Applied metabolomics in drug discovery.

Science.gov (United States)

Cuperlovic-Culf, M; Culf, A S

2016-08-01

The metabolic profile is a direct signature of phenotype and biochemical activity following any perturbation. Metabolites are small molecules present in a biological system including natural products as well as drugs and their metabolism by-products depending on the biological system studied. Metabolomics can provide activity information about possible novel drugs and drug scaffolds, indicate interesting targets for drug development and suggest binding partners of compounds. Furthermore, metabolomics can be used for the discovery of novel natural products and in drug development. Metabolomics can enhance the discovery and testing of new drugs and provide insight into the on- and off-target effects of drugs. This review focuses primarily on the application of metabolomics in the discovery of active drugs from natural products and the analysis of chemical libraries and the computational analysis of metabolic networks. Metabolomics methodology, both experimental and analytical is fast developing. At the same time, databases of compounds are ever growing with the inclusion of more molecular and spectral information. An increasing number of systems are being represented by very detailed metabolic network models. Combining these experimental and computational tools with high throughput drug testing and drug discovery techniques can provide new promising compounds and leads.

Computational methods in drug discovery

OpenAIRE

Sumudu P. Leelananda; Steffen Lindert

2016-01-01

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery project...

Drug-diagnostics co-development in oncology

Directory of Open Access Journals (Sweden)

Richard eSimon

2013-12-01

Full Text Available Developments in genomics are providing a biological basis for the heterogeneity of clinical course and response to treatment that have long been apparent to clinicians The ability to molecularly characterize of human diseases presents new opportunities to develop more effective treatments and new challenges for the design and analysis of clinical trials.In oncology, treatment of broad populations with regimens that benefit a minority of patients is less economically sustainable with expensive molecularly targeted therapeutics. The established molecular heterogeneity of human diseases requires the development of new paradigms for the design and analysis of randomized clinical trials as a reliable basis for predictive medicine. We review prospective designs for the development of new therapeutics and predictive biomarkers to inform their use. We cover designs for a wide range of settings. At one extreme is the development of a new drug with a single candidate biomarker and strong biological evidence that marker negative patients are unlikely to benefit from the new drug. At the other extreme are phase III clinical trials involving both genome-wide discovery of a predictive classifier and internal validation of that classifier. We have outlined a prediction based approach to the analysis of randomized clinical trials that both preserves the type I error and provides a reliable internally validated basis for predicting which patients are most likely or unlikely to benefit from a new regimen.

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

Directory of Open Access Journals (Sweden)

Nicholas Ekow Thomford

2018-05-01

Full Text Available The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of “active compound” has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of ‘organ-on chip’ and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery.

Science.gov (United States)

Thomford, Nicholas Ekow; Senthebane, Dimakatso Alice; Rowe, Arielle; Munro, Daniella; Seele, Palesa; Maroyi, Alfred; Dzobo, Kevin

2018-05-25

The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review

Toxins and drug discovery.

Science.gov (United States)

Harvey, Alan L

2014-12-15

Components from venoms have stimulated many drug discovery projects, with some notable successes. These are briefly reviewed, from captopril to ziconotide. However, there have been many more disappointments on the road from toxin discovery to approval of a new medicine. Drug discovery and development is an inherently risky business, and the main causes of failure during development programmes are outlined in order to highlight steps that might be taken to increase the chances of success with toxin-based drug discovery. These include having a clear focus on unmet therapeutic needs, concentrating on targets that are well-validated in terms of their relevance to the disease in question, making use of phenotypic screening rather than molecular-based assays, and working with development partners with the resources required for the long and expensive development process. Copyright © 2014 The Author. Published by Elsevier Ltd.. All rights reserved.

Bioinformatics in translational drug discovery.

Science.gov (United States)

Wooller, Sarah K; Benstead-Hume, Graeme; Chen, Xiangrong; Ali, Yusuf; Pearl, Frances M G

2017-08-31

Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications. © 2017 The Author(s).

Regulatory aspects of oncology drug safety evaluation: Past practice, current issues, and the challenge of new drugs

International Nuclear Information System (INIS)

Rosenfeldt, Hans; Kropp, Timothy; Benson, Kimberly; Ricci, M. Stacey; McGuinn, W. David; Verbois, S. Leigh

2010-01-01

The drug development of new anti-cancer agents is streamlined in response to the urgency of bringing effective drugs to market for patients with limited life expectancy. FDA's regulation of oncology drugs has evolved from the practices set forth in Arnold Lehman's seminal work published in the 1950s through the current drafting of a new International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) safety guidance for anti-cancer drug nonclinical evaluations. The ICH combines the efforts of the regulatory authorities of Europe, Japan, and the United States and the pharmaceutical industry from these three regions to streamline the scientific and technical aspects of drug development. The recent development of new oncology drug classes with novel mechanisms of action has improved survival rates for some cancers but also brings new challenges for safety evaluation. Here we present the legacy of Lehman and colleagues in the context of past and present oncology drug development practices and focus on some of the current issues at the center of an evolving harmonization process that will generate a new safety guidance for oncology drugs, ICH S9. The purpose of this new guidance will be to facilitate oncology drug development on a global scale by standardizing regional safety requirements.

Observational study of drug-drug interactions in oncological inpatients

Directory of Open Access Journals (Sweden)

María Sacramento Díaz-Carrasco

2018-01-01

Full Text Available Objective: To determine the prevalence of potential clinically relevant drug- drug interactions in adult oncological inpatients, as well as to describe the most frequent interactions. A standard database was used. Method: An observational, transversal, and descriptive study including patients admitted to the Oncology Service of a reference hospital. All prescriptions were collected twice a week during a month. They were analysed using Lexicomp® database, recording all interactions classified with a level of risk: C, D or X. Results: A total of 1 850 drug-drug interactions were detected in 218 treatments. The prevalence of treatments with at least one clinically relevant interaction was 95%, being 94.5% for those at level C and 26.1% for levels D and X. The drugs most commonly involved in the interactions detected were opioid analgesics, antipsychotics (butyrophenones, benzodiazepines, pyrazolones, glucocorticoids and heparins, whereas interactions with antineoplastics were minimal, highlighting those related to paclitaxel and between metamizole and various antineoplastics. Conclusions: The prevalence of clinically relevant drug-drug interactions rate was very high, highlighting the high risk percentage of them related to level of risk X. Due to the frequency of onset and potential severity, highlighted the concomitant use of central nervous system depressants drugs with risk of respiratory depression, the risk of onset of anticholinergic symptoms when combining morphine or haloperidol with butylscopolamine, ipratropium bromide or dexchlorpheniramine and the multiple interactions involving metamizole.

Oncology drugs for orphan indications: how are HTA processes evolving for this specific drug category?

Science.gov (United States)

Adkins, Elizabeth M; Nicholson, Lindsay; Floyd, David; Ratcliffe, Mark; Chevrou-Severac, Helene

2017-01-01

Orphan drugs (ODs) are intended for the diagnosis, prevention, or treatment of rare diseases. Many cancer subtypes, including all childhood cancers, are defined as rare diseases, and over one-third of ODs are now intended to treat oncology indications. However, market access for oncology ODs is becoming increasingly challenging; ODs are prone to significant uncertainty around their cost-effectiveness, while payers must balance the need for these vital innovations with growing sensitivity to rising costs. The objective of this review was to evaluate different mechanisms that have been introduced to facilitate patient access to oncology ODs in five different countries (Australia, Canada, England, France, and Sweden), using eight oncology ODs and non-orphan oncology drugs as examples of their application. A targeted literature review of health technology assessment (HTA) agency websites was undertaken to identify country-specific guidance and HTA documentation for recently evaluated oncology ODs and non-orphan oncology drugs. None of these countries were found to have explicit HTA criteria for the assessment of ODs, and therefore, oncology ODs are assessed through the usual HTA process. However, distinct and additional processes are adopted to facilitate access to oncology ODs. Review of eight case-study drugs showed that these additional assessment processes were rarely used, and decisions were largely driven by proving cost-effectiveness using standard incremental cost-effectiveness ratio (ICER) thresholds. The predominant implication arising from this study is that application of standard HTA criteria to oncology ODs in many countries fails to take into account any uncertainties around their clinical- and cost-effectiveness, resulting in disparities in HTA reimbursement decisions based on differences in addressing or accepting uncertainty. In order to address this issue, HTA agencies should adopt a more flexible approach to cost-effectiveness, as typified by the

Maximum Entropy in Drug Discovery

Directory of Open Access Journals (Sweden)

Chih-Yuan Tseng

2014-07-01

Full Text Available Drug discovery applies multidisciplinary approaches either experimentally, computationally or both ways to identify lead compounds to treat various diseases. While conventional approaches have yielded many US Food and Drug Administration (FDA-approved drugs, researchers continue investigating and designing better approaches to increase the success rate in the discovery process. In this article, we provide an overview of the current strategies and point out where and how the method of maximum entropy has been introduced in this area. The maximum entropy principle has its root in thermodynamics, yet since Jaynes’ pioneering work in the 1950s, the maximum entropy principle has not only been used as a physics law, but also as a reasoning tool that allows us to process information in hand with the least bias. Its applicability in various disciplines has been abundantly demonstrated. We give several examples of applications of maximum entropy in different stages of drug discovery. Finally, we discuss a promising new direction in drug discovery that is likely to hinge on the ways of utilizing maximum entropy.

The Medicaid Rebate: Changes in Oncology Drug Prices After the Affordable Care Act.

Science.gov (United States)

Bonakdar Tehrani, Ali; Carroll, Norman V

2017-08-01

Prescription drug spending is a significant component of Medicaid total expenditures. The Affordable Care Act (ACA) includes a provision that increases the Medicaid rebate for both brand-name and generic drugs. This study examines the extent to which oncology drug prices changed after the increase in the Medicaid rebate in 2010. A pre-post study design was used to evaluate the correlation between the Medicaid rebate increase and oncology drug prices after 2010 using 2006-2013 State Drug Utilization Data. The results show that the average annual price of top-selling cancer drugs in 2006, adjusted for inflation and secular changes in drug prices, have increased by US$154 and US$235 for branded and competitive brand drugs, respectively, following the 2010 ACA; however, generic oncology drug prices showed no significant changes. The findings from this study indicate that oncology drug prices have increased after the 2010 ACA, and suggest that pharmaceutical companies may have increased their drug prices to offset increases in Medicaid rebates.

78 FR 12762 - Joint Meeting of the Medical Imaging Drugs Advisory Committee and the Oncologic Drugs Advisory...

Science.gov (United States)

2013-02-25

...] Joint Meeting of the Medical Imaging Drugs Advisory Committee and the Oncologic Drugs Advisory Committee... be open to the public. Name of Committees: Medical Imaging Drugs Advisory Committee and the Oncologic... Special Medical Programs. [FR Doc. 2013-04141 Filed 2-22-13; 8:45 am] BILLING CODE 4160-01-P ...

The Value of Specialty Oncology Drugs

Science.gov (United States)

Goldman, Dana P; Jena, Anupam B; Lakdawalla, Darius N; Malin, Jennifer L; Malkin, Jesse D; Sun, Eric

2010-01-01

Objective To estimate patients' elasticity of demand, willingness to pay, and consumer surplus for five high-cost specialty medications treating metastatic disease or hematologic malignancies. Data Source/Study Setting Claims data from 71 private health plans from 1997 to 2005. Study Design This is a revealed preference analysis of the demand for specialty drugs among cancer patients. We exploit differences in plan generosity to examine how utilization of specialty oncology drugs varies with patient out-of-pocket costs. Data Collection/Extraction Methods We extracted key variables from administrative health insurance claims records. Principal Findings A 25 percent reduction in out-of-pocket costs leads to a 5 percent increase in the probability that a patient initiates specialty cancer drug therapy. Among patients who initiate, a 25 percent reduction in out-of-pocket costs reduces the number of treatments (claims) by 1–3 percent, depending on the drug. On average, the value of these drugs to patients who use them is about four times the total cost paid by the patient and his or her insurer, although this ratio may be lower for oral specialty therapies. Conclusions The decision to initiate therapy with specialty oncology drugs is responsive to price, but not highly so. Among patients who initiate therapy, the amount of treatment is equally responsive. The drugs we examine are highly valued by patients in excess of their total costs, although oral agents warrant further scrutiny as copayments increase. PMID:19878344

Deep Learning in Drug Discovery.

Science.gov (United States)

Gawehn, Erik; Hiss, Jan A; Schneider, Gisbert

2016-01-01

Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Solution NMR Spectroscopy in Target-Based Drug Discovery.

Science.gov (United States)

Li, Yan; Kang, Congbao

2017-08-23

Solution NMR spectroscopy is a powerful tool to study protein structures and dynamics under physiological conditions. This technique is particularly useful in target-based drug discovery projects as it provides protein-ligand binding information in solution. Accumulated studies have shown that NMR will play more and more important roles in multiple steps of the drug discovery process. In a fragment-based drug discovery process, ligand-observed and protein-observed NMR spectroscopy can be applied to screen fragments with low binding affinities. The screened fragments can be further optimized into drug-like molecules. In combination with other biophysical techniques, NMR will guide structure-based drug discovery. In this review, we describe the possible roles of NMR spectroscopy in drug discovery. We also illustrate the challenges encountered in the drug discovery process. We include several examples demonstrating the roles of NMR in target-based drug discoveries such as hit identification, ranking ligand binding affinities, and mapping the ligand binding site. We also speculate the possible roles of NMR in target engagement based on recent processes in in-cell NMR spectroscopy.

