WorldWideScience

Sample records for predicting drug susceptibility

  1. 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...

  2. Treatment Default amongst Patients with Tuberculosis in Urban Morocco: Predicting and Explaining Default and Post-Default Sputum Smear and Drug Susceptibility Results

    Science.gov (United States)

    Ghali, Iraqi; Kizub, Darya; Billioux, Alexander C.; Bennani, Kenza; Bourkadi, Jamal Eddine; Benmamoun, Abderrahmane; Lahlou, Ouafae; Aouad, Rajae El; Dooley, Kelly E.

    2014-01-01

    Setting Public tuberculosis (TB) clinics in urban Morocco. Objective Explore risk factors for TB treatment default and develop a prediction tool. Assess consequences of default, specifically risk for transmission or development of drug resistance. Design Case-control study comparing patients who defaulted from TB treatment and patients who completed it using quantitative methods and open-ended questions. Results were interpreted in light of health professionals’ perspectives from a parallel study. A predictive model and simple tool to identify patients at high risk of default were developed. Sputum from cases with pulmonary TB was collected for smear and drug susceptibility testing. Results 91 cases and 186 controls enrolled. Independent risk factors for default included current smoking, retreatment, work interference with adherence, daily directly observed therapy, side effects, quick symptom resolution, and not knowing one’s treatment duration. Age >50 years, never smoking, and having friends who knew one’s diagnosis were protective. A simple scoring tool incorporating these factors was 82.4% sensitive and 87.6% specific for predicting default in this population. Clinicians and patients described additional contributors to default and suggested locally-relevant intervention targets. Among 89 cases with pulmonary TB, 71% had sputum that was smear positive for TB. Drug resistance was rare. Conclusion The causes of default from TB treatment were explored through synthesis of qualitative and quantitative data from patients and health professionals. A scoring tool with high sensitivity and specificity to predict default was developed. Prospective evaluation of this tool coupled with targeted interventions based on our findings is warranted. Of note, the risk of TB transmission from patients who default treatment to others is likely to be high. The commonly-feared risk of drug resistance, though, may be low; a larger study is required to confirm these findings

  3. Treatment default amongst patients with tuberculosis in urban Morocco: predicting and explaining default and post-default sputum smear and drug susceptibility results.

    Science.gov (United States)

    Cherkaoui, Imad; Sabouni, Radia; Ghali, Iraqi; Kizub, Darya; Billioux, Alexander C; Bennani, Kenza; Bourkadi, Jamal Eddine; Benmamoun, Abderrahmane; Lahlou, Ouafae; Aouad, Rajae El; Dooley, Kelly E

    2014-01-01

    Public tuberculosis (TB) clinics in urban Morocco. Explore risk factors for TB treatment default and develop a prediction tool. Assess consequences of default, specifically risk for transmission or development of drug resistance. Case-control study comparing patients who defaulted from TB treatment and patients who completed it using quantitative methods and open-ended questions. Results were interpreted in light of health professionals' perspectives from a parallel study. A predictive model and simple tool to identify patients at high risk of default were developed. Sputum from cases with pulmonary TB was collected for smear and drug susceptibility testing. 91 cases and 186 controls enrolled. Independent risk factors for default included current smoking, retreatment, work interference with adherence, daily directly observed therapy, side effects, quick symptom resolution, and not knowing one's treatment duration. Age >50 years, never smoking, and having friends who knew one's diagnosis were protective. A simple scoring tool incorporating these factors was 82.4% sensitive and 87.6% specific for predicting default in this population. Clinicians and patients described additional contributors to default and suggested locally-relevant intervention targets. Among 89 cases with pulmonary TB, 71% had sputum that was smear positive for TB. Drug resistance was rare. The causes of default from TB treatment were explored through synthesis of qualitative and quantitative data from patients and health professionals. A scoring tool with high sensitivity and specificity to predict default was developed. Prospective evaluation of this tool coupled with targeted interventions based on our findings is warranted. Of note, the risk of TB transmission from patients who default treatment to others is likely to be high. The commonly-feared risk of drug resistance, though, may be low; a larger study is required to confirm these findings.

  4. Treatment default amongst patients with tuberculosis in urban Morocco: predicting and explaining default and post-default sputum smear and drug susceptibility results.

    Directory of Open Access Journals (Sweden)

    Imad Cherkaoui

    Full Text Available Public tuberculosis (TB clinics in urban Morocco.Explore risk factors for TB treatment default and develop a prediction tool. Assess consequences of default, specifically risk for transmission or development of drug resistance.Case-control study comparing patients who defaulted from TB treatment and patients who completed it using quantitative methods and open-ended questions. Results were interpreted in light of health professionals' perspectives from a parallel study. A predictive model and simple tool to identify patients at high risk of default were developed. Sputum from cases with pulmonary TB was collected for smear and drug susceptibility testing.91 cases and 186 controls enrolled. Independent risk factors for default included current smoking, retreatment, work interference with adherence, daily directly observed therapy, side effects, quick symptom resolution, and not knowing one's treatment duration. Age >50 years, never smoking, and having friends who knew one's diagnosis were protective. A simple scoring tool incorporating these factors was 82.4% sensitive and 87.6% specific for predicting default in this population. Clinicians and patients described additional contributors to default and suggested locally-relevant intervention targets. Among 89 cases with pulmonary TB, 71% had sputum that was smear positive for TB. Drug resistance was rare.The causes of default from TB treatment were explored through synthesis of qualitative and quantitative data from patients and health professionals. A scoring tool with high sensitivity and specificity to predict default was developed. Prospective evaluation of this tool coupled with targeted interventions based on our findings is warranted. Of note, the risk of TB transmission from patients who default treatment to others is likely to be high. The commonly-feared risk of drug resistance, though, may be low; a larger study is required to confirm these findings.

  5. The Predictive Role of Difficulties in Emotion Regulation and Self-Control with Susceptibility to Addiction in Drug-Dependent Individuals

    Directory of Open Access Journals (Sweden)

    Mahmoud Shirazi

    2015-06-01

    Full Text Available Objective: The present study aimed to examine the predictive role of difficulties in emotion regulation and self-control in potential for addiction among drug-dependent individuals. Method: This was a correlational study which falls within the category of descriptive studies. The statistical population of the current study included all patients under treatment in outpatient health centers in Bam, among whom 315 individuals were selected through cluster sampling method as the participants of the study. Difficulties in Emotion Regulation Scale, Self-Control Scale, and Addiction Susceptibility Questionnaire were used for data collection purposes. Results: The results indicated that difficulties engaging in goal directed behavior, impulse control difficulties, lack of emotional awareness, and lack of emotional clarity (dimensions of difficulties in emotion regulation had a significant positive correlation with potential for addiction. In addition, there was a negative significant relationship between self-control and potential for addiction among drug-dependent individuals. Conclusion: In addition to common methods of abstinence from drug dependence, teaching self-control and emotional control techniques to addicted patients can help them reduce their dependence.

  6. Antiretroviral drug susceptibility among drug-naive adults with recent HIV infection in Rakai, Uganda.

    Science.gov (United States)

    Eshleman, Susan H; Laeyendecker, Oliver; Parkin, Neil; Huang, Wei; Chappey, Colombe; Paquet, Agnes C; Serwadda, David; Reynolds, Steven J; Kiwanuka, Noah; Quinn, Thomas C; Gray, Ronald; Wawer, Maria

    2009-04-27

    To analyze antiretroviral drug susceptibility in HIV from recently infected adults in Rakai, Uganda, prior to the availability of antiretroviral drug treatment. Samples obtained at the time of HIV seroconversion (1998-2003) were analyzed using the GeneSeq HIV and PhenoSense HIV assays (Monogram Biosciences, Inc., South San Francisco, California, USA). Test results were obtained for 104 samples (subtypes: 26A, 1C, 66D, 9A/D, 1C/D, 1 intersubtype recombinant). Mutations used for genotypic surveillance of transmitted antiretroviral drug resistance were identified in six samples: three had nucleoside reverse transcriptase inhibitor (NRTI) surveillance mutations (two had M41L, one had K219R), and three had protease inhibitor surveillance mutations (I47V, F53L, N88D); none had nonnucleoside reverse transcriptase inhibitor (NNRTI) surveillance mutations. Other resistance-associated mutations were identified in some samples. However, none of the samples had a sufficient number of mutations to predict reduced antiretroviral drug susceptibility. Ten (9.6%) of the samples had reduced phenotypic susceptibility to at least one drug (one had partial susceptibility to didanosine, one had nevirapine resistance, and eight had resistance or partial susceptibility to at least one protease inhibitor). Fifty-three (51%) of the samples had hypersusceptibility to at least one drug (seven had zidovudine hypersusceptibility, 28 had NNRTI hypersusceptibility, 34 had protease inhibitor hypersusceptibility). Delavirdine hypersusceptibility was more frequent in subtype A than D. In subtype D, efavirenz hypersusceptibility was associated with substitutions at codon 11 in HIV-reverse transcriptase. Phenotyping detected reduced antiretroviral drug susceptibility and hypersusceptibility in HIV from some antiretroviral-naive Ugandan adults that was not predicted by genotyping. Phenotyping may complement genotyping for analysis of antiretroviral drug susceptibility in populations with nonsubtype B

  7. The Predictive Role of Difficulties in Emotion Regulation and Self-Control with Susceptibility to Addiction in Drug-Dependent Individuals

    OpenAIRE

    Mahmoud Shirazi; Monavar Janfaza

    2015-01-01

    Objective: The present study aimed to examine the predictive role of difficulties in emotion regulation and self-control in potential for addiction among drug-dependent individuals. Method: This was a correlational study which falls within the category of descriptive studies. The statistical population of the current study included all patients under treatment in outpatient health centers in Bam, among whom 315 individuals were selected through cluster sampling method as the participants of t...

  8. The usefulness of microscopic observation for drug susceptibility of ...

    African Journals Online (AJOL)

    The usefulness of microscopic observation for drug susceptibility of. Mycobacterium tuberculosis complex in routine clinical microbiology laboratory. S.E. MSHANA1,2*, C. IMIRZALIOGLU2, E. DOMANN2 and T. CHAKRABORTY2. 1 We l Bugando Un vers ty College of Health Sc ences, P.O. Box 1464, Mwanza, Tanzan a.

  9. Bacterial isolates and drug susceptibility patterns of urinary tract ...

    African Journals Online (AJOL)

    Urinary tract infection (UTI) during pregnancy may cause complications such as pyelonephritis, hypertensive disease of pregnancy, anaemia, chronic renal failure, premature delivery and foetal mortality. This study aimed to identify the etiologic agents of UTI and to determine the patterns of antimicrobial drug susceptibility ...

  10. Antimicrobial drug susceptibility of Neisseria meningitidis strains isolated from carriers

    Directory of Open Access Journals (Sweden)

    Dayamí García

    2000-06-01

    Full Text Available When it is necessary to determine the susceptibility of Neisseria meningitidis (Nm strains to antimicrobial drugs, it is important to consider that it should be analyzed in a double context. One of them related to the use of drugs in a specific medical treatment; and the other; to chemoprophylatic drugs, both with the same purpose: the accurate selection of the “in vivo” antimicrobial agent. This requires the study of the sensitivity and resistance of strains isolated in both carriers and patients. With the aim of further studying the behavior of the strains that currently circulate in Cuba, an antimicrobial drug susceptibility study was conducted in 90 strains isolated from carriers during the first half of 1998. The agar dilution method was used to determine the minimum inhibitory concentrations (MICs to: penicillin, ampicillin, rifampin, sulfadiazine, chloramphenicol, ciprofloxacin, ceftriaxone, cefotaxime. The study of the three latter drugs was done for the first time in our country. The search for β- lactamase-producer strains was also performed. There was a predominance of penicillin sensitive strains (82,2% with an intermediate sensitivity to ampicillin (57,8%, while 70% of the strains were sensitive to sulfadiazine. Regarding the rest of the antimicrobial drugs, 100% of the strains were sensitive. The paper shows the MICs for each drug as well as the phenotypic characteristics of the strains with the penicillin and sulfadiazine sensitivity and resistance patterns. No β-lactamase-producer strains were found.

  11. Iron Deprivation Affects Drug Susceptibilities of Mycobacteria Targeting Membrane Integrity

    Directory of Open Access Journals (Sweden)

    Rahul Pal

    2015-01-01

    Full Text Available Multidrug resistance (MDR acquired by Mycobacterium tuberculosis (MTB through continuous deployment of antitubercular drugs warrants immediate search for novel targets and mechanisms. The ability of MTB to sense and become accustomed to changes in the host is essential for survival and confers the basis of infection. A crucial condition that MTB must surmount is iron limitation, during the establishment of infection, since iron is required by both bacteria and humans. This study focuses on how iron deprivation affects drug susceptibilities of known anti-TB drugs in Mycobacterium smegmatis, a “surrogate of MTB.” We showed that iron deprivation leads to enhanced potency of most commonly used first line anti-TB drugs that could be reverted upon iron supplementation. We explored that membrane homeostasis is disrupted upon iron deprivation as revealed by enhanced membrane permeability and hypersensitivity to membrane perturbing agent leading to increased passive diffusion of drug and TEM images showing detectable differences in cell envelope thickness. Furthermore, iron seems to be indispensable to sustain genotoxic stress suggesting its possible role in DNA repair machinery. Taken together, we for the first time established a link between cellular iron and drug susceptibility of mycobacteria suggesting iron as novel determinant to combat MDR.

  12. Drug susceptibility of fungi isolated from ICU patients

    Science.gov (United States)

    Modrzewska, Barbara D; Kurnatowska,, Anna J; Khalid, Katarzyna

    Candida species can be a reason of infections associated with high morbidity and mortality. The risk of invasive candidosis for patients admitted to intensive care units (ICUs) is increased due to immunosuppressive states, prolonged length of stay, broad-spectrum antibiotics and Candida colonization. The aim of the study was to determine selected properties of fungi isolated from patients treated in the ICUs of hospitals in Lodz. The materials were collected from the oral cavity, the tracheostomy or endotracheal tube and urine from 16 children and 35 adult. In total, 127 samples were examined to differentiate the fungal strains with used morphological and biochemical methods. Candida species were isolated from adult patients (82.9%), but were not isolated from any of the children; C. albicans was the predominant fungus (61.7%), much less frequent were C. glabrata (12.8%), C. tropicalis (6.4%) and C. kefyr, C. dubliniensis (4.3% each).The susceptibility of fungi to antimycotic drugs revealed that almost all of the strains were susceptible to nystatin (97.9%) and to amphotericin B (72.3%), and resistant to fluconazole (72.3%) and ketoconazole (57.5%). No isolation of fungi from children remaining in ICU may be an evidence of high sanitary regime at these wards; fungi from the genus Candida are the etiological factors for ICU infections; 3/5 of them are caused by C. albicans, mostly of the code 2 576 174, characteristic for strains isolated from hospitalized patients; it is necessary to determine the species of the fungus and its susceptibility to drugs, which allows to conduct effective therapy; prophylactic administration of fluconazole leads to an increase in the number of strains resistant to this chemotherapeutic agent; in the antifungal local treatment, nystatin should be a drug of choice as the drug to which most fungi are susceptible.

  13. Microbial sensor for drug susceptibility testing of Mycobacterium tuberculosis.

    Science.gov (United States)

    Zhang, Z-T; Wang, D-B; Li, C-Y; Deng, J-Y; Zhang, J-B; Bi, L-J; Zhang, X-E

    2018-01-01

    Drug susceptibility testing (DST) of clinical isolates of Mycobacterium tuberculosis is critical in treating tuberculosis. We demonstrate the possibility of using a microbial sensor to perform DST of M. tuberculosis and shorten the time required for DST. The sensor is made of an oxygen electrode with M. tuberculosis cells attached to its surface. This sensor monitors the residual oxygen consumption of M. tuberculosis cells after treatment with anti-TB drugs with glycerine as a carbon source. In principle, after drug pretreatment for 4-5 days, the response differences between the sensors made of drug-sensitive isolates are distinguishable from the sensors made of drug-resistant isolates. The susceptibility of the M. tuberculosis H37Ra strain, its mutants and 35 clinical isolates to six common anti-TB drugs: rifampicin, isoniazid, streptomycin, ethambutol, levofloxacin and para-aminosalicylic acid were tested using the proposed method. The results agreed well with the gold standard method (LJ) and were determined in significantly less time. The whole procedure takes approximately 11 days and therefore has the potential to inform clinical decisions. To our knowledge, this is the first study that demonstrates the possible application of a dissolved oxygen electrode-based microbial sensor in M. tuberculosis drug resistance testing. This study used the microbial sensor to perform DST of M. tuberculosis and shorten the time required for DST. The overall detection result of the microbial sensor agreed well with that of the conventional LJ proportion method and takes less time than the existing phenotypic methods. In future studies, we will build an O 2 electrode array microbial sensor reactor to enable a high-throughput drug resistance analysis. © 2017 The Authors. Journal of Applied Microbiology published by John Wiley & Sons Ltd on behalf of The Society for Applied Microbiology.

  14. Human Gut Microbiota Predicts Susceptibility to Vibrio cholerae Infection.

    Science.gov (United States)

    Midani, Firas S; Weil, Ana A; Chowdhury, Fahima; Begum, Yasmin A; Khan, Ashraful I; Debela, Meti D; Durand, Heather K; Reese, Aspen T; Nimmagadda, Sai N; Silverman, Justin D; Ellis, Crystal N; Ryan, Edward T; Calderwood, Stephen B; Harris, Jason B; Qadri, Firdausi; David, Lawrence A; LaRocque, Regina C

    2018-04-12

    Cholera is a public health problem worldwide and the risk factors for infection are only partially understood. We prospectively studied household contacts of cholera patients to compare those who were infected with those who were not. We constructed predictive machine learning models of susceptibility using baseline gut microbiota data. We identified bacterial taxa associated with susceptibility to Vibrio cholerae infection and tested these taxa for interactions with V. cholerae in vitro. We found that machine learning models based on gut microbiota predicted V. cholerae infection as well as models based on known clinical and epidemiological risk factors. A 'predictive gut microbiota' of roughly 100 bacterial taxa discriminated between contacts who developed infection and those who did not. Susceptibility to cholera was associated with depleted levels of microbes from the phylum Bacteroidetes. By contrast, a microbe associated with cholera by our modeling framework, Paracoccus aminovorans, promoted the in vitro growth of V. cholerae. Gut microbiota structure, clinical outcome, and age were also linked. These findings support the hypothesis that abnormal gut microbial communities are a host factor related to V. cholerae susceptibility.

  15. Rapid drug susceptibility test of mycobacterium tuberculosis by bioluminescence sensor

    Science.gov (United States)

    Lu, Bin; Xu, Shunqing; Chen, Zifei; Zhou, Yikai

    2001-09-01

    With the persisting increase of drug-resistant stains of M. Tuberculosis around the world, rapid and sensitive detection of antibiotic of M. Tuberculosis is becoming more and more important. In the present study, drug susceptibility of M. tuberculosis were detected by recombination mycobacteriophage combined with bioluminescence sensor. It is based on the use of recombination mycobacteriophage which can express firefly luciferase when it infects viable mycobacteria, and can effectively produce quantifiable photon. Meanwhile, in mycobacterium cells treated with active antibiotic, no light is observed. The emitted light is recorded by a bioluminscence sensor, so the result of drug-resistant test can be determined by the naked eye. 159 stains of M. tuberculosis were applied to this test on their resistant to rifampin, streptomycin and isoniazid. It is found that the agreement of this assay with Liewenstein- Jensen slat is: rifampin 95.60 percent, isoniazid 91.82 percent, streptomycin 88.68 percent, which showed that it is a fast and practical method to scene and detect drug resistant of mycobacterium stains.

  16. Predictive susceptibility analysis of typhoon induced landslides in Central Taiwan

    Science.gov (United States)

    Shou, Keh-Jian; Lin, Zora

    2017-04-01

    Climate change caused by global warming affects Taiwan significantly for the past decade. The increasing frequency of extreme rainfall events, in which concentrated and intensive rainfalls generally cause geohazards including landslides and debris flows. The extraordinary, such as 2004 Mindulle and 2009 Morakot, hit Taiwan and induced serious flooding and landslides. This study employs rainfall frequency analysis together with the atmospheric general circulation model (AGCM) downscaling estimation to understand the temporal rainfall trends, distributions, and intensities in the adopted Wu River watershed in Central Taiwan. To assess the spatial hazard of the landslides, landslide susceptibility analysis was also applied. Different types of rainfall factors were tested in the susceptibility models for a better accuracy. In addition, the routes of typhoons were also considered in the predictive analysis. The results of predictive analysis can be applied for risk prevention and management in the study area.

  17. Machine learning modelling for predicting soil liquefaction susceptibility

    Directory of Open Access Journals (Sweden)

    P. Samui

    2011-01-01

    Full Text Available This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN based on multi-layer perceptions (MLP that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N160] and cyclic stress ratio (CSR. Further, an attempt has been made to simplify the models, requiring only the two parameters [(N160 and peck ground acceleration (amax/g], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

  18. 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...

  19. Drug susceptibility testing of Mycobacterium tuberculosis to fluoroquinolones

    DEFF Research Database (Denmark)

    Johansen, I S; Larsen, A R; Sandven, P

    2003-01-01

    In the first attempt to establish a quality assurance programme for susceptibility testing of Mycobacterium tuberculosis to fluoroquinolones, 20 strains with different fluoroquinolone susceptibility patterns were distributed by the Supranational Reference Laboratory in Stockholm to the other...

  20. Trypanosoma cruzi benznidazole susceptibility in vitro does not predict the therapeutic outcome of human Chagas disease

    Directory of Open Access Journals (Sweden)

    Margoth Moreno

    2010-11-01

    Full Text Available Therapeutic failure of benznidazole (BZ is widely documented in Chagas disease and has been primarily associated with variations in the drug susceptibility of Trypanosoma cruzi strains. In humans, therapeutic success has been assessed by the negativation of anti-T. cruzi antibodies, a process that may take up to 10 years. A protocol for early screening of the drug resistance of infective strains would be valuable for orienting physicians towards alternative therapies, with a combination of existing drugs or new anti-T. cruzi agents. We developed a procedure that couples the isolation of parasites by haemoculture with quantification of BZ susceptibility in the resultant epimastigote forms. BZ activity was standardized with reference strains, which showed IC50 to BZ between 7.6-32 µM. The assay was then applied to isolates from seven chronic patients prior to administration of BZ therapy. The IC50 of the strains varied from 15.6 ± 3-51.4 ± 1 µM. Comparison of BZ susceptibility of the pre-treatment isolates of patients considered cured by several criteria and of non-cured patients indicates that the assay does not predict therapeutic outcome. A two-fold increase in BZ resistance in the post-treatment isolates of two patients was verified. Based on the profile of nine microsatellite loci, sub-population selection in non-cured patients was ruled out.

  1. Comparative drug susceptibility study of five clonal strains of Trichomonas vaginalis in vitro

    Institute of Scientific and Technical Information of China (English)

    Hemantkumar Somabhai Chaudhari; Prati Pal Singh

    2011-01-01

    Objective: To produce comparative data on a group of Trichomonas vaginalis clonal strains with varied drug responses using identical methods and materials. Methods: Five clonal strains of Trichomonas vaginalis were isolated from reference strain using agar plate technique. The variability of growth kinetic and susceptibility of clonal strain to metronidazole, tinidazole, satranidazole and nitazoxanide were observed in 96 well microtitre plate. Results: Among these clonal strains there was a good correlation between rates of growth with the relative susceptibility of the strains to drugs in vitro. Regarding metronidazole, tinidazole and satranidazole susceptibility, different degrees of susceptibility were determined. However, no difference in nitazoxanide susceptibility was found between the clonal strain tested and a reference strain.Conclusions: This is the first description of biological variability in clonal stock of Trichomonas vaginalis. Different degrees of drug susceptibility were determined among clonal strains tested. Further studies will be necessary to ascertain the importance of this variability in clinical infection.

  2. Simple, direct drug susceptibility testing technique for diagnosis of drug-resistant tuberculosis in resource-poor settings.

    Science.gov (United States)

    Kim, C-K; Joo, Y-T; Lee, E P; Park, Y K; Kim, H-J; Kim, S J

    2013-09-01

    The Korean Institute of Tuberculosis, Seoul, Republic of Korea. To develop a simple, direct drug susceptibility testing (DST) technique using Kudoh-modified Ogawa (KMO) medium. The critical concentrations of isoniazid (INH), rifampicin (RMP), kanamycin (KM) and ofloxacin (OFX) for KMO medium were calibrated by comparing the minimal inhibitory concentrations (MICs) against clinical isolates of Mycobacterium tuberculosis on KMO with those on Löwenstein-Jensen (LJ). The performance of the direct KMO DST technique was evaluated on 186 smear-positive sputum specimens and compared with indirect LJ DST. Agreement of MICs on direct vs. indirect DST was high for INH, RMP and OFX. KM MICs on KMO were ∼10 g/ml higher than those on LJ. The critical concentrations of INH, RMP, OFX and KM for KMO were therefore set at 0.2, 40.0, 2.0, and 40.0 g/ml. The evaluation of direct DST of smear-positive sputum specimens showed 100% agreement with indirect LJ DST for INH and RMP. However, the respective susceptible and resistant predictive values were 98.8% and 100% for OFX, and 100% and 80% for KM. Direct DST using KMO is useful, with clear advantages of a shorter turnaround time, procedural simplicity and low cost compared to indirect DST. It may be most indicated in resource-poor settings for programmatic management of drug-resistant tuberculosis.

  3. Disinfectant-susceptibility of multi-drug-resistant Mycobacterium tuberculosis isolated in Japan

    Directory of Open Access Journals (Sweden)

    Noriko Shinoda

    2016-02-01

    Full Text Available Abstract Background Multi-drug-resistant Mycobacterium tuberculosis has been an important problem in public health around the world. However, limited information about disinfectant-susceptibility of multi-drug-resistant strain of M. tuberculosis was available. Findings We studied susceptibility of several Japanese isolates of multi-drug-resistant M. tuberculosis against disinfectants, which are commonly used in clinical and research laboratories. We selected a laboratory reference strain (H37Rv and eight Japanese isolates, containing five drug-susceptible strains and three multi-drug-resistant strains, and determined profiles of susceptibility against eight disinfectants. The M. tuberculosis strains were distinguished into two groups by the susceptibility profile. There was no relationship between multi-drug-resistance and disinfectant-susceptibility in the M. tuberculosis strains. Cresol soap and oxydol were effective against all strains we tested, regardless of drug resistance. Conclusions Disinfectant-resistance is independent from multi-drug-resistance in M. tuberculosis. Cresol soap and oxydol were effective against all strains we tested, regardless of drug resistance.

  4. Recapitulation of Clinical Individual Susceptibility to Drug-Induced QT Prolongation in Healthy Subjects Using iPSC-Derived Cardiomyocytes

    Directory of Open Access Journals (Sweden)

    Tadahiro Shinozawa

    2017-02-01

    Full Text Available To predict drug-induced serious adverse events (SAE in clinical trials, a model using a panel of cells derived from human induced pluripotent stem cells (hiPSCs of individuals with different susceptibilities could facilitate major advancements in translational research in terms of safety and pharmaco-economics. However, it is unclear whether hiPSC-derived cells can recapitulate interindividual differences in drug-induced SAE susceptibility in populations not having genetic disorders such as healthy subjects. Here, we evaluated individual differences in SAE susceptibility based on an in vitro model using hiPSC-derived cardiomyocytes (hiPSC-CMs as a pilot study. hiPSCs were generated from blood samples of ten healthy volunteers with different susceptibilities to moxifloxacin (Mox-induced QT prolongation. Different Mox-induced field potential duration (FPD prolongation values were observed in the hiPSC-CMs from each individual. Interestingly, the QT interval was significantly positively correlated with FPD at clinically relevant concentrations (r > 0.66 in multiple analyses including concentration-QT analysis. Genomic analysis showed no interindividual significant differences in known target-binding sites for Mox and other drugs such as the hERG channel subunit, and baseline QT ranges were normal. The results suggest that hiPSC-CMs from healthy subjects recapitulate susceptibility to Mox-induced QT prolongation and provide proof of concept for in vitro preclinical trials.

  5. 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.

  6. Susceptibility of Selected Multi-Drug Resistant Clinical Isolates to ...

    African Journals Online (AJOL)

    2018-03-01

    Mar 1, 2018 ... under the terms of the Creative Commons. Attribution ... relies on traditional medicine for their primary ... Collection and identification of plant materials: ..... versus carbapenem- susceptible Gram-negative microorganisms: Risk.

  7. Drug Susceptibility of Mycobacterium tuberculosis Beijing Genotype and Association with MDR TB

    Science.gov (United States)

    ten Kate, Marian T.; de Knegt, Gerjo J.; Kremer, Kristin; Aarnoutse, Rob E.; Boeree, Martin J.; Verbrugh, Henri A.; van Soolingen, Dick; Bakker-Woudenberg, Irma A.J.M.

    2012-01-01

    To determine differences in the ability of Mycobacterium tuberculosis strains to withstand antituberculosis drug treatment, we compared the activity of antituberculosis drugs against susceptible Beijing and East-African/Indian genotype M. tuberculosis strains. Beijing genotype strains showed high rates of mutation within a wide range of drug concentrations, possibly explaining this genotype’s association with multidrug-resistant tuberculosis. PMID:22469099

  8. Neural responses to exclusion predict susceptibility to social influence.

    Science.gov (United States)

    Falk, Emily B; Cascio, Christopher N; O'Donnell, Matthew Brook; Carp, Joshua; Tinney, Francis J; Bingham, C Raymond; Shope, Jean T; Ouimet, Marie Claude; Pradhan, Anuj K; Simons-Morton, Bruce G

    2014-05-01

    Social influence is prominent across the lifespan, but sensitivity to influence is especially high during adolescence and is often associated with increased risk taking. Such risk taking can have dire consequences. For example, in American adolescents, traffic-related crashes are leading causes of nonfatal injury and death. Neural measures may be especially useful in understanding the basic mechanisms of adolescents' vulnerability to peer influence. We examined neural responses to social exclusion as potential predictors of risk taking in the presence of peers in recently licensed adolescent drivers. Risk taking was assessed in a driving simulator session occurring approximately 1 week after the neuroimaging session. Increased activity in neural systems associated with the distress of social exclusion and mentalizing during an exclusion episode predicted increased risk taking in the presence of a peer (controlling for solo risk behavior) during a driving simulator session outside the neuroimaging laboratory 1 week later. These neural measures predicted risky driving behavior above and beyond self-reports of susceptibility to peer pressure and distress during exclusion. These results address the neural bases of social influence and risk taking; contribute to our understanding of social and emotional function in the adolescent brain; and link neural activity in specific, hypothesized, regions to risk-relevant outcomes beyond the neuroimaging laboratory. Results of this investigation are discussed in terms of the mechanisms underlying risk taking in adolescents and the public health implications for adolescent driving. Copyright © 2014 Society for Adolescent Health and Medicine. All rights reserved.

  9. In silico site-directed mutagenesis informs species-specific predictions of chemical susceptibility derived from the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool

    Science.gov (United States)

    The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool was developed to address needs for rapid, cost effective methods of species extrapolation of chemical susceptibility. Specifically, the SeqAPASS tool compares the primary sequence (Level 1), functiona...

  10. Susceptibility of Aeromonas Hydophila Isolates to Antimicrobial Drugs

    Directory of Open Access Journals (Sweden)

    Igor Stojanov

    2010-05-01

    Full Text Available Aeromonas hydrophila is a microorganism widely distributed in nature: in water, soil, food. It is also part of the normal bacterial flora of many animals. As an opportune microorganism it is a secondary biological agent that contributes to the occurrence of a fish disease and its deterioration. Frequently, its presence is an indication of bad zoohygiene and zootechnical conditions in fish ponds. Reduced quality and quantity of feed, mechanical injuries, parasitosis, seasonal oscillation in temperature present some of the factors that produce favorable conditions for bacterial proliferation of aeromonas in fish ponds, so clinical symptoms of the disease occur. Aeromonas is almost always present in clinical isolates and may be unjustly accused for bad health of fish. Antibiotic therapy is applied even when the clinical findings are clear, what certainly effects the susceptibility to chemotherapeutics. The subject of our work was bacteriological examination of the material obtained from the carps with the observed skin changes and the carps without these changes. Also, antimicrobial susceptibility of Aeromonas hydrophila was tested. The aim of this research was to determined the presence of Aeromonas hydrophilia in the carp ponds and to test antibiotic susceptibility. The material consisted of the samples from the fish ponds where the carps were with and without changed skin. The method the isolation of Aeromonas hydrophila was used. The diffusion disk technique was used for testing antibiotic susceptibility. The isolates were tested for their susceptibility to Florephenikol, Flumequine, Olaqindox and Oxitetracycline. The obtained results point that antimicrobial susceptibility was the same regardless of the origin of the samples, i.e. the resistance was the same for both groups of samples (the strains isolated from the fish with skin changes and the strains from fish without changes on skin. The strains were highly resistant: 35% were resistant to

  11. What is the mechanism for persistent coexistence of drug-susceptible and drug-resistant strains of Streptococcus pneumoniae?

    Science.gov (United States)

    Colijn, Caroline; Cohen, Ted; Fraser, Christophe; Hanage, William; Goldstein, Edward; Givon-Lavi, Noga; Dagan, Ron; Lipsitch, Marc

    2010-01-01

    The rise of antimicrobial resistance in many pathogens presents a major challenge to the treatment and control of infectious diseases. Furthermore, the observation that drug-resistant strains have risen to substantial prevalence but have not replaced drug-susceptible strains despite continuing (and even growing) selective pressure by antimicrobial use presents an important problem for those who study the dynamics of infectious diseases. While simple competition models predict the exclusion of one strain in favour of whichever is ‘fitter’, or has a higher reproduction number, we argue that in the case of Streptococcus pneumoniae there has been persistent coexistence of drug-sensitive and drug-resistant strains, with neither approaching 100 per cent prevalence. We have previously proposed that models seeking to understand the origins of coexistence should not incorporate implicit mechanisms that build in stable coexistence ‘for free’. Here, we construct a series of such ‘structurally neutral’ models that incorporate various features of bacterial spread and host heterogeneity that have been proposed as mechanisms that may promote coexistence. We ask to what extent coexistence is a typical outcome in each. We find that while coexistence is possible in each of the models we consider, it is relatively rare, with two exceptions: (i) allowing simultaneous dual transmission of sensitive and resistant strains lets coexistence become a typical outcome, as does (ii) modelling each strain as competing more strongly with itself than with the other strain, i.e. self-immunity greater than cross-immunity. We conclude that while treatment and contact heterogeneity can promote coexistence to some extent, the in-host interactions between strains, particularly the interplay between coinfection, multiple infection and immunity, play a crucial role in the long-term population dynamics of pathogens with drug resistance. PMID:19940002

  12. 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

  13. A Review of Moxifloxacin for the Treatment of Drug-Susceptible Tuberculosis.

    Science.gov (United States)

    Naidoo, Anushka; Naidoo, Kogieleum; McIlleron, Helen; Essack, Sabiha; Padayatchi, Nesri

    2017-11-01

    Moxifloxacin, an 8-methoxy quinolone, is an important drug in the treatment of multidrug-resistant tuberculosis and is being investigated in novel drug regimens with pretomanid, bedaquiline, and pyrazinamide, or rifapentine, for the treatment of drug-susceptible tuberculosis. Early results of these studies are promising. Although current evidence does not support the use of moxifloxacin in treatment-shortening regimens for drug-susceptible tuberculosis, it may be recommended in patients unable to tolerate standard first-line drug regimens or for isoniazid monoresistance. Evidence suggests that the standard 400-mg dose of moxifloxacin used in the treatment of tuberculosis may be suboptimal in some patients, leading to worse tuberculosis treatment outcomes and emergence of drug resistance. Furthermore, a drug interaction with the rifamycins results in up to 31% reduced plasma concentrations of moxifloxacin when these are combined for treatment of drug-susceptible tuberculosis, although the clinical relevance of this interaction is unclear. Moxifloxacin exhibits extensive interindividual pharmacokinetic variability. Higher doses of moxifloxacin may be needed to achieve drug exposures required for improved clinical outcomes. Further study is, however, needed to determine the safety of proposed higher doses and clinically validated targets for drug exposure to moxifloxacin associated with improved tuberculosis treatment outcomes. We discuss in this review the evidence for the use of moxifloxacin in drug-susceptible tuberculosis and explore the role of moxifloxacin pharmacokinetics, pharmacodynamics, and drug interactions with rifamycins, on tuberculosis treatment outcomes when used in first-line tuberculosis drug regimens. © 2017, The American College of Clinical Pharmacology.

