putamen in children are associated with subsequent development of secondary ADHD. Depiction software is useful in guiding statistical analysis of image data.

  5. The mastermind approach to CNS drug therapy: translational prediction of human brain distribution, target site kinetics, and therapeutic effects

    OpenAIRE

    de Lange, Elizabeth CM

    2013-01-01

    Despite enormous advances in CNS research, CNS disorders remain the world?s leading cause of disability. This accounts for more hospitalizations and prolonged care than almost all other diseases combined, and indicates a high unmet need for good CNS drugs and drug therapies. Following dosing, not only the chemical properties of the drug and blood?brain barrier (BBB) transport, but also many other processes will ultimately determine brain target site kinetics and consequently the CNS effects. ...

  6. Oral delivery of anticancer drugs

    DEFF Research Database (Denmark)

    Thanki, Kaushik; Gangwal, Rahul P; Sangamwar, Abhay T

    2013-01-01

    The present report focuses on the various aspects of oral delivery of anticancer drugs. The significance of oral delivery in cancer therapeutics has been highlighted which principally includes improvement in quality of life of patients and reduced health care costs. Subsequently, the challenges...... incurred in the oral delivery of anticancer agents have been especially emphasized. Sincere efforts have been made to compile the various physicochemical properties of anticancer drugs from either literature or predicted in silico via GastroPlus™. The later section of the paper reviews various emerging...... trends to tackle the challenges associated with oral delivery of anticancer drugs. These invariably include efflux transporter based-, functional excipient- and nanocarrier based-approaches. The role of drug nanocrystals and various others such as polymer based- and lipid based...

  7. Enhancement of the predicted drug hepatotoxicity in gel entrapped hepatocytes within polysulfone-g-poly (ethylene glycol) modified hollow fiber

    International Nuclear Information System (INIS)

    Shen Chong; Zhang Guoliang; Meng Qin

    2010-01-01

    Collagen gel-based 3D cultures of hepatocytes have been proposed for evaluation of drug hepatotoxicity because of their more reliability than traditional monolayer culture. The collagen gel entrapment of hepatocytes in hollow fibers has been proven to well reflect the drug hepatotoxicity in vivo but was limited by adsorption of hydrophobic drugs onto hollow fibers. This study aimed to investigate the impact of hollow fibers on hepatocyte performance and drug hepatotoxicity. Polysulfone-g-poly (ethylene glycol) (PSf-g-PEG) hollow fiber was fabricated and applied for the first time to suppress the drug adsorption. Then, the impact of hollow fibers was evaluated by detecting the hepatotoxicity of eight selected drugs to gel entrapped hepatocytes within PSf and PSf-g-PEG hollow fibers, or without hollow fibers. The hepatocytes in PSf-g-PEG hollow fiber showed the highest sensitivity to drug hepatotoxicity, while those in PSf hollow fiber and cylindrical gel without hollow fiber underestimated the hepatotoxicity due to either drug adsorption or low hepatic functions. Therefore, the 3D culture of gel entrapped hepatocytes within PSf-g-PEG hollow fiber would be a promising tool for investigation of drug hepatotoxicity in vitro.

  8. Towards a better prediction of peak concentration, volume of distribution and half-life after oral drug administration in man, using allometry.

    Science.gov (United States)

    Sinha, Vikash K; Vaarties, Karin; De Buck, Stefan S; Fenu, Luca A; Nijsen, Marjoleen; Gilissen, Ron A H J; Sanderson, Wendy; Van Uytsel, Kelly; Hoeben, Eva; Van Peer, Achiel; Mackie, Claire E; Smit, Johan W

    2011-05-01

    It is imperative that new drugs demonstrate adequate pharmacokinetic properties, allowing an optimal safety margin and convenient dosing regimens in clinical practice, which then lead to better patient compliance. Such pharmacokinetic properties include suitable peak (maximum) plasma drug concentration (C(max)), area under the plasma concentration-time curve (AUC) and a suitable half-life (t(½)). The C(max) and t(½) following oral drug administration are functions of the oral clearance (CL/F) and apparent volume of distribution during the terminal phase by the oral route (V(z)/F), each of which may be predicted and combined to estimate C(max) and t(½). Allometric scaling is a widely used methodology in the pharmaceutical industry to predict human pharmacokinetic parameters such as clearance and volume of distribution. In our previous published work, we have evaluated the use of allometry for prediction of CL/F and AUC. In this paper we describe the evaluation of different allometric scaling approaches for the prediction of C(max), V(z)/F and t(½) after oral drug administration in man. Twenty-nine compounds developed at Janssen Research and Development (a division of Janssen Pharmaceutica NV), covering a wide range of physicochemical and pharmacokinetic properties, were selected. The C(max) following oral dosing of a compound was predicted using (i) simple allometry alone; (ii) simple allometry along with correction factors such as plasma protein binding (PPB), maximum life-span potential or brain weight (reverse rule of exponents, unbound C(max) approach); and (iii) an indirect approach using allometrically predicted