Arthritis Genetics Analysis Aids Drug Discovery

Science.gov (United States)

... NIH Research Matters January 13, 2014 Arthritis Genetics Analysis Aids Drug Discovery An international research team identified 42 new ... Edition Distracted Driving Raises Crash Risk Arthritis Genetics Analysis Aids Drug Discovery Oxytocin Affects Facial Recognition Connect with Us ...

Open source drug discovery--a new paradigm of collaborative research in tuberculosis drug development.

Science.gov (United States)

Bhardwaj, Anshu; Scaria, Vinod; Raghava, Gajendra Pal Singh; Lynn, Andrew Michael; Chandra, Nagasuma; Banerjee, Sulagna; Raghunandanan, Muthukurussi V; Pandey, Vikas; Taneja, Bhupesh; Yadav, Jyoti; Dash, Debasis; Bhattacharya, Jaijit; Misra, Amit; Kumar, Anil; Ramachandran, Srinivasan; Thomas, Zakir; Brahmachari, Samir K

2011-09-01

It is being realized that the traditional closed-door and market driven approaches for drug discovery may not be the best suited model for the diseases of the developing world such as tuberculosis and malaria, because most patients suffering from these diseases have poor paying capacity. To ensure that new drugs are created for patients suffering from these diseases, it is necessary to formulate an alternate paradigm of drug discovery process. The current model constrained by limitations for collaboration and for sharing of resources with confidentiality hampers the opportunities for bringing expertise from diverse fields. These limitations hinder the possibilities of lowering the cost of drug discovery. The Open Source Drug Discovery project initiated by Council of Scientific and Industrial Research, India has adopted an open source model to power wide participation across geographical borders. Open Source Drug Discovery emphasizes integrative science through collaboration, open-sharing, taking up multi-faceted approaches and accruing benefits from advances on different fronts of new drug discovery. Because the open source model is based on community participation, it has the potential to self-sustain continuous development by generating a storehouse of alternatives towards continued pursuit for new drug discovery. Since the inventions are community generated, the new chemical entities developed by Open Source Drug Discovery will be taken up for clinical trial in a non-exclusive manner by participation of multiple companies with majority funding from Open Source Drug Discovery. This will ensure availability of drugs through a lower cost community driven drug discovery process for diseases afflicting people with poor paying capacity. Hopefully what LINUX the World Wide Web have done for the information technology, Open Source Drug Discovery will do for drug discovery. Copyright © 2011 Elsevier Ltd. All rights reserved.

The in silico drug discovery toolbox: applications in lead discovery and optimization.

Science.gov (United States)

Bruno, Agostino; Costantino, Gabriele; Sartori, Luca; Radi, Marco

2017-11-06

Discovery and development of a new drug is a long lasting and expensive journey that takes around 15 years from starting idea to approval and marketing of new medication. Despite the R&D expenditures have been constantly increasing in the last few years, number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. From this point of view, it is clear that if we want to increase drug-discovery success rate and reduce costs associated with development of a new drug, a comprehensive evaluation/prediction of potential safety issues should be conducted as soon as possible during early drug discovery phase. In the present review, we will analyse the early steps of drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Introduction to fragment-based drug discovery.

Science.gov (United States)

Erlanson, Daniel A

2012-01-01

Fragment-based drug discovery (FBDD) has emerged in the past decade as a powerful tool for discovering drug leads. The approach first identifies starting points: very small molecules (fragments) that are about half the size of typical drugs. These fragments are then expanded or linked together to generate drug leads. Although the origins of the technique date back some 30 years, it was only in the mid-1990s that experimental techniques became sufficiently sensitive and rapid for the concept to be become practical. Since that time, the field has exploded: FBDD has played a role in discovery of at least 18 drugs that have entered the clinic, and practitioners of FBDD can be found throughout the world in both academia and industry. Literally dozens of reviews have been published on various aspects of FBDD or on the field as a whole, as have three books (Jahnke and Erlanson, Fragment-based approaches in drug discovery, 2006; Zartler and Shapiro, Fragment-based drug discovery: a practical approach, 2008; Kuo, Fragment based drug design: tools, practical approaches, and examples, 2011). However, this chapter will assume that the reader is approaching the field with little prior knowledge. It will introduce some of the key concepts, set the stage for the chapters to follow, and demonstrate how X-ray crystallography plays a central role in fragment identification and advancement.

77 FR 31025 - Oncologic Drugs Advisory Committee; Notice of Meeting

Science.gov (United States)

2012-05-24

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2012-N-0001] Oncologic Drugs Advisory Committee; Notice of Meeting AGENCY: Food and Drug Administration, HHS. ACTION: Notice. This notice announces a forthcoming meeting of a public advisory committee of the Food and Drug...

77 FR 58399 - Oncologic Drugs Advisory Committee; Notice of Meeting

Science.gov (United States)

2012-09-20

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2012-N-0001] Oncologic Drugs Advisory Committee; Notice of Meeting AGENCY: Food and Drug Administration, HHS. ACTION: Notice. This notice announces a forthcoming meeting of a public advisory committee of the Food and Drug...

78 FR 13348 - Oncologic Drugs Advisory Committee; Notice of Meeting

Science.gov (United States)

2013-02-27

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2013-N-0001] Oncologic Drugs Advisory Committee; Notice of Meeting AGENCY: Food and Drug Administration, HHS. ACTION: Notice. This notice announces a forthcoming meeting of a public advisory committee of the Food and Drug...

77 FR 32125 - Oncologic Drugs Advisory Committee; Notice of Meeting

Science.gov (United States)

2012-05-31

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2012-N-0001] Oncologic Drugs Advisory Committee; Notice of Meeting AGENCY: Food and Drug Administration, HHS. ACTION: Notice. This notice announces a forthcoming meeting of a public advisory committee of the Food and Drug...

75 FR 9419 - Oncologic Drugs Advisory Committee; Notice of Meeting

Science.gov (United States)

2010-03-02

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2010-N-0001] Oncologic Drugs Advisory Committee; Notice of Meeting AGENCY: Food and Drug Administration, HHS. ACTION: Notice. This notice announces a forthcoming meeting of a public advisory committee of the Food and Drug...

78 FR 48690 - Oncologic Drugs Advisory Committee; Notice of Meeting

Science.gov (United States)

2013-08-09

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2013-N-0001] Oncologic Drugs Advisory Committee; Notice of Meeting AGENCY: Food and Drug Administration, HHS. ACTION: Notice. This notice announces a forthcoming meeting of a public advisory committee of the Food and Drug...

77 FR 5813 - Oncologic Drugs Advisory Committee; Notice of Meeting

Science.gov (United States)

2012-02-06

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2012-N-0001] Oncologic Drugs Advisory Committee; Notice of Meeting AGENCY: Food and Drug Administration, HHS. ACTION: Notice. This notice announces a forthcoming meeting of a public advisory committee of the Food and Drug...

75 FR 71450 - Oncologic Drugs Advisory Committee; Amendment of Notice

Science.gov (United States)

2010-11-23

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2010-N-0001] Oncologic Drugs Advisory Committee; Amendment of Notice AGENCY: Food and Drug Administration, HHS. ACTION: Notice. SUMMARY: The Food and Drug Administration (FDA) is announcing an amendment to the notice of a...

76 FR 82309 - Oncologic Drugs Advisory Committee; Notice of Meeting

Science.gov (United States)

2011-12-30

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2011-N-0002] Oncologic Drugs Advisory Committee; Notice of Meeting AGENCY: Food and Drug Administration, HHS. ACTION: Notice. This notice announces a forthcoming meeting of a public advisory committee of the Food and Drug...

76 FR 11489 - Oncologic Drugs Advisory Committee; Notice of Meeting

Science.gov (United States)

2011-03-02

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2011-N-0002] Oncologic Drugs Advisory Committee; Notice of Meeting AGENCY: Food and Drug Administration, HHS. ACTION: Notice. This notice announces a forthcoming meeting of a public advisory committee of the Food and Drug...

77 FR 25184 - Oncologic Drugs Advisory Committee; Notice of Meeting

Science.gov (United States)

2012-04-27

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2012-N-0001] Oncologic Drugs Advisory Committee; Notice of Meeting AGENCY: Food and Drug Administration, HHS. ACTION: Notice. This notice announces a forthcoming meeting of a public advisory committee of the Food and Drug...

76 FR 44595 - Oncologic Drugs Advisory Committee; Notice of Meeting

Science.gov (United States)

2011-07-26

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2011-N-0002] Oncologic Drugs Advisory Committee; Notice of Meeting AGENCY: Food and Drug Administration, HHS. ACTION: Notice. This notice announces a forthcoming meeting of a public advisory committee of the Food and Drug...

76 FR 82310 - Oncologic Drugs Advisory Committee; Notice of Meeting

Science.gov (United States)

2011-12-30

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2011-N-0002] Oncologic Drugs Advisory Committee; Notice of Meeting AGENCY: Food and Drug Administration, HHS. ACTION: Notice. This notice announces a forthcoming meeting of a public advisory committee of the Food and Drug...

76 FR 65736 - Oncologic Drugs Advisory Committee; Notice of Meeting

Science.gov (United States)

2011-10-24

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2011-N-0002] Oncologic Drugs Advisory Committee; Notice of Meeting AGENCY: Food and Drug Administration, HHS. ACTION: Notice. This notice announces a forthcoming meeting of a public advisory committee of the Food and Drug...

75 FR 75680 - Oncologic Drugs Advisory Committee; Notice of Meeting

Science.gov (United States)

2010-12-06

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2010-N-0001] Oncologic Drugs Advisory Committee; Notice of Meeting AGENCY: Food and Drug Administration, HHS. ACTION: Notice. This notice announces a forthcoming meeting of a public advisory committee of the Food and Drug...

Virtual drug discovery: beyond computational chemistry?

Science.gov (United States)

Gilardoni, Francois; Arvanites, Anthony C

2010-02-01

This editorial looks at how a fully integrated structure that performs all aspects in the drug discovery process, under one company, is slowly disappearing. The steps in the drug discovery paradigm have been slowly increasing toward virtuality or outsourcing at various phases of product development in a company's candidate pipeline. Each step in the process, such as target identification and validation and medicinal chemistry, can be managed by scientific teams within a 'virtual' company. Pharmaceutical companies to biotechnology start-ups have been quick in adopting this new research and development business strategy in order to gain flexibility, access the best technologies and technical expertise, and decrease product developmental costs. In today's financial climate, the term virtual drug discovery has an organizational meaning. It represents the next evolutionary step in outsourcing drug development.

77 FR 37911 - Oncologic Drugs Advisory Committee; Amendment of Notice

Science.gov (United States)

2012-06-25

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2012-N-0001] Oncologic Drugs Advisory Committee; Amendment of Notice AGENCY: Food and Drug Administration, HHS. ACTION: Notice. The Food and Drug Administration (FDA) is announcing an amendment to the notice of meeting of the...

Bridging Adult Experience to Pediatrics in Oncology Drug Development.

Science.gov (United States)

Leong, Ruby; Zhao, Hong; Reaman, Gregory; Liu, Qi; Wang, Yaning; Stewart, Clinton F; Burckart, Gilbert

2017-10-01

Pediatric drug development in the United States has grown under the current regulations made permanent by the Food and Drug Administration Safety and Innovation Act of 2012. Over 1200 pediatric studies have now been submitted to the US FDA, but there is still a high rate of failure to obtain pediatric labeling for the indication pursued. Pediatric oncology represents special problems in that the disease is most often dissimilar to any cancer found in the adult population. Therefore, the development of drug dosing in pediatric oncology patients represents a special challenge. Potential approaches to pediatric dosing in oncology patients include extrapolation of efficacy from adult studies in those few cases where the disease is similar, inclusion of adolescent patients in adult trials when possible, and bridging the adult dose to the pediatric dose. An analysis of the recommended phase 2 dose for 40 molecularly targeted agents in pediatric patients provides some insight into current practices. Increased knowledge of tumor biology and efforts to identify and validate molecular targets and genetic abnormalities that drive childhood cancers can lead to increased opportunities for precision medicine in the treatment of pediatric cancers. © 2017, The American College of Clinical Pharmacology.

Applying genetics in inflammatory disease drug discovery

DEFF Research Database (Denmark)

Folkersen, Lasse; Biswas, Shameek; Frederiksen, Klaus Stensgaard

2015-01-01

, with several notable exceptions, the journey from a small-effect genetic variant to a functional drug has proven arduous, and few examples of actual contributions to drug discovery exist. Here, we discuss novel approaches of overcoming this hurdle by using instead public genetics resources as a pragmatic guide...... alongside existing drug discovery methods. Our aim is to evaluate human genetic confidence as a rationale for drug target selection....