  14. SeqAPASS: Predicting chemical susceptibility to threatened/endangered species

    Science.gov (United States)

    Conservation of a molecular target across species can be used as a line-of-evidence to predict the likelihood of chemical susceptibility. The web-based Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS; https://seqapass.epa.gov/seqapass/) application was devel...

  15. Application of luciferase assay for ATP to antimicrobial drug susceptibility

    Science.gov (United States)

    Chappelle, E. W.; Picciolo, G. L.; Vellend, H.; Tuttle, S. A.; Barza, M. J.; Weinstein, L. (Inventor)

    1977-01-01

    The susceptibility of bacteria, particularly those derived from body fluids, to antimicrobial agents is determined in terms of an ATP index measured by culturing a bacterium in a growth medium. The amount of ATP is assayed in a sample of the cultured bacterium by measuring the amount of luminescent light emitted when the bacterial ATP is reacted with a luciferase-luciferin mixture. The sample of the cultured bacterium is subjected to an antibiotic agent. The amount of bacterial adenosine triphosphate is assayed after treatment with the antibiotic by measuring the luminescent light resulting from the reaction. The ATP index is determined from the values obtained from the assay procedures.

  16. A Microfluidic Channel Method for Rapid Drug-Susceptibility Testing of Pseudomonas aeruginosa.

    Directory of Open Access Journals (Sweden)

    Yoshimi Matsumoto

    Full Text Available The recent global increase in the prevalence of antibiotic-resistant bacteria and lack of development of new therapeutic agents emphasize the importance of selecting appropriate antimicrobials for the treatment of infections. However, to date, the development of completely accelerated drug susceptibility testing methods has not been achieved despite the availability of a rapid identification method. We proposed an innovative rapid method for drug susceptibility testing for Pseudomonas aeruginosa that provides results within 3 h. The drug susceptibility testing microfluidic (DSTM device was prepared using soft lithography. It consisted of five sets of four microfluidic channels sharing one inlet slot, and the four channels are gathered in a small area, permitting simultaneous microscopic observation. Antimicrobials were pre-introduced into each channel and dried before use. Bacterial suspensions in cation-adjusted Mueller-Hinton broth were introduced from the inlet slot and incubated for 3 h. Susceptibilities were microscopically evaluated on the basis of differences in cell numbers and shapes between drug-treated and control cells, using dedicated software. The results of 101 clinically isolated strains of P. aeruginosa obtained using the DSTM method strongly correlated with results obtained using the ordinary microbroth dilution method. Ciprofloxacin, meropenem, ceftazidime, and piperacillin caused elongation in susceptible cells, while meropenem also induced spheroplast and bulge formation. Morphological observation could alternatively be used to determine the susceptibility of P. aeruginosa to these drugs, although amikacin had little effect on cell shape. The rapid determination of bacterial drug susceptibility using the DSTM method could also be applicable to other pathogenic species, and it could easily be introduced into clinical laboratories without the need for expensive instrumentation.

  17. Resistance mechanisms and drug susceptibility testing of nontuberculous mycobacteria.

    NARCIS (Netherlands)

    Ingen, J. van; Boeree, M.J.; Soolingen, D. van; Mouton, J.W.

    2012-01-01

    Nontuberculous mycobacteria (NTM) are increasingly recognized as causative agents of opportunistic infections in humans. For most NTM infections the therapy of choice is drug treatment, but treatment regimens differ by species, in particular between slow (e.g. Mycobacterium avium complex,

  18. Meta-analysis and genome-wide interpretation of genetic susceptibility to drug addiction

    Science.gov (United States)

    2011-01-01

    Background Classical genetic studies provide strong evidence for heritable contributions to susceptibility to developing dependence on addictive substances. Candidate gene and genome-wide association studies (GWAS) have sought genes, chromosomal regions and allelic variants likely to contribute to susceptibility to drug addiction. Results Here, we performed a meta-analysis of addiction candidate gene association studies and GWAS to investigate possible functional mechanisms associated with addiction susceptibility. From meta-data retrieved from 212 publications on candidate gene association studies and 5 GWAS reports, we linked a total of 843 haplotypes to addiction susceptibility. We mapped the SNPs in these haplotypes to functional and regulatory elements in the genome and estimated the magnitude of the contributions of different molecular mechanisms to their effects on addiction susceptibility. In addition to SNPs in coding regions, these data suggest that haplotypes in gene regulatory regions may also contribute to addiction susceptibility. When we compared the lists of genes identified by association studies and those identified by molecular biological studies of drug-regulated genes, we observed significantly higher participation in the same gene interaction networks than expected by chance, despite little overlap between the two gene lists. Conclusions These results appear to offer new insights into the genetic factors underlying drug addiction. PMID:21999673

  19. 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.

  20. Mycobacterium abscessus subsp. abscessus Lung Disease: Drug Susceptibility Testing in Sputum Culture Negative Conversion

    Directory of Open Access Journals (Sweden)

    Takehiko Kobayashi

    2018-01-01

    Full Text Available Background: Among Mycobacterium abscessus complex infections, patients with M. abscessus subsp. abscessus (MAA lung disease are difficult to treat and no standard therapy has been established. Few reports have investigated the drug susceptibility of these strains. We retrospectively investigated how in vitro drug susceptibility testing (DST of MAA affects the induction of sputum conversion using pharmacotherapy. Methods: Patients with MAA lung disease diagnosed and treated between 2010 and 2014 at our hospital were enrolled and divided into Group A (sputum conversion without relapse within 1 year and Group B (persistent positive cultured or negative conversion with relapse. MAA was identified in M. abscessus using sequence with genotyping, and DST of MAA was performed. Results: We assessed 23 patients (9 males and 14 females. There were 8 patients in Group A and 15 in Group B. Higher prevalence of susceptible isolates for clarithromycin (CAM susceptibility on day 14 was noted in Group A than in Group B (P = 0.03 and no significant difference observed in the two groups for other drugs. Conclusions: In vitro DST of MAA, especially CAM susceptibility on day 14, affected the results of negative conversion. No other drugs were found to affect sputum culture negative conversion.

  1. Heightened vulnerability to MDR-TB epidemics after controlling drug-susceptible TB.

    Directory of Open Access Journals (Sweden)

    Jason D Bishai

    2010-09-01

    Full Text Available Prior infection with one strain TB has been linked with diminished likelihood of re-infection by a new strain. This paper attempts to determine the role of declining prevalence of drug-susceptible TB in enabling future epidemics of MDR-TB.A computer simulation of MDR-TB epidemics was developed using an agent-based model platform programmed in NetLogo (See http://mdr.tbtools.org/. Eighty-one scenarios were created, varying levels of treatment quality, diagnostic accuracy, microbial fitness cost, and the degree of immunogenicity elicited by drug-susceptible TB. Outcome measures were the number of independent MDR-TB cases per trial and the proportion of trials resulting in MDR-TB epidemics for a 500 year period after drug therapy for TB is introduced.MDR-TB epidemics propagated more extensively after TB prevalence had fallen. At a case detection rate of 75%, improving therapeutic compliance from 50% to 75% can reduce the probability of an epidemic from 45% to 15%. Paradoxically, improving the case-detection rate from 50% to 75% when compliance with DOT is constant at 75% increases the probability of MDR-TB epidemics from 3% to 45%.The ability of MDR-TB to spread depends on the prevalence of drug-susceptible TB. Immunologic protection conferred by exposure to drug-susceptible TB can be a crucial factor that prevents MDR-TB epidemics when TB treatment is poor. Any single population that successfully reduces its burden of drug-susceptible TB will have reduced herd immunity to externally or internally introduced strains of MDR-TB and can experience heightened vulnerability to an epidemic. Since countries with good TB control may be more vulnerable, their self interest dictates greater promotion of case detection and DOTS implementation in countries with poor control to control their risk of MDR-TB.

  2. Drug susceptibility testing of Mycobacterium Avium subsp. Avium isolates from naturally infected domestic pigeons to avian tuberculosis

    Directory of Open Access Journals (Sweden)

    Kaveh Parvandar

    2016-01-01

    Conclusion: We suggest drug susceptibility testing for more nontuberculous mycobateria, particularly M. avium complex isolated from infected birds and humans, as well as molecular basics of drug sensitivity in order to detect resistance genes of pathogenic M. avium subsp. avium.

  3. In-vitro antimycobacterial drug susceptibility testing of non-tubercular mycobacteria by tetrazolium microplate assay.

    Science.gov (United States)

    Sankar, Manimuthu Mani; Gopinath, Krishnamoorthy; Singla, Roopak; Singh, Sarman

    2008-07-11

    Non-tubercular mycobacteria (NTM) has not been given due attention till the recent epidemic of HIV. This has led to increasing interest of health care workers in their biology, epidemiology and drug resistance. However, timely detection and drug susceptibility profiling of NTM isolates are always difficult in resource poor settings like India. Furthermore, no standardized methodology or guidelines are available to reproduce the results with clinical concordance. To find an alternative and rapid method for performing the drug susceptibility assay in a resource limited settings like India, we intended to evaluate the utility of Tetrazolium microplate assay (TEMA) in comparison with proportion method for reporting the drug resistance in clinical isolates of NTM. A total of fifty-five NTM isolates were tested for susceptibility against Streptomycin, Rifampicin, Ethambutol, Ciprofloxacin, Ofloxacin, Azithromycin, and Clarithromycin by TEMA and the results were compared with agar proportion method (APM). Of the 55 isolates, 23 (41.8%) were sensitive to all the drugs and the remaining 32 (58.2%) were resistant to at least one drug. TEMA had very good concordance with APM except with minor discrepancies. Susceptibility results were obtained in the median of 5 to 9 days by TEMA. The NTM isolates were highly sensitive against Ofloxacin (98.18% sensitive) and Ciprofloxacin (90.09% sensitive). M. mucogenicum was sensitive only to Clarithromycin and resistant to all the other drugs tested. The concordance between TEMA and APM ranged between 96.4 - 100%. Tetrazolium Microplate Assay is a rapid and highly reproducible method. However, it must be performed only in tertiary level Mycobacteriology laboratories with proper bio-safety conditions.

  4. Transmission of Multidrug-Resistant and Drug-Susceptible Tuberculosis within Households: A Prospective Cohort Study

    Science.gov (United States)

    Grandjean, Louis; Gilman, Robert H.; Martin, Laura; Soto, Esther; Castro, Beatriz; Lopez, Sonia; Coronel, Jorge; Castillo, Edith; Alarcon, Valentina; Lopez, Virginia; San Miguel, Angela; Quispe, Neyda; Asencios, Luis; Dye, Christopher; Moore, David A. J.

    2015-01-01

    Background The “fitness” of an infectious pathogen is defined as the ability of the pathogen to survive, reproduce, be transmitted, and cause disease. The fitness of multidrug-resistant tuberculosis (MDRTB) relative to drug-susceptible tuberculosis is cited as one of the most important determinants of MDRTB spread and epidemic size. To estimate the relative fitness of drug-resistant tuberculosis cases, we compared the incidence of tuberculosis disease among the household contacts of MDRTB index patients to that among the contacts of drug-susceptible index patients. Methods and Findings This 3-y (2010–2013) prospective cohort household follow-up study in South Lima and Callao, Peru, measured the incidence of tuberculosis disease among 1,055 household contacts of 213 MDRTB index cases and 2,362 household contacts of 487 drug-susceptible index cases. A total of 35/1,055 (3.3%) household contacts of 213 MDRTB index cases developed tuberculosis disease, while 114/2,362 (4.8%) household contacts of 487 drug-susceptible index patients developed tuberculosis disease. The total follow-up time for drug-susceptible tuberculosis contacts was 2,620 person-years, while the total follow-up time for MDRTB contacts was 1,425 person-years. Using multivariate Cox regression to adjust for confounding variables including contact HIV status, contact age, socio-economic status, and index case sputum smear grade, the hazard ratio for tuberculosis disease among MDRTB household contacts was found to be half that for drug-susceptible contacts (hazard ratio 0.56, 95% CI 0.34–0.90, p = 0.017). The inference of transmission in this study was limited by the lack of genotyping data for household contacts. Capturing incident disease only among household contacts may also limit the extrapolation of these findings to the community setting. Conclusions The low relative fitness of MDRTB estimated by this study improves the chances of controlling drug-resistant tuberculosis. However, fitter

  5. [Circadian rhythm in susceptibility of mice to the anti-tumor drug carboplatin].

    Science.gov (United States)

    Lu, X H; Yin, L J

    1994-12-01

    The platinum-containing compounds has become a major chemical agent in the treatment of cancer. A circadian rhythm in the susceptibility of rodents and human being to cisplatin has been demonstrated, the maximal tolerance being found in the animal's active phase. Carboplatin is a second generation analog. Two studies were performed on mice with carboplatin under 12:12 light dark cycle to study its chronotoxicity and chronoeffectiveness. In study I, single intraperitoneal injection of 192mg/kg (LD50) carboplatin was given to four groups of mice at four different circadian stage. It was found that at 50% the overall mortality of mice, there was a mortality difference of 28% for mice receiving the drug at 9 a.m. to 71% for mice receiving drug at 9 p.m. It demonstrated that carboplatin was better tolerated in the animal's early sleep phase. In study II, S180 tumor-bearing mice were treated with 50mg/kg of carboplatin. The longest mean survival time and the lowest marrow toxicity occurred in the group which received the drug at the beginning of the sleep phase. It showed that the susceptibility of mice to carboplatin is circadian stage dependent. These data clearly demonstrate that, by timing the administration of drugs according to body rhythms, such as the host susceptibility-resistance rhythm to a drug, one can gain a therapeutic advantage over an approach which ignores such rhythms.

  6. Evaluation of rapid radiometric method for drug susceptibility testing of Mycobacterium tuberculosis

    International Nuclear Information System (INIS)

    Siddiqi, S.H.; Libonati, J.P.; Middlebrook, G.

    1981-01-01

    A total of 106 isolates of Mycobacterium tuberculosis were tested for drug susceptibility by the conventional 7H11 plate method and by a new rapid radiometric method using special 7H12 liquid medium with 14 C-labeled substrate. Results obtained by the two methods were compared for rapidity, sensitivity, and specificity of the new test method. There was 98% overall agreement between the results obtained by the two methods. Of a total of 424 drug tests, only 8 drug results did not agree, mostly in the case of streptomycin. This new procedure was found to be rapid, with 87% of the tests results reportable within 4 days and 98% reportable within 5 days as compared to the usual 3 weeks required with the conventional indirect susceptibility test method. The results of this preliminary study indicate that the rapid radiometric method seems to have the potential for routine laboratory use and merits further investigations

  7. Spatial agreement of predicted patterns in landslide susceptibility maps

    Czech Academy of Sciences Publication Activity Database

    Sterlacchini, S.; Ballabio, C.; Blahůt, Jan; Masetti, J.; Sorichetta, A.

    2011-01-01

    Roč. 125, č. 1 (2011), s. 51-61 ISSN 0169-555X Institutional research plan: CEZ:AV0Z30460519 Keywords : landslide susceptibility * weights of evidence * success rate Subject RIV: DE - Earth Magnetism, Geodesy, Geography Impact factor: 2.520, year: 2011 http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V93-511TN6P-3&_user=1407143&_coverDate=01%2F01%2F2011&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000052620&_version=1&_urlVersion=0&_userid=1407143&md5=d4e3ac0e1295373f2203f99a1aa8e905&searchtype=a

  8. Genetic selection for coping style predicts stressor susceptibility

    NARCIS (Netherlands)

    Veenema, AH; Meijer, OC; de Kloet, ER; Koolhaas, JM

    Genetically selected aggressive (SAL) and nonaggressive (LAL) male wild house-mice which show distinctly different coping styles, also display a differential regulation of the hypothalamic-pituitary-adrenal axis after exposure to an acute stressor. To test the hypothesis that coping style predicts

  9. A simple field kit for the determination of drug susceptibility in Plasmodium falciparum.

    Science.gov (United States)

    Nguyen-Dinh, P; Magloire, R; Chin, W

    1983-05-01

    A field kit has been developed which greatly simplifies the performance of the 48-hour in vitro test for drug resistance in Plasmodium falciparum. The kit uses an easily reconstituted lyophilized culture medium, and requires only a fingerprick blood sample. In parallel tests with 13 isolates of P. falciparum in Haiti, the new technique had a success rate equal to that of the previously described method, with comparable results in terms of parasite susceptibility in vitro to chloroquine and pyrimethamine.

  10. Predictive modeling of gingivitis severity and susceptibility via oral microbiota.

    Science.gov (United States)

    Huang, Shi; Li, Rui; Zeng, Xiaowei; He, Tao; Zhao, Helen; Chang, Alice; Bo, Cunpei; Chen, Jie; Yang, Fang; Knight, Rob; Liu, Jiquan; Davis, Catherine; Xu, Jian

    2014-09-01

    Predictive modeling of human disease based on the microbiota holds great potential yet remains challenging. Here, 50 adults underwent controlled transitions from naturally occurring gingivitis, to healthy gingivae (baseline), and to experimental gingivitis (EG). In diseased plaque microbiota, 27 bacterial genera changed in relative abundance and functional genes including 33 flagellar biosynthesis-related groups were enriched. Plaque microbiota structure exhibited a continuous gradient along the first principal component, reflecting transition from healthy to diseased states, which correlated with Mazza Gingival Index. We identified two host types with distinct gingivitis sensitivity. Our proposed microbial indices of gingivitis classified host types with 74% reliability, and, when tested on another 41-member cohort, distinguished healthy from diseased individuals with 95% accuracy. Furthermore, the state of the microbiota in naturally occurring gingivitis predicted the microbiota state and severity of subsequent EG (but not the state of the microbiota during the healthy baseline period). Because the effect of disease is greater than interpersonal variation in plaque, in contrast to the gut, plaque microbiota may provide advantages in predictive modeling of oral diseases.

  11. 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...

  12. Evaluation of the microscopic observational drug susceptibility assay for rapid and efficient diagnosis of multi-drug resistant tuberculosis

    Directory of Open Access Journals (Sweden)

    R P Lazarus

    2012-01-01

    Full Text Available Purpose: Tuberculosis (TB is endemic in India and the burden of multi-drug-resistant tuberculosis (MDR-TB is high. Early detection of MDR-TB is of primary importance in controlling the spread of TB. The microscopic observational drug susceptibility (MODS assay has been described as a cost-effective and rapid method by which mycobacterial culture and the drug susceptibility test (DST can be done at the same time. Materials and Methods: A total of 302 consecutive sputum samples that were received in an accredited mycobacteriology laboratory for conventional culture and DST were evaluated by the MODS assay. Results: In comparison with conventional culture on Lowenstein Jensen (LJ media, the MODS assay showed a sensitivity of 94.12% and a specificity of 89.39% and its concordance with the DST by the proportion method on LJ media to isoniazid and rifampicin was 90.8% and 91.5%, respectively. The turnaround time for results by MODS was 9 days compared to 21 days by culture on LJ media and an additional 42 days for DST by the 1% proportion method. The cost of performing a single MODS assay was Rs. 250/-, compared to Rs. 950/- for culture and 1st line DST on LJ. Conclusion: MODS was found to be a sensitive and rapid alternative method for performing culture and DST to identify MDR-TB in resource poor settings.

  13. 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; ...

  14. Estimating Fitness by Competition Assays between Drug Susceptible and Resistant Mycobacterium tuberculosis of Predominant Lineages in Mumbai, India

    Science.gov (United States)

    Bhatter, Purva; Chatterjee, Anirvan; D'souza, Desiree; Tolani, Monica; Mistry, Nerges

    2012-01-01

    Background Multi Drug Resistant Tuberculosis (MDR TB) is a threat to global tuberculosis control. A significant fitness cost has been associated with DR strains from specific lineages. Evaluation of the influence of the competing drug susceptible strains on fitness of drug resistant strains may have an important bearing on understanding the spread of MDR TB. The aim of this study was to evaluate the fitness of MDR TB strains, from a TB endemic region of western India: Mumbai, belonging to 3 predominant lineages namely CAS, Beijing and MANU in the presence of drug susceptible strains from the same lineages. Methodology Drug susceptible strains from a single lineage were mixed with drug resistant strain, bearing particular non synonymous mutation (rpoB D516V; inhA, A16G; katG, S315T1/T2) from the same or different lineages. Fitness of M.tuberculosis (M.tb) strains was evaluated using the difference in growth rates obtained by using the CFU assay system. Conclusion/Significance While MANU were most fit amongst the drug susceptible strains of the 3 lineages, only Beijing MDR strains were found to grow in the presence of any of the competing drug susceptible strains. A disproportionate increase in Beijing MDR could be an alarm for an impending epidemic in this locale. In addition to particular non synonymous substitutions, the competing strains in an environment may impact the fitness of circulating drug resistant strains. PMID:22479407

  15. 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.

  16. [Antimicrobial susceptibility and drug-resistance genes of Yersinia spp. of retailed poultry in 4 provinces of China].

    Science.gov (United States)

    Peng, Z X; Zou, M Y; Xu, J; Guan, W Y; Li, Y; Liu, D R; Zhang, S S; Hao, Q; Yan, S F; Wang, W; Yu, D M; Li, F Q

    2018-04-06

    Objective: To monitor the antimicrobial resistance and drug-resistance genes of Yersinia enterocolitis , Y. intermedia and Y. frederiksenii recovered from retailed fresh poultry of 4 provinces of China. Methods: The susceptibility of 25 isolated Yersinia spp. to 14 classes and 25 kinds of antibiotics was determined by broth microdilution method according to CLSI (Clinical and Laboratory Standards Institute). The antibiotic resistance genes were predicted with antibiotic resistance genes database (ARDB) using whole genome sequences of Yersinia spp. Results: In all 22 Y. enterocolitis tested, 63.7% (14 isolates), 22.8% (5 isolates), 4.6% and 4.6% of 1 isolates exhibited the resistance to cefoxitin, ampicillin-sulbactam, nitrofurantoin and trimethoprim-sulfamethoxazole, respectively. All the 25 isolates were multi-drug resistant to more than 3 antibiotics, while 64.0% of isolates were resistant to more than 4 antibiotics. A few Y. enterocolitis isolates of this study were intermediate to ceftriaxone and ciprofloxacin. Most Yersinia spp. isolates contained antibiotic resistance genes mdtG, ksgA, bacA, blaA, rosAB and acrB , and 5 isolates recovered from fresh chicken also contained dfrA 1, catB 2 and ant 3 ia . Conclusion: The multi-drug resistant Yersinia spp. isolated from retailed fresh poultry is very serious in the 4 provinces of China, and their contained many kinds of drug-resistance genes.

  17. Flow cytometry susceptibility testing for conventional antifungal drugs and Comparison with the NCCLS Broth Macrodilution Test

    Directory of Open Access Journals (Sweden)

    M.J. Najafzadeh

    2009-08-01

    Full Text Available Introduction: During the last decade, the incidence of fungal infection has been increased in many countries. Because of the advent of resistant to antifungal agents, determination of an efficient strategic plan for treatment of fungal disease is an important issue in clinical mycology. Many methods have been introduced and developed for determination of invitro susceptibility tests. During the recent years, flow cytometry has developed to solving the problem and many papers have documented the usefulness of this technique. Materials and methods: As the first step, the invitro susceptibility of standard PTCC (Persian Type of Culture Collection strain and some clinical isolates of Candida consisting of Candida albicans, C. dubliniensis, C. glabrata, C. kefyer and C. parapsilosis were evaluated by macrodilution broth method according to NCCLS (National Committee for Clinical Laboratory Standards guidelines and flow cytometry susceptibility test. Results:  The data indicated that macro dilution broth methods and flow cytometry have the same results in determination of MIC (Minimum Inhibitory Concentration for amphotericin B, clotrimazole, fluconazole, ketoconazole and miconazole in C. albicans PTCC 5027 as well as clinical Candida isolates, such as C.albicans, C.dubliniensis, C.glabrata C.kefyr, and C.parapsilosis. Discussion: Comparing the results obtained by macrodilution broth and flow cytometry methods revealed that flow cytometry was faster. It is suggested that flow cytometry susceptibility test can be used as a powerful tool for determination of MIC and administration of the best antifungal drug in treatment of patients with Candida infections.

  18. Ergothioneine Maintains Redox and Bioenergetic Homeostasis Essential for Drug Susceptibility and Virulence of Mycobacterium tuberculosis

    Directory of Open Access Journals (Sweden)

    Vikram Saini

    2016-01-01

    Full Text Available The mechanisms by which Mycobacterium tuberculosis (Mtb maintains metabolic equilibrium to survive during infection and upon exposure to antimycobacterial drugs are poorly characterized. Ergothioneine (EGT and mycothiol (MSH are the major redox buffers present in Mtb, but the contribution of EGT to Mtb redox homeostasis and virulence remains unknown. We report that Mtb WhiB3, a 4Fe-4S redox sensor protein, regulates EGT production and maintains bioenergetic homeostasis. We show that central carbon metabolism and lipid precursors regulate EGT production and that EGT modulates drug sensitivity. Notably, EGT and MSH are both essential for redox and bioenergetic homeostasis. Transcriptomic analyses of EGT and MSH mutants indicate overlapping but distinct functions of EGT and MSH. Last, we show that EGT is critical for Mtb survival in both macrophages and mice. This study has uncovered a dynamic balance between Mtb redox and bioenergetic homeostasis, which critically influences Mtb drug susceptibility and pathogenicity.

  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. Radiometric studies on the oxidation of (I-14C) fatty acids by drug-susceptible and drug-resistant mycobacteria

    International Nuclear Information System (INIS)

    Camargo, E.E.; Kopajtic, T.M.; Hopkins, G.K.; Cannon, N.P.; Wagner Junior, H.N.

    1987-01-01

    A radiometric assay system has been used to study oxidation patterns of (l - 14 C) fatty acids by drug-susceptible and drug-resistant organisms of the genus Mycobacterium (M. tuberculosis - H 37 Rv and Erdman, M. bovis, M. avium, M. intracellulare, M.Kansasii and M. chelonei). The organisms were inoculated in sterile reaction vials containing liquid 7H9 medium, 10% ADC enrichment and 1.0 uli of one of the (l- 14 C) fatty acids (butyric, hexanoic, octanoic, decanoic, lauric, myristic, palmitic, stearic, oleic, linoleic, linolenic). Vials were incubated at 37 0 C and the 14 CO 2 envolved was measured daily for 3 days with a Bactec R-301 instrument. (M.A.C.) [pt

  1. In vitro antimalarial drug susceptibility in Thai border areas from 1998–2003

    Directory of Open Access Journals (Sweden)

    Mungthin Mathirut

    2005-08-01

    Full Text Available Abstract Background The Thai-Myanmar and Thai-Cambodia borders have been historically linked with the emergence and spread of Plasmodium falciparum parasites resistant to antimalarial drugs. Indeed, the areas are often described as harbouring multi-drug resistant parasites. These areas of Thailand have experienced significant changes in antimalarial drug exposure patterns over the past decade. This study describes the in vitro antimalarial susceptibility patterns of 95 laboratory-adapted P. falciparum isolates, collected between 1998 and 2003,. Methods Ninety five P. falciparum isolates were collected from five sites in Thailand between 1998 and 2003. After laboratory adaptation to in vitro culture, the susceptibility of these parasites to a range of established antimalarial drugs (chloroquine [CQ], mefloquine [MQ], quinine [QN] and dihydroartemisinin [DHA] was determined by the isotopic microtest. Results Mefloquine (MQ sensitivity remained poorest in areas previously described as MQ-resistant areas. Sensitivity to MQ of parasites from this area was significantly lower than those from areas reported to harbour moderate (p = 0.002 of low level MQ resistance (p = 000001. Importantly for all drugs tested, there was a considerable range in absolute parasite sensitivities. There was a weak, but statistically positive correlation between parasite sensitivity to CQ and sensitivity to both QN and MQ and a positive correlation between MQ and QN. In terms of geographical distribution, parasites from the Thai-Cambodia were tended to be less sensitive to all drugs tested compared to the Thai-Myanmar border. Parasite sensitivity to all drugs was stable over the 6-year collection period with the exception of QN. Conclusion This study highlights the high degree of variability in parasite drug sensitivity in Thailand. There were geographical differences in the pattern of resistance which might reflect differences in drug usage in each area. In contrast to many

  2. Evaluation of MGIT 960 System for the Second-Line Drugs Susceptibility Testing of Mycobacterium tuberculosis

    Directory of Open Access Journals (Sweden)

    Hyejin Kim

    2013-01-01

    Full Text Available Many laboratories validate DST of the second-line drugs by BACTEC MGIT 960 system. The objective of this study is to evaluate the critical concentration and perform DST for the 2nd line drugs. We evaluated 193 clinical strains of M. tuberculosis isolated from patients in South Korea. Testing the critical concentration of six second-line drugs was performed by MGIT 960 and compared with L-J proportion method. The critical concentration was determined to establish the most one that gave the difference between drug resistance and susceptibility in MGIT960 system. Good agreement of the following concentrations was found: Concordance was 95% for 0.5 μg/mL of moxifloxacin; 93.6%, 1.0 μg/mL of levofloxacin; 97.5%, 2.5 μg/mL of kanamycin; 90.6%, 2.5 μg/mL of capreomycin; 86.2%, 5.0 μg/mL of ethionamide; and 90.8%, 2.0 μg/mL of ρ-aminosalicylic acid. The critical concentrations of the four drugs, moxifloxacin, levofloxacin, kanamycin, and capreomycin, were concordant and reliable for testing 2nd line drug resistance. Further study of ethionamide and ρ-aminosalicylic acid is required.

  3. Multiple drug-susceptibility screening in Mycobacterium bovis: new nucleotide polymorphisms in the embB gene among ethambutol susceptible strains

    Directory of Open Access Journals (Sweden)

    Cinzia Marianelli

    2015-04-01

    Conclusion: All M. bovis isolates were sensitive to the most common antituberculosis drugs used for treatment. There was a good agreement between the d-REMA assay and the agar based reference method. Among ethambutol susceptible isolates, four new embB mutations were found.

  4. Miltefosine and antimonial drug susceptibility of Leishmania Viannia species and populations in regions of high transmission in Colombia.

    Directory of Open Access Journals (Sweden)

    Olga Lucía Fernández

    2014-05-01

    Full Text Available Pentavalent antimonials have been the first line treatment for dermal leishmaniasis in Colombia for over 30 years. Miltefosine is administered as second line treatment since 2005. The susceptibility of circulating populations of Leishmania to these drugs is unknown despite clinical evidence supporting the emergence of resistance.In vitro susceptibility was determined for intracellular amastigotes of 245 clinical strains of the most prevalent Leishmania Viannia species in Colombia to miltefosine (HePC and/or meglumine antimoniate (Sb(V; 163, (80% were evaluated for both drugs. Additionally, susceptibility to Sb(V was examined in two cohorts of 85 L. V. panamensis strains isolated between 1980-1989 and 2000-2009 in the municipality of Tumaco. Susceptibility to each drug differed among strains of the same species and between species. Whereas 68% of L. V. braziliensis strains presented in vitro resistance to HePC, 69% were sensitive to Sb(V. Resistance to HePC and Sb(V occurred respectively, in 20% y 21% of L. panamensis strains. Only 3% of L. V. guyanensis were resistant to HePC, and none to Sb(V. Drug susceptibility differed between geographic regions and time periods. Subpopulations having disparate susceptibility to Sb(V were discerned among L. V. panamensis strains isolated during 1980-1990 in Tumaco where resistant strains belonged to zymodeme 2.3, and sensitive strains to zymodeme 2.2.Large scale evaluation of clinical strains of Leishmania Viannia species demonstrated species, population, geographic, and epidemiologic differences in susceptibility to meglumine antimoniate and miltefosine, and provided baseline information for monitoring susceptibility to these drugs. Sensitive and resistant clinical strains within each species, and zymodeme as a proxy marker of antimony susceptibility for L. V. panamensis, will be useful in deciphering factors involved in susceptibility and the distribution of sensitive and resistant populations.

  5. Renal endothelial function and blood flow predict the individual susceptibility to adriamycin-induced renal damage

    NARCIS (Netherlands)

    Ochodnicky, Peter; Henning, Robert H.; Buikema, Hendrik; Kluppel, Alex C. A.; van Wattum, Marjolein; de Zeeuw, Dick; van Dokkum, Richard P. E.

    Background. Susceptibility to renal injury varies among individuals. Previously, we found that individual endothelial function of healthy renal arteries in vitro predicted severity of renal damage after 5/6 nephrectomy. Here we hypothesized that individual differences in endothelial function in

  6. Renal endothelial function and blood flow predict the individual susceptibility to adriamycin-induced renal damage

    NARCIS (Netherlands)

    Ochodnicky, Peter; Henning, Robert H.; Buikema, Hendrik; Kluppel, Alex C. A.; van Wattum, Marjolein; de Zeeuw, Dick; van Dokkum, Richard P. E.

    2009-01-01

    Susceptibility to renal injury varies among individuals. Previously, we found that individual endothelial function of healthy renal arteries in vitro predicted severity of renal damage after 5/6 nephrectomy. Here we hypothesized that individual differences in endothelial function in vitro and renal

  7. Prediction Equations Overestimate the Energy Requirements More for Obesity-Susceptible Individuals.

    Science.gov (United States)

    McLay-Cooke, Rebecca T; Gray, Andrew R; Jones, Lynnette M; Taylor, Rachael W; Skidmore, Paula M L; Brown, Rachel C

    2017-09-13

    Predictive equations to estimate resting metabolic rate (RMR) are often used in dietary counseling and by online apps to set energy intake goals for weight loss. It is critical to know whether such equations are appropriate for those susceptible to obesity. We measured RMR by indirect calorimetry after an overnight fast in 26 obesity susceptible (OSI) and 30 obesity resistant (ORI) individuals, identified using a simple 6-item screening tool. Predicted RMR was calculated using the FAO/WHO/UNU (Food and Agricultural Organisation/World Health Organisation/United Nations University), Oxford and Miflin-St Jeor equations. Absolute measured RMR did not differ significantly between OSI versus ORI (6339 vs. 5893 kJ·d -1 , p = 0.313). All three prediction equations over-estimated RMR for both OSI and ORI when measured RMR was ≤5000 kJ·d -1 . For measured RMR ≤7000 kJ·d -1 there was statistically significant evidence that the equations overestimate RMR to a greater extent for those classified as obesity susceptible with biases ranging between around 10% to nearly 30% depending on the equation. The use of prediction equations may overestimate RMR and energy requirements particularly in those who self-identify as being susceptible to obesity, which has implications for effective weight management.

  8. Drug Susceptibility Testing of 31 Antimicrobial Agents on Rapidly Growing Mycobacteria Isolates from China.

    Science.gov (United States)

    Pang, Hui; Li, Guilian; Zhao, Xiuqin; Liu, Haican; Wan, Kanglin; Yu, Ping

    2015-01-01

    Several species of rapidly growing mycobacteria (RGM) are now recognized as human pathogens. However, limited data on effective drug treatments against these organisms exists. Here, we describe the species distribution and drug susceptibility profiles of RGM clinical isolates collected from four southern Chinese provinces from January 2005 to December 2012. Clinical isolates (73) were subjected to in vitro testing with 31 antimicrobial agents using the cation-adjusted Mueller-Hinton broth microdilution method. The isolates included 55 M. abscessus, 11 M. fortuitum, 3 M. chelonae, 2 M. neoaurum, and 2 M. septicum isolates. M. abscessus (75.34%) and M. fortuitum (15.07%), the most common species, exhibited greater antibiotic resistance than the other three species. The isolates had low resistance to amikacin, linezolid, and tigecycline, and high resistance to first-line antituberculous agents, amoxicillin-clavulanic acid, rifapentine, dapsone, thioacetazone, and pasiniazid. M. abscessus and M. fortuitum were highly resistant to ofloxacin and rifabutin, respectively. The isolates showed moderate resistance to the other antimicrobial agents. Our results suggest that tigecycline, linezolid, clofazimine, and cefmetazole are appropriate choices for M. abscessus infections. Capreomycin, sulfamethoxazole, tigecycline, clofazimine, and cefmetazole are potentially good choices for M. fortuitum infections. Our drug susceptibility data should be useful to clinicians.