Individualization of anticancer therapy; molecular targets of novel drugs in oncology

Directory of Open Access Journals (Sweden)

Katarzyna Regulska

2012-11-01

Full Text Available Deregulation of cellular signal transduction, caused by gene mutations, has been recognized as a basic factor of cancer initiation, promotion and progression. Thus, the ability to control the activity of overstimulated signal molecules by the use of appropriate inhibitors became the idea of targeted cancer therapy, which has provided an effective tool to normalize the molecular disorders in malignant cells and to treat certain types of cancer. The molecularly targeted drugs are divided into two major pharmaceutical classes: monoclonal antibodies and small-molecule kinase inhibitors. This review presents a summary of their characteristics, analyzing their chemical structures, specified molecular targets, mechanisms of action and indications for use. Also the molecules subjected to preclinical trials or phase I, II and III clinical trials evaluating their efficiency and safety are presented. Moreover, the article discusses further perspectives for development of targeted therapies focusing on three major directions: systematic searching and discovery of new targets that are oncogenic drivers, improving the pharmacological properties of currently known drugs, and developing strategies to overcome drug resistance. Finally, the role of proper pharmacodiagnostics as a key to rational anticancer therapy has been emphasized since the verification of reliable predictive biomarkers is a basis of individualized medicine in oncology. 

Characteristics of potential drug-related problems among oncology patients

NARCIS (Netherlands)

Bulsink, Arjan; Imholz, Alex L. T.; Brouwers, Jacobus R. B. J.; Jansman, Frank G. A.

Background Oncology patients are more at risk for drug related problems because of treatment with (combinations of) anticancer drugs, as they have a higher risk for organ failure or altered metabolism with progression of their disease. Objective The aim of this study was to characterize and to

Orphan diseases: state of the drug discovery art.

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Volmar, Claude-Henry; Wahlestedt, Claes; Brothers, Shaun P

2017-06-01

Since 1983 more than 300 drugs have been developed and approved for orphan diseases. However, considering the development of novel diagnosis tools, the number of rare diseases vastly outpaces therapeutic discovery. Academic centers and nonprofit institutes are now at the forefront of rare disease R&D, partnering with pharmaceutical companies when academic researchers discover novel drugs or targets for specific diseases, thus reducing the failure risk and cost for pharmaceutical companies. Considerable progress has occurred in the art of orphan drug discovery, and a symbiotic relationship now exists between pharmaceutical industry, academia, and philanthropists that provides a useful framework for orphan disease therapeutic discovery. Here, the current state-of-the-art of drug discovery for orphan diseases is reviewed. Current technological approaches and challenges for drug discovery are considered, some of which can present somewhat unique challenges and opportunities in orphan diseases, including the potential for personalized medicine, gene therapy, and phenotypic screening.

Weighted similarity-based clustering of chemical structures and bioactivity data in early drug discovery.

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Perualila-Tan, Nolen Joy; Shkedy, Ziv; Talloen, Willem; Göhlmann, Hinrich W H; Moerbeke, Marijke Van; Kasim, Adetayo

2016-08-01

The modern process of discovering candidate molecules in early drug discovery phase includes a wide range of approaches to extract vital information from the intersection of biology and chemistry. A typical strategy in compound selection involves compound clustering based on chemical similarity to obtain representative chemically diverse compounds (not incorporating potency information). In this paper, we propose an integrative clustering approach that makes use of both biological (compound efficacy) and chemical (structural features) data sources for the purpose of discovering a subset of compounds with aligned structural and biological properties. The datasets are integrated at the similarity level by assigning complementary weights to produce a weighted similarity matrix, serving as a generic input in any clustering algorithm. This new analysis work flow is semi-supervised method since, after the determination of clusters, a secondary analysis is performed wherein it finds differentially expressed genes associated to the derived integrated cluster(s) to further explain the compound-induced biological effects inside the cell. In this paper, datasets from two drug development oncology projects are used to illustrate the usefulness of the weighted similarity-based clustering approach to integrate multi-source high-dimensional information to aid drug discovery. Compounds that are structurally and biologically similar to the reference compounds are discovered using this proposed integrative approach.

RAS - Screens & Assays - Drug Discovery

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The RAS Drug Discovery group aims to develop assays that will reveal aspects of RAS biology upon which cancer cells depend. Successful assay formats are made available for high-throughput screening programs to yield potentially effective drug compounds.

[Artificial Intelligence in Drug Discovery].

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Fujiwara, Takeshi; Kamada, Mayumi; Okuno, Yasushi

2018-04-01

According to the increase of data generated from analytical instruments, application of artificial intelligence(AI)technology in medical field is indispensable. In particular, practical application of AI technology is strongly required in "genomic medicine" and "genomic drug discovery" that conduct medical practice and novel drug development based on individual genomic information. In our laboratory, we have been developing a database to integrate genome data and clinical information obtained by clinical genome analysis and a computational support system for clinical interpretation of variants using AI. In addition, with the aim of creating new therapeutic targets in genomic drug discovery, we have been also working on the development of a binding affinity prediction system for mutated proteins and drugs by molecular dynamics simulation using supercomputer "Kei". We also have tackled for problems in a drug virtual screening. Our developed AI technology has successfully generated virtual compound library, and deep learning method has enabled us to predict interaction between compound and target protein.

Fragment-based drug discovery and molecular docking in drug design.

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Wang, Tao; Wu, Mian-Bin; Chen, Zheng-Jie; Chen, Hua; Lin, Jian-Ping; Yang, Li-Rong

2015-01-01

Fragment-based drug discovery (FBDD) has caused a revolution in the process of drug discovery and design, with many FBDD leads being developed into clinical trials or approved in the past few years. Compared with traditional high-throughput screening, it displays obvious advantages such as efficiently covering chemical space, achieving higher hit rates, and so forth. In this review, we focus on the most recent developments of FBDD for improving drug discovery, illustrating the process and the importance of FBDD. In particular, the computational strategies applied in the process of FBDD and molecular-docking programs are highlighted elaborately. In most cases, docking is used for predicting the ligand-receptor interaction modes and hit identification by structurebased virtual screening. The successful cases of typical significance and the hits identified most recently are discussed.

Outlook of the process of storage of oncology drugs from the logistic operator in Colombia

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Pedro Vicente Sánchez-Calvera

2014-05-01

Full Text Available This article mainly on the problem of  storage of oncology drugs, which is an important element in the supply chain, applied in the context of warehouse management of oncology drugs, in the logistics operator and providers of health services institutions (IPS for its acronym in Spanish of Bogotá DC and in the contex of a macro project "Methodological proposal for the definition of policies , rules of negotation and coordination in supply management of oncology drugs in Colombia," funded by Colciencias and the Universidad Nacional of Colombia. This article presents the current situation of oncology drugs, marking a characterization, diagnosis and identification and startegies evaluated under different scenarios. Finally, it is presented a proposal of improvement applied tranversely to storage processes including labels such as just in Time and the application of information and communication technologies (ICT.

Mathematical modeling for novel cancer drug discovery and development.

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Zhang, Ping; Brusic, Vladimir

2014-10-01

Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments. This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment. Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.

Systems Pharmacology in Small Molecular Drug Discovery

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Wei Zhou

2016-02-01

Full Text Available Drug discovery is a risky, costly and time-consuming process depending on multidisciplinary methods to create safe and effective medicines. Although considerable progress has been made by high-throughput screening methods in drug design, the cost of developing contemporary approved drugs did not match that in the past decade. The major reason is the late-stage clinical failures in Phases II and III because of the complicated interactions between drug-specific, human body and environmental aspects affecting the safety and efficacy of a drug. There is a growing hope that systems-level consideration may provide a new perspective to overcome such current difficulties of drug discovery and development. The systems pharmacology method emerged as a holistic approach and has attracted more and more attention recently. The applications of systems pharmacology not only provide the pharmacodynamic evaluation and target identification of drug molecules, but also give a systems-level of understanding the interaction mechanism between drugs and complex disease. Therefore, the present review is an attempt to introduce how holistic systems pharmacology that integrated in silico ADME/T (i.e., absorption, distribution, metabolism, excretion and toxicity, target fishing and network pharmacology facilitates the discovery of small molecular drugs at the system level.

International Drug Discovery Science and Technology--BIT's Seventh Annual Congress.

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Bodovitz, Steven

2010-01-01

BIT's Seventh Annual International Drug Discovery Science and Technology Congress, held in Shanghai, included topics covering new therapeutic and technological developments in the field of drug discovery. This conference report highlights selected presentations on open-access approaches to R&D, novel and multifactorial targets, and technologies that assist drug discovery. Investigational drugs discussed include the anticancer agents astuprotimut-r (GlaxoSmithKline plc) and AS-1411 (Antisoma plc).

Four disruptive strategies for removing drug discovery bottlenecks.

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Ekins, Sean; Waller, Chris L; Bradley, Mary P; Clark, Alex M; Williams, Antony J

2013-03-01

Drug discovery is shifting focus from industry to outside partners and, in the process, creating new bottlenecks. Technologies like high throughput screening (HTS) have moved to a larger number of academic and institutional laboratories in the USA, with little coordination or consideration of the outputs and creating a translational gap. Although there have been collaborative public-private partnerships in Europe to share pharmaceutical data, the USA has seemingly lagged behind and this may hold it back. Sharing precompetitive data and models may accelerate discovery across the board, while finding the best collaborators, mining social media and mobile approaches to open drug discovery should be evaluated in our efforts to remove drug discovery bottlenecks. We describe four strategies to rectify the current unsustainable situation. Copyright © 2012 Elsevier Ltd. All rights reserved.

"Drug" Discovery with the Help of Organic Chemistry.

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Itoh, Yukihiro; Suzuki, Takayoshi

2017-01-01

The first step in "drug" discovery is to find compounds binding to a potential drug target. In modern medicinal chemistry, the screening of a chemical library, structure-based drug design, and ligand-based drug design, or a combination of these methods, are generally used for identifying the desired compounds. However, they do not necessarily lead to success and there is no infallible method for drug discovery. Therefore, it is important to explore medicinal chemistry based on not only the conventional methods but also new ideas. So far, we have found various compounds as drug candidates. In these studies, some strategies based on organic chemistry have allowed us to find drug candidates, through 1) construction of a focused library using organic reactions and 2) rational design of enzyme inhibitors based on chemical reactions catalyzed by the target enzyme. Medicinal chemistry based on organic chemical reactions could be expected to supplement the conventional methods. In this review, we present drug discovery with the help of organic chemistry showing examples of our explorative studies on histone deacetylase inhibitors and lysine-specific demethylase 1 inhibitors.

Mass spectrometry-driven drug discovery for development of herbal medicine.

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Zhang, Aihua; Sun, Hui; Wang, Xijun

2018-05-01

Herbal medicine (HM) has made a major contribution to the drug discovery process with regard to identifying products compounds. Currently, more attention has been focused on drug discovery from natural compounds of HM. Despite the rapid advancement of modern analytical techniques, drug discovery is still a difficult and lengthy process. Fortunately, mass spectrometry (MS) can provide us with useful structural information for drug discovery, has been recognized as a sensitive, rapid, and high-throughput technology for advancing drug discovery from HM in the post-genomic era. It is essential to develop an efficient, high-quality, high-throughput screening method integrated with an MS platform for early screening of candidate drug molecules from natural products. We have developed a new chinmedomics strategy reliant on MS that is capable of capturing the candidate molecules, facilitating their identification of novel chemical structures in the early phase; chinmedomics-guided natural product discovery based on MS may provide an effective tool that addresses challenges in early screening of effective constituents of herbs against disease. This critical review covers the use of MS with related techniques and methodologies for natural product discovery, biomarker identification, and determination of mechanisms of action. It also highlights high-throughput chinmedomics screening methods suitable for lead compound discovery illustrated by recent successes. © 2016 Wiley Periodicals, Inc.

Science of the science, drug discovery and artificial neural networks.

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Patel, Jigneshkumar

2013-03-01

Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.

Computational neuropharmacology: dynamical approaches in drug discovery.

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Aradi, Ildiko; Erdi, Péter

2006-05-01

Computational approaches that adopt dynamical models are widely accepted in basic and clinical neuroscience research as indispensable tools with which to understand normal and pathological neuronal mechanisms. Although computer-aided techniques have been used in pharmaceutical research (e.g. in structure- and ligand-based drug design), the power of dynamical models has not yet been exploited in drug discovery. We suggest that dynamical system theory and computational neuroscience--integrated with well-established, conventional molecular and electrophysiological methods--offer a broad perspective in drug discovery and in the search for novel targets and strategies for the treatment of neurological and psychiatric diseases.

Budget impact analysis of drugs for ultra-orphan non-oncological diseases in Europe.

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Schlander, Michael; Adarkwah, Charles Christian; Gandjour, Afschin

2015-02-01

Ultra-orphan diseases (UODs) have been defined by a prevalence of less than 1 per 50,000 persons. However, little is known about budget impact of ultra-orphan drugs. For analysis, the budget impact analysis (BIA) had a time horizon of 10 years (2012-2021) and a pan-European payer's perspective, based on prevalence data for UODs for which patented drugs are available and/or for which drugs are in clinical development. A total of 18 drugs under patent protection or orphan drug designation for non-oncological UODs were identified. Furthermore, 29 ultra-orphan drugs for non-oncological diseases under development that have the potential of reaching the market by 2021 were found. Total budget impact over 10 years was estimated to be €15,660 and €4965 million for approved and pipeline ultra-orphan drugs, respectively (total: €20,625 million). The analysis does not support concerns regarding an uncontrolled growth in expenditures for drugs for UODs.