  9. Drug Susceptibility Testing of 31 Antimicrobial Agents on Rapidly Growing Mycobacteria Isolates from China

    Directory of Open Access Journals (Sweden)

    Hui Pang

    2015-01-01

    Full Text Available Objectives. Several species of rapidly growing mycobacteria (RGM are now recognized as human pathogens. However, limited data on effective drug treatments against these organisms exists. Here, we describe the species distribution and drug susceptibility profiles of RGM clinical isolates collected from four southern Chinese provinces from January 2005 to December 2012. Methods. Clinical isolates (73 were subjected to in vitro testing with 31 antimicrobial agents using the cation-adjusted Mueller-Hinton broth microdilution method. The isolates included 55 M. abscessus, 11 M. fortuitum, 3 M. chelonae, 2 M. neoaurum, and 2 M. septicum isolates. Results. M. abscessus (75.34% and M. fortuitum (15.07%, the most common species, exhibited greater antibiotic resistance than the other three species. The isolates had low resistance to amikacin, linezolid, and tigecycline, and high resistance to first-line antituberculous agents, amoxicillin-clavulanic acid, rifapentine, dapsone, thioacetazone, and pasiniazid. M. abscessus and M. fortuitum were highly resistant to ofloxacin and rifabutin, respectively. The isolates showed moderate resistance to the other antimicrobial agents. Conclusions. Our results suggest that tigecycline, linezolid, clofazimine, and cefmetazole are appropriate choices for M. abscessus infections. Capreomycin, sulfamethoxazole, tigecycline, clofazimine, and cefmetazole are potentially good choices for M. fortuitum infections. Our drug susceptibility data should be useful to clinicians.

  10. In vitro susceptibility of nematophagous fungi to antiparasitic drugs: interactions and implications for biological control

    Directory of Open Access Journals (Sweden)

    J. N. Vieira

    Full Text Available Abstract The fast anthelmintic resistance development has shown a limited efficiency in the control of animal’s endoparasitosis and has promoted research using alternative control methods. The use of chemicals in animal anthelmintic treatment, in association with nematophagous fungi used for biological control, is a strategy that has proven to be effective in reducing the nematode population density in farm animals. This study aims to verify the in vitro susceptibility of the nematophagous fungi Arthrobotrys oligospora, Duddingtonia flagrans and Paecilomyces lilacinus against the antiparasitic drugs albendazole, thiabendazole, ivermectin, levamisole and closantel by using the Minimum Inhibitory Concentration (MIC. MICs ranged between 4.0 and 0.031 µg/mL for albendazole, thiabendazole and ivermectin, between 0.937 and 0.117 µg/mL for levamisole, and between 0.625 and 0.034 µg/mL for closantel. The results showed that all antiparasitic drugs had an in vitro inhibitory effect on nematophagous fungi, which could compromise their action as agents of biological control. D. flagrans was the most susceptible species to all drugs.

  11. Pathogens Causing Blood Stream Infections and their Drug Susceptibility Profile in Immunocompromised Patients

    International Nuclear Information System (INIS)

    Fayyaz, M.; Mirza, I.A.; Ikram, A.; Hussain, A.; Ghafoor, T.; Shujat, U.

    2013-01-01

    Objective: To determine the types of pathogens causing blood stream infections and their drug susceptibility profile in immunocompromised patients. Study Design: Cross-sectional, observational study. Place and Duration of Study: Department of Microbiology, Armed Forces Institute of Pathology, Rawalpindi, from January to September 2012. Methodology: Blood culture bottles received from immunocompromised patients were dealt by two methods, brain heart infusion (BHI) broth based manual method and automated BACTEC system. The samples yielding positive growth from either of two methods were further analyzed. The identification of isolates was done with the help of biochemical reactions and rapid tests. Antimicrobial susceptibility of the isolates was carried out as per recommendations of Clinical and Laboratory Standards Institute (CLSI). Results: Out of the 938 blood culture specimens received from immunocompromised patients, 188 (20%) yielded positive growth. Out of these, 89 (47.3%) isolates were Gram positive and Gram negative each, while 10 (5.3%) isolates were fungi (Candida spp.). In case of Gram positive isolates, 75 (84.3%) were Staphylococcus spp. and 51 (67%) were Methicillin resistant. Amongst Gram negative group 49 (55.1%) isolates were of enterobacteriaceae family, while 40 (44.9%) were non-lactose fermenters (NLF). In vitro antimicrobial susceptibility of Staphylococci revealed 100% susceptibility to vancomycin and linezolid. The enterobacteriaceae isolates had better susceptibility against amikacin 85.7% compared to tigecycline 61.2% and imipenem 59.2%. For NLF, the in vitro efficacy of aminoglycosides was 72.5%. Conclusion: The frequency of Gram positive and Gram negative organisms causing blood stream infections in immunocompromised patients was equal. Vancomycin in case of Gram positive and amikacin for Gram negative organisms revealed better in vitro efficacy as compared to other antibiotics. (author)

  12. Pathogens causing blood stream infections and their drug susceptibility profile in immunocompromised patients.

    Science.gov (United States)

    Fayyaz, Muhammad; Mirza, Irfan Ali; Ikram, Aamer; Hussain, Aamir; Ghafoor, Tahir; Shujat, Umer

    2013-12-01

    To determine the types of pathogens causing blood stream infections and their drug susceptibility profile in immunocompromised patients. Cross-sectional, observational study. Department of Microbiology, Armed Forces Institute of Pathology, Rawalpindi, from January to September 2012. Blood culture bottles received from immunocompromised patients were dealt by two methods, brain heart infusion (BHI) broth based manual method and automated BACTEC system. The samples yielding positive growth from either of two methods were further analyzed. The identification of isolates was done with the help of biochemical reactions and rapid tests. Antimicrobial susceptibility of the isolates was carried out as per recommendations of Clinical and Laboratory Standards Institute (CLSI). Out of the 938 blood culture specimens received from immunocompromised patients, 188 (20%) yielded positive growth. Out of these, 89 (47.3%) isolates were Gram positive and Gram negative each, while 10 (5.3%) isolates were fungi (Candida spp.). In case of Gram positive isolates, 75 (84.3%) were Staphylococcus spp. and 51 (67%) were Methicillin resistant. Amongst Gram negative group 49 (55.1%) isolates were of enterobacteriaceae family, while 40 (44.9%) were non-lactose fermenters (NLF). In vitro antimicrobial susceptibility of Staphylococci revealed 100% susceptibility to vancomycin and linezolid. The enterobacteriaceae isolates had better susceptibility against amikacin 85.7% compared to tigecycline 61.2% and imipenem 59.2%. For NLF, the in vitro efficacy of aminoglycosides was 72.5%. The frequency of Gram positive and Gram negative organisms causing blood stream infections in immunocompromised patients was equal. Vancomycin in case of Gram positive and amikacin for Gram negative organisms revealed better in vitro efficacy as compared to other antibiotics.

  13. A New Criterion for Prediction of Hot Tearing Susceptibility of Cast Alloys

    Science.gov (United States)

    Nasresfahani, Mohamad Reza; Niroumand, Behzad

    2014-08-01

    A new criterion for prediction of hot tearing susceptibility of cast alloys is suggested which takes into account the effects of both important mechanical and metallurgical factors and is believed to be less sensitive to the presence of volume defects such as bifilms and inclusions. The criterion was validated by studying the hot tearing tendency of Al-Cu alloy. In conformity with the experimental results, the new criterion predicted reduction of hot tearing tendency with increasing the copper content.

  14. Differential Response of Coral Assemblages to Thermal Stress Underscores the Complexity in Predicting Bleaching Susceptibility.

    Science.gov (United States)

    Chou, Loke Ming; Toh, Tai Chong; Toh, Kok Ben; Ng, Chin Soon Lionel; Cabaitan, Patrick; Tun, Karenne; Goh, Eugene; Afiq-Rosli, Lutfi; Taira, Daisuke; Du, Rosa Celia Poquita; Loke, Hai Xin; Khalis, Aizat; Li, Jinghan; Song, Tiancheng

    2016-01-01

    Coral bleaching events have been predicted to occur more frequently in the coming decades with global warming. The susceptibility of corals to bleaching during thermal stress episodes is dependent on many factors and an understanding of these underlying drivers is crucial for conservation management. In 2013, a mild bleaching episode ensued in response to elevated sea temperature on the sediment-burdened reefs in Singapore. Surveys of seven sites highlighted variable bleaching susceptibility among coral genera-Pachyseris and Podabacia were the most impacted (31% of colonies of both genera bleached). The most susceptible genera such as Acropora and Pocillopora, which were expected to bleach, did not. Susceptibility varied between less than 6% and more than 11% of the corals bleached, at four and three sites respectively. Analysis of four of the most bleached genera revealed that a statistical model that included a combination of the factors (genus, colony size and site) provided a better explanation of the observed bleaching patterns than any single factor alone. This underscored the complexity in predicting the coral susceptibility to future thermal stress events and the importance of monitoring coral bleaching episodes to facilitate more effective management of coral reefs under climate change.

  15. Differential Response of Coral Assemblages to Thermal Stress Underscores the Complexity in Predicting Bleaching Susceptibility

    Science.gov (United States)

    Toh, Kok Ben; Ng, Chin Soon Lionel; Cabaitan, Patrick; Tun, Karenne; Goh, Eugene; Afiq-Rosli, Lutfi; Taira, Daisuke; Du, Rosa Celia Poquita; Loke, Hai Xin; Khalis, Aizat; Li, Jinghan; Song, Tiancheng

    2016-01-01

    Coral bleaching events have been predicted to occur more frequently in the coming decades with global warming. The susceptibility of corals to bleaching during thermal stress episodes is dependent on many factors and an understanding of these underlying drivers is crucial for conservation management. In 2013, a mild bleaching episode ensued in response to elevated sea temperature on the sediment-burdened reefs in Singapore. Surveys of seven sites highlighted variable bleaching susceptibility among coral genera–Pachyseris and Podabacia were the most impacted (31% of colonies of both genera bleached). The most susceptible genera such as Acropora and Pocillopora, which were expected to bleach, did not. Susceptibility varied between less than 6% and more than 11% of the corals bleached, at four and three sites respectively. Analysis of four of the most bleached genera revealed that a statistical model that included a combination of the factors (genus, colony size and site) provided a better explanation of the observed bleaching patterns than any single factor alone. This underscored the complexity in predicting the coral susceptibility to future thermal stress events and the importance of monitoring coral bleaching episodes to facilitate more effective management of coral reefs under climate change. PMID:27438593

  16. Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India

    Science.gov (United States)

    Kumar, Deepak; Thakur, Manoj; Dubey, Chandra S.; Shukla, Dericks P.

    2017-10-01

    In recent years, various machine learning techniques have been applied for landslide susceptibility mapping. In this study, three different variants of support vector machine viz., SVM, Proximal Support Vector Machine (PSVM) and L2-Support Vector Machine - Modified Finite Newton (L2-SVM-MFN) have been applied on the Mandakini River Basin in Uttarakhand, India to carry out the landslide susceptibility mapping. Eight thematic layers such as elevation, slope, aspect, drainages, geology/lithology, buffer of thrusts/faults, buffer of streams and soil along with the past landslide data were mapped in GIS environment and used for landslide susceptibility mapping in MATLAB. The study area covering 1625 km2 has merely 0.11% of area under landslides. There are 2009 pixels for past landslides out of which 50% (1000) landslides were considered as training set while remaining 50% as testing set. The performance of these techniques has been evaluated and the computational results show that L2-SVM-MFN obtains higher prediction values (0.829) of receiver operating characteristic curve (AUC-area under the curve) as compared to 0.807 for PSVM model and 0.79 for SVM. The results obtained from L2-SVM-MFN model are found to be superior than other SVM prediction models and suggest the usefulness of this technique to problem of landslide susceptibility mapping where training data is very less. However, these techniques can be used for satisfactory determination of susceptible zones with these inputs.

  17. Differential Response of Coral Assemblages to Thermal Stress Underscores the Complexity in Predicting Bleaching Susceptibility.

    Directory of Open Access Journals (Sweden)

    Loke Ming Chou

    Full Text Available Coral bleaching events have been predicted to occur more frequently in the coming decades with global warming. The susceptibility of corals to bleaching during thermal stress episodes is dependent on many factors and an understanding of these underlying drivers is crucial for conservation management. In 2013, a mild bleaching episode ensued in response to elevated sea temperature on the sediment-burdened reefs in Singapore. Surveys of seven sites highlighted variable bleaching susceptibility among coral genera-Pachyseris and Podabacia were the most impacted (31% of colonies of both genera bleached. The most susceptible genera such as Acropora and Pocillopora, which were expected to bleach, did not. Susceptibility varied between less than 6% and more than 11% of the corals bleached, at four and three sites respectively. Analysis of four of the most bleached genera revealed that a statistical model that included a combination of the factors (genus, colony size and site provided a better explanation of the observed bleaching patterns than any single factor alone. This underscored the complexity in predicting the coral susceptibility to future thermal stress events and the importance of monitoring coral bleaching episodes to facilitate more effective management of coral reefs under climate change.

  18. 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

  19. 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.

  20. A dual reporter cell assay for identifying serotype and drug susceptibility of herpes simplex virus.

    Science.gov (United States)

    Lu, Wen-Wen; Sun, Jun-Ren; Wu, Szu-Sian; Lin, Wan-Hsuan; Kung, Szu-Hao

    2011-08-15

    A dual reporter cell assay (DRCA) that allows real-time detection of herpes simplex virus (HSV) infection was developed. This was achieved by stable transfection of cells with an expression cassette that contains the dual reporter genes, secreted alkaline phosphatase (SEAP) and enhanced green fluorescent protein (EGFP), under the control of an HSV early gene promoter. Baby hamster kidney (BHK) and Chinese hamster ovary (CHO) cell lines were used as parental cell lines because the former is permissive for both HSV serotypes, HSV-1 and HSV-2, whereas the latter is susceptible to infection only by HSV-2. The DRCA permitted differential detection of HSV-1 and HSV-2 by observation of EGFP-positive cells, as substantiated by screening a total of 35 samples. The BHK-based cell line is sensitive to a viral titer as low as a single plaque-forming unit with a robust assay window as measured by a chemiluminescent assay. Evaluations of the DRCA with representative acyclovir-sensitive and acyclovir-resistant HSV strains demonstrated that their drug susceptibilities were accurately determined by a 48-h format. In summary, this novel DRCA is a useful means for serotyping of HSV in real time as well as a rapid screening method for determining anti-HSV susceptibilities. Copyright © 2011 Elsevier Inc. All rights reserved.

  1. Mortality among MDR-TB cases: comparison with drug-susceptible tuberculosis and associated factors.

    Directory of Open Access Journals (Sweden)

    Kocfa Chung-Delgado

    Full Text Available An increase in multidrug-resistant tuberculosis (MDR-TB cases is evident worldwide. Its management implies a complex treatment, high costs, more toxic anti-tuberculosis drug use, longer treatment time and increased treatment failure and mortality. The aims of this study were to compare mortality between MDR and drug-susceptible cases of tuberculosis, and to determine risk factors associated with mortality among MDR-TB cases.A retrospective cohort study was performed using data from clinical records of the National Strategy for Prevention and Control of Tuberculosis in Lima, Peru. In the first objective, MDR-TB, compared to drug-susceptible cases, was the main exposure variable and time to death, censored at 180 days, the outcome of interest. For the second objective, different variables obtained from clinical records were assessed as potential risk factors for death among MDR-TB cases. Cox regression analysis was used to determine hazard ratios (HR and 95% confidence intervals (95%CI. A total of 1,232 patients were analyzed: mean age 30.9 ±14.0 years, 60.0% were males. 61 patients (5.0% died during treatment, whereas the MDR-TB prevalence was 19.2%. MDR-TB increased the risk of death during treatment (HR = 7.5; IC95%: 4.1-13.4 when compared to presumed drug-susceptible cases after controlling for potential confounders. Education level (p = 0.01, previous TB episodes (p<0.001, diabetes history (p<0.001 and HIV infection (p = 0.04 were factors associated with mortality among MDR-TB cases.MDR-TB is associated with an increased risk of death during treatment. Lower education, greater number of previous TB episodes, diabetes history, and HIV infection were independently associated with mortality among MDR-TB cases. New strategies for appropriate MDR-TB detection and management should be implemented, including drug sensitivity tests, diabetes and HIV screening, as well as guarantee for a complete adherence to therapy.

  2. 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.

  3. Candida Species From Eye Infections: Drug Susceptibility, Virulence Factors, and Molecular Characterization.

    Science.gov (United States)

    Ranjith, Konduri; Sontam, Bhavani; Sharma, Savitri; Joseph, Joveeta; Chathoth, Kanchana N; Sama, Kalyana C; Murthy, Somasheila I; Shivaji, Sisinthy

    2017-08-01

    To determine the type of Candida species in ocular infections and to investigate the relationship of antifungal susceptibility profile to virulence factors. Fifty isolates of yeast-like fungi from patients with keratitis, endophthalmitis, and orbital cellulitis were identified by Vitek-2 compact system and DNA sequencing of ITS1-5.8S-ITS2 regions of the rRNA gene, followed by phylogenetic analysis for phenotypic and genotypic identification, respectively. Minimum inhibitory concentration of six antifungal drugs was determined by E test/microbroth dilution methods. Phenotypic and genotypic methods were used to determine the virulence factors. Phylogenetic analysis showed the clustering of all isolates into eight distinct groups with a major cluster formed Candida parapsilosis (n = 21), which was the most common species by both Vitek 2 and DNA sequencing. Using χ2 test no significant difference was noted between the techniques except that Vitek 2 did not identify C. viswanathii, C. orthopsilosis, and two non-Candida genera. Of 43 tested Candida isolates high susceptibility to amphotericin B (39/43, 90.6%) and natamycin (43/43, 100%) was noted. While none of the isolates produced coagulase, all produced esterase and catalase. The potential to form biofilm was detected in 23/43 (53.4%) isolates. Distribution of virulence factors by heat map analysis showed difference in metabolic activity of biofilm producers from nonbiofilm producers. Identified by Vitek 2 and DNA sequencing methods C. parapsilosis was the most common species associated with eye infections. Irrespective of the virulence factors elaborated, the Candida isolates were susceptible to commonly used antifungal drugs such as amphotericin B and natamycin.

  4. Drug susceptibility testing in microaerophilic parasites: Cysteine strongly affects the effectivities of metronidazole and auranofin, a novel and promising antimicrobial

    Directory of Open Access Journals (Sweden)

    David Leitsch

    2017-12-01

    Full Text Available The microaerophilic parasites Entamoeba histolytica, Trichomonas vaginalis, and Giardia lamblia annually cause hundreds of millions of human infections which are treated with antiparasitic drugs. Metronidazole is the most often prescribed drug but also other drugs are in use, and novel drugs with improved characteristics are constantly being developed. One of these novel drugs is auranofin, originally an antirheumatic which has been relabelled for the treatment of parasitic infections. Drug effectivity is arguably the most important criterion for its applicability and is commonly assessed in susceptibility assays using in vitro cultures of a given pathogen. However, drug susceptibility assays can be strongly affected by certain compounds in the growth media. In the case of microaerophilic parasites, cysteine which is added in large amounts as an antioxidant is an obvious candidate because it is highly reactive and known to modulate the toxicity of metronidazole in several microaerophilic parasites.In this study, it was attempted to reduce cysteine concentrations as far as possible without affecting parasite viability by performing drug susceptibility assays under strictly anaerobic conditions in an anaerobic cabinet. Indeed, T. vaginalis and E. histolytica could be grown without any cysteine added and the cysteine concentration necessary to maintain G. lamblia could be reduced to 20%. Susceptibilities to metronidazole were found to be clearly reduced in the presence of cysteine. With auranofin the protective effect of cysteine was extreme, providing protection to concentrations up to 100-fold higher as observed in the absence of cysteine. With three other drugs tested, albendazole, furazolidone and nitazoxanide, all in use against G. lamblia, the effect of cysteine was less pronounced. Oxygen was found to have a less marked impact on metronidazole and auranofin than cysteine but bovine bile which is standardly used in growth media for G

  5. Observation of reversible, rapid changes in drug susceptibility of hypoxic tumor cells in a microfluidic device

    Energy Technology Data Exchange (ETDEWEB)

    Germain, Todd; Ansari, Megan; Pappas, Dimitri, E-mail: d.pappas@ttu.edu

    2016-09-14

    Hypoxia is a major stimulus for increased drug resistance and for survival of tumor cells. Work from our group and others has shown that hypoxia increases resistance to anti-cancer compounds, radiation, and other damage-pathway cytotoxic agents. In this work we utilize a microfluidic culture system capable of rapid switching of local oxygen concentrations to determine changes in drug resistance in prostate cancer cells. We observed rapid adaptation to hypoxia, with drug resistance to 2 μM staurosporine established within 30 min of hypoxia. Annexin-V/Sytox Green apoptosis assays over 9 h showed 78.0% viability, compared to 84.5% viability in control cells (normoxic cells with no staurosporine). Normoxic cells exposed to the same staurosporine concentration had a viability of 48.6% after 9 h. Hypoxia adaptation was rapid and reversible, with Hypoxic cells treated with 20% oxygen for 30 min responding to staurosporine with 51.6% viability after drug treatment for 9 h. Induction of apoptosis through the receptor-mediated pathway, which bypasses anti-apoptosis mechanisms induced by hypoxia, resulted in 39.4 ± 7% cell viability. The rapid reversibility indicates co-treatment of oxygen with anti-cancer compounds may be a potential therapeutic target. - Highlights: • Microfluidic system switches rapidly between normoxia and hypoxia (5 min). • Observation of rapid adaptation of PC3 cells to hypoxia and normoxia (30 min). • Drug susceptibility in tumor cells restored after chip switched to normoxia for 30 min.

  6. Curiosity predicts smoking experimentation independent of susceptibility in a US national sample.

    Science.gov (United States)

    Nodora, Jesse; Hartman, Sheri J; Strong, David R; Messer, Karen; Vera, Lisa E; White, Martha M; Portnoy, David B; Choiniere, Conrad J; Vullo, Genevieve C; Pierce, John P

    2014-12-01

    To improve smoking prevention efforts, better methods for identifying at-risk youth are needed. The widely used measure of susceptibility to smoking identifies at-risk adolescents; however, it correctly identifies only about one third of future smokers. Adding curiosity about smoking to this susceptibility index may allow us to identify a greater proportion of future smokers while they are still pre-teens. We use longitudinal data from a recent national study on parenting to prevent problem behaviors. Only oldest children between 10 and 13years of age were eligible. Participants were identified by RDD survey and followed for 6years. All baseline never smokers with at least one follow-up assessment were included (n=878). The association of curiosity about smoking with future smoking behavior was assessed. Then, curiosity was added to form an enhanced susceptibility index and sensitivity, specificity and positive predictive value were calculated. Among committed never smokers at baseline, those who were 'definitely not curious' were less likely to progress toward smoking than both those who were 'probably not curious' (ORadj=1.89; 95% CI=1.03-3.47) or 'probably/definitely curious' (ORadj=2.88; 95% CI=1.11-7.45). Incorporating curiosity into the susceptibility index increased the proportion identified as at-risk to smoke from 25.1% to 46.9%. The sensitivity (true positives) for this enhanced susceptibility index for both experimentation and established smoking increased from 37-40% to over 50%, although the positive predictive value did not improve. The addition of curiosity significantly improves the identification and classification of which adolescents will experiment with smoking or become established smokers. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Rapid, radiolabeled-microculture method that uses macrophages for in vitro evaluation of Mycobacterium leprae viability and drug susceptibility.

    Science.gov (United States)

    Mittal, A; Sathish, M; Seshadri, P S; Nath, I

    1983-04-01

    This paper describes a microculture rapid assay using radiolabeling and mouse macrophages to determine the viability and the drug susceptibility or resistance of Mycobacterium leprae. Comparison of M. leprae resident macrophage cultures maintained in 96-well flat-bottomed plates showed results for viability and susceptibility or resistance to dapsone that were similar to results for concurrent cultures in Leighton tubes with greater numbers of bacilli and macrophages.

  8. Rapid, Radiolabeled-Microculture Method That Uses Macrophages for In Vitro Evaluation of Mycobacterium leprae Viability and Drug Susceptibility

    OpenAIRE

    Mittal, A.; Sathish, M.; Seshadri, P. S.; Nath, Indira

    1983-01-01

    This paper describes a microculture rapid assay using radiolabeling and mouse macrophages to determine the viability and the drug susceptibility or resistance of Mycobacterium leprae. Comparison of M. leprae resident macrophage cultures maintained in 96-well flat-bottomed plates showed results for viability and susceptibility or resistance to dapsone that were similar to results for concurrent cultures in Leighton tubes with greater numbers of bacilli and macrophages.

  9. Culture and drug susceptibility testing among previously treated tuberculosis patients in the Dominican Republic, 2014

    Directory of Open Access Journals (Sweden)

    Katia J. Romero Mercado

    Full Text Available ABSTRACT Multidrug-resistant tuberculosis (MDR-TB is a major public health concern that threatens global progress toward effective TB control. The risk of MDR-TB is increased in patients who have received previous TB treatment. This article describes the performance of culture and drug susceptibility testing (DST in patients registered as previously treated TB patients in the Dominican Republic in 2014, based on operational research that followed a retrospective cohort design and used routine program data. Under the current system of TB culturing and DST, the majority of patients with previously treated TB do not undergo DST, and those who do often experience considerable delay in obtaining their results. The lack of DST and delay in receiving DST results leads to underestimation of the number of MDR-TB cases and hinders the timely initiation of MDR-TB treatment.

  10. 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

  11. A method for evaluating antiviral drug susceptibility of Epstein-Barr virus

    Directory of Open Access Journals (Sweden)

    Charlotte A Romain

    2010-01-01

    Full Text Available Charlotte A Romain1, Henry H Balfour Jr1,2, Heather E Vezina1,3, Carol J Holman11Department of Laboratory Medicine and Pathology, 2Department of Pediatrics, 3Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USAAbstract: We developed an in vitro Epstein-Barr virus (EBV drug susceptibility assay using P3HR1 cells or lymphoblastoid cells from subjects with infectious mononucleosis, which were grown in the presence of various concentrations of acyclovir (ACV, ganciclovir (GCV or R-9-[4-hydroxy-2-(hydroxymethylbutyl]guanine (H2G and 12-O-tetradecanoyl-phorbol-13-acetate (TPA. On day 7, total cellular DNA was extracted and EBV DNA was detected using an in-house quantitative real-time polymerase chain reaction (PCR method. All three drugs had in vitro activity against EBV in both the laboratory standard producer cell line P3HR1 and in subject-derived lymphoblastoid cell lines. The median 50% inhibitory concentrations (IC50s in P3HR1 cells were: ACV, 3.4 μM; GCV, 2.6 μM; and H2G, 2.7 μM and in 3 subject-derived cells were: ACV, 2.5 μM; GCV, 1.7 μM; and H2G, 1.9 μM. Our assay can be used to screen candidate anti-EBV drugs. Because we can measure the IC50 of patients’ strains of EBV, this assay may also be useful for monitoring viral resistance especially in immunocompomised hosts receiving antiviral drugs for prevention or treatment of EBV diseases.Keywords: Epstein-Barr virus, ganciclovir, acyclovir, valomaciclovir, H2G, antivirals

  12. 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.

  13. 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.

  14. Characterization of drug susceptibility of Mycobacterium tuberculosis isolated from new cases of tuberculosis concurrent with HIV infection

    Directory of Open Access Journals (Sweden)

    G. V. Panov

    2015-01-01

    Full Text Available The paper characterizes drug susceptibility in Mycobacterium tuberculosis isolated from new cases of tuberculosis concurrent with HIV infection. The investigators have studied the spectrum of drug resistance in Mycobacterium tuberculosis isolated from new cases of tuberculosis concurrent with and without HIV infection (172 and 309 clinical isolates, respectively. There are differences in the rate of primary drug resistance to antituberculosis drugs in patients with and without HIV infection (59 and 43.5% of the cases, respectively. The HIV-infected have also shown high rifampicin resistance rates in Mycobacterium tuberculosis (41.7%. The reasons for these differences are as yet unknown and call for further investigation.

  15. Environmental isolation, biochemical identification, and antifungal drug susceptibility of Cryptococcus species

    Directory of Open Access Journals (Sweden)

    Valter Luis Iost Teodoro

    2013-12-01

    Full Text Available Introduction The incidence of opportunistic fungal infections has increased in recent years and is considered an important public health problem. Among systemic and opportunistic mycoses, cryptococcosis is distinguished by its clinical importance due to the increased risk of infection in individuals infected by human immunodeficiency virus. Methods To determine the occurrence of pathogenic Cryptococcus in pigeon excrement in the City of Araraquara, samples were collected from nine environments, including state and municipal schools, abandoned buildings, parks, and a hospital. The isolates were identified using classical tests, and susceptibility testing for the antifungal drugs (fluconazole, itraconazole, voriconazole, and amphotericin B independently was also performed. After collection, the excrement samples were plated on Niger agar and incubated at room temperature. Results A total of 87 bird dropping samples were collected, and 66.6% were positive for the genus Cryptococcus. The following species were identified: Cryptococcus neoformans (17.2%, Cryptococcus gattii (5.2%, Cryptococcus ater (3.5%, Cryptococcus laurentti (1.7%, and Cryptococcus luteolus (1.7%. A total of 70.7% of the isolates were not identified to the species level and are referred to as Cryptococcus spp. throughout the manuscript. Conclusions Although none of the isolates demonstrated resistance to antifungal drugs, the identification of infested areas, the proper control of birds, and the disinfection of these environments are essential for the epidemiological control of cryptococcosis.

  16. 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.

  17. 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...

  18. 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

  19. 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.

  20. Analysis of metal and biocides resistance genes in drug resistance and susceptible Salmonella enterica from food animals

    Science.gov (United States)

    Background Generally drug resistant bacteria carry antibiotic resistance genes and heavy metal and biocide resistance genes on large conjugative plasmids. The presence of these metal and biocide resistance genes in susceptible bacteria are not assessed comprehensively. Hence, WGS data of susceptib...

  1. Antimicrobial drug resistance of Salmonella enterica serovar typhi in asia and molecular mechanism of reduced susceptibility to the fluoroquinolones

    NARCIS (Netherlands)

    Chau, Tran Thuy; Campbell, James Ian; Galindo, Claudia M.; van Minh Hoang, Nguyen; Diep, To Song; Nga, Tran Thu Thi; van Vinh Chau, Nguyen; Tuan, Phung Quoc; Page, Anne Laure; Ochiai, R. Leon; Schultsz, Constance; Wain, John; Bhutta, Zulfiqar A.; Parry, Christopher M.; Bhattacharya, Sujit K.; Dutta, Shanta; Agtini, Magdarina; Dong, Baiqing; Honghui, Yang; Anh, Dang Duc; Canh, Do Gia; Naheed, Aliya; Albert, M. John; Phetsouvanh, Rattanaphone; Newton, Paul N.; Basnyat, Buddha; Arjyal, Amit; La, Tran Thi Phi; Rang, Nguyen Ngoc; Phuong, Le Thi; van Be Bay, Phan; von Seidlein, Lorenz; Dougan, Gordon; Clemens, John D.; Vinh, Ha; Hien, Tran Tinh; Chinh, Nguyen Tran; Acosta, Camilo J.; Farrar, Jeremy; Dolecek, Christiane

    2007-01-01

    This study describes the pattern and extent of drug resistance in 1,774 strains of Salmonella enterica serovar Typhi isolated across Asia between 1993 and 2005 and characterizes the molecular mechanisms underlying the reduced susceptibilities to fluoroquinolones of these strains. For 1,393 serovar

  2. Profiling gene expression of antimony response genes in Leishmania (Viannia) panamensis and infected macrophages and its relationship with drug susceptibility.

    Science.gov (United States)

    Barrera, Maria Claudia; Rojas, Laura Jimena; Weiss, Austin; Fernandez, Olga; McMahon-Pratt, Diane; Saravia, Nancy G; Gomez, Maria Adelaida

    2017-12-01

    The mechanisms of Leishmania resistance to antimonials have been primarily determined in experimentally derived Leishmania strains. However, their participation in the susceptibility phenotype in field isolates has not been conclusively established. Being an intracellular parasite, the activity of antileishmanials is dependent on internalization of drugs into host cells and effective delivery to the intracellular compartments inhabited by the parasite. In this study we quantified and comparatively analyzed the gene expression of nine molecules involved in mechanisms of xenobiotic detoxification and Leishmania resistance to antimonial drugs in resistant and susceptible laboratory derived and clinical L.(Viannia) panamensis strains(n=19). In addition, we explored the impact of Leishmania susceptibility to antimonials on the expression of macrophage gene products having putative functions in transport, accumulation and metabolism of antimonials. As previously shown for other Leishmania species, a trend of increased abcc3 and lower aqp-1 expression was observed in the laboratory derived Sb-resistant L.(V.) panamensis line. However, this was not found in clinical strains, in which the expression of abca2 was significantly higher in resistant strains as both, promastigotes and intracellular amastigotes. The effect of drug susceptibility on host cell gene expression was evaluated on primary human macrophages from patients with cutaneous leishmaniasis (n=17) infected ex-vivo with the matched L.(V.) panamensis strains isolated at diagnosis, and in THP-1 cells infected with clinical strains (n=6) and laboratory adapted L.(V.) panamensis lines. Four molecules, abcb1 (p-gp), abcb6, aqp-9 and mt2a were differentially modulated by drug resistant and susceptible parasites, and among these, a consistent and significantly increased expression of the xenobiotic scavenging molecule mt2a was observed in macrophages infected with Sb-susceptible L. (V.) panamensis. Our results

  3. 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...

  4. 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.

  5. 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.

  6. 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....

  7. 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.

  8. 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.

  9. 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...

  10. 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.

  11. 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.

  12. Klebsiella pneumoniae yfiRNB operon affects biofilm formation, polysaccharide production and drug susceptibility.

    Science.gov (United States)

    Huertas, Mónica G; Zárate, Lina; Acosta, Iván C; Posada, Leonardo; Cruz, Diana P; Lozano, Marcela; Zambrano, María M

    2014-12-01

    Klebsiella pneumoniae is an opportunistic pathogen important in hospital-acquired infections, which are complicated by the rise of drug-resistant strains and the capacity of cells to adhere to surfaces and form biofilms. In this work, we carried out an analysis of the genes in the K. pneumoniae yfiRNB operon, previously implicated in biofilm formation. The results indicated that in addition to the previously reported effect on type 3 fimbriae expression, this operon also affected biofilm formation due to changes in cellulose as part of the extracellular matrix. Deletion of yfiR resulted in enhanced biofilm formation and an altered colony phenotype indicative of cellulose overproduction when grown on solid indicator media. Extraction of polysaccharides and treatment with cellulase were consistent with the presence of cellulose in biofilms. The enhanced cellulose production did not, however, correlate with virulence as assessed using a Caenorhabditis elegans assay. In addition, cells bearing mutations in genes of the yfiRNB operon varied with respect to the WT control in terms of susceptibility to the antibiotics amikacin, ciprofloxacin, imipenem and meropenem. These results indicated that the yfiRNB operon is implicated in the production of exopolysaccharides that alter cell surface characteristics and the capacity to form biofilms--a phenotype that does not necessarily correlate with properties related with survival, such as resistance to antibiotics. © 2014 The Authors.

  13. A Low Cost/Low Power Open Source Sensor System for Automated Tuberculosis Drug Susceptibility Testing

    Directory of Open Access Journals (Sweden)

    Kyukwang Kim

    2016-06-01

    Full Text Available In this research an open source, low power sensor node was developed to check the growth of mycobacteria in a culture bottle with a nitrate reductase assay method for a drug susceptibility test. The sensor system reports the temperature and color sensor output frequency change of the culture bottle when the device is triggered. After the culture process is finished, a nitrite ion detecting solution based on a commercial nitrite ion detection kit is injected into the culture bottle by a syringe pump to check bacterial growth by the formation of a pigment by the reaction between the solution and the color sensor. Sensor status and NRA results are broadcasted via a Bluetooth low energy beacon. An Android application was developed to collect the broadcasted data, classify the status of cultured samples from multiple devices, and visualize the data for the end users, circumventing the need to examine each culture bottle manually during a long culture period. The authors expect that usage of the developed sensor will decrease the cost and required labor for handling large amounts of patient samples in local health centers in developing countries. All 3D-printerable hardware parts, a circuit diagram, and software are available online.