Fragment-based drug discovery and its application to challenging drug targets.

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Price, Amanda J; Howard, Steven; Cons, Benjamin D

2017-11-08

Fragment-based drug discovery (FBDD) is a technique for identifying low molecular weight chemical starting points for drug discovery. Since its inception 20 years ago, FBDD has grown in popularity to the point where it is now an established technique in industry and academia. The approach involves the biophysical screening of proteins against collections of low molecular weight compounds (fragments). Although fragments bind to proteins with relatively low affinity, they form efficient, high quality binding interactions with the protein architecture as they have to overcome a significant entropy barrier to bind. Of the biophysical methods available for fragment screening, X-ray protein crystallography is one of the most sensitive and least prone to false positives. It also provides detailed structural information of the protein-fragment complex at the atomic level. Fragment-based screening using X-ray crystallography is therefore an efficient method for identifying binding hotspots on proteins, which can then be exploited by chemists and biologists for the discovery of new drugs. The use of FBDD is illustrated here with a recently published case study of a drug discovery programme targeting the challenging protein-protein interaction Kelch-like ECH-associated protein 1:nuclear factor erythroid 2-related factor 2. © 2017 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.

Advancing Drug Discovery through Enhanced Free Energy Calculations.

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Abel, Robert; Wang, Lingle; Harder, Edward D; Berne, B J; Friesner, Richard A

2017-07-18

A principal goal of drug discovery project is to design molecules that can tightly and selectively bind to the target protein receptor. Accurate prediction of protein-ligand binding free energies is therefore of central importance in computational chemistry and computer aided drug design. Multiple recent improvements in computing power, classical force field accuracy, enhanced sampling methods, and simulation setup have enabled accurate and reliable calculations of protein-ligands binding free energies, and position free energy calculations to play a guiding role in small molecule drug discovery. In this Account, we outline the relevant methodological advances, including the REST2 (Replica Exchange with Solute Temperting) enhanced sampling, the incorporation of REST2 sampling with convential FEP (Free Energy Perturbation) through FEP/REST, the OPLS3 force field, and the advanced simulation setup that constitute our FEP+ approach, followed by the presentation of extensive comparisons with experiment, demonstrating sufficient accuracy in potency prediction (better than 1 kcal/mol) to substantially impact lead optimization campaigns. The limitations of the current FEP+ implementation and best practices in drug discovery applications are also discussed followed by the future methodology development plans to address those limitations. We then report results from a recent drug discovery project, in which several thousand FEP+ calculations were successfully deployed to simultaneously optimize potency, selectivity, and solubility, illustrating the power of the approach to solve challenging drug design problems. The capabilities of free energy calculations to accurately predict potency and selectivity have led to the advance of ongoing drug discovery projects, in challenging situations where alternative approaches would have great difficulties. The ability to effectively carry out projects evaluating tens of thousands, or hundreds of thousands, of proposed drug candidates

Protein Complex Production from the Drug Discovery Standpoint.

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Moarefi, Ismail

2016-01-01

Small molecule drug discovery critically depends on the availability of meaningful in vitro assays to guide medicinal chemistry programs that are aimed at optimizing drug potency and selectivity. As it becomes increasingly evident, most disease relevant drug targets do not act as a single protein. In the body, they are instead generally found in complex with protein cofactors that are highly relevant for their correct function and regulation. This review highlights selected examples of the increasing trend to use biologically relevant protein complexes for rational drug discovery to reduce costly late phase attritions due to lack of efficacy or toxicity.

Cell and small animal models for phenotypic drug discovery

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Szabo M

2017-06-01

Full Text Available Mihaly Szabo,1 Sara Svensson Akusjärvi,1 Ankur Saxena,1 Jianping Liu,2 Gayathri Chandrasekar,1 Satish S Kitambi1 1Department of Microbiology Tumor, and Cell Biology, 2Department of Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden Abstract: The phenotype-based drug discovery (PDD approach is re-emerging as an alternative platform for drug discovery. This review provides an overview of the various model systems and technical advances in imaging and image analyses that strengthen the PDD platform. In PDD screens, compounds of therapeutic value are identified based on the phenotypic perturbations produced irrespective of target(s or mechanism of action. In this article, examples of phenotypic changes that can be detected and quantified with relative ease in a cell-based setup are discussed. In addition, a higher order of PDD screening setup using small animal models is also explored. As PDD screens integrate physiology and multiple signaling mechanisms during the screening process, the identified hits have higher biomedical applicability. Taken together, this review highlights the advantages gained by adopting a PDD approach in drug discovery. Such a PDD platform can complement target-based systems that are currently in practice to accelerate drug discovery. Keywords: phenotype, screening, PDD, discovery, zebrafish, drug

Advances in fragment-based drug discovery platforms.

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Orita, Masaya; Warizaya, Masaichi; Amano, Yasushi; Ohno, Kazuki; Niimi, Tatsuya

2009-11-01

Fragment-based drug discovery (FBDD) has been established as a powerful alternative and complement to traditional high-throughput screening techniques for identifying drug leads. At present, this technique is widely used among academic groups as well as small biotech and large pharmaceutical companies. In recent years, > 10 new compounds developed with FBDD have entered clinical development, and more and more attention in the drug discovery field is being focused on this technique. Under the FBDD approach, a fragment library of relatively small compounds (molecular mass = 100 - 300 Da) is screened by various methods and the identified fragment hits which normally weakly bind to the target are used as starting points to generate more potent drug leads. Because FBDD is still a relatively new drug discovery technology, further developments and optimizations in screening platforms and fragment exploitation can be expected. This review summarizes recent advances in FBDD platforms and discusses the factors important for the successful application of this technique. Under the FBDD approach, both identifying the starting fragment hit to be developed and generating the drug lead from that starting fragment hit are important. Integration of various techniques, such as computational technology, X-ray crystallography, NMR, surface plasmon resonance, isothermal titration calorimetry, mass spectrometry and high-concentration screening, must be applied in a situation-appropriate manner.

Pathways to new drug discovery in neuropsychiatry

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Berk Michael

2012-11-01

Full Text Available Abstract There is currently a crisis in drug discovery for neuropsychiatric disorders, with a profound, yet unexpected drought in new drug development across the spectrum. In this commentary, the sources of this dilemma and potential avenues to redress the issue are explored. These include a critical review of diagnostic issues and of selection of participants for clinical trials, and the mechanisms for identifying new drugs and new drug targets. Historically, the vast majority of agents have been discovered serendipitously or have been modifications of existing agents. Serendipitous discoveries, based on astute clinical observation or data mining, remain a valid option, as is illustrated by the suggestion in the paper by Wahlqvist and colleagues that treatment with sulfonylurea and metformin reduces the risk of affective disorder. However, the identification of agents targeting disorder-related biomarkers is currently proving particularly fruitful. There is considerable hope for genetics as a purist, pathophysiologically valid pathway to drug discovery; however, it is unclear whether the science is ready to meet this promise. Fruitful paradigms will require a break from the orthodoxy, and creativity and risk may well be the fingerprints of success. See related article http://www.biomedcentral.com/1741-7015/10/150

Recent advances in inkjet dispensing technologies: applications in drug discovery.

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Zhu, Xiangcheng; Zheng, Qiang; Yang, Hu; Cai, Jin; Huang, Lei; Duan, Yanwen; Xu, Zhinan; Cen, Peilin

2012-09-01

Inkjet dispensing technology is a promising fabrication methodology widely applied in drug discovery. The automated programmable characteristics and high-throughput efficiency makes this approach potentially very useful in miniaturizing the design patterns for assays and drug screening. Various custom-made inkjet dispensing systems as well as specialized bio-ink and substrates have been developed and applied to fulfill the increasing demands of basic drug discovery studies. The incorporation of other modern technologies has further exploited the potential of inkjet dispensing technology in drug discovery and development. This paper reviews and discusses the recent developments and practical applications of inkjet dispensing technology in several areas of drug discovery and development including fundamental assays of cells and proteins, microarrays, biosensors, tissue engineering, basic biological and pharmaceutical studies. Progression in a number of areas of research including biomaterials, inkjet mechanical systems and modern analytical techniques as well as the exploration and accumulation of profound biological knowledge has enabled different inkjet dispensing technologies to be developed and adapted for high-throughput pattern fabrication and miniaturization. This in turn presents a great opportunity to propel inkjet dispensing technology into drug discovery.

Integration of distributed computing into the drug discovery process.

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von Korff, Modest; Rufener, Christian; Stritt, Manuel; Freyss, Joel; Bär, Roman; Sander, Thomas

2011-02-01

Grid computing offers an opportunity to gain massive computing power at low costs. We give a short introduction into the drug discovery process and exemplify the use of grid computing for image processing, docking and 3D pharmacophore descriptor calculations. The principle of a grid and its architecture are briefly explained. More emphasis is laid on the issues related to a company-wide grid installation and embedding the grid into the research process. The future of grid computing in drug discovery is discussed in the expert opinion section. Most needed, besides reliable algorithms to predict compound properties, is embedding the grid seamlessly into the discovery process. User friendly access to powerful algorithms without any restrictions, that is, by a limited number of licenses, has to be the goal of grid computing in drug discovery.

Revisiting lab-on-a-chip technology for drug discovery.

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Neuži, Pavel; Giselbrecht, Stefan; Länge, Kerstin; Huang, Tony Jun; Manz, Andreas

2012-08-01

The field of microfluidics or lab-on-a-chip technology aims to improve and extend the possibilities of bioassays, cell biology and biomedical research based on the idea of miniaturization. Microfluidic systems allow more accurate modelling of physiological situations for both fundamental research and drug development, and enable systematic high-volume testing for various aspects of drug discovery. Microfluidic systems are in development that not only model biological environments but also physically mimic biological tissues and organs; such 'organs on a chip' could have an important role in expediting early stages of drug discovery and help reduce reliance on animal testing. This Review highlights the latest lab-on-a-chip technologies for drug discovery and discusses the potential for future developments in this field.

Use of combinatorial chemistry to speed drug discovery.

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Rádl, S

1998-10-01

IBC's International Conference on Integrating Combinatorial Chemistry into the Discovery Pipeline was held September 14-15, 1998. The program started with a pre-conference workshop on High-Throughput Compound Characterization and Purification. The agenda of the main conference was divided into sessions of Synthesis, Automation and Unique Chemistries; Integrating Combinatorial Chemistry, Medicinal Chemistry and Screening; Combinatorial Chemistry Applications for Drug Discovery; and Information and Data Management. This meeting was an excellent opportunity to see how big pharma, biotech and service companies are addressing the current bottlenecks in combinatorial chemistry to speed drug discovery. (c) 1998 Prous Science. All rights reserved.

Natural Products as Leads in Schistosome Drug Discovery

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Bruno J. Neves

2015-01-01

Full Text Available Schistosomiasis is a neglected parasitic tropical disease that claims around 200,000 human lives every year. Praziquantel (PZQ, the only drug recommended by the World Health Organization for the treatment and control of human schistosomiasis, is now facing the threat of drug resistance, indicating the urgent need for new effective compounds to treat this disease. Therefore, globally, there is renewed interest in natural products (NPs as a starting point for drug discovery and development for schistosomiasis. Recent advances in genomics, proteomics, bioinformatics, and cheminformatics have brought about unprecedented opportunities for the rapid and more cost-effective discovery of new bioactive compounds against neglected tropical diseases. This review highlights the main contributions that NP drug discovery and development have made in the treatment of schistosomiasis and it discusses how integration with virtual screening (VS strategies may contribute to accelerating the development of new schistosomidal leads, especially through the identification of unexplored, biologically active chemical scaffolds and structural optimization of NPs with previously established activity.

Drug-Target Kinetics in Drug Discovery.

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Tonge, Peter J

2018-01-17

The development of therapies for the treatment of neurological cancer faces a number of major challenges including the synthesis of small molecule agents that can penetrate the blood-brain barrier (BBB). Given the likelihood that in many cases drug exposure will be lower in the CNS than in systemic circulation, it follows that strategies should be employed that can sustain target engagement at low drug concentration. Time dependent target occupancy is a function of both the drug and target concentration as well as the thermodynamic and kinetic parameters that describe the binding reaction coordinate, and sustained target occupancy can be achieved through structural modifications that increase target (re)binding and/or that decrease the rate of drug dissociation. The discovery and deployment of compounds with optimized kinetic effects requires information on the structure-kinetic relationships that modulate the kinetics of binding, and the molecular factors that control the translation of drug-target kinetics to time-dependent drug activity in the disease state. This Review first introduces the potential benefits of drug-target kinetics, such as the ability to delineate both thermodynamic and kinetic selectivity, and then describes factors, such as target vulnerability, that impact the utility of kinetic selectivity. The Review concludes with a description of a mechanistic PK/PD model that integrates drug-target kinetics into predictions of drug activity.

Admission of new oncological drugs to the Slovenian health care system and their accessibility to the patients

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Mitja Kos

2007-09-01

Full Text Available Background: The aim of the present study is to analyze the admission of anticancer drugs to the Slovenian healthcare system, and to evaluate patients’ accessibility to these medications.Methods: Admission and accessibility to anticancer drugs was evaluated by analysing: differences in registration time among USA, selected member states of EU, and Slovenia; time from the registration to the first use in Slovenia; differences between the first use in Slovenia and the first use in selected member states of EU and by analysing the market of oncology drugs. The study included drugs of the ATC groups such as Antitumor medications (cytostatics (ATC = L01 and Endocrine treatment (ATC = L02 that were used in Slovenia for the first time between 1999 to 2005. Registration data for Slovenia and EU was obtained from the registration documention of selected drugs at the Agency for Medicinal Products and Medical Devices of the Republic of Slovenia, and the European Agency for the Evaluation of Medicinal Products (EMEA. Registration data for USA was obtained from the registration documention of selected drugs at the Food and Drug Administration (FDA. The information upon the first use of drugs in Slovenia was acquired from the PharMIS system, and the information upon the first use of drugs in selected member states of EU was obtained from the study on patients’ access to oncology drugs by Nils Wilking and Bengt Jönsson, performed from 2005. Data for the market analysis of oncology drugs was obtained from the PharMIS system.Results: In previous years a delay in registration time between Slovenia and compared states was present for some oncology drugs. Along with the acceptance of Slovenia as a new member state of EU, on 1st May 2004, registration process in Slovenia became a part of the registration system of the European Agency for the Evaluation of Medicinal Products (EMEA. Majority of drugs had a time difference between the registration and the first use

SemaTyP: a knowledge graph based literature mining method for drug discovery.

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Sang, Shengtian; Yang, Zhihao; Wang, Lei; Liu, Xiaoxia; Lin, Hongfei; Wang, Jian

2018-05-30

Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.

Implementing Genome-Driven Oncology

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Hyman, David M.; Taylor, Barry S.; Baselga, José

2017-01-01

Early successes in identifying and targeting individual oncogenic drivers, together with the increasing feasibility of sequencing tumor genomes, have brought forth the promise of genome-driven oncology care. As we expand the breadth and depth of genomic analyses, the biological and clinical complexity of its implementation will be unparalleled. Challenges include target credentialing and validation, implementing drug combinations, clinical trial designs, targeting tumor heterogeneity, and deploying technologies beyond DNA sequencing, among others. We review how contemporary approaches are tackling these challenges and will ultimately serve as an engine for biological discovery and increase our insight into cancer and its treatment. PMID:28187282

Novel opportunities for computational biology and sociology in drug discovery

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Yao, Lixia

2009-01-01

Drug discovery today is impossible without sophisticated modeling and computation. In this review we touch on previous advances in computational biology and by tracing the steps involved in pharmaceutical development, we explore a range of novel, high value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry ties for scientific and human benefit. Attention to these opportunities could promise punctuated advance, and will complement the well-established computational work on which drug discovery currently relies. PMID:19674801

Financing drug discovery for orphan diseases

OpenAIRE

Fagnan, David Erik; Gromatzky, Austin A.; Stein, Roger Mark; Fernandez, Jose-Maria; Lo, Andrew W.

2014-01-01

Recently proposed ‘megafund’ financing methods for funding translational medicine and drug development require billions of dollars in capital per megafund to de-risk the drug discovery process enough to issue long-term bonds. Here, we demonstrate that the same financing methods can be applied to orphan drug development but, because of the unique nature of orphan diseases and therapeutics (lower development costs, faster FDA approval times, lower failure rates and lower correlation of failures...

AutoDrug: fully automated macromolecular crystallography workflows for fragment-based drug discovery

International Nuclear Information System (INIS)

Tsai, Yingssu; McPhillips, Scott E.; González, Ana; McPhillips, Timothy M.; Zinn, Daniel; Cohen, Aina E.; Feese, Michael D.; Bushnell, David; Tiefenbrunn, Theresa; Stout, C. David; Ludaescher, Bertram; Hedman, Britt; Hodgson, Keith O.; Soltis, S. Michael

2013-01-01

New software has been developed for automating the experimental and data-processing stages of fragment-based drug discovery at a macromolecular crystallography beamline. A new workflow-automation framework orchestrates beamline-control and data-analysis software while organizing results from multiple samples. AutoDrug is software based upon the scientific workflow paradigm that integrates the Stanford Synchrotron Radiation Lightsource macromolecular crystallography beamlines and third-party processing software to automate the crystallography steps of the fragment-based drug-discovery process. AutoDrug screens a cassette of fragment-soaked crystals, selects crystals for data collection based on screening results and user-specified criteria and determines optimal data-collection strategies. It then collects and processes diffraction data, performs molecular replacement using provided models and detects electron density that is likely to arise from bound fragments. All processes are fully automated, i.e. are performed without user interaction or supervision. Samples can be screened in groups corresponding to particular proteins, crystal forms and/or soaking conditions. A single AutoDrug run is only limited by the capacity of the sample-storage dewar at the beamline: currently 288 samples. AutoDrug was developed in conjunction with RestFlow, a new scientific workflow-automation framework. RestFlow simplifies the design of AutoDrug by managing the flow of data and the organization of results and by orchestrating the execution of computational pipeline steps. It also simplifies the execution and interaction of third-party programs and the beamline-control system. Modeling AutoDrug as a scientific workflow enables multiple variants that meet the requirements of different user groups to be developed and supported. A workflow tailored to mimic the crystallography stages comprising the drug-discovery pipeline of CoCrystal Discovery Inc. has been deployed and successfully

Zebrafish models in neuropsychopharmacology and CNS drug discovery.

Science.gov (United States)

Khan, Kanza M; Collier, Adam D; Meshalkina, Darya A; Kysil, Elana V; Khatsko, Sergey L; Kolesnikova, Tatyana; Morzherin, Yury Yu; Warnick, Jason E; Kalueff, Allan V; Echevarria, David J

2017-07-01

Despite the high prevalence of neuropsychiatric disorders, their aetiology and molecular mechanisms remain poorly understood. The zebrafish (Danio rerio) is increasingly utilized as a powerful animal model in neuropharmacology research and in vivo drug screening. Collectively, this makes zebrafish a useful tool for drug discovery and the identification of disordered molecular pathways. Here, we discuss zebrafish models of selected human neuropsychiatric disorders and drug-induced phenotypes. As well as covering a broad range of brain disorders (from anxiety and psychoses to neurodegeneration), we also summarize recent developments in zebrafish genetics and small molecule screening, which markedly enhance the disease modelling and the discovery of novel drug targets. © 2017 The British Pharmacological Society.

Fragment-based drug discovery as alternative strategy to the drug development for neglected diseases.

Science.gov (United States)

Mello, Juliana da Fonseca Rezende E; Gomes, Renan Augusto; Vital-Fujii, Drielli Gomes; Ferreira, Glaucio Monteiro; Trossini, Gustavo Henrique Goulart

2017-12-01

Neglected diseases (NDs) affect large populations and almost whole continents, representing 12% of the global health burden. In contrast, the treatment available today is limited and sometimes ineffective. Under this scenery, the Fragment-Based Drug Discovery emerged as one of the most promising alternatives to the traditional methods of drug development. This method allows achieving new lead compounds with smaller size of fragment libraries. Even with the wide Fragment-Based Drug Discovery success resulting in new effective therapeutic agents against different diseases, until this moment few studies have been applied this approach for NDs area. In this article, we discuss the basic Fragment-Based Drug Discovery process, brief successful ideas of general applications and show a landscape of its use in NDs, encouraging the implementation of this strategy as an interesting way to optimize the development of new drugs to NDs. © 2017 John Wiley & Sons A/S.

Integration of Antibody Array Technology into Drug Discovery and Development.

Science.gov (United States)

Huang, Wei; Whittaker, Kelly; Zhang, Huihua; Wu, Jian; Zhu, Si-Wei; Huang, Ruo-Pan

Antibody arrays represent a high-throughput technique that enables the parallel detection of multiple proteins with minimal sample volume requirements. In recent years, antibody arrays have been widely used to identify new biomarkers for disease diagnosis or prognosis. Moreover, many academic research laboratories and commercial biotechnology companies are starting to apply antibody arrays in the field of drug discovery. In this review, some technical aspects of antibody array development and the various platforms currently available will be addressed; however, the main focus will be on the discussion of antibody array technologies and their applications in drug discovery. Aspects of the drug discovery process, including target identification, mechanisms of drug resistance, molecular mechanisms of drug action, drug side effects, and the application in clinical trials and in managing patient care, which have been investigated using antibody arrays in recent literature will be examined and the relevance of this technology in progressing this process will be discussed. Protein profiling with antibody array technology, in addition to other applications, has emerged as a successful, novel approach for drug discovery because of the well-known importance of proteins in cell events and disease development.

Use of allosteric targets in the discovery of safer drugs.

Science.gov (United States)

Grover, Ashok Kumar

2013-01-01

The need for drugs with fewer side effects cannot be overemphasized. Today, most drugs modify the actions of enzymes, receptors, transporters and other molecules by directly binding to their active (orthosteric) sites. However, orthosteric site configuration is similar in several proteins performing related functions and this leads to a lower specificity of a drug for the desired protein. Consequently, such drugs may have adverse side effects. A new basis of drug discovery is emerging based on the binding of the drug molecules to sites away (allosteric) from the orthosteric sites. It is possible to find allosteric sites which are unique and hence more specific as targets for drug discovery. Of many available examples, two are highlighted here. The first is caloxins - a new class of highly specific inhibitors of plasma membrane Ca²⁺ pumps. The second concerns the modulation of receptors for the neurotransmitter acetylcholine, which binds to 12 types of receptors. Exploitation of allosteric sites has led to the discovery of drugs which can selectively modulate the activation of only 1 (M1 muscarinic) out of the 12 different types of acetylcholine receptors. These drugs are being tested for schizophrenia treatment. It is anticipated that the drug discovery exploiting allosteric sites will lead to more effective therapeutic agents with fewer side effects. Copyright © 2013 S. Karger AG, Basel.

Polypharmacology of Approved Anticancer Drugs.

Science.gov (United States)

Amelio, Ivano; Lisitsa, Andrey; Knight, Richard A; Melino, Gerry; Antonov, Alexey V

2017-01-01

The major drug discovery efforts in oncology have been concentrated on the development of selective molecules that are supposed to act specifically on one anticancer mechanism by modulating a single or several closely related drug targets. However, a bird's eye view on data from multiple available bioassays implies that most approved anticancer agents do, in fact, target many more proteins with different functions. Here we will review and systematize currently available information on the targets of several anticancer drugs along with revision of their potential mechanisms of action. Polypharmacology of the current antineoplastic agents suggests that drug clinical efficacy in oncology can be achieved only via modulation of multiple cellular mechanisms. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

In silico pharmacology for a multidisciplinary drug discovery process.

Science.gov (United States)

Ortega, Santiago Schiaffino; Cara, Luisa Carlota López; Salvador, María Kimatrai

2012-01-01

The process of bringing new and innovative drugs, from conception and synthesis through to approval on the market can take the pharmaceutical industry 8-15 years and cost approximately $1.8 billion. Two key technologies are improving the hit-to-drug timeline: high-throughput screening (HTS) and rational drug design. In the latter case, starting from some known ligand-based or target-based information, a lead structure will be rationally designed to be tested in vitro or in vivo. Computational methods are part of many drug discovery programs, including the assessment of ADME (absorption-distribution-metabolism-excretion) and toxicity (ADMET) properties of compounds at the early stages of discovery/development with impressive results. The aim of this paper is to review, in a simple way, some of the most popular strategies used by modelers and some successful applications on computational chemistry to raise awareness of its importance and potential for an actual multidisciplinary drug discovery process.

Preclinical experimental models of drug metabolism and disposition in drug discovery and development

Directory of Open Access Journals (Sweden)

Donglu Zhang

2012-12-01

Full Text Available Drug discovery and development involve the utilization of in vitro and in vivo experimental models. Different models, ranging from test tube experiments to cell cultures, animals, healthy human subjects, and even small numbers of patients that are involved in clinical trials, are used at different stages of drug discovery and development for determination of efficacy and safety. The proper selection and applications of correct models, as well as appropriate data interpretation, are critically important in decision making and successful advancement of drug candidates. In this review, we discuss strategies in the applications of both in vitro and in vivo experimental models of drug metabolism and disposition.

Targeting cysteine proteases in trypanosomatid disease drug discovery.