  14. 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

  15. 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...

  16. Species used for drug testing reveal different inhibition susceptibility for 17beta-hydroxysteroid dehydrogenase type 1.

    Directory of Open Access Journals (Sweden)

    Gabriele Möller

    Full Text Available Steroid-related cancers can be treated by inhibitors of steroid metabolism. In searching for new inhibitors of human 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD 1 for the treatment of breast cancer or endometriosis, novel substances based on 15-substituted estrone were validated. We checked the specificity for different 17beta-HSD types and species. Compounds were tested for specificity in vitro not only towards recombinant human 17beta-HSD types 1, 2, 4, 5 and 7 but also against 17beta-HSD 1 of several other species including marmoset, pig, mouse, and rat. The latter are used in the processes of pharmacophore screening. We present the quantification of inhibitor preferences between human and animal models. Profound differences in the susceptibility to inhibition of steroid conversion among all 17beta-HSDs analyzed were observed. Especially, the rodent 17beta-HSDs 1 were significantly less sensitive to inhibition compared to the human ortholog, while the most similar inhibition pattern to the human 17beta-HSD 1 was obtained with the marmoset enzyme. Molecular docking experiments predicted estrone as the most potent inhibitor. The best performing compound in enzymatic assays was also highly ranked by docking scoring for the human enzyme. However, species-specific prediction of inhibitor performance by molecular docking was not possible. We show that experiments with good candidate compounds would out-select them in the rodent model during preclinical optimization steps. Potentially active human-relevant drugs, therefore, would no longer be further developed. Activity and efficacy screens in heterologous species systems must be evaluated with caution.

  17. 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.

  18. Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion

    Science.gov (United States)

    Rahmati, Omid; Tahmasebipour, Nasser; Haghizadeh, Ali; Pourghasemi, Hamid Reza; Feizizadeh, Bakhtiar

    2017-12-01

    Gully erosion constitutes a serious problem for land degradation in a wide range of environments. The main objective of this research was to compare the performance of seven state-of-the-art machine learning models (SVM with four kernel types, BP-ANN, RF, and BRT) to model the occurrence of gully erosion in the Kashkan-Poldokhtar Watershed, Iran. In the first step, a gully inventory map consisting of 65 gully polygons was prepared through field surveys. Three different sample data sets (S1, S2, and S3), including both positive and negative cells (70% for training and 30% for validation), were randomly prepared to evaluate the robustness of the models. To model the gully erosion susceptibility, 12 geo-environmental factors were selected as predictors. Finally, the goodness-of-fit and prediction skill of the models were evaluated by different criteria, including efficiency percent, kappa coefficient, and the area under the ROC curves (AUC). In terms of accuracy, the RF, RBF-SVM, BRT, and P-SVM models performed excellently both in the degree of fitting and in predictive performance (AUC values well above 0.9), which resulted in accurate predictions. Therefore, these models can be used in other gully erosion studies, as they are capable of rapidly producing accurate and robust gully erosion susceptibility maps (GESMs) for decision-making and soil and water management practices. Furthermore, it was found that performance of RF and RBF-SVM for modelling gully erosion occurrence is quite stable when the learning and validation samples are changed.

  19. [Identification and drug susceptibility testing of Mycobacterium thermoresistibile and Mycobacterium elephantis isolated from a cow with mastitis].

    Science.gov (United States)

    Li, W B; Ji, L Y; Xu, D L; Liu, H C; Zhao, X Q; Wu, Y M; Wan, K L

    2018-05-10

    Objective: To understand the etiological characteristics and drug susceptibility of Mycobacterium thermoresistibile and Mycobacterium elephantis isolated from a cow with mastitis and provide evidence for the prevention and control of infectious mastitis in cows. Methods: The milk sample was collected from a cow with mastitis, which was pretreated with 4 % NaOH and inoculated with L-J medium for Mycobacterium isolation. The positive cultures were initially identified by acid-fast staining and multi-loci PCR, then Mycobacterium species was identified by the multiple loci sequence analysis (MLSA) with 16S rRNA , hsp65 , ITS and SodA genes. The drug sensitivity of the isolates to 27 antibiotics was tested by alamar blue assay. Results: Two anti-acid stain positive strains were isolated from the milk of a cow with mastitis, which were identified as non- tuberculosis mycobacterium by multi-loci PCR, and multi-loci nucleic acid sequence analysis indicated that one strain was Mycobacterium thermoresistibile and another one was Mycobacterium elephantis . The results of the drug susceptibility test showed that the two strains were resistant to most antibiotics, including rifampicin and isoniazid, but they were sensitive to amikacin, moxifloxacin, levofloxacin, ethambutol, streptomycin, tobramycin, ciprofloxacin and linezolid. Conclusions: Mycobacterium thermoresistibile and Mycobacterium elephantis were isolated in a cow with mastitis and the drug susceptibility spectrum of the pathogens were unique. The results of the study can be used as reference for the prevention and control the infection in cows.

  20. 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…

  1. 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

  2. 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.

  3. 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

  4. 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.

  5. 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

  6. Antimicrobial Drug Resistance of Salmonella enterica Serovar Typhi in Asia and Molecular Mechanism of Reduced Susceptibility to the Fluoroquinolones▿

    OpenAIRE

    Chau, Tran Thuy; Campbell, James Ian; Galindo, Claudia M.; Van Minh Hoang, Nguyen; Diep, To Song; Nga, Tran Thu Thi; Van Vinh Chau, Nguyen; Tuan, Phung Quoc; Page, Anne Laure; Ochiai, R. Leon; Schultsz, Constance; Wain, John; Bhutta, Zulfiqar A.; Parry, Christopher M.; Bhattacharya, Sujit K.

    2007-01-01

    This study describes the pattern and extent of drug resistance in 1,774 strains of Salmonella enterica serovar Typhi isolated across Asia between 1993 and 2005 and characterizes the molecular mechanisms underlying the reduced susceptibilities to fluoroquinolones of these strains. For 1,393 serovar Typhi strains collected in southern Vietnam, the proportion of multidrug resistance has remained high since 1993 (50% in 2004) and there was a dramatic increase in nalidixic acid resistance between ...

  7. The value of microscopic-observation drug susceptibility assay in the diagnosis of tuberculosis and detection of multidrug resistance.

    Science.gov (United States)

    Sertel Şelale, Denİz; Uzun, Meltem

    2018-01-01

    Inexpensive, rapid, and reliable tests for detecting the presence and drug susceptibility of Mycobacterium tuberculosis complex (MTBC) are urgently needed to control the transmission of tuberculosis. In this study, we aimed to assess the accuracy and speed of the microscopic-observation drug susceptibility (MODS) assay in the identification of MTBC and detection of multidrug resistance. Sputum samples from patients suspected to have tuberculosis were simultaneously tested with MODS and conventional culture [Löwenstein-Jensen (LJ) culture, BACTEC MGIT™ 960 (MGIT) system], and drug susceptibility testing (MGIT system) methods. A total of 331 sputum samples were analyzed. Sensitivity and specificity of MODS assay for detection of MTBC strains were 96% and 98.8%, respectively. MODS assay detected multidrug resistant MTBC isolates with 92.3% sensitivity and 96.6% specificity. Median time to culture positivity was similar for MGIT (8 days) and MODS culture (8 days), but was significantly longer with LJ culture (20 days) (p tuberculosis and detection of multidrug resistance. © 2017 APMIS. Published by John Wiley & Sons Ltd.

  8. Polymorphism in Mitochondrial Group I Introns among Cryptococcus neoformans and Cryptococcus gattii Genotypes and Its Association with Drug Susceptibility

    Directory of Open Access Journals (Sweden)

    Felipe E. E. S. Gomes

    2018-02-01

    Full Text Available Cryptococcosis, one of the most important systemic mycosis in the world, is caused by different genotypes of Cryptococcus neoformans and Cryptococcus gattii, which differ in their ecology, epidemiology, and antifungal susceptibility. Therefore, the search for new molecular markers for genotyping, pathogenicity and drug susceptibility is necessary. Group I introns fulfill the requisites for such task because (i they are polymorphic sequences; (ii their self-splicing is inhibited by some drugs; and (iii their correct splicing under parasitic conditions is indispensable for pathogen survival. Here, we investigated the presence of group I introns in the mitochondrial LSU rRNA gene in 77 Cryptococcus isolates and its possible relation to drug susceptibility. Sequencing revealed two new introns in the LSU rRNA gene. All the introns showed high sequence similarity to other mitochondrial introns from distinct fungi, supporting the hypothesis of an ancient non-allelic invasion. Intron presence was statistically associated with those genotypes reported to be less pathogenic (p < 0.001. Further virulence assays are needed to confirm this finding. In addition, in vitro antifungal tests indicated that the presence of LSU rRNA introns may influence the minimum inhibitory concentration (MIC of amphotericin B and 5-fluorocytosine. These findings point to group I introns in the mitochondrial genome of Cryptococcus as potential molecular markers for antifungal resistance, as well as therapeutic targets.

  9. 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.

  10. Individual behavioral characteristics of wild-type rats predict susceptibility to experimental autoimmune encephalomyelitis

    NARCIS (Netherlands)

    Kavelaars, A; Heijnen, CJ; Tennekes, R; Bruggink, JE; Koolhaas, JM

    1999-01-01

    Neuroendocrine-immune interactions are thought to be important in determining susceptibility to autoimmune disease. Animal studies have revealed that differences in susceptibility to experimental autoimmune encephalomyelitis (EAE) are related to:reactivity in the hypothalamo-pituitary-adrenal axis.

  11. 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.)

  12. High-throughput behavioral phenotyping of drug and alcohol susceptibility traits in the expanded panel of BXD recombinant inbred strains

    Energy Technology Data Exchange (ETDEWEB)

    Philip, Vivek M [ORNL; Ansah, T [University of Tennessee Health Science Center, Memphis; Blaha, C, [University of Tennessee Health Science Center, Memphis; Cook, Melloni N. [University of Memphis; Hamre, Kristin M. [University of Tennessee Health Science Center, Memphis; Lariviere, William R [University of Pittsburgh; Matthews, Douglas B [Baylor University; Goldowitz, Daniel [University of British Columbia, Vancouver; Chesler, Elissa J [ORNL

    2010-01-01

    Genetic reference populations, particularly the BXD recombinant inbred strains, are a valuable resource for the discovery of the bio-molecular substrates and genetic drivers responsible for trait variation and co- ariation. This approach can be profitably applied in the analysis of susceptibility and mechanisms of drug and alcohol use disorders for which many predisposing behaviors may predict occurrence and manifestation of increased preference for these substances. Many of these traits are modeled by common mouse behavioral assays, facilitating the detection of patterns and sources of genetic co-regulation of predisposing phenotypes and substance consumption. Members of the Tennessee Mouse Genome Consortium have obtained behavioral phenotype data from 260 measures related to multiple behavioral assays across several domains: self-administration, response to, and withdrawal from cocaine, MDMA, morphine and alcohol; novelty seeking; behavioral despair and related neurological phenomena; pain sensitivity; stress sensitivity; anxiety; hyperactivity; and sleep/wake cycles. All traits have been measured in both sexes and the recently expanded panel of 69 additional BXD recombinant inbred strains (N=69). Sex differences and heritability estimates were obtained for each trait, and a comparison of early (N = 32) and recent BXD RI lines was performed. Primary data is publicly available for heritability, sex difference and genetic analyses using www.GeneNetwork.org. These analyses include QTL detection and genetic analysis of gene expression. Stored results from these analyses are available at http://ontologicaldiscovery.org for comparison to other genomic analysis results. Together with the results of related studies, these data form a public resource for integrative systems genetic analysis of neurobehavioral traits.

  13. 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

  14. A cost-effective smartphone-based antimicrobial susceptibility test reader for drug resistance testing (Conference Presentation)

    Science.gov (United States)

    Feng, Steve W.; Tseng, Derek; Di Carlo, Dino; Garner, Omai B.; Ozcan, Aydogan

    2017-03-01

    Antimicrobial susceptibility testing (AST) is commonly used for determining microbial drug resistance, but routine testing, which can significantly reduce the spread of multi-drug resistant organisms, is not regularly performed in resource-limited and field-settings due to technological challenges and lack of trained diagnosticians. We developed a portable cost-effective smartphone-based colorimetric 96-well microtiter plate (MTP) reader capable of automated AST without the need for a trained diagnostician. This system is composed of a smartphone used in conjunction with a 3D-printed opto-mechanical attachment, which holds a set of inexpensive light-emitting-diodes and fiber-optic cables coupled to the 96-well MTP for enabling the capture of the transmitted light through each well by the smartphone camera. Images of the MTP plate are captured at multiple exposures and uploaded to a local or remote server (e.g., a laptop) for automated processing/analysis of the results using a custom-designed smartphone application. Each set of images are combined to generate a high dynamic-range image and analyzed for well turbidity (indicative of bacterial growth), followed by interpretative analysis per plate to determine minimum inhibitory concentration (MIC) and drug susceptibility for the specific bacterium. Results are returned to the originating device within 1 minute and shown to the user in tabular form. We demonstrated the capability of this platform using MTPs prepared with 17 antibiotic drugs targeting Gram-negative bacteria and tested 82 patient isolate MTPs of Klebsiella pneumoniae, achieving well turbidity accuracy of 98.19%, MIC accuracy of 95.15%, and drug susceptibility interpretation accuracy of 99.06%, meeting the FDA defined criteria for AST.

  15. In vitro susceptibility patterns of clinically important Trichophyton and Epidermophyton species against nine antifungal drugs

    NARCIS (Netherlands)

    Badali, Hamid; Mohammadi, Rasoul; Mashedi, Olga; de Hoog, G Sybren; Meis, Jacques F

    Despite the common, worldwide, occurrence of dermatophytes, little information is available regarding susceptibility profiles against currently available and novel antifungal agents. A collection of sixty-eight clinical Trichophyton species and Epidermophyton floccosum were previously identified and

  16. 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

  17. In silico prediction of sex-based differences in human susceptibility to cardiac ventricular tachyarrhythmias

    Directory of Open Access Journals (Sweden)

    Pei-Chi eYang

    2012-09-01

    Full Text Available Sex-based differences in human susceptibility to cardiac ventricular tachyarrhythmias likely result from the emergent effects of multiple intersecting processes that fundamentally differ in male and female hearts. Included are measured differences in the genes encoding key cardiac ion channels and effects of sex steroid hormones to acutely modify electrical activity. At the genome scale, human females have recently been shown to have lower expression of genes encoding key cardiac repolarizing potassium currents and connexin43, the primary ventricular gap junction subunit. Human males and females also have distinct sex steroid hormones. Here, we developed mathematical models for male and female ventricular human heart cells by incorporating experimentally determined genomic differences and effects of sex steroid hormones into the O’Hara-Rudy model. These male and female model cells and tissues then were used to predict how various sex-based differences underlie arrhythmia risk. Genomic-based differences in ion channel expression were alone sufficient to determine longer female cardiac action potential durations (APD in both epicardial and endocardial cells compared to males. Subsequent addition of sex steroid hormones exacerbated these differences, as testosterone further shortened APDs, while estrogen and progesterone application resulted in disparate effects on APDs. Our results indicate that incorporation of experimentally determined genomic differences from human hearts in conjunction with sex steroid hormones are consistent with clinically observed differences in QT interval, T-wave shape and morphology, and critically, in the higher vulnerability of adult human females to Torsades de Pointes type arrhythmias. The model suggests that female susceptibility to alternans stems from longer female action potentials, while reentrant arrhythmia derives largely from sex-based differences in conduction play an important role in arrhythmia

  18. Molecular characterization and drug susceptibility profile of a Mycobacterium avium subspecies avium isolate from a dog with disseminated infection.

    Science.gov (United States)

    Armas, Federica; Furlanello, Tommaso; Camperio, Cristina; Trotta, Michele; Novari, Gianluca; Marianelli, Cinzia

    2016-01-12

    Mycobacterium avium complex (MAC) infections have been described in many mammalian species including humans and pets. We isolated and molecularly typed the causative agent of a rare case of disseminated mycobacteriosis in a dog. We identified the pathogen as a M. avium subspecies avium by sequencing the partial genes gyrB and rpsA. Considering the zoonotic potential of this infection, and in an attempt to ensure the most effective treatment for the animal, we also determined the drug susceptibility profile of the isolate to the most common drugs used to treat MAC disease in humans. The pathogen was tested in vitro against the macrolide clarithromycin, as well as against amikacin, ciprofloxacin, rifampicin, ethambutol and linezolid by the resazurin microdilution assay. It was found to be sensitive to all tested drugs save ethambutol. Despite the fact that the pathogen was sensitive to the therapies administered, the dog's overall clinical status worsened, and the animal died shortly after antimicrobial susceptibility results became available. Nucleotide sequencing of the embB gene, the target gene most commonly associated with ethambutol resistance, showed new missense mutations when compared to sequences available in public databases. In conclusion, we molecularly identified the MAC pathogen and determined its drug susceptibility profile in a relatively short period of time (seven days). We also characterized new genetic mutations likely to have been involved in the observed ethambutol resistance. Our results confirm the usefulness of both the gyrB and the rpsA genes as biomarkers for an accurate identification and differentiation of MAC pathogens.

  19. 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.

  20. Exploring the Predictive Validity of the Susceptibility to Smoking Construct for Tobacco Cigarettes, Alternative Tobacco Products, and E-Cigarettes.

    Science.gov (United States)

    Cole, Adam G; Kennedy, Ryan David; Chaurasia, Ashok; Leatherdale, Scott T

    2017-12-06

    Within tobacco prevention programming, it is useful to identify youth that are at risk for experimenting with various tobacco products and e-cigarettes. The susceptibility to smoking construct is a simple method to identify never-smoking students that are less committed to remaining smoke-free. However, the predictive validity of this construct has not been tested within the Canadian context or for the use of other tobacco products and e-cigarettes. This study used a large, longitudinal sample of secondary school students that reported never using tobacco cigarettes and non-current use of alternative tobacco products or e-cigarettes at baseline in Ontario, Canada. The sensitivity, specificity, and positive and negative predictive values of the susceptibility construct for predicting tobacco cigarette, e-cigarette, cigarillo or little cigar, cigar, hookah, and smokeless tobacco use one and two years after baseline measurement were calculated. At baseline, 29.4% of the sample was susceptible to future tobacco product or e-cigarette use. The sensitivity of the construct ranged from 43.2% (smokeless tobacco) to 59.5% (tobacco cigarettes), the specificity ranged from 70.9% (smokeless tobacco) to 75.9% (tobacco cigarettes), and the positive predictive value ranged from 2.6% (smokeless tobacco) to 32.2% (tobacco cigarettes). Similar values were calculated for each measure of the susceptibility construct. A significant number of youth that did not currently use tobacco products or e-cigarettes at baseline reported using tobacco products and e-cigarettes over a two-year follow-up period. The predictive validity of the susceptibility construct was high and the construct can be used to predict other tobacco product and e-cigarette use among youth. This study presents the predictive validity of the susceptibility construct for the use of tobacco cigarettes among secondary school students in Ontario, Canada. It also presents a novel use of the susceptibility construct for

  1. M. tuberculosis genotypic diversity and drug susceptibility pattern in HIV- infected and non-HIV-infected patients in northern Tanzania

    Directory of Open Access Journals (Sweden)

    van Soolingen Dick

    2007-05-01

    Full Text Available Abstract Background Tuberculosis (TB is a major health problem and HIV is the major cause of the increase in TB. Sub-Saharan Africa is endemic for both TB and HIV infection. Determination of the prevalence of M. tuberculosis strains and their drug susceptibility is important for TB control. TB positive culture, BAL fluid or sputum samples from 130 patients were collected and genotyped. The spoligotypes were correlated with anti-tuberculous drug susceptibility in HIV-infected and non-HIV patients from Tanzania. Results One-third of patients were TB/HIV co-infected. Forty-seven spoligotypes were identified. Fourteen isolates (10.8% had new and unique spoligotypes while 116 isolates (89.2% belonged to 33 known spoligotypes. The major spoligotypes contained nine clusters: CAS1-Kili 30.0%, LAM11- ZWE 14.6%, ND 9.2%, EAI 6.2%, Beijing 5.4%, T-undefined 4.6%, CAS1-Delhi 3.8%, T1 3.8% and LAM9 3.8%. Twelve (10.8% of the 111 phenotypically tested strains were resistant to anti-TB drugs. Eight (7.2% were monoresistant strains: 7 to isoniazid (INH and one to streptomycin. Four strains (3.5% were resistant to multiple drugs: one (0.9% was resistant to INH and streptomycin and the other three (2.7% were MDR strains: one was resistant to INH, rifampicin and ethambutol and two were resistant to all four anti-TB drugs. Mutation in the katG gene codon 315 and the rpoB hotspot region showed a low and high sensitivity, respectively, as predictor of phenotypic drug resistance. Conclusion CAS1-Kili and LAM11-ZWE were the most common families. Strains of the Beijing family and CAS1-Kili were not or least often associated with resistance, respectively. HIV status was not associated with spoligotypes, resistance or previous TB treatment.

  2. Genotyping and drug susceptibility testing of mycobacterial isolates from population-based tuberculosis prevalence survey in Ghana.

    Science.gov (United States)

    Addo, Kennedy Kwasi; Addo, Samuel Ofori; Mensah, Gloria Ivy; Mosi, Lydia; Bonsu, Frank Adae

    2017-12-02

    Mycobacterium tuberculosis complex (MTBC) and Non-tuberculosis Mycobacterium (NTM) infections differ clinically, making rapid identification and drug susceptibility testing (DST) very critical for infection control and drug therapy. This study aims to use World Health Organization (WHO) approved line probe assay (LPA) to differentiate mycobacterial isolates obtained from tuberculosis (TB) prevalence survey in Ghana and to determine their drug resistance patterns. A retrospective study was conducted whereby a total of 361 mycobacterial isolates were differentiated and their drug resistance patterns determined using GenoType Mycobacterium Assays: MTBC and CM/AS for differentiating MTBC and NTM as well MTBDRplus and NTM-DR for DST of MTBC and NTM respectively. Out of 361 isolates, 165 (45.7%) MTBC and 120 (33.2%) NTM (made up of 14 different species) were identified to the species levels whiles 76 (21.1%) could not be completely identified. The MTBC comprised 161 (97.6%) Mycobacterium tuberculosis and 4 (2.4%) Mycobacterium africanum. Isoniazid and rifampicin monoresistant MTBC isolates were 18/165 (10.9%) and 2/165(1.2%) respectively whiles 11/165 (6.7%) were resistant to both drugs. Majority 42/120 (35%) of NTM were M. fortuitum. DST of 28 M. avium complex and 8 M. abscessus complex species revealed that all were susceptible to macrolides (clarithromycin, azithromycin) and aminoglycosides (kanamycin, amikacin, and gentamicin). Our research signifies an important contribution to TB control in terms of knowledge of the types of mycobacterium species circulating and their drug resistance patterns in Ghana.

  3. Motivated malleability: Frontal cortical asymmetry predicts the susceptibility to social influence.

    Science.gov (United States)

    Schnuerch, Robert; Pfattheicher, Stefan

    2017-07-16

    Humans, just as many other animals, regulate their behavior in terms of approaching stimuli associated with pleasure and avoiding stimuli linked to harm. A person's current and chronic motivational direction - that is, approach versus avoidance orientation - is reliably reflected in the asymmetry of frontal cortical low-frequency oscillations. Using resting electroencephalography (EEG), we show that frontal asymmetry is predictive of the tendency to yield to social influence: Stronger right- than left-side frontolateral activation during a resting-state session prior to the experiment was robustly associated with a stronger inclination to adopt a peer group's judgments during perceptual decision-making (Study 1). We posit that this reflects the role of a person's chronic avoidance orientation in socially adjusted behavior. This claim was strongly supported by additional survey investigations (Studies 2a, 2b, 2c), all of which consistently revealed that trait avoidance was positively linked to the susceptibility to social influence. The present contribution thus stresses the relevance of chronic avoidance orientation in social conformity, refining (yet not contradicting) the longstanding view that socially influenced behavior is motivated by approach-related goals. Moreover, our findings valuably underscore and extend our knowledge on the association between frontal cortical asymmetry and a variety of psychological variables.

  4. Spatial prediction of ground subsidence susceptibility using an artificial neural network.

    Science.gov (United States)

    Lee, Saro; Park, Inhye; Choi, Jong-Kuk

    2012-02-01

    Ground subsidence in abandoned underground coal mine areas can result in loss of life and property. We analyzed ground subsidence susceptibility (GSS) around abandoned coal mines in Jeong-am, Gangwon-do, South Korea, using artificial neural network (ANN) and geographic information system approaches. Spatial data of subsidence area, topography, and geology, as well as various ground-engineering data, were collected and used to create a raster database of relevant factors for a GSS map. Eight major factors causing ground subsidence were extracted from the existing ground subsidence area: slope, depth of coal mine, distance from pit, groundwater depth, rock-mass rating, distance from fault, geology, and land use. Areas of ground subsidence were randomly divided into a training set to analyze GSS using the ANN and a test set to validate the predicted GSS map. Weights of each factor's relative importance were determined by the back-propagation training algorithms and applied to the input factor. The GSS was then calculated using the weights, and GSS maps were created. The process was repeated ten times to check the stability of analysis model using a different training data set. The map was validated using area-under-the-curve analysis with the ground subsidence areas that had not been used to train the model. The validation showed prediction accuracies between 94.84 and 95.98%, representing overall satisfactory agreement. Among the input factors, "distance from fault" had the highest average weight (i.e., 1.5477), indicating that this factor was most important. The generated maps can be used to estimate hazards to people, property, and existing infrastructure, such as the transportation network, and as part of land-use and infrastructure planning.

  5. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

    Science.gov (United States)

    Saro, Lee; Woo, Jeon Seong; Kwan-Young, Oh; Moung-Jin, Lee

    2016-02-01

    The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs) followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS). These factors were analysed using artificial neural network (ANN) and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50%) and a test set (50%). A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10%) was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%). Of the weights used in the artificial neural network model, `slope' yielded the highest weight value (1.330), and `aspect' yielded the lowest value (1.000). This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

  6. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

    Directory of Open Access Journals (Sweden)

    Saro Lee

    2016-02-01

    Full Text Available The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS. These factors were analysed using artificial neural network (ANN and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50% and a test set (50%. A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10% was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%. Of the weights used in the artificial neural network model, ‘slope’ yielded the highest weight value (1.330, and ‘aspect’ yielded the lowest value (1.000. This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

  7. [Drug susceptibility test guided therapy and novel empirical quadruple therapy for Helicobacter pylori infection: a network Meta-analysis].

    Science.gov (United States)

    Gou, Q Y; Yu, R B; Shi, R H

    2017-05-10

    Objective: To compare the efficacy and the risk of adverse effect of drug susceptibility test guided therapy and novel empirical quadruple therapy for Helicobacter ( H .) pylori infection. Methods: Literature retrieval was conducted by using major databases. Related papers published up to June 2015 were considered eligible if they were randomized control trials comparing different pharmacological formulations for H. pylori infection and used in a network Meta-analysis and a single rate Meta-analysis to evaluate the relative and absolute rates of H. pylori eradication and the risk of adverse effect. The Jadad score was used to evaluate the methodological quality. Funnel plot was constructed to evaluate the risk of publication bias. Begg's rank correlation test or Egger's regression intercept test was done for the asymmetry of funnel plot. Results: Twenty randomized control trials for the treatment of 6 753 initial treated patients with H. pylori infection were included. Drug susceptibility test guided therapy was significantly superior to concomitant therapy, hybrid therapy, sequential therapy and bismuth quadruple therapy. The culture-based therapy had the highest likelihood of improving clinical efficacy, with lowest risk of adverse effect. Concomitant therapy had the highest probability of causing adverse effect despite its effectiveness. Hybrid therapy and bismuth quadruple therapy were associated with lower risk of adverse effect and higher effectiveness. Conclusion: Drug susceptibility test guided therapy showed superiority to other 4 interventions for H. pylori eradication mentioned above. Hybrid therapy and bismuth quadruple therapy might be applied in the settings where the culture-based strategy is not available.

  8. 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

    (fast/late Na+ and Ca2+ currents exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca2+-transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.

  9. 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

    (fast/late Na + and Ca 2+ currents) exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca 2+ -transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.

  10. Second line drug susceptibility testing to inform the treatment of rifampin-resistant tuberculosis: a quantitative perspective

    Directory of Open Access Journals (Sweden)

    Emily A. Kendall

    2017-03-01

    Full Text Available Treatment failure and resistance amplification are common among patients with rifampin-resistant tuberculosis (TB. Drug susceptibility testing (DST for second-line drugs is recommended for these patients, but logistical difficulties have impeded widespread implementation of second-line DST in many settings. To provide a quantitative perspective on the decision to scale up second-line DST, we synthesize literature on the prevalence of second-line drug resistance, the expected clinical and epidemiologic benefits of using second-line DST to ensure that patients with rifampin-resistant TB receive effective regimens, and the costs of implementing (or not implementing second-line DST for all individuals diagnosed with rifampin-resistant TB. We conclude that, in most settings, second-line DST could substantially improve treatment outcomes for patients with rifampin-resistant TB, reduce transmission of drug-resistant TB, prevent amplification of drug resistance, and be affordable or even cost-saving. Given the large investment made in each patient treated for rifampin-resistant TB, these payoffs would come at relatively small incremental cost. These anticipated benefits likely justify addressing the real challenges faced in implementing second-line DST in most high-burden settings.

  11. 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.

  12. 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.

  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. In vitro drug susceptibility of Mycobacterium tuberculosis for amikacin, kanamycin and capreomycin.

    NARCIS (Netherlands)

    Dijkstra, J A; van der Laan, T; Akkerman, O W; Bolhuis, M S; de Lange, W C M; Kosterink, J G W; van der Werf, T S; Alffenaar, J W C; van Soolingen, D

    2018-01-01

    Amikacin, kanamycin and capreomycin are listed among the most important 2nd line drugs for multidrug resistant tuberculosis. Although amikacin and kanamycin are administered in the same dose and show the same pharmacokinetics, they have different WHO breakpoints suggesting that the two drugs have a

  15. Global Phenotypic Characterization of Effects of Fluoroquinolone Resistance Selection on the Metabolic Activities and Drug Susceptibilities of Clostridium perfringens Strains

    Directory of Open Access Journals (Sweden)

    Miseon Park

    2014-01-01

    Full Text Available Fluoroquinolone resistance affects toxin production of Clostridium perfringens strains differently. To investigate the effect of fluoroquinolone resistance selection on global changes in metabolic activities and drug susceptibilities, four C. perfringens strains and their norfloxacin-, ciprofloxacin-, and gatifloxacin-resistant mutants were compared in nearly 2000 assays, using phenotype microarray plates. Variations among mutant strains resulting from resistance selection were observed in all aspects of metabolism. Carbon utilization, pH range, osmotic tolerance, and chemical sensitivity of resistant strains were affected differently in the resistant mutants depending on both the bacterial genotype and the fluoroquinolone to which the bacterium was resistant. The susceptibilities to gentamicin and erythromycin of all resistant mutants except one increased, but some resistant strains were less susceptible to amoxicillin, cefoxitin, ceftriaxone, chloramphenicol, and metronidazole than their wild types. Sensitivity to ethidium bromide decreased in some resistant mutants and increased in others. Microarray analysis of two gatifloxacin-resistant mutants showed changes in metabolic activities that were correlated with altered expression of various genes. Both the chemical structures of fluoroquinolones and the genomic makeup of the wild types influenced the changes found in resistant mutants, which may explain some inconsistent reports of the effects of therapeutic use of fluoroquinolones on clinical isolates of bacteria.

  16. A Prospective Study of Tuberculosis Drug Susceptibility in Sabah, Malaysia, and an Algorithm for Management of Isoniazid Resistance

    Science.gov (United States)

    Rashid Ali, Muhammad Redzwan S.; Parameswaran, Uma; William, Timothy; Bird, Elspeth; Wilkes, Christopher S.; Lee, Wai Khew; Yeo, Tsin Wen; Anstey, Nicholas M.; Ralph, Anna P.

    2015-01-01

    Introduction. The burden of tuberculosis is high in eastern Malaysia, and rates of Mycobacterium tuberculosis drug resistance are poorly defined. Our objectives were to determine M. tuberculosis susceptibility and document management after receipt of susceptibility results. Methods. Prospective study of adult outpatients with smear-positive pulmonary tuberculosis (PTB) in Sabah, Malaysia. Additionally, hospital clinicians accessed the reference laboratory for clinical purposes during the study. Results. 176 outpatients were enrolled; 173 provided sputum samples. Mycobacterial culture yielded M. tuberculosis in 159 (91.9%) and nontuberculous Mycobacterium (NTM) in three (1.7%). Among outpatients there were no instances of multidrug resistant M. tuberculosis (MDR-TB). Seven people (4.5%) had isoniazid resistance (INH-R); all were switched to an appropriate second-line regimen for varying durations (4.5–9 months). Median delay to commencement of the second-line regimen was 13 weeks. Among 15 inpatients with suspected TB, 2 had multidrug resistant TB (one extensively drug resistant), 2 had INH-R, and 4 had NTM. Conclusions. Current community rates of MDR-TB in Sabah are low. However, INH-resistance poses challenges, and NTM is an important differential diagnosis in this setting, where smear microscopy is the usual diagnostic modality. To address INH-R management issues in our setting, we propose an algorithm for the treatment of isoniazid-resistant PTB. PMID:25838829

  17. An in vitro study of antifungal drug susceptibility of Candida species isolated from human immunodeficiency virus seropositive and human immunodeficiency virus seronegative individuals in Lucknow population Uttar Pradesh.

    Science.gov (United States)

    Dar, Mohammad Shafi; Sreedar, Gadiputi; Shukla, Abhilasha; Gupta, Prashant; Rehan, Ahmad Danish; George, Jiji

    2015-01-01

    Candidiasis is the most common opportunistic infection in human immunodeficiency virus (HIV) seropositive patients, starting from asymptomatic colonization to pathogenic forms and gradual colonization of non-albicans in patients with advanced immunosuppression leads to resistance for azole group of antifungal drugs with high rate of morbidity and mortality. To isolate the Candida species and determine of antifungal drug susceptibility against fluconazole, itraconazole, nystatin, amphotericin B, and clotrimazolein HIV seropositive and control individuals, with or without clinical oropharyngeal candidiasis (OPC). Includes samples from faucial region of 70 subjects with and without clinical candidiasis in HIV seropositive and controls were aseptically inoculated onto Sabaraud's Dextrose Agar media and yeasts were identified for the specific species by Corn Meal Agar, sugar fermentation and heat tolerance tests. Antifungal drug susceptibility of the isolated species was done against above-mentioned drugs by E-test and disc diffusion method. The commonly isolated species in HIV seropositive and controls were Candida albicans, Candida glabrata and Candida tropicalis Candida guilliermondii and Candida dubliniensis isolated only in HIV seropositive patients. Susceptibility against selected antifungal drugs was observed more in HIV-negative individuals whereas susceptible dose-dependent and resistance were predominant in HIV-positive patients. Resistance is the major problem in the therapy of OPC, especially in HIV seropositive patients due to aggressive and prolonged use of antifungal agents, therefore, our study emphasizes the need for antifungal drug susceptibility testing whenever antifungal treatment is desired, especially in HIV-infected subjects.

  18. Direct nitrate reductase assay versus microscopic observation drug susceptibility test for rapid detection of MDR-TB in Uganda.

    Directory of Open Access Journals (Sweden)

    Freddie Bwanga

    Full Text Available The most common method for detection of drug resistant (DR TB in resource-limited settings (RLSs is indirect susceptibility testing on Lowenstein-Jensen medium (LJ which is very time consuming with results available only after 2-3 months. Effective therapy of DR TB is therefore markedly delayed and patients can transmit resistant strains. Rapid and accurate tests suitable for RLSs in the diagnosis of DR TB are thus highly needed. In this study we compared two direct techniques--Nitrate Reductase Assay (NRA and Microscopic Observation Drug Susceptibility (MODS for rapid detection of MDR-TB in a high burden RLS. The sensitivity, specificity, and proportion of interpretable results were studied. Smear positive sputum was collected from 245 consecutive re-treatment TB patients attending a TB clinic in Kampala, Uganda. Samples were processed at the national reference laboratory and tested for susceptibility to rifampicin and isoniazid with direct NRA, direct MODS and the indirect LJ proportion method as reference. A total of 229 specimens were confirmed as M. tuberculosis, of these interpretable results were obtained in 217 (95% with either the NRA or MODS. Sensitivity, specificity and kappa agreement for MDR-TB diagnosis was 97%, 98% and 0.93 with the NRA; and 87%, 95% and 0.78 with the MODS, respectively. The median time to results was 10, 7 and 64 days with NRA, MODS and the reference technique, respectively. The cost of laboratory supplies per sample was low, around 5 USD, for the rapid tests. The direct NRA and MODS offered rapid detection of resistance almost eight weeks earlier than with the reference method. In the study settings, the direct NRA was highly sensitive and specific. We consider it to have a strong potential for timely detection of MDR-TB in RLS.

  19. Predictive modeling of freezing and thawing of frost-susceptible soils.

    Science.gov (United States)

    2015-09-01

    Frost depth is an essential factor in design of various transportation infrastructures. In frost : susceptible soils, as soils freezes, water migrates through the soil voids below the freezing line : towards the freezing front and causes excessive he...

  20. 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.

  1. 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

  2. Drug susceptibility of Mycobacterium tuberculosis in a rural area of Bangladesh and its relevance to the national treatment regimens.

    Science.gov (United States)

    Van Deun, A; Aung, K J; Chowdhury, S; Saha, S; Pankaj, A; Ashraf, A; Rigouts, L; Fissette, K; Portaels, F

    1999-02-01

    Greater Mymensingh District, a rural area of Bangladesh, at the start of the National Tuberculosis Programme (NTP). To determine the prevalence of initial and acquired drug resistance of Mycobacterium tuberculosis, and to assess the appropriateness of the NTP's standard regimens. Sampling of pre-treatment sputum from all newly registered smear-positive cases in five centres covering the area. Culture and susceptibility testing in a supra-national reference laboratory. Initial resistance to isoniazid (H) was 5.4%, and to rifampicin (R) 0.5%. Acquired H and R resistance were 25.9% and 7.4%, respectively. Multidrug resistance (MDR) was observed in one new case only and in 5.6% of previously treated patients. Changing the present NTP indication for retreatment regimen to one month of previous H intake would increase coverage of H-resistant cases from 52% to 89%, adding 6% to drug costs. The prevalence of drug resistance is surprisingly low in Bangladesh, but could rise with improving economic conditions. The NTP regimens for smear-positive cases are appropriate, all the more so since the human immunodeficiency virus is virtually absent. Indications for the retreatment regimen should be extended to include all patients treated for at least one month with any drug. The NTP regimen for smear-negative cases runs the risk of leading to MDR under present field conditions.

  3. In vitro susceptibility of antifungal drugs against Sporothrix brasiliensis recovered from cats with sporotrichosis in Brazil.

    Science.gov (United States)

    Brilhante, Raimunda Sâmia Nogueira; Rodrigues, Anderson Messias; Sidrim, José Júlio Costa; Rocha, Marcos Fábio Gadelha; Pereira, Sandro Antonio; Gremião, Isabella Dib Ferreira; Schubach, Tânia Maria Pacheco; de Camargo, Zoilo Pires

    2016-03-01

    Sporotrichosis is an important subcutaneous mycosis of humans and animals. Classically, the disease is acquired upon traumatic inoculation of Sporothrix propagules from contaminated soil and plant debris. In addition, the direct horizontal transmission of Sporothrix among animals and the resulting zoonotic infection in humans highlight an alternative and efficient rout of transmission through biting and scratching. Sporothrix brasiliensis is the most virulent species of the Sporothrix schenckii complex and is responsible for the long-lasting outbreak of feline sporotrichosis in Brazil. However, antifungal susceptibility data of animal-borne isolates is scarce. Therefore, this study evaluated the in vitro activity of amphotericin B, caspofungin, itraconazole, voriconazole, fluconazole, and ketoconazole against animal-borne isolates of S. brasiliensis. The susceptibility tests were performed through broth microdilution (M38-A2). The results show the relevant activity of itraconazole, amphotericin B, and ketoconazole against S. brasiliensis, with the following MIC ranges: 0.125-2, 0.125-4 and 0.0312-2 μg/ml, respectively. Caspofungin was moderately effective, displaying higher variation in MIC values (0.25-64 μg/ml). Voriconazole (2-64 μg/ml) and fluconazole (62.5-500 μg/ml) showed low activity against S. brasiliensis strains. This study contributed to the characterization of the in vitro antifungal susceptibility of strains of S. brasiliensis recovered from cats with sporotrichosis, which have recently been considered the main source of human infections. © The Author 2015. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  4. Technical Note: Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models

    Directory of Open Access Journals (Sweden)

    S. Pereira

    2012-04-01

    Full Text Available The aim of this study is to identify the landslide predisposing factors' combination using a bivariate statistical model that best predicts landslide susceptibility. The best model is one that has simultaneously good performance in terms of suitability and predictive power and has been developed using variables that are conditionally independent. The study area is the Santa Marta de Penaguião council (70 km2 located in the Northern Portugal.

    In order to identify the best combination of landslide predisposing factors, all possible combinations using up to seven predisposing factors were performed, which resulted in 120 predictions that were assessed with a landside inventory containing 767 shallow translational slides. The best landslide susceptibility model was selected according to the model degree of fitness and on the basis of a conditional independence criterion. The best model was developed with only three landslide predisposing factors (slope angle, inverse wetness index, and land use and was compared with a model developed using all seven landslide predisposing factors.

    Results showed that it is possible to produce a reliable landslide susceptibility model using fewer landslide predisposing factors, which contributes towards higher conditional independence.

  5. A genome wide association study of Plasmodium falciparum susceptibility to 22 antimalarial drugs in Kenya.

    Directory of Open Access Journals (Sweden)

    Jason P Wendler

    Full Text Available Drug resistance remains a chief concern for malaria control. In order to determine the genetic markers of drug resistant parasites, we tested the genome-wide associations (GWA of sequence-based genotypes from 35 Kenyan P. falciparum parasites with the activities of 22 antimalarial drugs.Parasites isolated from children with acute febrile malaria were adapted to culture, and sensitivity was determined by in vitro growth in the presence of anti-malarial drugs. Parasites were genotyped using whole genome sequencing techniques. Associations between 6250 single nucleotide polymorphisms (SNPs and resistance to individual anti-malarial agents were determined, with false discovery rate adjustment for multiple hypothesis testing. We identified expected associations in the pfcrt region with chloroquine (CQ activity, and other novel loci associated with amodiaquine, quinazoline, and quinine activities. Signals for CQ and primaquine (PQ overlap in and around pfcrt, and interestingly the phenotypes are inversely related for these two drugs. We catalog the variation in dhfr, dhps, mdr1, nhe, and crt, including novel SNPs, and confirm the presence of a dhfr-164L quadruple mutant in coastal Kenya. Mutations implicated in sulfadoxine-pyrimethamine resistance are at or near fixation in this sample set.Sequence-based GWA studies are powerful tools for phenotypic association tests. Using this approach on falciparum parasites from coastal Kenya we identified known and previously unreported genes associated with phenotypic resistance to anti-malarial drugs, and observe in high-resolution haplotype visualizations a possible signature of an inverse selective relationship between CQ and PQ.

  6. 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.

  7. An analytical method for assessing stage-specific drug activity in Plasmodium vivax malaria: implications for ex vivo drug susceptibility testing.

    Directory of Open Access Journals (Sweden)

    Douglas H Kerlin

    Full Text Available The emergence of highly chloroquine (CQ resistant P. vivax in Southeast Asia has created an urgent need for an improved understanding of the mechanisms of drug resistance in these parasites, the development of robust tools for defining the spread of resistance, and the discovery of new antimalarial agents. The ex vivo Schizont Maturation Test (SMT, originally developed for the study of P. falciparum, has been modified for P. vivax. We retrospectively analysed the results from 760 parasite isolates assessed by the modified SMT to investigate the relationship between parasite growth dynamics and parasite susceptibility to antimalarial drugs. Previous observations of the stage-specific activity of CQ against P. vivax were confirmed, and shown to have profound consequences for interpretation of the assay. Using a nonlinear model we show increased duration of the assay and a higher proportion of ring stages in the initial blood sample were associated with decreased effective concentration (EC(50 values of CQ, and identify a threshold where these associations no longer hold. Thus, starting composition of parasites in the SMT and duration of the assay can have a profound effect on the calculated EC(50 for CQ. Our findings indicate that EC(50 values from assays with a duration less than 34 hours do not truly reflect the sensitivity of the parasite to CQ, nor an assay where the proportion of ring stage parasites at the start of the assay does not exceed 66%. Application of this threshold modelling approach suggests that similar issues may occur for susceptibility testing of amodiaquine and mefloquine. The statistical methodology which has been developed also provides a novel means of detecting stage-specific drug activity for new antimalarials.

  8. The RadGenomics project. Prediction for radio-susceptibility of individuals with genetic predisposition

    International Nuclear Information System (INIS)

    Imai, Takashi

    2003-01-01

    The ultimate goal of our project, named RadGenomics, is to elucidate the heterogeneity of the response to ionizing radiation arising from genetic variation among individuals, for the purpose of developing personalized radiation therapy regimens for cancer patients. Cancer patients exhibit patient-to-patient variability in normal tissue reactions after radiotherapy. Several observations support the hypothesis that the radiosensitivity of normal tissue is influenced by genetic factors. The rapid progression of human genome sequencing and the recent development of new technologies in molecular biology are providing new opportunities for elucidating the genetic basis of individual differences in susceptibility to radiation exposure. The development of a sufficiently robust, predictive assay enabling individual dose adjustment would improve the outcome of radiation therapy in patients. Our strategy for identification of DNA polymorphisms that contribute to the individual radiosensitivity is as follows. First, we have been categorizing DNA samples obtained from cancer patients, who have been kindly introduced to us through many collaborators, according to their clinical characteristics including the method and effect of treatment and side effects as scored by toxicity criteria, and also the result of an in vitro radiosensitivity assay, e.g., the micronuclei assay of their lymphocytes. Second, we have identified candidate genes for genotyping mainly by using our custom-designed oligonucleotide array with RNA samples, in which the probes were obtained from more than 40 cancer and 3 fibroblast cell lines whose radiosensitivity level was quite heterogeneous. We have also been studying the modification of proteins after irradiation of cells which may be caused by mainly phosphorylation or dephosphorylation, using mass spectrometry. Genes encoding the modified proteins and/or other proteins with which they interact such as specific protein kinases and phosphatases are also

  9. 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.

  10. 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.

  11. 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.

  12. Observation of reversible, rapid changes in drug susceptibility of hypoxic tumor cells in a microfluidic device.

    Science.gov (United States)

    Germain, Todd; Ansari, Megan; Pappas, Dimitri

    2016-09-14

    Hypoxia is a major stimulus for increased drug resistance and for survival of tumor cells. Work from our group and others has shown that hypoxia increases resistance to anti-cancer compounds, radiation, and other damage-pathway cytotoxic agents. In this work we utilize a microfluidic culture system capable of rapid switching of local oxygen concentrations to determine changes in drug resistance in prostate cancer cells. We observed rapid adaptation to hypoxia, with drug resistance to 2 μM staurosporine established within 30 min of hypoxia. Annexin-V/Sytox Green apoptosis assays over 9 h showed 78.0% viability, compared to 84.5% viability in control cells (normoxic cells with no staurosporine). Normoxic cells exposed to the same staurosporine concentration had a viability of 48.6% after 9 h. Hypoxia adaptation was rapid and reversible, with Hypoxic cells treated with 20% oxygen for 30 min responding to staurosporine with 51.6% viability after drug treatment for 9 h. Induction of apoptosis through the receptor-mediated pathway, which bypasses anti-apoptosis mechanisms induced by hypoxia, resulted in 39.4 ± 7% cell viability. The rapid reversibility indicates co-treatment of oxygen with anti-cancer compounds may be a potential therapeutic target. Copyright © 2016 Elsevier B.V. All rights reserved.

  13. A rapid culture system uninfluenced by an inoculum effect increases reliability and convenience for drug susceptibility testing of Mycobacterium tuberculosis.

    Science.gov (United States)

    Jung, Yong-Gyun; Kim, Hyejin; Lee, Sangyeop; Kim, Suyeoun; Jo, EunJi; Kim, Eun-Geun; Choi, Jungil; Kim, Hyun Jung; Yoo, Jungheon; Lee, Hye-Jeong; Kim, Haeun; Jung, Hyunju; Ryoo, Sungweon; Kwon, Sunghoon

    2018-06-05

    The Disc Agarose Channel (DAC) system utilizes microfluidics and imaging technologies and is fully automated and capable of tracking single cell growth to produce Mycobacterium tuberculosis (MTB) drug susceptibility testing (DST) results within 3~7 days. In particular, this system can be easily used to perform DSTs without the fastidious preparation of the inoculum of MTB cells. Inoculum effect is one of the major problems that causes DST errors. The DAC system was not influenced by the inoculum effect and produced reliable DST results. In this system, the minimum inhibitory concentration (MIC) values of the first-line drugs were consistent regardless of inoculum sizes ranging from ~10 3 to ~10 8 CFU/mL. The consistent MIC results enabled us to determine the critical concentrations for 12 anti-tuberculosis drugs. Based on the determined critical concentrations, further DSTs were performed with 254 MTB clinical isolates without measuring an inoculum size. There were high agreement rates (96.3%) between the DAC system and the absolute concentration method using Löwenstein-Jensen medium. According to these results, the DAC system is the first DST system that is not affected by the inoculum effect. It can thus increase reliability and convenience for DST of MTB. We expect that this system will be a potential substitute for conventional DST systems.

  14. Carcinogen susceptibility is regulated by genome architecture and predicts cancer mutagenesis.

    Science.gov (United States)

    García-Nieto, Pablo E; Schwartz, Erin K; King, Devin A; Paulsen, Jonas; Collas, Philippe; Herrera, Rafael E; Morrison, Ashby J

    2017-10-02

    The development of many sporadic cancers is directly initiated by carcinogen exposure. Carcinogens induce malignancies by creating DNA lesions (i.e., adducts) that can result in mutations if left unrepaired. Despite this knowledge, there has been remarkably little investigation into the regulation of susceptibility to acquire DNA lesions. In this study, we present the first quantitative human genome-wide map of DNA lesions induced by ultraviolet (UV) radiation, the ubiquitous carcinogen in sunlight that causes skin cancer. Remarkably, the pattern of carcinogen susceptibility across the genome of primary cells significantly reflects mutation frequency in malignant melanoma. Surprisingly, DNase-accessible euchromatin is protected from UV, while lamina-associated heterochromatin at the nuclear periphery is vulnerable. Many cancer driver genes have an intrinsic increase in carcinogen susceptibility, including the BRAF oncogene that has the highest mutation frequency in melanoma. These findings provide a genome-wide snapshot of DNA injuries at the earliest stage of carcinogenesis. Furthermore, they identify carcinogen susceptibility as an origin of genome instability that is regulated by nuclear architecture and mirrors mutagenesis in cancer. © 2017 The Authors.

  15. Parental consanguinity and susceptibility to drug abuse among offspring, a case-control study.

    Science.gov (United States)

    Saadat, Mostafa; Vakili-Ghartavol, Roghayyeh

    2010-11-30

    Consanguineous marriage is the union of individuals having at least one common ancestor. It is well established that consanguinity is a potential risk factor for many adverse health outcome of offspring. In the present case-control study we tested the hypothesis of an association between parental consanguinity marriages and risk of offspring substance abuse. The study was performed in Shiraz (Fars province, Iran). Here 156 male drug abusers (case group) and 264 randomly selected healthy blood donors, matched for age and gender as control group, were included in the study. The prevalence of parental consanguineous marriages in the studied sample was 39.1 and 28.0% among cases and controls, respectively. The difference was statistically significant. The substance abusers were more smokers and drinkers compared with the control group. There was significant negative linear trend between drug abuse and level of education. The participants stratified using drinking habits and then the analysis was carried out separately for drinker and non-drinker subjects. Among drinkers, neither before nor after adjusting for smoking status and educational level, parental consanguinity did not show association with risk of substance abuse. Among non-drinkers, after adjusting for smoking status and educational level, parental consanguineous marriage was significantly associated with increased risk of substance abuse. Our study supports a significant relationship between parental consanguinity and drug abuse among non-drinker subjects. Copyright © 2010 Elsevier Ltd. All rights reserved.

  16. Genetic polymorphisms of N-acetyltransferase 2 & susceptibility to antituberculosis drug-induced hepatotoxicity

    Directory of Open Access Journals (Sweden)

    Surendra K Sharma

    2016-01-01

    Full Text Available Background & objectives: The N-acetyltransferase 2 (NAT2 gene encodes an enzyme which both activates and deactivates arylamine and other drugs and carcinogens. This study was aimed to investigate the role of NAT2 gene polymorphism in anti-tuberculosis drug-induced hepatotoxicity (DIH. Methods: In this prospective study, polymerase chain reaction-restriction fragment length polymorphism results for NAT2 gene were compared between 185 tuberculosis patients who did not develop DIH and 105 tuberculosis patients who developed DIH while on anti-tuberculosis drugs. Results: Frequency of slow-acetylator genotype was commonly encountered and was not significantly different between DIH (82.8% and non-DIH (77.2% patients. However, the genotypic distribution of variant NAT2FNx015/FNx017 amongst slow-acetylator genotypes was significantly higher in DIH (56% group as compared to non-DIH (39% group (odds ratio 2.02; P=0.006. Interpretation & conclusions: The present study demonstrated no association between NAT2 genotype and DIH in the north Indian patients with tuberculosis.

  17. 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.

  18. Species distribution and drug susceptibility of candida in clinical isolates from a tertiary care centre at Indore

    Directory of Open Access Journals (Sweden)

    N Pahwa

    2014-01-01

    Full Text Available Background: The incidence of fungal infections has increased significantly, contributing to morbidity and mortality. This is caused by an alarming increase in infections with multi-drug resistant bacteria leading to overuse of broad-spectrum antimicrobials, which lead to overgrowth of Candida, thus enhancing its opportunity to cause disease. Candida are major human fungal pathogens that cause both mucosal and deep tissue infections. Objective : The aim of our study was to identify the distribution of Candida species among clinical isolates and their sensitivity pattern for common antifungal drugs. Materials and Methods : Two hundred and thirty-seven different clinical isolates of Candida were collected from patients visiting to a tertiary care centre of Indore from 2010 to 2012. Identification of Candida species as well as antifungal sensitivity testing was performed with Vitek2 Compact (Biomerieux France using vitek 2 cards for identification of yeast and yeast like organisms (ID-YST cards. Antifungal susceptibility testing was performed with Vitek2 "Fungal Susceptibility Card (AST YS01 kits respectively. Results : We found that the non-albicans Candida were more prevalent than Candida albicans in paediatric (60 year patients than other age group (4-18, 19-60 years patients and also in intensive care unit (ICU patients as compared to out patient department (OPD patients. Resistance rates for amphotericin B, fluconazole, flucytosine, itraconazole, and voriconazole were 2.9%, 5.9%, 0.0%, 4.2% and 2.5%%, respectively. All the strains of C. krusei were found resistant to fluconazole with intermediate sensitivity to flucytosine. Conclusion: Species-level identification of Candida and their antifungal sensitivity testing should be performed to achieve better clinical results.

  19. Antiretroviral neuropenetration scores better correlate with cognitive performance of HIV-infected patients after accounting for drug susceptibility.

    Science.gov (United States)

    Fabbiani, Massimiliano; Grima, Pierfrancesco; Milanini, Benedetta; Mondi, Annalisa; Baldonero, Eleonora; Ciccarelli, Nicoletta; Cauda, Roberto; Silveri, Maria C; De Luca, Andrea; Di Giambenedetto, Simona

    2015-01-01

    The aim of the study was to explore how viral resistance and antiretroviral central nervous system (CNS) penetration could impact on cognitive performance of HIV-infected patients. We performed a multicentre cross-sectional study enrolling HIV-infected patients undergoing neuropsychological testing, with a previous genotypic resistance test on plasma samples. CNS penetration-effectiveness (CPE) scores and genotypic susceptibility scores (GSS) were calculated for each regimen. A composite score (CPE-GSS) was then constructed. Factors associated with cognitive impairment were investigated by logistic regression analysis. A total of 215 patients were included. Mean CPE was 7.1 (95% CI 6.9, 7.3) with 206 (95.8%) patients showing a CPE≥6. GSS correction decreased the CPE value in 21.4% (mean 6.5, 95% CI 6.3, 6.7), 26.5% (mean 6.4, 95% CI 6.1, 6.6) and 24.2% (mean 6.4, 95% CI 6.2, 6.6) of subjects using ANRS, HIVDB and REGA rules, respectively. Overall, 66 (30.7%) patients were considered cognitively impaired. No significant association could be demonstrated between CPE and cognitive impairment. However, higher GSS-CPE was associated with a lower risk of cognitive impairment (CPE-GSSANRS odds ratio 0.75, P=0.022; CPE-GSSHIVDB odds ratio 0.77, P=0.038; CPE-GSSREGA odds ratio 0.78, P=0.038). Overall, a cutoff of CPE-GSS≥5 seemed the most discriminatory according to each different interpretation system. GSS-corrected CPE score showed a better correlation with neurocognitive performance than the standard CPE score. These results suggest that antiretroviral drug susceptibility, besides drug CNS penetration, can play a role in the control of HIV-associated neurocognitive disorders.

  20. Has introduction of rapid drug susceptibility testing at diagnosis impacted treatment outcomes among previously treated tuberculosis patients in Gujarat, India?

    Directory of Open Access Journals (Sweden)

    Paresh Dave

    Full Text Available Revised National TB Control Programme (RNTCP in India recommends that all previously-treated TB (PT patients are offered drug susceptibility testing (DST at diagnosis, using rapid diagnostics and screened out for rifampicin resistance before being treated with standardized, eight-month, retreatment regimen. This is intended to improve the early diagnosis of rifampicin resistance and its appropriate management and improve the treatment outcomes among the rest of the patients. In this state-wide study from Gujarat, India, we assess proportion of PT patients underwent rapid DST at diagnosis and the impact of this intervention on their treatment outcomes.This is a retrospective cohort study involving review of electronic patient-records maintained routinely under RNTCP. All PT patients registered for treatment in Gujarat during January-June 2013 were included. Information on DST and treatment outcomes were extracted from 'presumptive DR-TB patient register' and TB treatment register respectively. We performed a multivariate analysis to assess if getting tested is independently associated with unfavourable outcomes (death, loss-to-follow-up, failure, transfer out.Of 5,829 PT patients, 5306(91% were tested for drug susceptibility with rapid diagnostics. Overall, 71% (4,113 TB patients were successfully treated - 72% among tested versus 60% among non-tested. Patients who did not get tested at diagnosis had a 34% higher risk of unsuccessful outcomes as compared to those who got tested (aRR - 1.34; 95% CI 1.20-1.50 after adjusting for age, sex, HIV status and type of TB. Unfavourable outcomes (particularly failure and switched to category IV were higher among INH-resistant patients (39% as compared to INH-sensitive (29%.Offering DST at diagnosis improved the treatment outcomes among PT patients. However, even among tested, treatment outcomes remained suboptimal and were related to INH resistance and high loss-to-follow-up. These need to be addressed

  1. HIV-1 drug resistance prevalence, drug susceptibility and variant characterization in the Jacobi Medical Center paediatric cohort, Bronx, NY, USA.

    Science.gov (United States)

    de Mulder, M; York, V A; Wiznia, A A; Michaud, H A; Nixon, D F; Holguin, A; Rosenberg, M G

    2014-03-01

    With the advent of combined antiretroviral therapy (cART), perinatally HIV-infected children are surviving into adolescence and beyond. However, drug resistance mutations (DRMs) compromise viral control, affecting the long-term effectiveness of ART. The aims of this study were to detect and identify DRMs in a HIV-1 infected paediatric cohort. Paired plasma and dried blood spots (DBSs) specimens were obtained from HIV-1 perinatally infected patients attending the Jacobi Medical Center, New York, USA. Clinical, virological and immunological data for these patients were analysed. HIV-1 pol sequences were generated from samples to identify DRMs according to the International AIDS Society (IAS) 2011 list. Forty-seven perinatally infected patients were selected, with a median age of 17.7 years, of whom 97.4% were carrying subtype B. They had a mean viral load of 3143 HIV-1 RNA copies/mL and a mean CD4 count of 486 cells/μL at the time of sampling. Nineteen patients (40.4%) had achieved undetectable viraemia (40.5% had a CD4 count of > 500 cells/μL. Most of the patients (97.9%) had received cART, including protease inhibitor (PI)-based regimens in 59.6% of cases. The DRM prevalence was 54.1, 27.6 and 27.0% for nucleoside reverse transcriptase inhibitors (NRTIs), PIs and nonnucleoside reverse transcriptase inhibitors (NNRTIs), respectively. Almost two-thirds (64.9%) of the patients harboured DRMs to at least one drug class and 5.4% were triple resistant. The mean nucleotide similarity between plasma and DBS sequences was 97.9%. Identical DRM profiles were present in 60% of plasma-DBS paired sequences. A total of 30 DRMs were detected in plasma and 26 in DBSs, with 23 present in both. Although more perinatally HIV-1-infected children are reaching adulthood as a result of advances in cART, our study cohort presented a high prevalence of resistant viruses, especially viruses resistant to NRTIs. DBS specimens can be used for DRM detection. © 2013 British HIV Association.

  2. 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

  3. Cardiovascular Disease Biomarkers Predict Susceptibility or Resistance to Lung Injury in World Trade Center Dust Exposed Firefighters

    Science.gov (United States)

    Weiden, Michael D.; Naveed, Bushra; Kwon, Sophia; Cho, Soo Jung; Comfort, Ashley L.; Prezant, David J.; Rom, William N.; Nolan, Anna

    2013-01-01

    Pulmonary vascular loss is an early feature of chronic obstructive pulmonary disease. Biomarkers of inflammation and of metabolic syndrome, predicts loss of lung function in World Trade Center Lung Injury (WTC-LI). We investigated if other cardiovascular disease (CVD) biomarkers also predicted WTC-LI. This nested case-cohort study used 801 never smoker, WTC exposed firefighters with normal pre-9/11 lung function presenting for subspecialty pulmonary evaluation (SPE) before March, 2008. A representative sub-cohort of 124/801 with serum drawn within six months of 9/11 defined CVD biomarker distribution. Post-9/11/01 FEV1 at subspecialty exam defined cases: susceptible WTC-LI cases with FEV1≤77% predicted (66/801) and resistant WTC-LI cases with FEV1≥107% (68/801). All models were adjusted for WTC exposure intensity, BMI at SPE, age at 9/11, and pre-9/11 FEV1. Susceptible WTC-LI cases had higher levels of Apo-AII, CRP, and MIP-4 with significant RRs of 3.85, 3.93, and 0.26 respectively with an area under the curve (AUC) of 0.858. Resistant WTC-LI cases had significantly higher sVCAM and lower MPO with RRs of 2.24, and 2.89 respectively; AUC 0.830. Biomarkers of CVD in serum six-month post-9/11 predicted either susceptibility or resistance to WTC-LI. These biomarkers may define pathways producing or protecting subjects from pulmonary vascular disease and associated loss of lung function after an irritant exposure. PMID:22903969

  4. Susceptibility of coagulase positive staphylococci isolated from cow's mammary gland to antibacterial drugs

    Directory of Open Access Journals (Sweden)

    Savić-Rajić Nataša

    2009-01-01

    Full Text Available Coagulase positive staphylococci are one of the most common causes of chronic udder infection. Indiscriminate use of antimicrobial drugs and their presence in the environment where animals live has led to coagulase positive staphylococci strains resistant to antimicrobial means. Proper and timely treatment of sub-clinical mastitis, based on the most effective use of antimicrobial drugs, is the key to good health of the milk herd. The aim was to determine the antimicrobial efficacy of selected assets in relation to coagulase positive staphylococci isolated from samples of milk taken from individual udder quarters of cows in cases of udder infection from three farms with different mastitis prevalence. From a total of 9245 samples of milk taken from individual udder quarters of cows from three farms, 852 strains isolated were coagulase positive staphylococci. Coagulase positive staphylococci were isolated on blood agar and identified on the basis of macro-morphological characteristics and the coagulase and catalase test. The sensitivity of the coagulase positive staphylococci was tested by the Kirby Bauer agar diffusion method with the following antimicrobials: penicillin 6µg, amoxicillin / sulbactam (20 +10µg, cloxacillin 25 µg, cefalexin 30 µg, ceftiofur 30µg, linkomycin 15µg, 30 µg gentamycin and tetracycline 30 µg. Sensitivity testing of coagulase positive staphylococci, isolated in cases of intramammary cow infections, established a high degree of sensitivity in vitro towards penicilinasa resistant drugs (amoxicillin-sublactam, cloxacilin, cephalosporins of the first and third generations and linkomycin. The highest levels of resistance to penicillin (70.4% were found on a farm with a moderate prevalence of udder infection, then on the farm with the highest prevalence of intramammary infections (60.2% and the lowest on the farm with controlled levels of resistance of infection (43.7%. .

  5. Drug Susceptibility of Matrix-Encapsulated Candida albicans Nano-Biofilms

    Science.gov (United States)

    2014-02-01

    Filamentous hyphae can be seen attesting the presence of true biofilms. Figure 2. Kinetics of release of amphotericin B (AMB), caspofungin (CAS) from...the nano-biofilm growth in 3D is similar in the two matrices in the absence of any drugs (Fig. 3A,B). The cell wall along the hyphae appears as...demonstrate a 3D architecture with cell wall from hyphae (blue) superimposed with vacuoles of live cells (yellow). The viability of cells not exposed to any

  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. Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

    OpenAIRE

    Stone, Jennifer; Thompson, Deborah J.; dos-Santos-Silva, Isabel; Scott, Christopher; Tamimi, Rulla M.; Lindstrom, Sara; Kraft, Peter; Hazra, Aditi; Li, Jingmei; Eriksson, Louise; Czene, Kamila; Hall, Per; Jensen, Matt; Cunningham, Julie; Olson, Janet E.

    2015-01-01

    Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute non-dense area adjusted for study, age and BMI using mixed linear modeling. We found strong suppo...

  8. Audiovisual sentence recognition not predicted by susceptibility to the McGurk effect.

    Science.gov (United States)

    Van Engen, Kristin J; Xie, Zilong; Chandrasekaran, Bharath

    2017-02-01

    In noisy situations, visual information plays a critical role in the success of speech communication: listeners are better able to understand speech when they can see the speaker. Visual influence on auditory speech perception is also observed in the McGurk effect, in which discrepant visual information alters listeners' auditory perception of a spoken syllable. When hearing /ba/ while seeing a person saying /ga/, for example, listeners may report hearing /da/. Because these two phenomena have been assumed to arise from a common integration mechanism, the McGurk effect has often been used as a measure of audiovisual integration in speech perception. In this study, we test whether this assumed relationship exists within individual listeners. We measured participants' susceptibility to the McGurk illusion as well as their ability to identify sentences in noise across a range of signal-to-noise ratios in audio-only and audiovisual modalities. Our results do not show a relationship between listeners' McGurk susceptibility and their ability to use visual cues to understand spoken sentences in noise, suggesting that McGurk susceptibility may not be a valid measure of audiovisual integration in everyday speech processing.

  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. Evaluation of prediction capability, robustness, and sensitivity in non-linear landslide susceptibility models, Guantánamo, Cuba

    Science.gov (United States)

    Melchiorre, C.; Castellanos Abella, E. A.; van Westen, C. J.; Matteucci, M.

    2011-04-01

    This paper describes a procedure for landslide susceptibility assessment based on artificial neural networks, and focuses on the estimation of the prediction capability, robustness, and sensitivity of susceptibility models. The study is carried out in the Guantanamo Province of Cuba, where 186 landslides were mapped using photo-interpretation. Twelve conditioning factors were mapped including geomorphology, geology, soils, landuse, slope angle, slope direction, internal relief, drainage density, distance from roads and faults, rainfall intensity, and ground peak acceleration. A methodology was used that subdivided the database in 3 subsets. A training set was used for updating the weights. A validation set was used to stop the training procedure when the network started losing generalization capability, and a test set was used to calculate the performance of the network. A 10-fold cross-validation was performed in order to show that the results are repeatable. The prediction capability, the robustness analysis, and the sensitivity analysis were tested on 10 mutually exclusive datasets. The results show that by means of artificial neural networks it is possible to obtain models with high prediction capability and high robustness, and that an exploration of the effect of the individual variables is possible, even if they are considered as a black-box model.

  11. Role of spontaneous physical activity in prediction of susceptibility to activity based anorexia in male and female rats.

    Science.gov (United States)

    Perez-Leighton, Claudio E; Grace, Martha; Billington, Charles J; Kotz, Catherine M

    2014-08-01

    Anorexia nervosa (AN) is a chronic eating disorder affecting females and males, defined by body weight loss, higher physical activity levels and restricted food intake. Currently, the commonalities and differences between genders in etiology of AN are not well understood. Animal models of AN, such as activity-based anorexia (ABA), can be helpful in identifying factors determining individual susceptibility to AN. In ABA, rodents are given an access to a running wheel while food restricted, resulting in paradoxical increased physical activity levels and weight loss. Recent studies suggest that different behavioral traits, including voluntary exercise, can predict individual weight loss in ABA. A higher inherent drive for movement may promote development and severity of AN, but this hypothesis remains untested. In rodents and humans, drive for movement is defined as spontaneous physical activity (SPA), which is time spent in low-intensity, non-volitional movements. In this paper, we show that a profile of body weight history and behavioral traits, including SPA, can predict individual weight loss caused by ABA in male and female rats with high accuracy. Analysis of the influence of SPA on ABA susceptibility in males and females rats suggests that either high or low levels of SPA increase the probability of high weight loss in ABA, but with larger effects in males compared to females. These results suggest that the same behavioral profile can identify individuals at-risk of AN for both male and female populations and that SPA has predictive value for susceptibility to AN. Copyright © 2014 Elsevier Inc. All rights reserved.

  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. Evaluation of GenoType® MTBDRplus assay for rapid detection of drug susceptibility testing of multi-drug resistance tuberculosis in Northern India

    Directory of Open Access Journals (Sweden)

    Anand Kumar Maurya

    2013-01-01

    Full Text Available Background: The problem of multi-drug resistance tuberculosis (MDR-TB is growing in several hotspots throughout the world. Rapid and accurate diagnosis of MDR-TB is crucial to facilitate early treatment and to reduce its spread in the community. The aim of the present study was to evaluate the new, novel GenoType® MTBDRplus assay for rapid detection of drug susceptibility testing (DST of MDR-TB cases in Northern India. Materials and Methods: A total of 550 specimens were collected from highly suspected drug resistant from pulmonary and extra-pulmonary TB cases. All the specimens were processed by Ziehl- Neelsen staining, culture, differentiation by the GenoType® CM assay, first line DST using BacT/ALERT 3D system and GenoType® MTBDRplus assay. The concordance of the GenoType® MTBDRplus assay was calculated in comparison with conventional DST results. Results: Overall the sensitivity for detection of rifampicin, isoniazid and MDR-TB resistance by GenoType® MTBDRplus assay was 98.0%, 98.4% and 98.2% respectively. Out of 55 MDR-TB strains, 45 (81.8%, 52 (94.5% and 17 (30.9% strains showed mutation in rpoB, katG and inhA genes respectively (P < 0.05. The most prominent mutations in rpoB, katG and inhA genes were; 37 (67.3% in S531L, 52 (94.5% in S315T1 and 11 (20% in C15T regions respectively (P < 0.05. Conclusions: Our study demonstrated a high concordance between the GenoType® MTBDRplus assay resistance patterns and those were observed by conventional DST with good sensitivity, specificity with short turnaround times and to control new cases of MDR-TB in countries with a high prevalence of MDR-TB.