Science.gov (United States)

Ferreira, Leonardo G; Andricopulo, Adriano D

2017-12-01

Chagas disease and human African trypanosomiasis are endemic conditions in Latin America and Africa, respectively, for which no effective and safe therapy is available. Efforts in drug discovery have focused on several enzymes from these protozoans, among which cysteine proteases have been validated as molecular targets for pharmacological intervention. These enzymes are expressed during the entire life cycle of trypanosomatid parasites and are essential to many biological processes, including infectivity to the human host. As a result of advances in the knowledge of the structural aspects of cysteine proteases and their role in disease physiopathology, inhibition of these enzymes by small molecules has been demonstrated to be a worthwhile approach to trypanosomatid drug research. This review provides an update on drug discovery strategies targeting the cysteine peptidases cruzain from Trypanosoma cruzi and rhodesain and cathepsin B from Trypanosoma brucei. Given that current chemotherapy for Chagas disease and human African trypanosomiasis has several drawbacks, cysteine proteases will continue to be actively pursued as valuable molecular targets in trypanosomatid disease drug discovery efforts. Copyright © 2017. Published by Elsevier Inc.

From crystal to compound: structure-based antimalarial drug discovery.

Science.gov (United States)

Drinkwater, Nyssa; McGowan, Sheena

2014-08-01

Despite a century of control and eradication campaigns, malaria remains one of the world's most devastating diseases. Our once-powerful therapeutic weapons are losing the war against the Plasmodium parasite, whose ability to rapidly develop and spread drug resistance hamper past and present malaria-control efforts. Finding new and effective treatments for malaria is now a top global health priority, fuelling an increase in funding and promoting open-source collaborations between researchers and pharmaceutical consortia around the world. The result of this is rapid advances in drug discovery approaches and technologies, with three major methods for antimalarial drug development emerging: (i) chemistry-based, (ii) target-based, and (iii) cell-based. Common to all three of these approaches is the unique ability of structural biology to inform and accelerate drug development. Where possible, SBDD (structure-based drug discovery) is a foundation for antimalarial drug development programmes, and has been invaluable to the development of a number of current pre-clinical and clinical candidates. However, as we expand our understanding of the malarial life cycle and mechanisms of resistance development, SBDD as a field must continue to evolve in order to develop compounds that adhere to the ideal characteristics for novel antimalarial therapeutics and to avoid high attrition rates pre- and post-clinic. In the present review, we aim to examine the contribution that SBDD has made to current antimalarial drug development efforts, covering hit discovery to lead optimization and prevention of parasite resistance. Finally, the potential for structural biology, particularly high-throughput structural genomics programmes, to identify future targets for drug discovery are discussed.

Hierarchical virtual screening approaches in small molecule drug discovery.

Science.gov (United States)

Kumar, Ashutosh; Zhang, Kam Y J

2015-01-01

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery. Copyright © 2014 Elsevier Inc. All rights reserved.

Application of PBPK modelling in drug discovery and development at Pfizer.

Science.gov (United States)

Jones, Hannah M; Dickins, Maurice; Youdim, Kuresh; Gosset, James R; Attkins, Neil J; Hay, Tanya L; Gurrell, Ian K; Logan, Y Raj; Bungay, Peter J; Jones, Barry C; Gardner, Iain B

2012-01-01

Early prediction of human pharmacokinetics (PK) and drug-drug interactions (DDI) in drug discovery and development allows for more informed decision making. Physiologically based pharmacokinetic (PBPK) modelling can be used to answer a number of questions throughout the process of drug discovery and development and is thus becoming a very popular tool. PBPK models provide the opportunity to integrate key input parameters from different sources to not only estimate PK parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. Using examples from the literature and our own company, we have shown how PBPK techniques can be utilized through the stages of drug discovery and development to increase efficiency, reduce the need for animal studies, replace clinical trials and to increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however, some limitations need to be addressed to realize its application and utility more broadly.

Translational medicine and drug discovery

National Research Council Canada - National Science Library

Littman, Bruce H; Krishna, Rajesh

2011-01-01

..., and examples of their application to real-life drug discovery and development. The latest thinking is presented by researchers from many of the world's leading pharmaceutical companies, including Pfizer, Merck, Eli Lilly, Abbott, and Novartis, as well as from academic institutions and public- private partnerships that support translational research...

Drug discovery for Chagas disease should consider Trypanosoma cruzi strain diversity

Directory of Open Access Journals (Sweden)

Bianca Zingales

2014-09-01

Full Text Available This opinion piece presents an approach to standardisation of an important aspect of Chagas disease drug discovery and development: selecting Trypanosoma cruzi strains for in vitro screening. We discuss the rationale for strain selection representing T. cruzi diversity and provide recommendations on the preferred parasite stage for drug discovery, T. cruzi discrete typing units to include in the panel of strains and the number of strains/clones for primary screens and lead compounds. We also consider experimental approaches for in vitro drug assays. The Figure illustrates the current Chagas disease drug-discovery and development landscape.

[Application of Imaging Mass Spectrometry for Drug Discovery].

Science.gov (United States)

Hayasaka, Takahiro

2016-01-01

Imaging mass spectrometry (IMS) can reveal the distribution of biomolecules on tissue sections. In this process, the biomolecules are directly ionized within tissue sections using matrix-assisted laser desorption/ionization, and then their distribution is visualized by pseudo-color based on the relative signal intensity. The biomolecules, such as fatty acids, phospholipids, glycolipids, peptides, proteins, and neurotransmitters, have been analyzed at a spatial resolution of 5 μm. A special instrument for IMS analysis was developed by Shimadzu. The IMS analysis does not require the labeling of biomolecules and is capable of analyzing all the ionized biomolecules. Interest in this method has expanded to many research fields, including biology, agriculture, medicine, and pharmacology. The technique is especially relevant to the drug discovery process. As practiced currently, drug discovery is expensive and time consuming, requiring the preparation of probes for each drug and its metabolites, followed by systematic probe tracking in animal models. The IMS technique is expected to overcome these drawbacks by revealing the distribution of drugs and their metabolites using only a single analysis. In this symposium, I introduced the methodology and applications of IMS and discussed the feasibility of its application to drug discovery in the near future.

Net present value approaches for drug discovery.

Science.gov (United States)

Svennebring, Andreas M; Wikberg, Jarl Es

2013-12-01

Three dedicated approaches to the calculation of the risk-adjusted net present value (rNPV) in drug discovery projects under different assumptions are suggested. The probability of finding a candidate drug suitable for clinical development and the time to the initiation of the clinical development is assumed to be flexible in contrast to the previously used models. The rNPV of the post-discovery cash flows is calculated as the probability weighted average of the rNPV at each potential time of initiation of clinical development. Practical considerations how to set probability rates, in particular during the initiation and termination of a project is discussed.

Bead-based screening in chemical biology and drug discovery

DEFF Research Database (Denmark)

Komnatnyy, Vitaly V.; Nielsen, Thomas Eiland; Qvortrup, Katrine

2018-01-01

libraries for early drug discovery. Among the various library forms, the one-bead-one-compound (OBOC) library, where each bead carries many copies of a single compound, holds the greatest potential for the rapid identification of novel hits against emerging drug targets. However, this potential has not yet...... been fully realized due to a number of technical obstacles. In this feature article, we review the progress that has been made towards bead-based library screening and applications to the discovery of bioactive compounds. We identify the key challenges of this approach and highlight key steps needed......High-throughput screening is an important component of the drug discovery process. The screening of libraries containing hundreds of thousands of compounds requires assays amanable to miniaturisation and automization. Combinatorial chemistry holds a unique promise to deliver structural diverse...

Computer-Aided Drug Discovery in Plant Pathology.

Science.gov (United States)

Shanmugam, Gnanendra; Jeon, Junhyun

2017-12-01

Control of plant diseases is largely dependent on use of agrochemicals. However, there are widening gaps between our knowledge on plant diseases gained from genetic/mechanistic studies and rapid translation of the knowledge into target-oriented development of effective agrochemicals. Here we propose that the time is ripe for computer-aided drug discovery/design (CADD) in molecular plant pathology. CADD has played a pivotal role in development of medically important molecules over the last three decades. Now, explosive increase in information on genome sequences and three dimensional structures of biological molecules, in combination with advances in computational and informational technologies, opens up exciting possibilities for application of CADD in discovery and development of agrochemicals. In this review, we outline two categories of the drug discovery strategies: structure- and ligand-based CADD, and relevant computational approaches that are being employed in modern drug discovery. In order to help readers to dive into CADD, we explain concepts of homology modelling, molecular docking, virtual screening, and de novo ligand design in structure-based CADD, and pharmacophore modelling, ligand-based virtual screening, quantitative structure activity relationship modelling and de novo ligand design for ligand-based CADD. We also provide the important resources available to carry out CADD. Finally, we present a case study showing how CADD approach can be implemented in reality for identification of potent chemical compounds against the important plant pathogens, Pseudomonas syringae and Colletotrichum gloeosporioides .

Medicinal chemistry inspired fragment-based drug discovery.

Science.gov (United States)

Lanter, James; Zhang, Xuqing; Sui, Zhihua

2011-01-01

Lead generation can be a very challenging phase of the drug discovery process. The two principal methods for this stage of research are blind screening and rational design. Among the rational or semirational design approaches, fragment-based drug discovery (FBDD) has emerged as a useful tool for the generation of lead structures. It is particularly powerful as a complement to high-throughput screening approaches when the latter failed to yield viable hits for further development. Engagement of medicinal chemists early in the process can accelerate the progression of FBDD efforts by incorporating drug-friendly properties in the earliest stages of the design process. Medium-chain acyl-CoA synthetase 2b and ketohexokinase are chosen as examples to illustrate the importance of close collaboration of medicinal chemists, crystallography, and modeling. Copyright © 2011 Elsevier Inc. All rights reserved.

Biomimicry as a basis for drug discovery.

Science.gov (United States)

Kolb, V M

1998-01-01

Selected works are discussed which clearly demonstrate that mimicking various aspects of the process by which natural products evolved is becoming a powerful tool in contemporary drug discovery. Natural products are an established and rich source of drugs. The term "natural product" is often used synonymously with "secondary metabolite." Knowledge of genetics and molecular evolution helps us understand how biosynthesis of many classes of secondary metabolites evolved. One proposed hypothesis is termed "inventive evolution." It invokes duplication of genes, and mutation of the gene copies, among other genetic events. The modified duplicate genes, per se or in conjunction with other genetic events, may give rise to new enzymes, which, in turn, may generate new products, some of which may be selected for. Steps of the inventive evolution can be mimicked in several ways for purpose of drug discovery. For example, libraries of chemical compounds of any imaginable structure may be produced by combinatorial synthesis. Out of these libraries new active compounds can be selected. In another example, genetic system can be manipulated to produce modified natural products ("unnatural natural products"), from which new drugs can be selected. In some instances, similar natural products turn up in species that are not direct descendants of each other. This is presumably due to a horizontal gene transfer. The mechanism of this inter-species gene transfer can be mimicked in therapeutic gene delivery. Mimicking specifics or principles of chemical evolution including experimental and test-tube evolution also provides leads for new drug discovery.

Synthetic biology for pharmaceutical drug discovery

Directory of Open Access Journals (Sweden)

Trosset JY

2015-12-01

Full Text Available Jean-Yves Trosset,1 Pablo Carbonell2,3 1Bioinformation Research Laboratory, Sup’Biotech, Villejuif, France; 2Faculty of Life Sciences, SYNBIOCHEM Centre, Manchester Institute of Biotechnology, University of Manchester, Manchester, UK; 3Department of Experimental and Health Sciences (DCEXS, Research Programme on Biomedical Informatics (GRIB, Hospital del Mar Medical Research Institute (IMIM, Universitat Pompeu Fabra (UPF, Barcelona, Spain Abstract: Synthetic biology (SB is an emerging discipline, which is slowly reorienting the field of drug discovery. For thousands of years, living organisms such as plants were the major source of human medicines. The difficulty in resynthesizing natural products, however, often turned pharmaceutical industries away from this rich source for human medicine. More recently, progress on transformation through genetic manipulation of biosynthetic units in microorganisms has opened the possibility of in-depth exploration of the large chemical space of natural products derivatives. Success of SB in drug synthesis culminated with the bioproduction of artemisinin by microorganisms, a tour de force in protein and metabolic engineering. Today, synthetic cells are not only used as biofactories but also used as cell-based screening platforms for both target-based and phenotypic-based approaches. Engineered genetic circuits in synthetic cells are also used to decipher disease mechanisms or drug mechanism of actions and to study cell–cell communication within bacteria consortia. This review presents latest developments of SB in the field of drug discovery, including some challenging issues such as drug resistance and drug toxicity. Keywords: metabolic engineering, plant synthetic biology, natural products, synthetic quorum sensing, drug resistance

Drugs from the Oceans: Marine Natural Products as Leads for Drug Discovery.

Science.gov (United States)

Altmann, Karl-Heinz

2017-10-25

The marine environment harbors a vast number of species that are the source of a wide array of structurally diverse bioactive secondary metabolites. At this point in time, roughly 27'000 marine natural products are known, of which eight are (were) at the origin of seven marketed drugs, mostly for the treatment of cancer. The majority of these drugs and also of drug candidates currently undergoing clinical evaluation (excluding antibody-drug conjugates) are unmodified natural products, but synthetic chemistry has played a central role in the discovery and/or development of all but one of the approved marine-derived drugs. More than 1000 new marine natural products have been isolated per year over the last decade, but the pool of new and unique structures is far from exhausted. To fully leverage the potential offered by the structural diversity of marine-produced secondary metabolites for drug discovery will require their broad assessment for different bioactivities and the productive interplay between new fermentation technologies, synthetic organic chemistry, and medicinal chemistry, in order to secure compound supply and enable lead optimization.