  14. Software-aided approach to investigate peptide structure and metabolic susceptibility of amide bonds in peptide drugs based on high resolution mass spectrometry.

    Directory of Open Access Journals (Sweden)

    Tatiana Radchenko

    Full Text Available Interest in using peptide molecules as therapeutic agents due to high selectivity and efficacy is increasing within the pharmaceutical industry. However, most peptide-derived drugs cannot be administered orally because of low bioavailability and instability in the gastrointestinal tract due to protease activity. Therefore, structural modifications peptides are required to improve their stability. For this purpose, several in-silico software tools have been developed such as PeptideCutter or PoPS, which aim to predict peptide cleavage sites for different proteases. Moreover, several databases exist where this information is collected and stored from public sources such as MEROPS and ExPASy ENZYME databases. These tools can help design a peptide drug with increased stability against proteolysis, though they are limited to natural amino acids or cannot process cyclic peptides, for example. We worked to develop a new methodology to analyze peptide structure and amide bond metabolic stability based on the peptide structure (linear/cyclic, natural/unnatural amino acids. This approach used liquid chromatography / high resolution, mass spectrometry to obtain the analytical data from in vitro incubations. We collected experimental data for a set (linear/cyclic, natural/unnatural amino acids of fourteen peptide drugs and four substrate peptides incubated with different proteolytic media: trypsin, chymotrypsin, pepsin, pancreatic elastase, dipeptidyl peptidase-4 and neprilysin. Mass spectrometry data was analyzed to find metabolites and determine their structures, then all the results were stored in a chemically aware manner, which allows us to compute the peptide bond susceptibility by using a frequency analysis of the metabolic-liable bonds. In total 132 metabolites were found from the various in vitro conditions tested resulting in 77 distinct cleavage sites. The most frequent observed cleavage sites agreed with those reported in the literature. The

  15. 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.

  16. Antimalarial drug susceptibility testing of Plasmodium falciparum in Brazil using a radioisotope method

    Directory of Open Access Journals (Sweden)

    Cerutti Junior Crispim

    1999-01-01

    Full Text Available From March 1996 to August 1997, a study was carried out in a malaria endemic area of the Brazilian Amazon region. In vivo sensitivity evaluation to antimalarial drugs was performed in 129 patients. Blood samples (0.5 ml were drawn from each patient and cryopreserved to proceed to in vitro studies. In vitro sensitivity evaluation performed using a radioisotope method was carried out with the cryopreserved samples from September to December 1997. Thirty-one samples were tested for chloroquine, mefloquine, halofantrine, quinine, arteether and atovaquone. Resistance was evidenced in 96.6% (29/30 of the samples tested for chloroquine, 3.3% (1/30 for quinine, none (0/30 for mefloquine and none for halofantrine (0/30. Overall low sensitivity was evidenced in 10% of the samples tested for quinine, 22.5% tested for halofantrine and in 20% tested for mefloquine. Means of IC 50 values were 132.2 (SD: 46.5 ng/ml for chloroquine, 130.6 (SD: 49.6 ng/ml for quinine, 3.4 (SD: 1.3 ng/ml for mefloquine, 0.7 (SD: 0.3 ng/ml for halofantrine, 1 (SD: 0.6 ng/ml for arteether and 0.4 (SD: 0.2 ng/ml for atovaquone. Means of chloroquine IC 50 of the tested samples were comparable to that of the chloroquine-resistant strain W2 (137.57 ng/ml and nearly nine times higher than that of the chloroquine-sensitive strain D6 (15.09 ng/ml. Means of quinine IC 50 of the tested samples were 1.7 times higher than that of the low sensitivity strain W2 (74.84 ng/ml and nearly five times higher than that of the quinine-sensitive strain D6 (27.53 ng/ml. These results disclose in vitro high resistance levels to chloroquine, low sensitivity to quinine and evidence of decreasing sensitivity to mefloquine and halofantrine in the area under evaluation.

  17. 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.

  18. Investigation of susceptibility of Staphylococcus species to some antibacterial drugs by disk diffusion and broth microdilution

    Directory of Open Access Journals (Sweden)

    Ašanin Jelena

    2012-01-01

    Full Text Available The objective of this work was to identify isolated Staphylococcus species and to investigate their sensitivity to some antibacterial drugs. The material used for these investigations were Staphylococcus isolates originating from milk samples. A total of 25 strains of Staphylococcus isolates were examined, including 24 from milk samples from cows with mastitis, and one strain was isolated from a milk sample from a cow following treatment for mastitis. For primary identification, catalase and oxidase tests were used, as well as the free coagulase test. Following the preliminary tests, the isolated strains were identified using commercial systems ID32 STAPH (bioMérieux, France and the BBL Crystal Gram-Positive ID Kit (Becton Dickinson, USA according to the enclosed instructions. The Staphylococcus isolates were examined for sensitivity to the following: oxacillin, penicillin, cefoxitin, gentamicin, erythromycin, chloramphenicol, tetracycline, ciprofloxacin, sulfametoxazol/trimetoprim, and vacomycin using the disk diffusion method and the broth microdilution method as recommended by the Clinical and Laboratory Strandards Institute - CLSI(2003, and the results were interpreted according to CLSI recommendations from 2008 and 2010. Antibiogram disks manufactured by Becton Dickinson (USA were used, and the broth microdilution method was applied using pure antibiotic substances from different manufacturers: erythromycin, chloramphenicol, cefoxitin, gentamicin, oxacillin, tetracycline (Sigma Aldrich, USA, sulfametoxazol (Fluka, USA, penicillin (Calbiochem, Germany, vancomycin (Abbott laboratories, USA, ciprofloxacin and trimetoprim (Zdravlje A.D., Serbia. All 25 strains were catalase positive and oxidase negative. Of the 25 strains, 19 were coagulase positive and 6 were coagulase negative.With the implementation of the disk diffusion method on 19 strains of S. aureus, 17 were established to be resistant to penicillin (89.5%, and 2 strains to gentamicin

  19. 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…

  20. Antimicrobial Drug Resistance of Salmonella enterica Serovar Typhi in Asia and Molecular Mechanism of Reduced Susceptibility to the Fluoroquinolones▿

    Science.gov (United States)

    Chau, Tran Thuy; Campbell, James Ian; Galindo, Claudia M.; Van Minh Hoang, Nguyen; Diep, To Song; Nga, Tran Thu Thi; Van Vinh Chau, Nguyen; Tuan, Phung Quoc; Page, Anne Laure; Ochiai, R. Leon; Schultsz, Constance; Wain, John; Bhutta, Zulfiqar A.; Parry, Christopher M.; Bhattacharya, Sujit K.; Dutta, Shanta; Agtini, Magdarina; Dong, Baiqing; Honghui, Yang; Anh, Dang Duc; Canh, Do Gia; Naheed, Aliya; Albert, M. John; Phetsouvanh, Rattanaphone; Newton, Paul N.; Basnyat, Buddha; Arjyal, Amit; La, Tran Thi Phi; Rang, Nguyen Ngoc; Phuong, Le Thi; Van Be Bay, Phan; von Seidlein, Lorenz; Dougan, Gordon; Clemens, John D.; Vinh, Ha; Hien, Tran Tinh; Chinh, Nguyen Tran; Acosta, Camilo J.; Farrar, Jeremy; Dolecek, Christiane

    2007-01-01

    This study describes the pattern and extent of drug resistance in 1,774 strains of Salmonella enterica serovar Typhi isolated across Asia between 1993 and 2005 and characterizes the molecular mechanisms underlying the reduced susceptibilities to fluoroquinolones of these strains. For 1,393 serovar Typhi strains collected in southern Vietnam, the proportion of multidrug resistance has remained high since 1993 (50% in 2004) and there was a dramatic increase in nalidixic acid resistance between 1993 (4%) and 2005 (97%). In a cross-sectional sample of 381 serovar Typhi strains from 8 Asian countries, Bangladesh, China, India, Indonesia, Laos, Nepal, Pakistan, and central Vietnam, collected in 2002 to 2004, various rates of multidrug resistance (16 to 37%) and nalidixic acid resistance (5 to 51%) were found. The eight Asian countries involved in this study are home to approximately 80% of the world's typhoid fever cases. These results document the scale of drug resistance across Asia. The Ser83→Phe substitution in GyrA was the predominant alteration in serovar Typhi strains from Vietnam (117/127 isolates; 92.1%). No mutations in gyrB, parC, or parE were detected in 55 of these strains. In vitro time-kill experiments showed a reduction in the efficacy of ofloxacin against strains harboring a single-amino-acid substitution at codon 83 or 87 of GyrA; this effect was more marked against a strain with a double substitution. The 8-methoxy fluoroquinolone gatifloxacin showed rapid killing of serovar Typhi harboring both the single- and double-amino-acid substitutions. PMID:17908946

  1. 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...

  2. 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

  3. Towards Point-of-Care Diagnosis of Pulmonary Tuberculosis and Drug Susceptibility Testing by Whole Genome Sequencing of DNA Isolated from Sputum

    Directory of Open Access Journals (Sweden)

    Kayzad S. Nilgiriwala

    2017-12-01

    Full Text Available Preliminary screening of pulmonary tuberculosis (TB in India still relies on sputum microscopy, which has low sensitivity leading to high rate of false negatives. Moreover, conventional phenotypic drug susceptibility testing (DST is conducted over a period of weeks leading to delays in correct treatment. Next generation sequencing technologies (Illumina and Oxford Nanopore have made it possible to sequence miniscule amount of DNA and generate enough data within a day for detecting specific microbes and their DST profile. Sputum samples from two pulmonary TB patients were processed by decontamination and DNA was isolated from the decontaminated sputum sediments. The isolated DNA was used for sequencing by Illumina and by MinION (Oxford Nanopore Technologies. The sequence data was used to diagnose TB and to determine the DST profiles for the first- and second-line drugs by Mykrobe Predictor. Validation was conducted by sequencing DNA (by Illumina isolated from pure growth culture from both the samples individually. DNA sequencing data (for both, Ilumina and MinION from one of the sputum samples indicated the presence of Mycobacterium tuberculosis (M. tb resistant to streptomycin, isoniazid, rifampicin and ethambutol and its lineage was predicted to be Beijing East Asia. The second sample indicated the presence of M. tb sensitive to the first- and second-line drugs by MinION and showed minor resistance call only to rifampicin by Illumina. Lineage of the second sample was predicted to be East Africa Indian Ocean, whereas Illumina data indicated it to be Delhi Central Asia. The two samples were correctly diagnosed for the presence of M. tb in the sputum DNA. Their DST profiles and lineage were also successfully determined from both the sequencing platforms (with minor discrepancies paving the way towards diagnosis and DST of TB from DNA isolated from sputum samples at point-of-care. Nanopore sequencing currently requires skilled personnel for DNA

  4. 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.

  5. Bacterial profile and drug susceptibility pattern of urinary tract infection in pregnant women at University of Gondar Teaching Hospital, Northwest Ethiopia.

    Science.gov (United States)

    Alemu, Agersew; Moges, Feleke; Shiferaw, Yitayal; Tafess, Ketema; Kassu, Afework; Anagaw, Belay; Agegn, Abebe

    2012-04-25

    Urinary tract infection (UTI) is a common health problem among pregnant women. Proper investigation and prompt treatment are needed to prevent serious life threatening condition and morbidity due to urinary tract infection that can occur in pregnant women. Recent report in Addis Ababa, Ethiopia indicated the prevalence of UTI in pregnant women was 11.6% and Gram negative bacteria was the predominant isolates and showed multi drug resistance. This study aimed to assess bacterial profile that causes urinary tract infection and their antimicrobial susceptibility pattern among pregnant women visiting antenatal clinic at University of Gondar Teaching Hospital, Northwest Ethiopia. A cross-sectional study was conducted at University of Gondar Teaching Hospital from March 22 to April 30, 2011. Mid stream urine samples were collected and inoculated into Cystine Lactose Electrolyte Deficient medium (CLED). Colony counts yielding bacterial growth of 105/ml of urine or more of pure isolates were regarded as significant bacteriuria for infection. Colony from CLED was sub cultured onto MacConkey agar and blood agar plates. Identification was done using cultural characteristics and a series of biochemical tests. A standard method of agar disc diffusion susceptibility testing method was used to determine susceptibility patterns of the isolates. The overall prevalence of UTI in pregnant women was 10.4%. The predominant bacterial pathogens were Escherichia coli 47.5% followed by coagulase-negative staphylococci 22.5%, Staphylococcus aureus 10%, and Klebsiella pneumoniae 10%. Gram negative isolates were resulted low susceptibility to co-trimoxazole (51.9%) and tetracycline (40.7%) whereas Gram positive showed susceptibility to ceftriaxon (84.6%) and amoxicillin-clavulanic acid (92.3%). Multiple drug resistance (resistance to two or more drugs) was observed in 95% of the isolates. Significant bacteriuria was observed in asymptomatic pregnant women. Periodic studies are recommended to

  6. Susceptibility Testing by Polymerase Chain Reaction DNA Quantitation: A Method to Measure Drug Resistance of Human Immunodeficiency Virus Type 1 Isolates

    Science.gov (United States)

    Eron, Joseph J.; Gorczyca, Paul; Kaplan, Joan C.; D'Aquila, Richard T.

    1992-04-01

    Polymerase chain reaction (PCR) DNA quantitation (PDQ) susceptibility testing rapidly and directly measures nucleoside sensitivity of human immunodeficiency virus type 1 (HIV-1) isolates. PCR is used to quantitate the amount of HIV-1 DNA synthesized after in vitro infection of peripheral blood mononuclear cells. The relative amounts of HIV-1 DNA in cell lysates from cultures maintained at different drug concentrations reflect drug inhibition of virus replication. The results of PDQ susceptibility testing of 2- or 3-day cultures are supported by assays measuring HIV-1 p24 antigen production in supernatants of 7- or 10-day cultures. DNA sequence analyses to identify mutations in the reverse transcriptase gene that cause resistance to 3'-azido-3'-deoxythymidine also support the PDQ results. With the PDQ method, both infectivity titration and susceptibility testing can be performed on supernatants from primary cultures of peripheral blood mononuclear cells. PDQ susceptibility testing should facilitate epidemiologic studies of the clinical significance of drug-resistant HIV-1 isolates.

  7. 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.

  8. Drug susceptibility to etravirine and darunavir among Human Immunodeficiency Virus Type 1-derived pseudoviruses in treatment-experienced patients with HIV/AIDS in South Korea.

    Science.gov (United States)

    Kwon, Oh-Kyung; Kim, Sung Soon; Rhee, Jee Eun; Kee, Mee-Kyung; Park, Mina; Oh, Hye-Ri; Choi, Ju-Yeon

    2015-04-09

    In South Korea, about 20 types of antiretroviral drugs are used in the treatment of patients with human immunodeficiency virus/acquired immune deficiency syndrome. Since 2010, raltegravir, etravirine, and darunavir have been spotlighted as new drugs for highly active antiretroviral therapy (HAART)-experienced adults with resistant HIV-1 in South Korea. In this study, we investigated potential susceptibility of pseudoviruses derived from treatment-experienced Korean patients to etravirine vs efavirenz and to darunavir vs amprenavir and indinavir using a modified single-round assay. Pseudoviruses derived from nine treatment-experienced patients infected with HIV-1 were investigated by comparison with the wild-type strain pNL4-3. The 50% inhibitory concentration (IC50) values were calculated and drug susceptibility was compared. The intensity of genotypic drug resistance was classified based on the 'SIR' interpretation of the Stanford data base. Drug susceptibility was generally higher for etravirine and darunavir compared with efavirenz, amprenavir, and indinavir in pseudoviruses derived from treatment-experienced patients. Pseudoviruses derived from patients KRB4025 and KRB8014, who exhibited long-term use of protease inhibitors, showed an outside of tested drug concentration, especially for amprenavir and indinavir. However, they exhibited a lower fold-change in resistance to darunavir. Etravirine and darunavir have been used in HAART since 2010 in South Korea. Therefore, these antiretroviral drugs together with other newly introduced antiretroviral drugs are interesting for the optimal treatment of patients with treatment failure. This study may help to find a more effective HAART in the case of HIV-1 infected patients that have difficulty being treated.

  9. 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...

  10. Culture and Next-generation sequencing-based drug susceptibility testing unveil high levels of drug-resistant-TB in Djibouti: results from the first national survey.

    Science.gov (United States)

    Tagliani, Elisa; Hassan, Mohamed Osman; Waberi, Yacine; De Filippo, Maria Rosaria; Falzon, Dennis; Dean, Anna; Zignol, Matteo; Supply, Philip; Abdoulkader, Mohamed Ali; Hassangue, Hawa; Cirillo, Daniela Maria

    2017-12-15

    Djibouti is a small country in the Horn of Africa with a high TB incidence (378/100,000 in 2015). Multidrug-resistant TB (MDR-TB) and resistance to second-line agents have been previously identified in the country but the extent of the problem has yet to be quantified. A national survey was conducted to estimate the proportion of MDR-TB among a representative sample of TB patients. Sputum was tested using XpertMTB/RIF and samples positive for MTB and resistant to rifampicin underwent first line phenotypic susceptibility testing. The TB supranational reference laboratory in Milan, Italy, undertook external quality assurance, genotypic testing based on whole genome and targeted-deep sequencing and phylogenetic studies. 301 new and 66 previously treated TB cases were enrolled. MDR-TB was detected in 34 patients: 4.7% of new and 31% of previously treated cases. Resistance to pyrazinamide, aminoglycosides and capreomycin was detected in 68%, 18% and 29% of MDR-TB strains respectively, while resistance to fluoroquinolones was not detected. Cluster analysis identified transmission of MDR-TB as a critical factor fostering drug resistance in the country. Levels of MDR-TB in Djibouti are among the highest on the African continent. High prevalence of resistance to pyrazinamide and second-line injectable agents have important implications for treatment regimens.

  11. 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.

  12. 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.

  13. 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.

  14. Leishmaniasis in the major endemic region of Plurinational State of Bolivia: Species identification, phylogeography and drug susceptibility implications.

    Science.gov (United States)

    Bilbao-Ramos, Pablo; Dea-Ayuela, M Auxiliadora; Cardenas-Alegría, Oscar; Salamanca, Efraín; Santalla-Vargas, José Antonio; Benito, Cesar; Flores, Ninoska; Bolás-Fernández, Francisco

    2017-12-01

    The Plurinational State of Bolivia is one of the Latin American countries with the highest prevalence of leishmaniasis, highlighting the lowlands of the Department of La Paz where about 50% of the total cases were reported. The control of the disease can be seriously compromised by the intrinsic variability of the circulating species that may limit the efficacy of treatment while favoring the emergence of resistance. Fifty-five isolates of Leishmania from cutaneous and mucocutaneous lesions from patients living in different provinces of the Department of La Paz were tested. Molecular characterization of isolates was carried out by 3 classical markers: the rRNA internal transcribed spacer 1 (ITS-1), the heat shock protein 70 (HSP70) and the mitochondrial cytochrome b (Cyt-b). These markers were amplified by PCR and their products digested by the restriction endonuclease enzymes AseI and HaeIII followed by subsequent sequencing of Cyt-b gene and ITS-1 region for subsequent phylogenetic analysis. The combined use of these 3 markers allowed us to assign 36 isolates (65.5%) to the complex Leishmania (Viannia) braziliensis, 4 isolates (7, 27%) to L. (Viannia) lainsoni. and the remaining 15 isolates (23.7%) to a local variant of L. (Leishmania) mexicana. Concerning in vitro drug susceptibility the amastigotes from all isolates where highly sensitive to Fungizone ® (mean IC 50 between 0.23 and 0.5μg/mL) whereas against Glucantime ® the sensitivity was moderate (mean IC 50 ranging from 50.84μg/mL for L. (V.) braziliensis to 18.23μg/mL for L. (L.) mexicana. L. (V.) lainsoni was not sensitive to Glucantime ® . The susceptibility to miltefosine was highly variable among species isolates, being L. (L.) mexicana the most sensitive, followed by L. (V.) braziliensis and L. (V.) lainsoni (mean IC 50 of 8.24μg/mL, 17.85μg/mL and 23.28μg/mL, respectively). Copyright © 2017. Published by Elsevier B.V.

  15. 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.

  16. Rapid, automated, nonradiometric susceptibility testing of Mycobacterium tuberculosis complex to four first-line antituberculous drugs used in standard short-course chemotherapy

    DEFF Research Database (Denmark)

    Johansen, Isik Somuncu; Thomsen, Vibeke Østergaard; Marjamäki, Merja

    2004-01-01

    The increasing prevalence of drug-resistant tuberculosis necessitates rapid and accurate susceptibility testing. The nonradiometric BACTEC Mycobacteria Growth Indicator Tube 960 (MGIT) system for susceptibility testing was evaluated on 222 clinical Mycobacterium tuberculosis complex isolates...... for isoniazid, rifampin, and ethambutol. Fifty-seven of the isolates were tested for pyrazinamide. Results were compared to those of radiometric BACTEC 460 system and discrepancies were resolved by the agar proportion method. We found an overall agreement of 99.0% for isoniazid, 99.5% for rifampin, 98.......2% for ethambutol, and 100% for pyrazinamide. After resolution of discrepancies, MGIT yielded no false susceptibility for rifampin and isoniazid. Although turnaround times were comparable, MGIT provides an advantage as inoculation can be done on any weekday as the growth is monitored automatically. The automated...

  17. 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.

  18. 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.

  19. Determinants of Default from Tuberculosis Treatment among Patients with Drug-Susceptible Tuberculosis in Karachi, Pakistan: A Mixed Methods Study.

    Science.gov (United States)

    Chida, Natasha; Ansari, Zara; Hussain, Hamidah; Jaswal, Maria; Symes, Stephen; Khan, Aamir J; Mohammed, Shama

    2015-01-01

    Non-adherence to tuberculosis therapy can lead to drug resistance, prolonged infectiousness, and death; therefore, understanding what causes treatment default is important. Pakistan has one of the highest burdens of tuberculosis in the world, yet there have been no qualitative studies in Pakistan that have specifically examined why default occurs. We conducted a mixed methods study at a tuberculosis clinic in Karachi to understand why patients with drug-susceptible tuberculosis default from treatment, and to identify factors associated with default. Patients attending this clinic pick up medications weekly and undergo family-supported directly observed therapy. In-depth interviews were administered to 21 patients who had defaulted. We also compared patients who defaulted with those who were cured, had completed, or had failed treatment in 2013. Qualitative analyses showed the most common reasons for default were the financial burden of treatment, and medication side effects and beliefs. The influence of finances on other causes of default was also prominent, as was concern about the effect of treatment on family members. In quantitative analysis, of 2120 patients, 301 (14.2%) defaulted. Univariate analysis found that male gender (OR: 1.34, 95% CI: 1.04-1.71), being 35-59 years of age (OR: 1.54, 95% CI: 1.14-2.08), or being 60 years of age or older (OR: 1.84, 95% CI: 1.17-2.88) were associated with default. After adjusting for gender, disease site, and patient category, being 35-59 years of age (aOR: 1.49, 95% CI: 1.10-2.03) or 60 years of age or older (aOR: 1.76, 95% CI: 1.12-2.77) were associated with default. In multivariate analysis age was the only variable associated with default. This lack of identifiable risk factors and our qualitative findings imply that default is complex and often due to extrinsic and medication-related factors. More tolerable medications, improved side effect management, and innovative cost-reduction measures are needed to reduce

  20. Determinants of Default from Tuberculosis Treatment among Patients with Drug-Susceptible Tuberculosis in Karachi, Pakistan: A Mixed Methods Study.

    Directory of Open Access Journals (Sweden)

    Natasha Chida

    Full Text Available Non-adherence to tuberculosis therapy can lead to drug resistance, prolonged infectiousness, and death; therefore, understanding what causes treatment default is important. Pakistan has one of the highest burdens of tuberculosis in the world, yet there have been no qualitative studies in Pakistan that have specifically examined why default occurs. We conducted a mixed methods study at a tuberculosis clinic in Karachi to understand why patients with drug-susceptible tuberculosis default from treatment, and to identify factors associated with default. Patients attending this clinic pick up medications weekly and undergo family-supported directly observed therapy.In-depth interviews were administered to 21 patients who had defaulted. We also compared patients who defaulted with those who were cured, had completed, or had failed treatment in 2013.Qualitative analyses showed the most common reasons for default were the financial burden of treatment, and medication side effects and beliefs. The influence of finances on other causes of default was also prominent, as was concern about the effect of treatment on family members. In quantitative analysis, of 2120 patients, 301 (14.2% defaulted. Univariate analysis found that male gender (OR: 1.34, 95% CI: 1.04-1.71, being 35-59 years of age (OR: 1.54, 95% CI: 1.14-2.08, or being 60 years of age or older (OR: 1.84, 95% CI: 1.17-2.88 were associated with default. After adjusting for gender, disease site, and patient category, being 35-59 years of age (aOR: 1.49, 95% CI: 1.10-2.03 or 60 years of age or older (aOR: 1.76, 95% CI: 1.12-2.77 were associated with default.In multivariate analysis age was the only variable associated with default. This lack of identifiable risk factors and our qualitative findings imply that default is complex and often due to extrinsic and medication-related factors. More tolerable medications, improved side effect management, and innovative cost-reduction measures are needed to

  1. 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.

  2. Predicting the susceptibility to gully initiation in data-poor regions

    Science.gov (United States)

    Dewitte, Olivier; Daoudi, Mohamed; Bosco, Claudio; Van Den Eeckhaut, Miet

    2015-01-01

    Permanent gullies are common features in many landscapes and quite often they represent the dominant soil erosion process. Once a gully has initiated, field evidence shows that gully channel formation and headcut migration rapidly occur. In order to prevent the undesired effects of gullying, there is a need to predict the places where new gullies might initiate. From detailed field measurements, studies have demonstrated strong inverse relationships between slope gradient of the soil surface (S) and drainage area (A) at the point of channel initiation across catchments in different climatic and morphological environments. Such slope-area thresholds (S-A) can be used to predict locations in the landscape where gullies might initiate. However, acquiring S-A requires detailed field investigations and accurate high resolution digital elevation data, which are usually difficult to acquire. To circumvent this issue, we propose a two-step method that uses published S-A thresholds and a logistic regression analysis (LR). S-A thresholds from the literature are used as proxies of field measurement. The method is calibrated and validated on a watershed, close to the town of Algiers, northern Algeria, where gully erosion affects most of the slopes. The gullies extend up to several kilometres in length and cover 16% of the study area. First we reconstruct the initiation areas of the existing gullies by applying S-A thresholds for similar environments. Then, using the initiation area map as the dependent variable with combinations of topographic and lithological predictor variables, we calibrate several LR models. It provides relevant results in terms of statistical reliability, prediction performance, and geomorphological significance. This method using S-A thresholds with data-driven assessment methods like LR proves to be efficient when applied to common spatial data and establishes a methodology that will allow similar studies to be undertaken elsewhere.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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...

  13. 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.

  14. Spatial prediction of landslide susceptibility in parts of Garhwal Himalaya, India, using the weight of evidence modelling.

    Science.gov (United States)

    Guri, Pardeep Kumar; Ray, P K Champati; Patel, Ramesh Chandra

    2015-06-01

    Garhwal Himalaya in northern India has emerged as one of the most prominent hot spots of landslide occurrences in the Himalaya mainly due to geological causes related to mountain building processes, steep topography and frequent occurrences of extreme precipitation events. As this region has many pilgrimage and tourist centres, it is visited by hundreds of thousands of people every year, and in the recent past, there has been rapid development to provide adequate roads and building infrastructure. Additionally, attempts are also made to harness hydropower by constructing tunnels, dams and reservoirs and thus altering vulnerable slopes at many places. As a result, the overall risk due to landslide hazards has increased many folds and, therefore, an attempt was made to assess landslide susceptibility using 'Weights of Evidence (WofE)', a well-known bivariate statistical modelling technique implemented in a much improved way using remote sensing and Geographic Information System. This methodology has dual advantage as it demonstrates how to derive critical parameters related to geology, geomorphology, slope, land use and most importantly temporal landslide distribution in one of the data scarce region of the world. Secondly, it allows to experiment with various combination of parameters to assess their cumulative effect on landslides. In total, 15 parameters related to geology, geomorphology, terrain, hydrology and anthropogenic factors and 2 different landslide inventories (prior to 2007 and 2008-2011) were prepared from high-resolution Indian remote sensing satellite data (Cartosat-1 and Resourcesat-1) and were validated by field investigation. Several combinations of parameters were carried out using WofE modelling, and finally using best combination of eight parameters, 76.5 % of overall landslides were predicted in 24 % of the total area susceptible to landslide occurrences. The study has highlighted that using such methodology landslide susceptibility assessment

  15. 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.

  16. 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.

  17. 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.

  18. 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....

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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

  8. 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

  9. Bacterial profile and drug susceptibility pattern of urinary tract infection in pregnant women at University of Gondar Teaching Hospital, Northwest Ethiopia

    Directory of Open Access Journals (Sweden)

    Alemu Agersew

    2012-04-01

    Full Text Available Abstract Background Urinary tract infection (UTI is a common health problem among pregnant women. Proper investigation and prompt treatment are needed to prevent serious life threatening condition and morbidity due to urinary tract infection that can occur in pregnant women. Recent report in Addis Ababa, Ethiopia indicated the prevalence of UTI in pregnant women was 11.6 % and Gram negative bacteria was the predominant isolates and showed multi drug resistance. This study aimed to assess bacterial profile that causes urinary tract infection and their antimicrobial susceptibility pattern among pregnant women visiting antenatal clinic at University of Gondar Teaching Hospital, Northwest Ethiopia. Methods A cross-sectional study was conducted at University of Gondar Teaching Hospital from March 22 to April 30, 2011. Mid stream urine samples were collected and inoculated into Cystine Lactose Electrolyte Deficient medium (CLED. Colony counts yielding bacterial growth of 105/ml of urine or more of pure isolates were regarded as significant bacteriuria for infection. Colony from CLED was sub cultured onto MacConkey agar and blood agar plates. Identification was done using cultural characteristics and a series of biochemical tests. A standard method of agar disc diffusion susceptibility testing method was used to determine susceptibility patterns of the isolates. Results The overall prevalence of UTI in pregnant women was 10.4 %. The predominant bacterial pathogens were Escherichia coli 47.5 % followed by coagulase-negative staphylococci 22.5 %, Staphylococcus aureus 10 %, and Klebsiella pneumoniae 10 %. Gram negative isolates were resulted low susceptibility to co-trimoxazole (51.9 % and tetracycline (40.7 % whereas Gram positive showed susceptibility to ceftriaxon (84.6 % and amoxicillin–clavulanic acid (92.3 %. Multiple drug resistance (resistance to two or more drugs was observed in 95 % of the isolates. Conclusion

  10. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques

    Science.gov (United States)

    Chen, Wei; Pourghasemi, Hamid Reza; Panahi, Mahdi; Kornejady, Aiding; Wang, Jiale; Xie, Xiaoshen; Cao, Shubo

    2017-11-01

    The spatial prediction of landslide susceptibility is an important prerequisite for the analysis of landslide hazards and risks in any area. This research uses three data mining techniques, such as an adaptive neuro-fuzzy inference system combined with frequency ratio (ANFIS-FR), a generalized additive model (GAM), and a support vector machine (SVM), for landslide susceptibility mapping in Hanyuan County, China. In the first step, in accordance with a review of the previous literature, twelve conditioning factors, including slope aspect, altitude, slope angle, topographic wetness index (TWI), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), and lithology, were selected. In the second step, a collinearity test and correlation analysis between the conditioning factors and landslides were applied. In the third step, we used three advanced methods, namely, ANFIS-FR, GAM, and SVM, for landslide susceptibility modeling. Subsequently, the results of their accuracy were validated using a receiver operating characteristic curve. The results showed that all three models have good prediction capabilities, while the SVM model has the highest prediction rate of 0.875, followed by the ANFIS-FR and GAM models with prediction rates of 0.851 and 0.846, respectively. Thus, the landslide susceptibility maps produced in the study area can be applied for management of hazards and risks in landslide-prone Hanyuan County.

  11. Distribution, detection of enterotoxigenic strains and antimicrobial drug susceptibility patterns of Bacteroides fragilis group in diarrheic and non-diarrheic feces from Brazilian infants

    Directory of Open Access Journals (Sweden)

    Débora Paula Ferreira

    2010-10-01

    Full Text Available Despite the importance of gastrointestinal diseases and their global distribution, affecting millions of individuals around the world, the role and antimicrobial susceptibility patterns of anaerobic bacteria such as those in the Bacteroides fragilis group (BFG are still unclear in young children. This study investigated the occurrence and distribution of species in the BFG and enterotoxigenic strains in the fecal microbiota of children and their antimicrobial susceptibility patterns. Diarrheic (n=110 and non-diarrheic (n=65 fecal samples from children aged 0-5 years old were evaluated. BFG strains were isolated and identified by conventional biochemical, physiological and molecular approaches. Alternatively, bacteria and enterotoxigenic strains were detected directly from feces by molecular biology. Antimicrobial drug susceptibility patterns were determined by the agar dilution method according to the guidelines for isolated bacteria. BFG was detected in 64.3% of the fecal samples (55% diarrheic and 80.4% non-diarrheic, and 4.6% were enterotoxigenic. Antimicrobial resistance was observed against ampicillin, ampicillin/sulbactam, piperacillin/tazobactam, meropenem, ceftriaxone, clindamycin and chloramphenicol. The data show that these bacteria are prevalent in fecal microbiota at higher levels in healthy children. The molecular methodology was more effective in identifying the B. fragilis group when compared to the biochemical and physiological techniques. The observation of high resistance levels stimulates thoughts about the indiscriminate use of antimicrobial drugs in early infancy. Further quantitative studies are needed to gain a better understanding of the role of these bacteria in acute diarrhea in children.

  12. Trends of Mycobacterium bovis Isolation and First-Line Anti-tuberculosis Drug Susceptibility Profile: A Fifteen-Year Laboratory-Based Surveillance.

    Science.gov (United States)

    Bobadilla-del Valle, Miriam; Torres-González, Pedro; Cervera-Hernández, Miguel Enrique; Martínez-Gamboa, Areli; Crabtree-Ramirez, Brenda; Chávez-Mazari, Bárbara; Ortiz-Conchi, Narciso; Rodríguez-Cruz, Luis; Cervantes-Sánchez, Axel; Gudiño-Enríquez, Tomasa; Cinta-Severo, Carmen; Sifuentes-Osornio, José; Ponce de León, Alfredo

    2015-09-01

    Mycobacterium tuberculosis causes the majority of tuberculosis (TB) cases in humans; however, in developing countries, human TB caused by M. bovis may be frequent but undetected. Human TB caused by M. bovis is considered a zoonosis; transmission is mainly through consumption of unpasteurized dairy products, and it is less frequently attributed to animal-to-human or human-to-human contact. We describe the trends of M. bovis isolation from human samples and first-line drug susceptibility during a 15-year period in a referral laboratory located in a tertiary care hospital in Mexico City. Data on mycobacterial isolates from human clinical samples were retrieved from the laboratory's database for the 2000-2014 period. Susceptibility to first-line drugs: rifampin, isoniazid, streptomycin (STR) and ethambutol was determined. We identified 1,165 isolates, 73.7% were M. tuberculosis and 26.2%, M. bovis. Among pulmonary samples, 16.6% were M. bovis. The proportion of M. bovis isolates significantly increased from 7.8% in 2000 to 28.4% in 2014 (X(2)trend, ptuberculosis isolates (10.9% vs.3.4%, ptuberculosis, respectively (p = 0.637). A rising trend of primary STR monoresistance was observed for both species (3.4% in 2000-2004 vs. 7.6% in 2010-2014; p = 0.02). There is a high prevalence and a rising trend of M. bovis isolates in our region. The proportion of pulmonary M. bovis isolates is higher than in previous reports. Additionally, we report high rates of primary anti-tuberculosis resistance and secondary MDR in both M. tuberculosis and M. bovis. This is one of the largest reports on drug susceptibility of M. bovis from human samples and shows a significant proportion of first-line anti-tuberculosis drug resistance.

  13. Trends of Mycobacterium bovis Isolation and First-Line Anti-tuberculosis Drug Susceptibility Profile: A Fifteen-Year Laboratory-Based Surveillance

    Science.gov (United States)

    Bobadilla-del Valle, Miriam; Torres-González, Pedro; Cervera-Hernández, Miguel Enrique; Martínez-Gamboa, Areli; Crabtree-Ramirez, Brenda; Chávez-Mazari, Bárbara; Ortiz-Conchi, Narciso; Rodríguez-Cruz, Luis; Cervantes-Sánchez, Axel; Gudiño-Enríquez, Tomasa; Cinta-Severo, Carmen; Sifuentes-Osornio, José; Ponce de León, Alfredo

    2015-01-01

    Background Mycobacterium tuberculosis causes the majority of tuberculosis (TB) cases in humans; however, in developing countries, human TB caused by M. bovis may be frequent but undetected. Human TB caused by M. bovis is considered a zoonosis; transmission is mainly through consumption of unpasteurized dairy products, and it is less frequently attributed to animal-to-human or human-to-human contact. We describe the trends of M. bovis isolation from human samples and first-line drug susceptibility during a 15-year period in a referral laboratory located in a tertiary care hospital in Mexico City. Methodology/Principal Findings Data on mycobacterial isolates from human clinical samples were retrieved from the laboratory’s database for the 2000–2014 period. Susceptibility to first-line drugs: rifampin, isoniazid, streptomycin (STR) and ethambutol was determined. We identified 1,165 isolates, 73.7% were M. tuberculosis and 26.2%, M. bovis. Among pulmonary samples, 16.6% were M. bovis. The proportion of M. bovis isolates significantly increased from 7.8% in 2000 to 28.4% in 2014 (X 2 trend, ptuberculosis isolates (10.9% vs.3.4%, ptuberculosis, respectively (p = 0.637). A rising trend of primary STR monoresistance was observed for both species (3.4% in 2000–2004 vs. 7.6% in 2010–2014; p = 0.02). Conclusions/Significance There is a high prevalence and a rising trend of M. bovis isolates in our region. The proportion of pulmonary M. bovis isolates is higher than in previous reports. Additionally, we report high rates of primary anti-tuberculosis resistance and secondary MDR in both M. tuberculosis and M. bovis. This is one of the largest reports on drug susceptibility of M. bovis from human samples and shows a significant proportion of first-line anti-tuberculosis drug resistance. PMID:26421930

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Microbiological quality of water from the rivers of Curitiba, Paraná State, Brazil, and the susceptibility to antimicrobial drugs and pathogenicity of Escherichia coli.

    Science.gov (United States)

    Giowanella, Melissa; Bozza, Angela; do Rocio Dalzoto, Patricia; Dionísio, Jair Alves; Andraus, Sumaia; Guimarães, Edson Luiz Gomes; Pimentel, Ida Chapaval

    2015-11-01

    Water safety is determined by several markers, and Escherichia coli is one of the most important indicators of water quality. The objective of this study was to evaluate the microbiological parameters in environmental samples of fresh water from rivers of Curitiba and its metropolitan area in Paraná State, Brazil. In addition, we evaluated the pathogenicity and susceptibility to antimicrobial drugs in E. coli. These evaluations were performed by quantitative and qualitative methods employing selective media for isolating thermotolerant coliforms and biochemical tests for identifying E. coli. Pathogenic strains of E. coli were detected by PCR multiplex using specific primers. From the water samples, 494 thermotolerant coliforms were obtained, of which 96 (19.43%) isolates were characterized as E. coli. Three isolates were identified as enteroaggregative E. coli, one as enterotoxigenic E. coli, one as enteropathogenic E. coli, and two carried the Eae virulence gene. E. coli susceptibility to commonly employed antimicrobial drugs was analyzed by the disc diffusion method. The results showed 49 (51.04%) isolates resistant to all the drugs assayed, 16 (16.67%) with an intermediate resistance to all drugs, and 31 (32.29%) intermediately or fully resistant to one or more drugs tested. The highest rate of resistance was observed for tetracycline 30 μg, streptomycin 10 μg, and ceftazidime 30 μg. Detection of E. coli is associated with water contamination by fecal material from humans and warm-blooded animals. The occurrence of resistant strains can be the result of the indiscriminate use of antimicrobial drugs and poor sanitation in the areas assayed.

  19. 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

  20. 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

  1. A quantitative evaluation of a qualitative risk assessment framework: Examining the assumptions and predictions of the Productivity Susceptibility Analysis (PSA)

    Science.gov (United States)

    2018-01-01

    Qualitative risk assessment frameworks, such as the Productivity Susceptibility Analysis (PSA), have been developed to rapidly evaluate the risks of fishing to marine populations and prioritize management and research among species. Despite being applied to over 1,000 fish populations, and an ongoing debate about the most appropriate method to convert biological and fishery characteristics into an overall measure of risk, the assumptions and predictive capacity of these approaches have not been evaluated. Several interpretations of the PSA were mapped to a conventional age-structured fisheries dynamics model to evaluate the performance of the approach under a range of assumptions regarding exploitation rates and measures of biological risk. The results demonstrate that the underlying assumptions of these qualitative risk-based approaches are inappropriate, and the expected performance is poor for a wide range of conditions. The information required to score a fishery using a PSA-type approach is comparable to that required to populate an operating model and evaluating the population dynamics within a simulation framework. In addition to providing a more credible characterization of complex system dynamics, the operating model approach is transparent, reproducible and can evaluate alternative management strategies over a range of plausible hypotheses for the system. PMID:29856869

  2. A successful antimicrobial therapeutic strategy for the discitis caused by Aggregatibacter actinomycetemcomitans under unknown drug susceptibility: A case report.

    Science.gov (United States)

    Uno, Shunsuke; Horiuchi, Yosuke; Uchida, Takae; Yonaha, Akiko; Miyata, Takanori; Nagano, Eiko; Kodama, Takao; Hasegawa, Naoki

    2018-04-20

    Aggregatibacter actinomycetemcomitans is well-known as the pathogen of gingivitis or periodontitis, and discitis or vertebral osteomyelitis cases caused by this organism have rarely been reported. Ampicillin or amoxicillin has been used in the previously reported discitis cases; however, no cases have been reported that is treated with levofloxacin. We report the first published case we chose levofloxacin to treat. We failed to perform the susceptibility testing because of the poor growth and fastidious nature of the organism, and the result of susceptibility of amoxicillin was unclear. Levofloxacin, which A. actinomycetemcomitans is usually susceptible to, can be an effective alternative oral antimicrobial agent in such cases. Copyright © 2018 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

  3. Temporal and seasonal changes of genetic polymorphisms associated with altered drug susceptibility to chloroquine, lumefantrine and quinine in Guinea-Bissau between 2003 and 2012

    DEFF Research Database (Denmark)

    Jovel, Irina Tatiana; Kofoed, Poul-Erik; Rombo, Lars

    2015-01-01

    BACKGROUND: Guinea-Bissau, West-Africa introduced artemether-lumefantrine in 2008 but quinine has also been commonly prescribed for treatment of uncomplicated Plasmodium falciparum malaria. An efficacious high-dose chloroquine treatment regimen was used previously. Temporal and seasonal changes...... of genetic polymorphisms associated with altered drug susceptibility to chloroquine, lumefantrine and quinine are described. METHODS: Pfcrt K76T, pfmdr1 gene copy numbers, N86Y, Y184F and 1034-1246 sequences were determined using PCR-based methods. Blood samples came from virtually all (n=1806) children aged.......001). CONCLUSIONS: Following the discontinuation of an effective chloroquine regimen highly artemether-lumefantrine susceptible P. falciparum (with pfcrt 76T) accumulated possibly due to suboptimal use of quinine and despite a fitness cost linked to 76T....

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Resistance to first-line anti-TB drugs is associated with reduced nitric oxide susceptibility in Mycobacterium tuberculosis

    DEFF Research Database (Denmark)

    Idh, Jonna; Mekonnen, Mekidim; Abate, Ebba

    2012-01-01

    The relative contribution of nitric oxide (NO) to the killing of Mycobacterium tuberculosis in human tuberculosis (TB) is controversial, although this has been firmly established in rodents. Studies have demonstrated that clinical strains of M. tuberculosis differ in susceptibility to NO, but how...

  9. Outcomes of treatment of drug-susceptible tuberculosis at public sector primary healthcare clinics in Johannesburg, South Africa: A retrospective cohort study.

    Science.gov (United States)

    Budgell, E P; Evans, D; Schnippel, K; Ive, P; Long, L; Rosen, S

    2016-09-05

    Despite the large number of tuberculosis (TB) patients treated in South Africa (SA), there are few descriptions in the published literature of drug-susceptible TB patient characteristics, mode of diagnosis or treatment outcomes in routine public sector treatment programmes. To enhance the evidence base for public sector TB treatment service delivery, we reported the characteristics of and outcomes for a retrospective cohort of adult TB patients at public sector clinics in the Johannesburg Metropolitan Municipality (JHB), SA. We collected medical record data for a retrospective cohort of adult (≥18 years) TB patients registered between 1 April 2011 and 31 March 2012 at three public sector clinics in JHB. Data were abstracted from National TB Programme clinic cards and the TB case registers routinely maintained at study sites. We report patient characteristics, mode of diagnosis, mode of treatment supervision, treatment characteristics, HIV status and treatment outcomes for this cohort. A total of 544 patients were enrolled in the cohort. Most (86%) were new TB cases, 81% had pulmonary TB, 58% were smear-positive at treatment initiation and 71% were HIV co-infected. Among 495 patients with treatment outcomes reported, 80% (n=394) had successful outcomes, 11% (n=55) were lost to follow-up, 8% (n=40) died and 1% (n=6) failed treatment. Primary healthcare clinics in JHB are achieving relatively high rates of success in treating drug-susceptible TB. Missing laboratory results were common, including follow-up smears, cultures and drug susceptibility tests, making it difficult to assess adherence to guidelines and leaving scope for substantial improvements in record-keeping at the clinics involved.

  10. Drug susceptibility of influenza A/H3N2 strains co-circulating during 2009 influenza pandemic: first report from Mumbai.

    Science.gov (United States)

    Gohil, Devanshi J; Kothari, Sweta T; Shinde, Pramod S; Chintakrindi, Anand S; Meharunkar, Rhuta; Warke, Rajas V; Kanyalkar, Meena A; Chowdhary, Abhay S; Deshmukh, Ranjana A

    2015-01-01

    From its first instance in 1977, resistance to amantadine, a matrix (M2) inhibitor has been increasing among influenza A/H3N2, thus propelling the use of oseltamivir, a neuraminidase (NA) inhibitor as a next line drug. Information on drug susceptibility to amantadine and neuraminidase inhibitors for influenza A/H3N2 viruses in India is limited with no published data from Mumbai. This study aimed at examining the sensitivity to M2 and NA inhibitors of influenza A/H3N2 strains isolated from 2009 to 2011 in Mumbai. Nasopharyngeal swabs positive for influenza A/H3N2 virus were inoculated on Madin-Darby canine kidney (MDCK) cell line for virus isolation. Molecular analysis of NA and M2 genes was used to detect known mutations contributing to resistance. Resistance to neuraminidase was assayed using a commercially available chemiluminescence based NA-Star assay kit. Genotypically, all isolates were observed to harbor mutations known to confer resistance to amantadine. However, no know mutations conferring resistance to NA inhibitors were detected. The mean IC50 value for oseltamivir was 0.25 nM. One strain with reduced susceptibility to the neuraminidase inhibitor (IC₅₀=4.08 nM) was isolated from a patient who had received oseltamivir treatment. Phylogenetic analysis postulate the emergence of amantadine resistance in Mumbai may be due to genetic reassortment with the strains circulating in Asia and North America. Surveillance of drug susceptibility helped us to identify an isolate with reduced sensitivity to oseltamivir. Therefore, we infer that such surveillance would help in understanding possible trends underlying the emergence of resistant variants in humans. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. 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.

  12. 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.

  13. 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.

  14. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

    Science.gov (United States)

    Pradhan, Biswajeet

    2013-02-01

    The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e

  15. 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.

  16. [Validation of the modified algorithm for predicting host susceptibility to viruses taking into account susceptibility parameters of primary target cell cultures and natural immunity factors].

    Science.gov (United States)

    Zhukov, V A; Shishkina, L N; Safatov, A S; Sergeev, A A; P'iankov, O V; Petrishchenko, V A; Zaĭtsev, B N; Toporkov, V S; Sergeev, A N; Nesvizhskiĭ, Iu V; Vorob'ev, A A

    2010-01-01

    The paper presents results of testing a modified algorithm for predicting virus ID50 values in a host of interest by extrapolation from a model host taking into account immune neutralizing factors and thermal inactivation of the virus. The method was tested for A/Aichi/2/68 influenza virus in SPF Wistar rats, SPF CD-1 mice and conventional ICR mice. Each species was used as a host of interest while the other two served as model hosts. Primary lung and trachea cells and secretory factors of the rats' airway epithelium were used to measure parameters needed for the purpose of prediction. Predicted ID50 values were not significantly different (p = 0.05) from those experimentally measured in vivo. The study was supported by ISTC/DARPA Agreement 450p.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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

  5. 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.

  6. Secondary metabolite profiles and antifungal drug susceptibility of Aspergillus fumigatus and closely related species, Aspergillus lentulus, Aspergillus udagawae, and Aspergillus viridinutans.

    Science.gov (United States)

    Tamiya, Hiroyuki; Ochiai, Eri; Kikuchi, Kazuyo; Yahiro, Maki; Toyotome, Takahito; Watanabe, Akira; Yaguchi, Takashi; Kamei, Katsuhiko

    2015-05-01

    The incidence of Aspergillus infection has been increasing in the past few years. Also, new Aspergillus fumigatus-related species, namely Aspergillus lentulus, Aspergillus udagawae, and Aspergillus viridinutans, were shown to infect humans. These fungi exhibit marked morphological similarities to A. fumigatus, albeit with different clinical courses and antifungal drug susceptibilities. The present study used liquid chromatography/time-of-flight mass spectrometry to identify the secondary metabolites secreted as virulence factors by these Aspergillus species and compared their antifungal susceptibility. The metabolite profiles varied widely among A. fumigatus, A. lentulus, A. udagawae, and A. viridinutans, producing 27, 13, 8, and 11 substances, respectively. Among the mycotoxins, fumifungin, fumiquinazoline A/B and D, fumitremorgin B, gliotoxin, sphingofungins, pseurotins, and verruculogen were only found in A. fumigatus, whereas auranthine was only found in A. lentulus. The amount of gliotoxin, one of the most abundant mycotoxins in A. fumigatus, was negligible in these related species. In addition, they had decreased susceptibility to antifungal agents such as itraconazole and voriconazole, even though metabolites that were shared in the isolates showing higher minimum inhibitory concentrations than epidemiological cutoff values were not detected. These strikingly different secondary metabolite profiles may lead to the development of more discriminative identification protocols for such closely related Aspergillus species as well as improved treatment outcomes. Copyright © 2015 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

  7. qPCR-High resolution melt analysis for drug susceptibility testing of Mycobacterium leprae directly from clinical specimens of leprosy patients.

    Science.gov (United States)

    Araujo, Sergio; Goulart, Luiz Ricardo; Truman, Richard W; Goulart, Isabela Maria B; Vissa, Varalakshmi; Li, Wei; Matsuoka, Masanori; Suffys, Philip; Fontes, Amanda B; Rosa, Patricia S; Scollard, David M; Williams, Diana L

    2017-06-01

    Real-Time PCR-High Resolution Melting (qPCR-HRM) analysis has been recently described for rapid drug susceptibility testing (DST) of Mycobacterium leprae. The purpose of the current study was to further evaluate the validity, reliability, and accuracy of this assay for M. leprae DST in clinical specimens. The specificity and sensitivity for determining the presence and susceptibility of M. leprae to dapsone based on the folP1 drug resistance determining region (DRDR), rifampin (rpoB DRDR) and ofloxacin (gyrA DRDR) was evaluated using 211 clinical specimens from leprosy patients, including 156 multibacillary (MB) and 55 paucibacillary (PB) cases. When comparing the results of qPCR-HRM DST and PCR/direct DNA sequencing, 100% concordance was obtained. The effects of in-house phenol/chloroform extraction versus column-based DNA purification protocols, and that of storage and fixation protocols of specimens for qPCR-HRM DST, were also evaluated. qPCR-HRM results for all DRDR gene assays (folP1, rpoB, and gyrA) were obtained from both MB (154/156; 98.7%) and PB (35/55; 63.3%) patients. All PCR negative specimens were from patients with low numbers of bacilli enumerated by an M. leprae-specific qPCR. We observed that frozen and formalin-fixed paraffin embedded (FFPE) tissues or archival Fite's stained slides were suitable for HRM analysis. Among 20 mycobacterial and other skin bacterial species tested, only M. lepromatosis, highly related to M. leprae, generated amplicons in the qPCR-HRM DST assay for folP1 and rpoB DRDR targets. Both DNA purification protocols tested were efficient in recovering DNA suitable for HRM analysis. However, 3% of clinical specimens purified using the phenol/chloroform DNA purification protocol gave false drug resistant data. DNA obtained from freshly frozen (n = 172), formalin-fixed paraffin embedded (FFPE) tissues (n = 36) or archival Fite's stained slides (n = 3) were suitable for qPCR-HRM DST analysis. The HRM-based assay was also able to

  8. Quality Control Guidelines for Disk Diffusion and Broth Microdilution Antimicrobial Susceptibility Tests with Seven Drugs for Veterinary Applications

    Science.gov (United States)

    Odland, Brant A.; Erwin, Meredith E.; Jones, Ronald N.

    2000-01-01

    This multicenter study proposes antimicrobial susceptibility (MIC and disk diffusion methods) quality control (QC) parameters for seven compounds utilized in veterinary health. Alexomycin, apramycin, tiamulin, tilmicosin, and tylosin were tested by broth microdilution against various National Committee for Clinical Laboratory Standards (NCCLS)-recommended QC organisms (Staphylococcus aureus ATCC 29213, Enterococcus faecalis ATCC 29212, Streptococcus pneumoniae ATCC 49619, Escherichia coli ATCC 25922, and Pseudomonas aeruginosa ATCC 27853). In addition, disk diffusion zone diameter QC limits were determined for apramycin, enrofloxacin, and premafloxacin by using E. coli ATCC 25922, P. aeruginosa ATCC 27853, and S. aureus ATCC 25923. The results from five or six participating laboratories produced ≥99.0% of MICs and ≥95.0% of the zone diameters within suggested guidelines. The NCCLS Subcommittee for Veterinary Antimicrobial Susceptibility Testing has recently approved these ranges for publication in the next M31 document. PMID:10618141

  9. 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...

  10. Conjugation of metronidazole with dextran: a potential pharmaceutical strategy to control colonic distribution of the anti-amebic drug susceptible to metabolism by colonic microbes.

    Science.gov (United States)

    Kim, Wooseong; Yang, Yejin; Kim, Dohoon; Jeong, Seongkeun; Yoo, Jin-Wook; Yoon, Jeong-Hyun; Jung, Yunjin

    2017-01-01

    Metronidazole (MTDZ), the drug of choice for the treatment of protozoal infections such as luminal amebiasis, is highly susceptible to colonic metabolism, which may hinder its conversion from a colon-specific prodrug to an effective anti-amebic agent targeting the entire large intestine. Thus, in an attempt to control the colonic distribution of the drug, a polymeric colon-specific prodrug, MTDZ conjugated to dextran via a succinate linker (Dex-SA-MTDZ), was designed. Upon treatment with dextranase for 8 h, the degree of Dex-SA-MTDZ depolymerization (%) with a degree of substitution (mg of MTDZ bound in 100 mg of Dex-SA-MTDZ) of 7, 17, and 30 was 72, 38, and 8, respectively, while that of dextran was 85. Depolymerization of Dex-SA-MTDZ was found to be necessary for the release of MTDZ, because dextranase pretreatment ensures that de-esterification occurs between MTDZ and the dextran backbone. In parallel, Dex-SA-MTDZ with a degree of substitution of 17 was found not to release MTDZ upon incubation with the contents of the small intestine and stomach of rats, but it released MTDZ when incubated with rat cecal contents (including microbial dextranases). Moreover, Dex-SA-MTDZ exhibited prolonged release of MTDZ, which contrasts with drug release by small molecular colon-specific prodrugs, MTDZ sulfate and N -nicotinoyl-2-{2-(2-methyl-5-nitroimidazol-1-yl)ethyloxy}-d,l-glycine. These prodrugs were eliminated very rapidly, and no MTDZ was detected in the cecal contents. Consistent with these in vitro results, we found that oral gavage of Dex-SA-MTDZ delivered MTDZ (as MTDZ conjugated to [depolymerized] dextran) to the distal colon. However, upon oral gavage of the small molecular prodrugs, no prodrugs were detected in the distal colon. Collectively, these data suggest that dextran conjugation is a potential pharmaceutical strategy to control the colonic distribution of drugs susceptible to colonic microbial metabolism.

  11. 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.

  12. Prevalence and drug susceptibility pattern of group B Streptococci (GBS) among pregnant women attending antenatal care (ANC) in Nekemte Referral Hospital (NRH), Nekemte, Ethiopia.

    Science.gov (United States)

    Mengist, Hylemariam Mihiretie; Zewdie, Olifan; Belew, Adugna; Dabsu, Regea

    2017-08-10

    The main objective of this study was to determine the prevalence and drug susceptibility pattern of group B Streptococci (GBS) among pregnant women. The specific objectives include; (1) To determine the prevalence of GBS colonization among pregnant women (2) To determine the drug susceptibility pattern of GBS among pregnant women and (3) To identify associated risk factors with GBS colonization among pregnant women. The median age of the participants was 24.5 years (range 16-38) and 86% participants were urban residents. The total prevalence of maternal GBS colonization from vaginal swab culture was 12.2% (22/180). The prevalence of GBS colonization rate was significantly higher in those pregnant women above 37 weeks of gestation [AOR, 95% CI 2.1 (1.2, 11.6), P = 0.03] and married ones [AOR, 95% CI 3.2 (1.8, 11.6), P < 0.021]. Twenty (91%) of GBS isolates were sensitive to vancomycin and the highest resistance was observed against penicillin G (77.3%). The prevalence of GBS colonization in this study was significantly high and differed by gestational age and marital status. None of the GBS isolates were resistant to vancomycin but higher resistance was shown against Penicillin G.

  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. Trends of Mycobacterium bovis Isolation and First-Line Anti-tuberculosis Drug Susceptibility Profile: A Fifteen-Year Laboratory-Based Surveillance.

    Directory of Open Access Journals (Sweden)

    Miriam Bobadilla-del Valle

    2015-09-01

    Full Text Available Mycobacterium tuberculosis causes the majority of tuberculosis (TB cases in humans; however, in developing countries, human TB caused by M. bovis may be frequent but undetected. Human TB caused by M. bovis is considered a zoonosis; transmission is mainly through consumption of unpasteurized dairy products, and it is less frequently attributed to animal-to-human or human-to-human contact. We describe the trends of M. bovis isolation from human samples and first-line drug susceptibility during a 15-year period in a referral laboratory located in a tertiary care hospital in Mexico City.Data on mycobacterial isolates from human clinical samples were retrieved from the laboratory's database for the 2000-2014 period. Susceptibility to first-line drugs: rifampin, isoniazid, streptomycin (STR and ethambutol was determined. We identified 1,165 isolates, 73.7% were M. tuberculosis and 26.2%, M. bovis. Among pulmonary samples, 16.6% were M. bovis. The proportion of M. bovis isolates significantly increased from 7.8% in 2000 to 28.4% in 2014 (X(2trend, p<0.001. Primary STR resistance was higher among M. bovis compared with M. tuberculosis isolates (10.9% vs.3.4%, p<0.001. Secondary multidrug resistance (MDR rates were 38.5% and 34.4% for M. bovis and M. tuberculosis, respectively (p = 0.637. A rising trend of primary STR monoresistance was observed for both species (3.4% in 2000-2004 vs. 7.6% in 2010-2014; p = 0.02.There is a high prevalence and a rising trend of M. bovis isolates in our region. The proportion of pulmonary M. bovis isolates is higher than in previous reports. Additionally, we report high rates of primary anti-tuberculosis resistance and secondary MDR in both M. tuberculosis and M. bovis. This is one of the largest reports on drug susceptibility of M. bovis from human samples and shows a significant proportion of first-line anti-tuberculosis drug resistance.

  2. Generation of pH responsive fluorescent nano capsules through simple steps for the oral delivery of low pH susceptible drugs

    Science.gov (United States)

    Radhakumary, Changerath; Sreenivasan, Kunnatheeri

    2016-11-01

    pH responsive nano capsules are promising as it can encapsulate low pH susceptible drugs like insulin and guard them from the hostile environments in the intestinal tract. The strong acidity of the gastro-intestinal tract and the presence of proteolytic enzymes are the tumbling blocks for the design of drug delivery vehicles through oral route for drugs like insulin. Nano capsules are normally built over templates which are subsequently removed by further steps. Such processes are complex and often lead into deformed and collapsed capsules. In this study, we choose calcium carbonate (CaCO3) nano particles to serve as template. Over CaCO3 nanoparticles, silica layers were built followed by polymethacrylic acid chains to acquire pH responsiveness. During the polymerization process of the methacrylic acid, the calcium carbonate core particles were dissolved leading to the formation of nano hollow capsules having a size that ranges from 225 to 246 nm and thickness from 19 to 58 nm. The methodology is simple and devoid of additional steps. The nano shells exhibited 80% release of the loaded model drug, insulin at pH 7.4 while at pH 2.0 the capsules nearly stopped the release of the drug. Polymethacrylic acid shows pH responsive swelling behavior that it swells at intestinal pH (7.0-7.5) and shrinks at gastric pH (˜2.0) thus enabling the safe unloading of the drug from the nano capsules.

  3. Susceptibility profiles of Nocardia isolates based on current taxonomy.

    Science.gov (United States)

    Schlaberg, Robert; Fisher, Mark A; Hanson, Kimberley E

    2014-01-01

    The genus Nocardia has undergone rapid taxonomic expansion in recent years, and an increasing number of species are recognized as human pathogens. Many established species have predictable antimicrobial susceptibility profiles, but sufficient information is often not available for recently described organisms. Additionally, the effectiveness of sulfonamides as first-line drugs for Nocardia has recently been questioned. This led us to review antimicrobial susceptibility patterns for a large number of molecularly identified clinical isolates. Susceptibility results were available for 1,299 isolates representing 39 different species or complexes, including 11 that were newly described, during a 6-year study period. All tested isolates were susceptible to linezolid. Resistance to trimethoprim-sulfamethoxazole (TMP-SMX) was rare (2%) except among Nocardia pseudobrasiliensis (31%) strains and strains of the N. transvalensis complex (19%). Imipenem susceptibility varied for N. cyriacigeorgica and N. farcinica, as did ceftriaxone susceptibility of the N. nova complex. Resistance to more than one of the most commonly used drugs (amikacin, ceftriaxone, TMP-SMX, and imipenem) was highest for N. pseudobrasiliensis (100%), N. transvalensis complex (83%), N. farcinica (68%), N. puris (57%), N. brasiliensis (51%), N. aobensis (50%), and N. amikacinitolerans (43%). Thus, while antimicrobial resistance can often be predicted, susceptibility testing should still be considered when combination therapy is warranted, for less well characterized species or those with variable susceptibility profiles, and for patients with TMP-SMX intolerance.

  4. 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.

  5. Epidemiology and Drug Susceptibility of Pseudomonas aeruginosa Strains isolated from Patients admitted to Zabol hospitals: Short Communication

    Directory of Open Access Journals (Sweden)

    Forough Heydari

    2015-12-01

    Full Text Available Background and Aim: Pseudomonas aeruginosa is one of the most important causative agents of nosocomial infections that threatens many lives .. Regarding the innate and adaptive ability of the bacteria species to become resistant to many antimicrobial agents, recognition of different antibiotic resistance patterns is extremely significant in assessing the validity of the monitoring programs. Also, the pattern of genetic isolates is essential in the management of infections caused by these bacteria. The purpose of this study was to determine genetic diversity and patterns of antimicrobial resistance of P. aeruginosa isolates using RAPD-PCR. Materials and Methods: The present study aimed at assessing the genetic diversity and antibiotic resistant pattern of P. aeruginosa isolates in the educational Zabol hospitals. Thus, antibiotic susceptibility of 100 isolates was determined applying Kirby-Bauer disk diffusion method. Results: RAPD-PCR data revealed  a high level of polymorphism among the isolates of P. aeruginosa in Sistan. But, no association was observed between antibiotic susceptibility and genetic diversity pattern. Conclusion: In the present study, we RAPD-PCR technique was found to be a useful means for the investigation of the genetic variation and epidemiological study among P. aeruginosa isolates collected from Sistan region.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. The influence of the CYP2D6*4 polymorphism on drug response and disease susceptibility

    NARCIS (Netherlands)

    M.J. Bijl (Monique)

    2009-01-01

    textabstractThis thesis is about the role of CYP2D6, a drug-metabolizing enzyme, in today’s pharmacotherapy. Cytochrome P450 2D6 (CYP2D6) is an important member of a large family of enzymes with the name cytochrome P450 which is abundantly present in most non-monocellular living organisms. Its

  11. Mutations Conferring Resistance to Viral DNA Polymerase Inhibitors in Camelpox Virus Give Different Drug-Susceptibility Profiles in Vaccinia Virus

    Czech Academy of Sciences Publication Activity Database

    Duraffour, S.; Andrei, G.; Topalis, D.; Krečmerová, Marcela; Crance, J. M.; Garin, D.; Snoeck, R.

    2012-01-01

    Roč. 86, č. 13 (2012), s. 7310-7325 ISSN 0022-538X Institutional support: RVO:61388963 Keywords : camelpox virus * CMLV * vaccinia virus VACV * acyclic nucleoside phosphonates * HPMPDAP * cidofovir * drug resistance Subject RIV: CC - Organic Chemistry Impact factor: 5.076, year: 2012

  12. Dirt cheap and without prescription: how susceptible are young US consumers to purchasing drugs from rogue internet pharmacies?

    Science.gov (United States)

    Ivanitskaya, Lana; Brookins-Fisher, Jodi; O Boyle, Irene; Vibbert, Danielle; Erofeev, Dmitry; Fulton, Lawrence

    2010-04-26

    Websites of many rogue sellers of medications are accessible through links in email spam messages or via web search engines. This study examined how well students enrolled in a U.S. higher education institution could identify clearly unsafe pharmacies. The aim is to estimate these health consumers vulnerability to fraud by illegitimate Internet pharmacies. Two Internet pharmacy websites, created specifically for this study, displayed multiple untrustworthy features modeled after five actual Internet drug sellers which the authors considered to be potentially dangerous to consumers. The websites had none of the safe pharmacy signs and nearly all of the danger signs specified in the Food and Drug Administration s (FDA s) guide to consumers. Participants were told that a neighborhood pharmacy charged US$165 for a one-month supply of Beozine, a bogus drug to ensure no pre-existing knowledge. After checking its price at two Internet pharmacies-$37.99 in pharmacy A and $57.60 in pharmacy B-the respondents were asked to indicate if each seller was a good place to buy the drug. Responses came from 1,914 undergraduate students who completed an online eHealth literacy assessment in 2005-2008. Participation rate was 78%. In response to "On a scale from 0-10, how good is this pharmacy as a place for buying Beozine?" many respondents gave favorable ratings. Specifically, 50% of students who reviewed pharmacy A and 37% of students who reviewed pharmacy B chose a rating above the scale midpoint. When explaining a low drug cost, these raters related it to low operation costs, ad revenue, pressure to lower costs due to comparison shopping, and/or high sales volume. Those who said that pharmacy A or B was "a very bad place" for purchasing the drug (25%), as defined by a score of 1 or less, related low drug cost to lack of regulation, low drug quality, and/or customer information sales. About 16% of students thought that people should be advised to buy cheaper drugs at pharmacies

  13. 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.

  14. 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.

  15. Infection control, genetic assessment of drug resistance and drug susceptibility testing in the current management of multidrug/extensively-resistant tuberculosis (M/XDR-TB) in Europe

    DEFF Research Database (Denmark)

    Bothamley, Graham H.; Lange, Christoph; Albrecht, Dirk

    2017-01-01

    AIM: Europe has the highest documented caseload and greatest increase in multidrug and extensively drug-resistant tuberculosis (M/XDR-TB) of all World Health Organization (WHO) regions. This survey examines how recommendations for M/XDR-TB management are being implemented. METHODS: TBNET is a pan...

  16. 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.

  17. 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.

  18. Genetic Predictions of Prion Disease Susceptibility in Carnivore Species Based on Variability of the Prion Gene Coding Region

    Science.gov (United States)

    Stewart, Paula; Campbell, Lauren; Skogtvedt, Susan; Griffin, Karen A.; Arnemo, Jon M.; Tryland, Morten; Girling, Simon; Miller, Michael W.; Tranulis, Michael A.; Goldmann, Wilfred

    2012-01-01

    Mammalian species vary widely in their apparent susceptibility to prion diseases. For example, several felid species developed prion disease (feline spongiform encephalopathy or FSE) during the bovine spongiform encephalopathy (BSE) epidemic in the United Kingdom, whereas no canine BSE cases were detected. Whether either of these or other groups of carnivore species can contract other prion diseases (e.g. chronic wasting disease or CWD) remains an open question. Variation in the host-encoded prion protein (PrPC) largely explains observed disease susceptibility patterns within ruminant species, and may explain interspecies differences in susceptibility as well. We sequenced and compared the open reading frame of the PRNP gene encoding PrPC protein from 609 animal samples comprising 29 species from 22 genera of the Order Carnivora; amongst these samples were 15 FSE cases. Our analysis revealed that FSE cases did not encode an identifiable disease-associated PrP polymorphism. However, all canid PrPs contained aspartic acid or glutamic acid at codon 163 which we propose provides a genetic basis for observed susceptibility differences between canids and felids. Among other carnivores studied, wolverine (Gulo gulo) and pine marten (Martes martes) were the only non-canid species to also express PrP-Asp163, which may impact on their prion diseases susceptibility. Populations of black bear (Ursus americanus) and mountain lion (Puma concolor) from Colorado showed little genetic variation in the PrP protein and no variants likely to be highly resistant to prions in general, suggesting that strain differences between BSE and CWD prions also may contribute to the limited apparent host range of the latter. PMID:23236380

  19. Genetic predictions of prion disease susceptibility in carnivore species based on variability of the prion gene coding region.

    Directory of Open Access Journals (Sweden)

    Paula Stewart

    Full Text Available Mammalian species vary widely in their apparent susceptibility to prion diseases. For example, several felid species developed prion disease (feline spongiform encephalopathy or FSE during the bovine spongiform encephalopathy (BSE epidemic in the United Kingdom, whereas no canine BSE cases were detected. Whether either of these or other groups of carnivore species can contract other prion diseases (e.g. chronic wasting disease or CWD remains an open question. Variation in the host-encoded prion protein (PrP(C largely explains observed disease susceptibility patterns within ruminant species, and may explain interspecies differences in susceptibility as well. We sequenced and compared the open reading frame of the PRNP gene encoding PrP(C protein from 609 animal samples comprising 29 species from 22 genera of the Order Carnivora; amongst these samples were 15 FSE cases. Our analysis revealed that FSE cases did not encode an identifiable disease-associated PrP polymorphism. However, all canid PrPs contained aspartic acid or glutamic acid at codon 163 which we propose provides a genetic basis for observed susceptibility differences between canids and felids. Among other carnivores studied, wolverine (Gulo gulo and pine marten (Martes martes were the only non-canid species to also express PrP-Asp163, which may impact on their prion diseases susceptibility. Populations of black bear (Ursus americanus and mountain lion (Puma concolor from Colorado showed little genetic variation in the PrP protein and no variants likely to be highly resistant to prions in general, suggesting that strain differences between BSE and CWD prions also may contribute to the limited apparent host range of the latter.

  20. Species-Specific and Drug-Specific Differences in Susceptibility of Candida Biofilms to Echinocandins: Characterization of Less Common Bloodstream Isolates

    Science.gov (United States)

    Simitsopoulou, Maria; Peshkova, Pavla; Tasina, Efthymia; Katragkou, Aspasia; Kyrpitzi, Daniela; Velegraki, Aristea; Walsh, Thomas J.