Large-scale automatic extraction of side effects associated with targeted anticancer drugs from full-text oncological articles.

Science.gov (United States)

Xu, Rong; Wang, QuanQiu

2015-06-01

Targeted anticancer drugs such as imatinib, trastuzumab and erlotinib dramatically improved treatment outcomes in cancer patients, however, these innovative agents are often associated with unexpected side effects. The pathophysiological mechanisms underlying these side effects are not well understood. The availability of a comprehensive knowledge base of side effects associated with targeted anticancer drugs has the potential to illuminate complex pathways underlying toxicities induced by these innovative drugs. While side effect association knowledge for targeted drugs exists in multiple heterogeneous data sources, published full-text oncological articles represent an important source of pivotal, investigational, and even failed trials in a variety of patient populations. In this study, we present an automatic process to extract targeted anticancer drug-associated side effects (drug-SE pairs) from a large number of high profile full-text oncological articles. We downloaded 13,855 full-text articles from the Journal of Oncology (JCO) published between 1983 and 2013. We developed text classification, relationship extraction, signaling filtering, and signal prioritization algorithms to extract drug-SE pairs from downloaded articles. We extracted a total of 26,264 drug-SE pairs with an average precision of 0.405, a recall of 0.899, and an F1 score of 0.465. We show that side effect knowledge from JCO articles is largely complementary to that from the US Food and Drug Administration (FDA) drug labels. Through integrative correlation analysis, we show that targeted drug-associated side effects positively correlate with their gene targets and disease indications. In conclusion, this unique database that we built from a large number of high-profile oncological articles could facilitate the development of computational models to understand toxic effects associated with targeted anticancer drugs. Copyright © 2015 Elsevier Inc. All rights reserved.

Accelerated approval of oncology products: the food and drug administration experience.

Science.gov (United States)

Johnson, John R; Ning, Yang-Min; Farrell, Ann; Justice, Robert; Keegan, Patricia; Pazdur, Richard

2011-04-20

We reviewed the regulatory history of the accelerated approval process and the US Food and Drug Administration (FDA) experience with accelerated approval of oncology products from its initiation in December 11, 1992, to July 1, 2010. The accelerated approval regulations allowed accelerated approval of products to treat serious or life-threatening diseases based on surrogate endpoints that are reasonably likely to predict clinical benefit. Failure to complete postapproval trials to confirm clinical benefit with due diligence could result in removal of the accelerated approval indication from the market. From December 11, 1992, to July 1, 2010, the FDA granted accelerated approval to 35 oncology products for 47 new indications. Clinical benefit was confirmed in postapproval trials for 26 of the 47 new indications, resulting in conversion to regular approval. The median time between accelerated approval and regular approval of oncology products was 3.9 years (range = 0.8-12.6 years) and the mean time was 4.7 years, representing a substantial time savings in terms of earlier availability of drugs to cancer patients. Three new indications did not show clinical benefit when confirmatory postapproval trials were completed and were subsequently removed from the market or had restricted distribution plans implemented. Confirmatory trials were not completed for 14 new indications. The five longest intervals from receipt of accelerated approval to July 1, 2010, without completion of trials to confirm clinical benefit were 10.5, 6.4, 5.5, 5.5, and 4.7 years. The five longest intervals between accelerated approval and successful conversion to regular approval were 12.6, 9.7, 8.1, 7.5, and 7.4 years. Trials to confirm clinical benefit should be part of the drug development plan and should be in progress at the time of an application seeking accelerated approval to prevent an ineffective drug from remaining on the market for an unacceptable time.

Accessing external innovation in drug discovery and development.

Science.gov (United States)

Tufféry, Pierre

2015-06-01

A decline in the productivity of the pharmaceutical industry research and development (R&D) pipeline has highlighted the need to reconsider the classical strategies of drug discovery and development, which are based on internal resources, and to identify new means to improve the drug discovery process. Accepting that the combination of internal and external ideas can improve innovation, ways to access external innovation, that is, opening projects to external contributions, have recently been sought. In this review, the authors look at a number of external innovation opportunities. These include increased interactions with academia via academic centers of excellence/innovation centers, better communication on projects using crowdsourcing or social media and new models centered on external providers such as built-to-buy startups or virtual pharmaceutical companies. The buzz for accessing external innovation relies on the pharmaceutical industry's major challenge to improve R&D productivity, a conjuncture favorable to increase interactions with academia and new business models supporting access to external innovation. So far, access to external innovation has mostly been considered during early stages of drug development, and there is room for enhancement. First outcomes suggest that external innovation should become part of drug development in the long term. However, the balance between internal and external developments in drug discovery can vary largely depending on the company strategies.

Traditional Chinese Medicine-Based Network Pharmacology Could Lead to New Multicompound Drug Discovery

Directory of Open Access Journals (Sweden)

Jian Li

2012-01-01

Full Text Available Current strategies for drug discovery have reached a bottleneck where the paradigm is generally “one gene, one drug, one disease.” However, using holistic and systemic views, network pharmacology may be the next paradigm in drug discovery. Based on network pharmacology, a combinational drug with two or more compounds could offer beneficial synergistic effects for complex diseases. Interestingly, traditional chinese medicine (TCM has been practicing holistic views for over 3,000 years, and its distinguished feature is using herbal formulas to treat diseases based on the unique pattern classification. Though TCM herbal formulas are acknowledged as a great source for drug discovery, no drug discovery strategies compatible with the multidimensional complexities of TCM herbal formulas have been developed. In this paper, we highlighted some novel paradigms in TCM-based network pharmacology and new drug discovery. A multiple compound drug can be discovered by merging herbal formula-based pharmacological networks with TCM pattern-based disease molecular networks. Herbal formulas would be a source for multiple compound drug candidates, and the TCM pattern in the disease would be an indication for a new drug.

Organic synthesis provides opportunities to transform drug discovery

Science.gov (United States)

Blakemore, David C.; Castro, Luis; Churcher, Ian; Rees, David C.; Thomas, Andrew W.; Wilson, David M.; Wood, Anthony

2018-03-01

Despite decades of ground-breaking research in academia, organic synthesis is still a rate-limiting factor in drug-discovery projects. Here we present some current challenges in synthetic organic chemistry from the perspective of the pharmaceutical industry and highlight problematic steps that, if overcome, would find extensive application in the discovery of transformational medicines. Significant synthesis challenges arise from the fact that drug molecules typically contain amines and N-heterocycles, as well as unprotected polar groups. There is also a need for new reactions that enable non-traditional disconnections, more C-H bond activation and late-stage functionalization, as well as stereoselectively substituted aliphatic heterocyclic ring synthesis, C-X or C-C bond formation. We also emphasize that syntheses compatible with biomacromolecules will find increasing use, while new technologies such as machine-assisted approaches and artificial intelligence for synthesis planning have the potential to dramatically accelerate the drug-discovery process. We believe that increasing collaboration between academic and industrial chemists is crucial to address the challenges outlined here.

Application of lean manufacturing concepts to drug discovery: rapid analogue library synthesis.

Science.gov (United States)

Weller, Harold N; Nirschl, David S; Petrillo, Edward W; Poss, Michael A; Andres, Charles J; Cavallaro, Cullen L; Echols, Martin M; Grant-Young, Katherine A; Houston, John G; Miller, Arthur V; Swann, R Thomas

2006-01-01

The application of parallel synthesis to lead optimization programs in drug discovery has been an ongoing challenge since the first reports of library synthesis. A number of approaches to the application of parallel array synthesis to lead optimization have been attempted over the years, ranging from widespread deployment by (and support of) individual medicinal chemists to centralization as a service by an expert core team. This manuscript describes our experience with the latter approach, which was undertaken as part of a larger initiative to optimize drug discovery. In particular, we highlight how concepts taken from the manufacturing sector can be applied to drug discovery and parallel synthesis to improve the timeliness and thus the impact of arrays on drug discovery.

Evolution of health technology assessment: best practices of the pan-Canadian Oncology Drug Review

Directory of Open Access Journals (Sweden)

Rocchi A

2015-06-01

Full Text Available Angela Rocchi,1 Isabelle Chabot,2 Judith Glennie3 1Athena Research Inc., Burlington, ON, 2EvAccess Inc., Vaudreuil-Dorion, QC, 3JL Glennie Consulting Inc., Aurora, ON, Canada Background: In 2007, Canada chose to develop a separate and distinct path for oncology drug health technology assessment (HTA. In 2013, the decision was made to transfer the pan-Canadian Oncology Drug Review (pCODR to the Canadian Agency for Drugs and Technologies in Health (CADTH, to align the pCODR and CADTH Common Drug Review processes while building on the best practices of both. The objective of this research was to conduct an examination of the best practices established by the pCODR. Methods: A qualitative research approach was taken to assess the policies, processes, and practices of the pCODR, based on internationally accepted best practice “principles” in HTA, with a particular focus on stakeholder engagement. Publicly available information regarding the approach of the pCODR was used to gauge the agency's performance against these principles. In addition, stakeholder observations and real-world experiences were gathered through key informant interviews to be inclusive of perspectives from patient advocacy groups, provincial and/or cancer agency decision-makers, community and academic oncologists, industry, expert committee members, and health economists. Results: This analysis indicated that, through the pCODR, oncology stakeholders have had a voice in and have come to trust the quality and relevance of oncology HTA as a vital tool to ensure the best decisions for Canadians with cancer and their health care system. It could be expected that adoption of the principles and processes of the pCODR would bring a similar level of engagement and trust to other HTA organizations in Canada and elsewhere. Conclusion: The results of this research led to recommendations for improvement and potential extrapolation of these best practices to other HTA organizations

The Critical Role of Organic Chemistry in Drug Discovery.

Science.gov (United States)

Rotella, David P

2016-10-19

Small molecules remain the backbone for modern drug discovery. They are conceived and synthesized by medicinal chemists, many of whom were originally trained as organic chemists. Support from government and industry to provide training and personnel for continued development of this critical skill set has been declining for many years. This Viewpoint highlights the value of organic chemistry and organic medicinal chemists in the complex journey of drug discovery as a reminder that basic science support must be restored.

The Proteomics Big Challenge for Biomarkers and New Drug-Targets Discovery

Science.gov (United States)

Savino, Rocco; Paduano, Sergio; Preianò, Mariaimmacolata; Terracciano, Rosa

2012-01-01

In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets. PMID:23203042

Scientific workflows as productivity tools for drug discovery.

Science.gov (United States)

Shon, John; Ohkawa, Hitomi; Hammer, Juergen

2008-05-01

Large pharmaceutical companies annually invest tens to hundreds of millions of US dollars in research informatics to support their early drug discovery processes. Traditionally, most of these investments are designed to increase the efficiency of drug discovery. The introduction of do-it-yourself scientific workflow platforms has enabled research informatics organizations to shift their efforts toward scientific innovation, ultimately resulting in a possible increase in return on their investments. Unlike the handling of most scientific data and application integration approaches, researchers apply scientific workflows to in silico experimentation and exploration, leading to scientific discoveries that lie beyond automation and integration. This review highlights some key requirements for scientific workflow environments in the pharmaceutical industry that are necessary for increasing research productivity. Examples of the application of scientific workflows in research and a summary of recent platform advances are also provided.

Drug discovery for alopecia: gone today, hair tomorrow.

Science.gov (United States)

Santos, Zenildo; Avci, Pinar; Hamblin, Michael R

2015-03-01

Hair loss or alopecia affects the majority of the population at some time in their life, and increasingly, sufferers are demanding treatment. Three main types of alopecia (androgenic [AGA], areata [AA] and chemotherapy-induced [CIA]) are very different, and have their own laboratory models and separate drug-discovery efforts. In this article, the authors review the biology of hair, hair follicle (HF) cycling, stem cells and signaling pathways. AGA, due to dihydrotesterone, is treated by 5-α reductase inhibitors, androgen receptor blockers and ATP-sensitive potassium channel-openers. AA, which involves attack by CD8(+)NK group 2D-positive (NKG2D(+)) T cells, is treated with immunosuppressives, biologics and JAK inhibitors. Meanwhile, CIA is treated by apoptosis inhibitors, cytokines and topical immunotherapy. The desire to treat alopecia with an easy topical preparation is expected to grow with time, particularly with an increasing aging population. The discovery of epidermal stem cells in the HF has given new life to the search for a cure for baldness. Drug discovery efforts are being increasingly centered on these stem cells, boosting the hair cycle and reversing miniaturization of HF. Better understanding of the molecular mechanisms underlying the immune attack in AA will yield new drugs. New discoveries in HF neogenesis and low-level light therapy will undoubtedly have a role to play.