    2013-01-01

    Candida species other than Candida albicans are increasingly recognized as causes of biofilm-associated infections. This is a comprehensive study that compared the in vitro activities of all three echinocandins against biofilms formed by different common and infrequently identified Candida isolates. We determined the activities of anidulafungin (ANID), caspofungin (CAS), and micafungin (MFG) against planktonic cells and biofilms of bloodstream isolates of C. albicans (15 strains), Candida parapsilosis (6 strains), Candida lusitaniae (16 strains), Candida guilliermondii (5 strains), and Candida krusei (12 strains) by XTT [2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide] assay. Planktonic and biofilm MICs were defined as ≥50% fungal damage. Planktonic cells of all Candida species were susceptible to the three echinocandins, with MICs of ≤1 mg/liter. By comparison, differences in the MIC profiles of biofilms in response to echinocandins existed among the Candida species. Thus, C. lusitaniae and C. guilliermondii biofilms were highly recalcitrant to all echinocandins, with MICs of ≥32 mg/liter. In contrast, the MICs of all three echinocandins for C. albicans and C. krusei biofilms were relatively low (MICs ≤ 1 mg/liter). While echinocandins exhibited generally high MICs against C. parapsilosis biofilms, MFG exhibited the lowest MICs against these isolates (4 mg/liter). A paradoxical growth effect was observed with CAS concentrations ranging from 8 to 64 mg/liter against C. albicans and C. parapsilosis biofilms but not against C. krusei, C. lusitaniae, or C. guilliermondii. While non-albicans Candida planktonic cells were susceptible to all echinocandins, there were drug- and species-specific differences in susceptibility among biofilms of the various Candida species, with C. lusitaniae and C. guilliermondii exhibiting profiles of high MICs of the three echinocandins. PMID:23529739

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. 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.

  6. 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.

  7. Evaluation of rapid MTT tube method for detection of drug susceptibility of mycobacterium tuberculosis to rifampicin and isoniazid

    Directory of Open Access Journals (Sweden)

    Raut U

    2008-01-01

    Full Text Available Purpose: To evaluate MTT method for detection of drug resistance to rifampicin and isoniazid in M.tuberculosis . This method utilises the ability of viable mycobacterial cells to reduce MTT( 3-4,5-dimethylthiazol-2-yl-2, 5-diphenyl tetrazolium bromide. Methods: The method was standardised with known resistant and sensitive strains of M.tuberculosis and was then extended to 50 clinical isolates. An inoculum of 10 7 cfu/mL was prepared in Middlebrook 7H9 medium supplemented with oleic acid, albumin, dextrose and catalase. For each drug three tubes were used, one with INH(0.2μg/mL or RIF(1μg/mL, another as inoculum control and third as blank control. These were incubated at 37°C for four and seven days respectively for RIF and INH after which MTT assay was performed. Results were read visually and by colorimeter at 570 nm. Relative optical density unit (RODU of 0.2 was taken as cut off. Results were compared with drug sensitivity obtained by proportion method using LJ medium. Results: For rifampicin, concordance with proportion method was 90% by visual and 94% by RODU. Sensitivity and specificity was 86.8% and 100% respectively by visual method and 95.2% and 87.5% respectively by RODU. For Isoniazid, concordance was 94% and sensitivity and specificity was 94.7 and 91.7% respectively by both visual and RODU. Conclusions: MTT assay proved to be rapid and cheap method for performing drug sensitivity of M.tuberculosis

  8. 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

  9. 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

  10. Ertapenem susceptibility of extended spectrum beta-lactamase-producing organisms

    Directory of Open Access Journals (Sweden)

    Selby Edward B

    2007-06-01

    Full Text Available Abstract Background Infections caused by multiply drug resistant organisms such as extended spectrum beta-lactamase (ESBL-producing Escherichia coli and Klebsiella pneumoniae are increasing. Carbapenems (imipenem and meropenem are the antibiotics commonly used to treat these agents. There is limited clinical data regarding the efficacy of the newest carbapenem, ertapenem, against these organisms. Ertapenem susceptibility of ESBL-producing E. coli and K. pneumoniae clinical isolates were evaluated and compared to imipenem to determine if imipenem susceptibility could be used as a surrogate for ertapenem susceptibility. Methods 100 ESBL isolates (n = 34 E. coli and n = 66 K. pneumoniae collected from 2005–2006 clinical specimens at WRAMC were identified and tested for susceptibility by Vitek Legacy [bioMerieux, Durham, NC]. Ertapenem susceptibility was performed via epsilometer test (E-test [AB Biodisk, Solna, Sweden]. Results 100% of ESBL isolates tested were susceptible to ertapenem. 100% of the same isolates were also susceptible to imipenem. Conclusion These results, based on 100% susceptibility, suggest that ertapenem may be an alternative to other carbapenems for the treatment of infections caused by ESBL-producing E. coli and K. pneumoniae. Clinical outcomes studies are needed to determine if ertapenem is effective for the treatment of infection caused by these organisms. However, due to lack of resistant isolates, we are unable to conclude whether imipenem susceptibility accurately predicts ertapenem susceptibility.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. Bacterial profile and drug susceptibility pattern of urinary tract infection in pregnant women at Tikur Anbessa Specialized Hospital Addis Ababa, Ethiopia.

    Science.gov (United States)

    Assefa, Addisu; Asrat, Daniel; Woldeamanuel, Yimtubezinash; G/Hiwot, Yirgu; Abdella, Ahmed; Melesse, Tadele

    2008-07-01

    Urinary tract infection (UTI) is a common complication of pregnancy. It may be symptomatic or asymptomatic. The aim of this cross sectional study was to identify bacterial agents and their antibiotic susceptibility pattern isolated from pregnant women with UTI attending antenatal clinic of Tikur Anbessa Specialized Hospital (TASH). Four hundred and fourteen pregnant women with asymptomatic UTI (n = 369) and symptomatic UTI (n = 45) were investigated for urinary tract infection from January to March 2005. The age range of both groups was 18 to 44 years. Bacteriological screening of mid-stream urine specimens revealed that 39/369 (10.6%) and 9/45 (20%) had significant bacteriuria in asymptomatic and symptomatic group, respectively (p = 0.10). The overall prevalence of urinary tract infection was 48/414 (11.6%). The bacterial pathogens isolated were predominantly E. coil (44%), followed by S. aureus (20%), coagulase-negative staphylococci (16%), and K. pneumoniae (8%). Others found in small in number included P. mirabilis, P. aeruginosa, Enterococcus spp. and non-Group A-beta hemolytic Streptococcus, this accounted 2% for each. The gram positive and negative bacteria accounted 40% and 60% respectively. The susceptibility pattern for gram-negative bacteria showed that most of the isolates (> 65% of the strains) were sensitive to amoxicillin-clavulanic acid (70%), chloramphenicol (83.3%), gentamicin (93.3%), kanamycin (93.3%), nitrofurantoin (87.7%) and trimethoprim-sulphamethoxazole (73.3%). Among the gram-positives, more than 60% of the isolates were sensitive to amoxicillin-clavulanic acid (100%), cephalothin (95%), chloramphenicol (70%), erythromycin (80%), gentamicin (85%), methicillin (83.3%), nitrofurantoin (100%) and trimethoprim-sulphamethoxazole (65%). Generally, amoxicillin-clavulanic acid, chloramphenicol, gentamicin, nitrofurantoin and trimethoprim-sulphamethoxazole were effective at least in 70% of the isolates. Multiple drug resistance (resistance two or

  17. PknB remains an essential and a conserved target for drug development in susceptible and MDR strains of M. Tuberculosis.

    Science.gov (United States)

    Gupta, Anamika; Pal, Sudhir K; Pandey, Divya; Fakir, Najneen A; Rathod, Sunita; Sinha, Dhiraj; SivaKumar, S; Sinha, Pallavi; Periera, Mycal; Balgam, Shilpa; Sekar, Gomathi; UmaDevi, K R; Anupurba, Shampa; Nema, Vijay

    2017-08-18

    The Mycobacterium tuberculosis (M.tb) protein kinase B (PknB) which is now proved to be essential for the growth and survival of M.tb, is a transmembrane protein with a potential to be a good drug target. However it is not known if this target remains conserved in otherwise resistant isolates from clinical origin. The present study describes the conservation analysis of sequences covering the inhibitor binding domain of PknB to assess if it remains conserved in susceptible and resistant clinical strains of mycobacteria picked from three different geographical areas of India. A total of 116 isolates from North, South and West India were used in the study with a variable profile of their susceptibilities towards streptomycin, isoniazid, rifampicin, ethambutol and ofloxacin. Isolates were also spoligotyped in order to find if the conservation pattern of pknB gene remain consistent or differ with different spoligotypes. The impact of variation as found in the study was analyzed using Molecular dynamics simulations. The sequencing results with 115/116 isolates revealed the conserved nature of pknB sequences irrespective of their susceptibility status and spoligotypes. The only variation found was in one strains wherein pnkB sequence had G to A mutation at 664 position translating into a change of amino acid, Valine to Isoleucine. After analyzing the impact of this sequence variation using Molecular dynamics simulations, it was observed that the variation is causing no significant change in protein structure or the inhibitor binding. Hence, the study endorses that PknB is an ideal target for drug development and there is no pre-existing or induced resistance with respect to the sequences involved in inhibitor binding. Also if the mutation that we are reporting for the first time is found again in subsequent work, it should be checked with phenotypic profile before drawing the conclusion that it would affect the activity in any way. Bioinformatics analysis in our study

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. 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...

  3. 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.

  4. 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.

  5. Like will to like: abundances of closely related species can predict susceptibility to intestinal colonization by pathogenic and commensal bacteria.

    Science.gov (United States)

    Stecher, Bärbel; Chaffron, Samuel; Käppeli, Rina; Hapfelmeier, Siegfried; Freedrich, Susanne; Weber, Thomas C; Kirundi, Jorum; Suar, Mrutyunjay; McCoy, Kathy D; von Mering, Christian; Macpherson, Andrew J; Hardt, Wolf-Dietrich

    2010-01-01

    The intestinal ecosystem is formed by a complex, yet highly characteristic microbial community. The parameters defining whether this community permits invasion of a new bacterial species are unclear. In particular, inhibition of enteropathogen infection by the gut microbiota ( = colonization resistance) is poorly understood. To analyze the mechanisms of microbiota-mediated protection from Salmonella enterica induced enterocolitis, we used a mouse infection model and large scale high-throughput pyrosequencing. In contrast to conventional mice (CON), mice with a gut microbiota of low complexity (LCM) were highly susceptible to S. enterica induced colonization and enterocolitis. Colonization resistance was partially restored in LCM-animals by co-housing with conventional mice for 21 days (LCM(con21)). 16S rRNA sequence analysis comparing LCM, LCM(con21) and CON gut microbiota revealed that gut microbiota complexity increased upon conventionalization and correlated with increased resistance to S. enterica infection. Comparative microbiota analysis of mice with varying degrees of colonization resistance allowed us to identify intestinal ecosystem characteristics associated with susceptibility to S. enterica infection. Moreover, this system enabled us to gain further insights into the general principles of gut ecosystem invasion by non-pathogenic, commensal bacteria. Mice harboring high commensal E. coli densities were more susceptible to S. enterica induced gut inflammation. Similarly, mice with high titers of Lactobacilli were more efficiently colonized by a commensal Lactobacillus reuteri(RR) strain after oral inoculation. Upon examination of 16S rRNA sequence data from 9 CON mice we found that closely related phylotypes generally display significantly correlated abundances (co-occurrence), more so than distantly related phylotypes. Thus, in essence, the presence of closely related species can increase the chance of invasion of newly incoming species into the gut

  6. Tenofovir alafenamide demonstrates broad cross-genotype activity against wild-type HBV clinical isolates and maintains susceptibility to drug-resistant HBV isolates in vitro.

    Science.gov (United States)

    Liu, Yang; Miller, Michael D; Kitrinos, Kathryn M

    2017-03-01

    Tenofovir alafenamide (TAF) is a novel prodrug of tenofovir (TFV). This study evaluated the antiviral activity of TAF against wild-type genotype A-H HBV clinical isolates as well as adefovir-resistant, lamivudine-resistant, and entecavir-resistant HBV isolates. Full length HBV genomes or the polymerase/reverse transcriptase (pol/RT) region from treatment-naïve patients infected with HBV genotypes A-H were amplified and cloned into an expression vector under the control of a CMV promoter. In addition, 11 drug resistant HBV constructs were created by site-directed mutagenesis of a full length genotype D construct. Activity of TAF was measured by transfection of each construct into HepG2 cells and assessment of HBV DNA levels following treatment across a range of TAF concentrations. TAF activity in vitro was similar against wild-type genotype A-H HBV clinical isolates. All lamivudine- and entecavir-resistant isolates and 4/5 adefovir-resistant isolates were found to be sensitive to inhibition by TAF in vitro as compared to the wild-type isolate. The adefovir-resistant isolate rtA181V + rtN236T exhibited low-level reduced susceptibility to TAF. TAF is similarly active in vitro against wild-type genotype A-H HBV clinical isolates. The TAF sensitivity results for all drug-resistant isolates are consistent with what has been observed with the parent drug TFV. The in vitro cell-based HBV phenotyping assay results support the use of TAF in treatment of HBV infected subjects with diverse HBV genotypes, in both treatment-naive and treatment-experienced HBV infected patients. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. A Faropenem, Linezolid, and Moxifloxacin Regimen for Both Drug-Susceptible and Multidrug-Resistant Tuberculosis in Children: FLAME Path on the Milky Way.

    Science.gov (United States)

    Deshpande, Devyani; Srivastava, Shashikant; Nuermberger, Eric; Pasipanodya, Jotam G; Swaminathan, Soumya; Gumbo, Tawanda

    2016-11-01

     The regimen of linezolid and moxifloxacin was found to be efficacious in the hollow fiber system model of pediatric intracellular tuberculosis. However, its kill rate was slower than the standard 3-drug regimen of isoniazid, rifampin, and pyrazinamide. We wanted to examine the effect of adding a third oral agent, faropenem, to this dual combination.  We performed a series of studies in the hollow fiber system model of intracellular Mycobacterium tuberculosis, by mimicking pediatric pharmacokinetics of each antibiotic. First, we varied the percentage of time that faropenem persisted above minimum inhibitory concentration (T MIC ) on the moxifloxacin-linezolid regimen. After choosing the best faropenem exposure, we performed experiments in which we varied the moxifloxacin and linezolid doses in the triple regimen. Finally, we performed longer-duration therapy validation experiments. Bacterial burden was quantified using both colony-forming units per milliliter (CFU/mL) and time to positivity (TTP). Kill slopes were modeled using exponential regression.  TTP was a more sensitive measure of bacterial burden than CFU/mL. A faropenem T MIC > 62% was associated with steepest microbial kill slope. Regimens of standard linezolid and moxifloxacin plus faropenem T MIC > 60%, as well as higher-dose moxifloxacin, achieved slopes equivalent to those of the standard regimen based by both TTP and CFU/mL over 28 days of treatment.  We have developed an oral faropenem-linezolid-moxifloxacin (FLAME) regimen that is free of first-line drugs. The regimen could be effective against both multidrug-resistant and drug-susceptible tuberculosis in children. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America.

  8. 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.

  9. 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.

  10. 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.

  11. [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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. Measuring enzymatic HIV-1 susceptibility to two reverse transcriptase inhibitors as a rapid and simple approach to HIV-1 drug-resistance testing.

    Directory of Open Access Journals (Sweden)

    Dieter Hoffmann

    Full Text Available Simple and cost-effective approaches for HIV drug-resistance testing are highly desirable for managing increasingly expanding HIV-1 infected populations who initiate antiretroviral therapy (ART, particularly in resource-limited settings. Non-nucleoside reverse trancriptase inhibitor (NNRTI-based regimens with an NRTI backbone containing lamivudine (3TC or emtricitabine (FTC are preferred first ART regimens. Failure with these drug combinations typically involves the selection of NNRTI- and/or 3TC/FTC-resistant viruses. Therefore, the availability of simple assays to measure both types of drug resistance is critical. We have developed a high throughput screening test for assessing enzymatic resistance of the HIV-1 RT in plasma to 3TC/FTC and NNRTIs. The test uses the sensitive "Amp-RT" assay with a newly-developed real-time PCR format to screen biochemically for drug resistance in single reactions containing either 3TC-triphosphate (3TC-TP or nevirapine (NVP. Assay cut-offs were defined based on testing a large panel of subtype B and non-subtype B clinical samples with known genotypic profiles. Enzymatic 3TC resistance correlated well with the presence of M184I/V, and reduced NVP susceptibility was strongly associated with the presence of K103N, Y181C/I, Y188L, and G190A/Q. The sensitivity and specificity for detecting resistance were 97.0% and 96.0% in samples with M184V, and 97.4% and 96.2% for samples with NNRTI mutations, respectively. We further demonstrate the utility of an HIV capture method in plasma by using magnetic beads coated with CD44 antibody that eliminates the need for ultracentifugation. Thus our results support the use of this simple approach for distinguishing WT from NNRTI- or 3TC/FTC-resistant viruses in clinical samples. This enzymatic testing is subtype-independent and can assist in the clinical management of diverse populations particularly in resource-limited settings.

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. Aerobic bacteria from mucous membranes, ear canals, and skin wounds of feral cats in Grenada, and the antimicrobial drug susceptibility of major isolates.

    Science.gov (United States)

    Hariharan, Harry; Matthew, Vanessa; Fountain, Jacqueline; Snell, Alicia; Doherty, Devin; King, Brittany; Shemer, Eran; Oliveira, Simone; Sharma, Ravindra N

    2011-03-01

    In a 2-year period 54 feral cats were captured in Grenada, West Indies, and a total of 383 samples consisting of swabs from rectum, vagina, ears, eyes, mouth, nose and wounds/abscesses, were cultured for aerobic bacteria and campylobacters. A total of 251 bacterial isolates were obtained, of which 205 were identified to species level and 46 to genus level. A commercial bacterial identification system (API/Biomerieux), was used for this purpose. The most common species was Escherichia coli (N=60), followed by Staphylococcus felis/simulans (40), S. hominis (16), S. haemolyticus (12), Streptococcus canis (9), Proteus mirabilis (8), Pasteurella multocida (7), Streptococcus mitis (7), Staphylococcus xylosus (7), S. capitis (6), S. chromogenes (4), S. sciuri (3), S. auricularis (2), S. lentus (2), S. hyicus (2), Streptococcus suis (2) and Pseudomonas argentinensis (2). Sixteen other isolates were identified to species level. A molecular method using 16S rRNA sequencing was used to confirm/identify 22 isolates. Salmonella or campylobacters were not isolated from rectal swabs. E. coli and S. felis/simulans together constituted 50% of isolates from vagina. S. felis/simulans was the most common species from culture positive ear and eye samples. P. multocida was isolated from 15% of mouth samples. Coagulase-negative staphylococci were the most common isolates from nose and wound swabs. Staphylococcus aureus, or S. intemedius/S. pseudintermedius were not isolated from any sample. Antimicrobial drug resistance was minimal, most isolates being susceptible to all drugs tested against, including tetracycline. Copyright © 2010 Elsevier Ltd. All rights reserved.

  6. 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

  7. 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.

  8. 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.

  9. 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

  10. Old Yellow Enzyme from Trypanosoma cruzi Exhibits In Vivo Prostaglandin F2α Synthase Activity and Has a Key Role in Parasite Infection and Drug Susceptibility

    Directory of Open Access Journals (Sweden)

    Florencia Díaz-Viraqué

    2018-03-01

    Full Text Available The discovery that trypanosomatids, unicellular organisms of the order Kinetoplastida, are capable of synthesizing prostaglandins raised questions about the role of these molecules during parasitic infections. Multiple studies indicate that prostaglandins could be related to the infection processes and pathogenesis in trypanosomatids. This work aimed to unveil the role of the prostaglandin F2α synthase TcOYE in the establishment of Trypanosoma cruzi infection, the causative agent of Chagas disease. This chronic disease affects several million people in Latin America causing high morbidity and mortality. Here, we propose a prokaryotic evolutionary origin for TcOYE, and then we used in vitro and in vivo experiments to show that T. cruzi prostaglandin F2α synthase plays an important role in modulating the infection process. TcOYE overexpressing parasites were less able to complete the infective cycle in cell culture infections and increased cardiac tissue parasitic load in infected mice. Additionally, parasites overexpressing the enzyme increased PGF2α synthesis from arachidonic acid. Finally, an increase in benznidazole and nifurtimox susceptibility in TcOYE overexpressing parasites showed its participation in activating the currently anti-chagasic drugs, which added to its observed ability to confer resistance to hydrogen peroxide, highlights the relevance of this enzyme in multiple events including host–parasite interaction.

  11. Prenatal stress alters progestogens to mediate susceptibility to sex-typical, stress-sensitive disorders, such as drug abuse: a review

    Directory of Open Access Journals (Sweden)

    Cheryl A Frye

    2011-10-01

    Full Text Available Maternal-offspring interactions begin prior to birth. Experiences of the mother during gestation play a powerful role in determining the developmental programming of the central nervous system. In particular, stress during gestation alters developmental programming of the offspring resulting in susceptibility to sex-typical and stress-sensitive neurodevelopmental, neuropsychiatric and neurodegenerative disorders. However, neither these effects, nor the underlying mechanisms, are well understood. Our hypothesis is that allopregnanolone, during gestation, plays a particularly vital role in mitigating effects of stress on the developing fetus and may mediate, in part, alterations apparent throughout the lifespan. Specifically, altered balance between glucocorticoids and progestogens during critical periods of development (stemming from psychological, immunological, and/or endocrinological stressors during gestation may permanently influence behavior, brain morphology, and/or neuroendocrine-sensitive processes. 5α-reduced progestogens are integral in the developmental programming of sex-typical, stress-sensitive, and/or disorder-relevant phenotypes. Prenatal stress may alter these responses and dysregulate allopregnanolone and its normative effects on stress axis function. As an example of a neurodevelopmental, neuropsychiatric and/or neurodegenerative process, this review focuses on responsiveness to drugs of abuse, which is sensitive to prenatal stress and progestogen milieu. This review explores the notion that allopregnanolone may effect, or be influenced by, prenatal stress, with consequences for neurodevelopmental-, neuropsychiatric- and/or neurodegenerative- relevant processes, such as addiction.

  12. Evaluation of Microscopic Observation Drug Susceptibility (MODS) and the string test for rapid diagnosis of pulmonary tuberculosis in HIV/AIDS patients in Bolivia.

    Science.gov (United States)

    Lora, Meredith H; Reimer-McAtee, Melissa J; Gilman, Robert H; Lozano, Daniel; Saravia, Ruth; Pajuelo, Monica; Bern, Caryn; Castro, Rosario; Espinoza, Magaly; Vallejo, Maya; Solano, Marco; Challapa, Roxana; Torrico, Faustino

    2015-06-06

    Tuberculosis (TB) is the most common opportunistic infection and the leading cause of death in HIV-positive people worldwide. Diagnosing TB is difficult, and is more challenging in resource-scarce settings where culture-based diagnostic methods rely on poorly sensitive smear microscopy by Ziehl-Neelsen stain (ZN). We performed a cross-sectional study examining the diagnostic utility of Microscopic Observation Drug Susceptibility liquid culture (MODS) versus traditional Ziehl-Neelsen staining (ZN) and Lowenstein Jensen culture (LJ) of pulmonary tuberculosis (TB) and multidrug-resistant tuberculosis (MDRTB) in HIV-infected patients in Bolivia. For sputum scarce individuals we assessed the value of the string test and induced sputum for TB diagnosis. The presence of Mycobacterium tuberculosis (Mtb) in the sputum of 107 HIV-positive patients was evaluated by ZN, LJ, and MODS. Gastric secretion samples obtained by the string test were evaluated by MODS in 102 patients. The TB-HIV co-infection rate of HIV patients with respiratory symptoms by sputum sample was 45 % (48/107); 46/48 (96 %) were positive by MODS, 38/48 (79 %) by LJ, and 30/48 (63 %) by ZN. The rate of MDRTB was 9 % (4/48). Median time to positive culture was 10 days by MODS versus 34 days by LJ (p Bolivia.

  13. 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,

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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)

  9. Investigation of Susceptibility of Mycobacterium tuberculosis Complex Strains Isolated from Clinical Samples Against the First and Second-Line Anti-tuberculosis Drugs by the Sensititre MycoTB Plate Method

    Directory of Open Access Journals (Sweden)

    Figen KAYSERİLİ ORHAN

    2018-03-01

    Full Text Available Introduction: Phenotypic methods for drug susceptibility testing of Mycobacterium tuberculosis complex (MTC to second-line drugs are not yet standardized. The Sensititre MycoTB Plate is a microtiter plate containing lyophilized antibiotics and configured for determination of MIC to first and second-line anti-tuberculosis drugs. The purpose of this study is to detect the susceptibility rates of MTC strains isolated from patients’ specimens for first and second-line anti-tuberculosis drugs. Materials and Methods: This study included 50 MTC strains isolated from various clinical specimens. Out of the 50 strains, 38 were isolated from sputum, three from cerebrospinal fluid, three from bronchoalveolar lavage, and six from other samples in this study. The susceptibility of strains to anti-tuberculosis drugs were determined by the Sensititre MycoTB Plate Method. Thawed isolates were subcultured, and dilutions were inoculated into MycoTB wells. The results were read at days 7, 14 and 21. Results: At the end of study, out of 50 MTC isolates, 7 (14% showed resistance to Isoniazid (INH, 5 (10% to streptomycin (SM, 4 (8% to ethambutol (EMB, 4 (8% to ethionamide (ETH, 3 (6% to rifampicin (RIF, 3 (6% to rifabutin (RFB, 2 (4% to kanamycin (KAN, 2 (4% to ofloxacin (OFL, 2 (4% to P-aminosalicyclic acid (PAS, 1 (2% to moxiflocacin (MOX, and 1 (2% to cycloserine (CYC. All strains were found sensitive to amikacin while 2 strains (4% were identified as multidrug-resistant tuberculosis (MDR-TB. Thirty-five strains (70% were sensitive to all drugs. Extensively drug resistant tuberculosis (XDR-TB was not determined in this study. Conclusion: This is the first study that tests second line anti-tuberculosis drugs in our location and provides us valuable data regarding MDR-TB and XDR-TB rates. The Sensititre MycoTB Plate Method is a fast, reliable and practical method and can be used to determine the susceptibility of first and second-line anti-tuberculosis drugs.

  10. 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.

  11. 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.

  12. 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

  13. 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

  14. Susceptibilidad "in vitro" de cepas de Cryptococcus a 5 drogas antifungicas "In vitro" susceptibility of Cryptococcus strains to 5 antifungal drugs

    Directory of Open Access Journals (Sweden)

    A. J. Bava

    1989-10-01

    Full Text Available Se estudió la susceptibilidad "in vitro" de 24 cepas de 3 especies del género Cryptococcus a 5 drogas antifúngicas (anfotericina B, 5 fluorocitosina, ketoconazol, itraconazol y miconazol. Las mismas se agruparon según su especie, variedad y origen de aislamiento. Para determinar la concentración inhibitoria mínima (C.I.M. de cada droga se empleó el método de dilución en agar con el medio básico nitrogenado para levaduras, adicionado de glucosa. Se obtuvo además la media geométrica de estos valores para cada grupo y se comparó cada uno de ellos. Los resultados obtenidos fueron homogéneos con la sola excepción de las cepas de Cryptococcus sp (no neoformans, en las cuales se detectaron elevados valores de C.I.M. para la 5 fluorocitosina.A comparative study of the "in vitro" susceptibility of 24 Cryptococcus strains to 5 antifungal drugs (amphotericin B, 5 fluorocytosine, miconazole, itraconazole and ketoconazole, was carried out. These strains were grouped according to species, varieties and isolation's origins. The minimum inhibitory concentration (M.I.C. was determinated by the agar dilution technique in yeast nitrogen base agar with dextrose. The mean geometrical of the M.I.C. values of each group was compared with the others. The results obtained were homogeneous with the only exception of the "non neoformans" strains, in which, higher M.I.C. to 5 fluorocytosine values were detected.

  15. Microscopic observation drug-susceptibility assay vs. Xpert® MTB/RIF for the diagnosis of tuberculosis in a rural African setting: a cost-utility analysis.

    Science.gov (United States)

    Wikman-Jorgensen, Philip E; Llenas-García, Jara; Pérez-Porcuna, Tomàs M; Hobbins, Michael; Ehmer, Jochen; Mussa, Manuel A; Ascaso, Carlos

    2017-06-01

    To compare the cost-utility of microscopic observation drug-susceptibility assay (MODS) and Xpert ® MTB/RIF implementation for tuberculosis (TB) diagnosis in rural northern Mozambique. Stochastic transmission compartmental TB model from the healthcare provider perspective with parameter input from direct measurements, systematic literature reviews and expert opinion. MODS and Xpert ® MTB/RIF were evaluated as replacement test of smear microscopy (SM) or as an add-on test after a negative SM. Costs were calculated in 2013 USD, effects in disability-adjusted life years (DALY). Willingness to pay threshold (WPT) was established at once the per capita Gross National Income of Mozambique. MODS as an add-on test to negative SM produced an incremental cost-effectiveness ratio (ICER) of 5647.89USD/DALY averted. MODS as a substitute for SM yielded an ICER of 5374.58USD/DALY averted. Xpert ® MTB/RIF as an add-on test to negative SM yielded ICER of 345.71USD/DALY averted. Xpert ® MTB/RIF as a substitute for SM obtained an ICER of 122.13USD/DALY averted. TB prevalence and risk of infection were the main factors impacting MODS and Xpert ® MTB/RIF ICER in the one-way sensitivity analysis. In the probabilistic sensitivity analysis, Xpert ® MTB/RIF was most likely to have an ICER below the WPT, whereas MODS was not. Our cost-utility analysis favours the implementation of Xpert ® MTB/RIF as a replacement of SM for all TB suspects in this rural high TB/HIV prevalence African setting. © 2017 John Wiley & Sons Ltd.

  16. A functional polymorphism of the microRNA-146a gene is associated with susceptibility to drug-resistant epilepsy and seizures frequency.

    Science.gov (United States)

    Cui, Lili; Tao, Hua; Wang, Yan; Liu, Zhou; Xu, Zhien; Zhou, Haihong; Cai, Yujie; Yao, Lifen; Chen, Beichu; Liang, Wandong; Liu, Yu; Cheng, Wanwen; Liu, Tingting; Ma, Guoda; Li, You; Zhao, Bin; Li, Keshen

    2015-04-01

    Epilepsy is the third most common chronic brain disorder and is characterized by an enduring predisposition for seizures. Recently, a growing body of evidence has suggested that microRNA-146a (miR-146a) is upregulated in the brains of epilepsy patients and of mouse models; furthermore, miR-146a may be involved in the development and progression of seizures through the regulation of inflammation and immune responses. In this report, we performed a case-control study to analyze the relationship between the two potentially functional single nucleotide polymorphisms (SNPs) of the miR-146a gene (rs2910464 and rs57095329) and the risk of epilepsy in a Chinese population comprising 249 cases and 249 healthy controls. Our study comprised 249 epilepsy patients and 249 healthy controls in two regions of China. The DNA was genotyped using the ABI PRISM SNapShot method. The statistical analysis was estimated using the chi-square test or Fisher's exact test. Our results indicated a significant association between the rs57095329 SNP of the miR-146a gene and the risk of drug resistant epilepsy (DRE) (genotypes, p = 0.0258 and alleles, p = 0.0108). Moreover, the rs57095329 A allele was found to be associated with a reduced risk of seizures frequency in DRE patients (all p epilepsy. Our data indicate that the rs57095329 polymorphism in the promoter region of miR-146a is involved in the genetic susceptibility to DRE and the seizures frequency. Copyright © 2015 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  17. Isolation and antimicrobial drug susceptibility pattern of bacterial pathogens from pediatric patients with otitis media in selected health institutions, Addis Ababa, Ethiopia: a prospective cross-sectional study.

    Science.gov (United States)

    Hailegiyorgis, Tewodros Tesfa; Sarhie, Wondemagegn Demsiss; Workie, Hailemariam Mekonnen

    2018-01-01

    Trimethoprim-Sulfamethoxazole, Penicillin G, Ampicillin, Amoxicillin, and Chloramphenicol. S. aureus and P. aeruginosa are the most common pathogens that contribute to otitis media as well most of the isolates show a high level of resistance to commonly used drugs to treat otitis media. Therefore, culture and susceptibility testes have paramount importance for the better management of otitis media and drug-resistant infections.

  18. Identification of drug susceptibility pattern and mycobacterial species in sputum smear positive pulmonary tuberculosis patients with and without HIV co-infection in north west Ethiopia

    DEFF Research Database (Denmark)

    Mekonen, Mekdem; Abate, Ebba; Aseffa, Abraham

    2010-01-01

    Ethiopia is among the high-burden countries of tuberculosis (TB) in the world Since mycobacterial culture and susceptibility testing are not routinely performed in Ethiopia, recent data on susceptibility patterns and the mycobacterial species cultured from sputum smear positive patients are limited....

  19. 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

  20. 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.

  1. 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.

  2. Susceptibility Testing

    Science.gov (United States)

    ... Marker Bicarbonate (Total CO2) Bilirubin Blood Culture Blood Gases Blood Ketones Blood Smear Blood Typing Blood Urea ... hours depending on the method used. There are commercial tests available that offer rapid susceptibility testing and ...

  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. Bacteriuria and antimicrobial susceptibility pattern of bacterial ...

    African Journals Online (AJOL)

    Bacterial isolates and drug susceptibility patterns of urinary tract infection among ... Key words: Urinary tract infection, pregnant women, antimicrobial drug ..... and premature labour as well as adverse outcome for the unborn child (Raz, 2003).

  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. Cost-effectiveness analysis of microscopic observation drug susceptibility test versus Xpert MTB/Rif test for diagnosis of pulmonary tuberculosis in HIV patients in Uganda.

    Science.gov (United States)

    Walusimbi, Simon; Kwesiga, Brendan; Rodrigues, Rashmi; Haile, Melles; de Costa, Ayesha; Bogg, Lennart; Katamba, Achilles

    2016-10-10

    Microscopic Observation Drug Susceptibility (MODS) and Xpert MTB/Rif (Xpert) are highly sensitive tests for diagnosis of pulmonary tuberculosis (PTB). This study evaluated the cost effectiveness of utilizing MODS versus Xpert for diagnosis of active pulmonary TB in HIV infected patients in Uganda. A decision analysis model comparing MODS versus Xpert for TB diagnosis was used. Costs were estimated by measuring and valuing relevant resources required to perform the MODS and Xpert tests. Diagnostic accuracy data of the tests were obtained from systematic reviews involving HIV infected patients. We calculated base values for unit costs and varied several assumptions to obtain the range estimates. Cost effectiveness was expressed as costs per TB patient diagnosed for each of the two diagnostic strategies. Base case analysis was performed using the base estimates for unit cost and diagnostic accuracy of the tests. Sensitivity analysis was performed using a range of value estimates for resources, prevalence, number of tests and diagnostic accuracy. The unit cost of MODS was US$ 6.53 versus US$ 12.41 of Xpert. Consumables accounted for 59 % (US$ 3.84 of 6.53) of the unit cost for MODS and 84 % (US$10.37 of 12.41) of the unit cost for Xpert. The cost effectiveness ratio of the algorithm using MODS was US$ 34 per TB patient diagnosed compared to US$ 71 of the algorithm using Xpert. The algorithm using MODS was more cost-effective compared to the algorithm using Xpert for a wide range of different values of accuracy, cost and TB prevalence. The cost (threshold value), where the algorithm using Xpert was optimal over the algorithm using MODS was US$ 5.92. MODS versus Xpert was more cost-effective for the diagnosis of PTB among HIV patients in our setting. Efforts to scale-up MODS therefore need to be explored. However, since other non-economic factors may still favour the use of Xpert, the current cost of the Xpert cartridge still needs to be reduced further by more than

  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. 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. 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.

  16. 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.

  17. 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.

  18. 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

  19. 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...

  20. 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

  1. 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).

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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...

  10. 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.

  11. 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

  12. 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 sy