An invertebrate model for CNS drug discovery

DEFF Research Database (Denmark)

Al-Qadi, Sonia; Schiøtt, Morten; Hansen, Steen Honoré

2015-01-01

BACKGROUND: ABC efflux transporters at the blood brain barrier (BBB), namely the P-glycoprotein (P-gp), restrain the development of central nervous system (CNS) drugs. Consequently, early screening of CNS drug candidates is pivotal to identify those affected by efflux activity. Therefore, simple,...... barriers. CONCLUSION: Findings suggest a conserved mechanism of brain efflux activity between insects and vertebrates, confirming that this model holds promise for inexpensive and high-throughput screening relative to in vivo models, for CNS drug discovery....

Perspectives on bioanalytical mass spectrometry and automation in drug discovery.

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Janiszewski, John S; Liston, Theodore E; Cole, Mark J

2008-11-01

The use of high speed synthesis technologies has resulted in a steady increase in the number of new chemical entities active in the drug discovery research stream. Large organizations can have thousands of chemical entities in various stages of testing and evaluation across numerous projects on a weekly basis. Qualitative and quantitative measurements made using LC/MS are integrated throughout this process from early stage lead generation through candidate nomination. Nearly all analytical processes and procedures in modern research organizations are automated to some degree. This includes both hardware and software automation. In this review we discuss bioanalytical mass spectrometry and automation as components of the analytical chemistry infrastructure in pharma. Analytical chemists are presented as members of distinct groups with similar skillsets that build automated systems, manage test compounds, assays and reagents, and deliver data to project teams. The ADME-screening process in drug discovery is used as a model to highlight the relationships between analytical tasks in drug discovery. Emerging software and process automation tools are described that can potentially address gaps and link analytical chemistry related tasks. The role of analytical chemists and groups in modern 'industrialized' drug discovery is also discussed.

[Fragment-based drug discovery: concept and aim].

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Tanaka, Daisuke

2010-03-01

Fragment-Based Drug Discovery (FBDD) has been recognized as a newly emerging lead discovery methodology that involves biophysical fragment screening and chemistry-driven fragment-to-lead stages. Although fragments, defined as structurally simple and small compounds (typically FBDD primarily turns our attention to weakly but specifically binding fragments (hit fragments) as the starting point of medicinal chemistry. Hit fragments are then promoted to more potent lead compounds through linking or merging with another hit fragment and/or attaching functional groups. Another positive aspect of FBDD is ligand efficiency. Ligand efficiency is a useful guide in screening hit selection and hit-to-lead phases to achieve lead-likeness. Owing to these features, a number of successful applications of FBDD to "undruggable targets" (where HTS and other lead identification methods failed to identify useful lead compounds) have been reported. As a result, FBDD is now expected to complement more conventional methodologies. This review, as an introduction of the following articles, will summarize the fundamental concepts of FBDD and will discuss its advantages over other conventional drug discovery approaches.

The discovery of drug-induced illness.

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Jick, H

1977-03-03

The increased use of drugs (and the concurrent increased risks of drug-induced illness) require definition of relevant research areas and strategy. For established marketed drugs, research needs depend on the magnitudes of risk of an illness from a drug and the base-line risk. With the drug risk high and the base-line risk low, the problem surfaces in premarketing studies or through the epidemic that develops after marketing. If the drug adds slightly to a high base-line risk, the effect is undetectable. When both risks are low, adverse effects can be discovered by chance, but systematic case-referent studies can speed discovery. If both risks are high, clinical trials and nonexperimental studies may be used. With both risks intermediate, systematic evaluations, especially case-referent studies are needed. Newly marketed drugs should be routinely evaluated through compulsory registration and follow-up study of the earliest users.

High throughput electrophysiology: new perspectives for ion channel drug discovery

DEFF Research Database (Denmark)

Willumsen, Niels J; Bech, Morten; Olesen, Søren-Peter

2003-01-01

. A cornerstone in current drug discovery is high throughput screening assays which allow examination of the activity of specific ion channels though only to a limited extent. Conventional patch clamp remains the sole technique with sufficiently high time resolution and sensitivity required for precise and direct....... The introduction of new powerful HTS electrophysiological techniques is predicted to cause a revolution in ion channel drug discovery....

Plant natural products research in tuberculosis drug discovery and ...

African Journals Online (AJOL)

Plant natural products research in tuberculosis drug discovery and development: A situation report ... African Journal of Biotechnology ... tuberculosis (XDR-TB), call for the development of new anti-tuberculosis drugs to combat this disease.

Patient-Reported Outcomes Labeling for Products Approved by the Office of Hematology and Oncology Products of the US Food and Drug Administration (2010-2014).

Science.gov (United States)

Gnanasakthy, Ari; DeMuro, Carla; Clark, Marci; Haydysch, Emily; Ma, Esprit; Bonthapally, Vijayveer

2016-06-01

To review the use of patient-reported outcome (PRO) data in medical product labeling granted by the US Food and Drug Administration (FDA) for new molecular entities and biologic license applications by the FDA Office of Hematology and Oncology Products (OHOP) between January 2010 and December 2014, to elucidate challenges faced by OHOP for approving PRO labeling, and to understand challenges faced by drug manufacturers to include PRO end points in oncology clinical trials. FDA Drug Approval Reports by Month were reviewed to obtain the number of new molecular entities and biologic license applications approved from 2010 to 2014. Drugs approved by the FDA OHOP during this period were selected for further review, focusing on brand and generic name; approval date; applicant; indication; PRO labeling describing treatment benefit, measures, end point status, and significant results; FDA reviewer feedback on PRO end points; and study design of registration trials. First in class, priority review, fast track, orphan drug, or accelerated approval status was retrieved for selected oncology drugs from 2011 to 2014. Descriptive analyses were performed by using Microsoft Excel 2010. Of 160 drugs approved by the FDA (2010-2014), 40 were approved by OHOP. Three (7.5%) of the 40 received PRO-related labeling (abiraterone acetate, ruxolitinib phosphate, and crizotinib). Compared with nononcology drugs (2011-2014), oncology drugs were more likely to be orphan and first in class. The majority of oncology drug reviews by FDA were fast track, priority, or accelerated. Although symptoms and functional decrements are common among patients with cancer, PRO labeling is rare in the United States, likely because of logistical hurdles and oncology study design. Recent developments within the FDA OHOP to capture PROs in oncology studies for the purpose of product labeling are encouraging. © 2016 by American Society of Clinical Oncology.

Systems biology-embedded target validation: improving efficacy in drug discovery.

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Vandamme, Drieke; Minke, Benedikt A; Fitzmaurice, William; Kholodenko, Boris N; Kolch, Walter

2014-01-01

The pharmaceutical industry is faced with a range of challenges with the ever-escalating costs of drug development and a drying out of drug pipelines. By harnessing advances in -omics technologies and moving away from the standard, reductionist model of drug discovery, there is significant potential to reduce costs and improve efficacy. Embedding systems biology approaches in drug discovery, which seek to investigate underlying molecular mechanisms of potential drug targets in a network context, will reduce attrition rates by earlier target validation and the introduction of novel targets into the currently stagnant market. Systems biology approaches also have the potential to assist in the design of multidrug treatments and repositioning of existing drugs, while stratifying patients to give a greater personalization of medical treatment. © 2013 Wiley Periodicals, Inc.

Drug Discovery in an Academic Setting: Playing to the Strengths

OpenAIRE

Huryn, Donna M.

2013-01-01

Drug discovery and medicinal chemistry initiatives in academia provide an opportunity to create a unique environment that is distinct from the traditional industrial model. Two characteristics of a university setting that are not usually associated with pharma are the ability to pursue high-risk projects and a depth of expertise, infrastructure, and capabilities in focused areas. Encouraging, supporting, and fostering drug discovery efforts that take advantage of these an...

Drug discovery and developments in developing countries ...

African Journals Online (AJOL)

the major burden being in developing countries. Many of ... The driving force for drug discovery and development by pharmaceutical firms ... world and particularly in the third world countries ..... GFHR (2000) Global Forum for Health Research:.

Scientific Prediction and Prophetic Patenting in Drug Discovery.

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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.

Can biochemistry drive drug discovery beyond simple potency measurements?

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Chène, Patrick

2012-04-01

Among the fields of expertise required to develop drugs successfully, biochemistry holds a key position in drug discovery at the interface between chemistry, structural biology and cell biology. However, taking the example of protein kinases, it appears that biochemical assays are mostly used in the pharmaceutical industry to measure compound potency and/or selectivity. This limited use of biochemistry is surprising, given that detailed biochemical analyses are commonly used in academia to unravel molecular recognition processes. In this article, I show that biochemistry can provide invaluable information on the dynamics and energetics of compound-target interactions that cannot be obtained on the basis of potency measurements and structural data. Therefore, an extensive use of biochemistry in drug discovery could facilitate the identification and/or development of new drugs. Copyright © 2012 Elsevier Ltd. All rights reserved.

An Ontology for Description of Drug Discovery Investigations

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Qi Da

2010-12-01

Full Text Available The paper presents an ontology for the description of Drug Discovery Investigation (DDI. This has been developed through the use of a Robot Scientist “Eve”, and in consultation with industry. DDI aims to define the principle entities and the relations in the research and development phase of the drug discovery pipeline. DDI is highly transferable and extendable due to its adherence to accepted standards, and compliance with existing ontology resources. This enables DDI to be integrated with such related ontologies as the Vaccine Ontology, the Advancing Clinico-Genomic Trials on Cancer Master Ontology, etc. DDI is available at http://purl.org/ddi/wikipedia or http://purl.org/ddi/home

Simulation with quantum mechanics/molecular mechanics for drug discovery.

Science.gov (United States)

Barbault, Florent; Maurel, François

2015-10-01

Biological macromolecules, such as proteins or nucleic acids, are (still) molecules and thus they follow the same chemical rules that any simple molecule follows, even if their size generally renders accurate studies unhelpful. However, in the context of drug discovery, a detailed analysis of ligand association is required for understanding or predicting their interactions and hybrid quantum mechanics/molecular mechanics (QM/MM) computations are relevant tools to help elucidate this process. In this review, the authors explore the use of QM/MM for drug discovery. After a brief description of the molecular mechanics (MM) technique, the authors describe the subtractive and additive techniques for QM/MM computations. The authors then present several application cases in topics involved in drug discovery. QM/MM have been widely employed during the last decades to study chemical processes such as enzyme-inhibitor interactions. However, despite the enthusiasm around this area, plain MM simulations may be more meaningful than QM/MM. To obtain reliable results, the authors suggest fixing several keystone parameters according to the underlying chemistry of each studied system.

Emerging concepts in biomarker discovery; The US-Japan workshop on immunological molecular markers in oncology

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Tahara, Hideaki; Sato, Marimo; Thurin, Magdalena; Wang, Ena; Butterfield, Lisa H; Disis, Mary L; Fox, Bernard A; Lee, Peter P; Khleif, Samir N; Wigginton, Jon M; Ambs, Stefan; Akutsu, Yasunori; Chaussabel, Damien; Doki, Yuichiro; Eremin, Oleg; Fridman, Wolf Hervé; Hirohashi, Yoshihiko; Imai, Kohzoh; Jacobson, James; Jinushi, Masahisa; Kanamoto, Akira; Kashani-Sabet, Mohammed; Kato, Kazunori; Kawakami, Yutaka; Kirkwood, John M; Kleen, Thomas O; Lehmann, Paul V; Liotta, Lance; Lotze, Michael T; Maio, Michele; Malyguine, Anatoli; Masucci, Giuseppe; Matsubara, Hisahiro; Mayrand-Chung, Shawmarie; Nakamura, Kiminori; Nishikawa, Hiroyoshi; Palucka, A Karolina; Petricoin, Emanuel F; Pos, Zoltan; Ribas, Antoni; Rivoltini, Licia; Sato, Noriyuki; Shiku, Hiroshi; Slingluff, Craig L; Streicher, Howard; Stroncek, David F; Takeuchi, Hiroya; Toyota, Minoru; Wada, Hisashi; Wu, Xifeng; Wulfkuhle, Julia; Yaguchi, Tomonori; Zeskind, Benjamin; Zhao, Yingdong; Zocca, Mai-Britt; Marincola, Francesco M

2009-01-01

Supported by the Office of International Affairs, National Cancer Institute (NCI), the "US-Japan Workshop on Immunological Biomarkers in Oncology" was held in March 2009. The workshop was related to a task force launched by the International Society for the Biological Therapy of Cancer (iSBTc) and the United States Food and Drug Administration (FDA) to identify strategies for biomarker discovery and validation in the field of biotherapy. The effort will culminate on October 28th 2009 in the "iSBTc-FDA-NCI Workshop on Prognostic and Predictive Immunologic Biomarkers in Cancer", which will be held in Washington DC in association with the Annual Meeting. The purposes of the US-Japan workshop were a) to discuss novel approaches to enhance the discovery of predictive and/or prognostic markers in cancer immunotherapy; b) to define the state of the science in biomarker discovery and validation. The participation of Japanese and US scientists provided the opportunity to identify shared or discordant themes across the distinct immune genetic background and the diverse prevalence of disease between the two Nations. Converging concepts were identified: enhanced knowledge of interferon-related pathways was found to be central to the understanding of immune-mediated tissue-specific destruction (TSD) of which tumor rejection is a representative facet. Although the expression of interferon-stimulated genes (ISGs) likely mediates the inflammatory process leading to tumor rejection, it is insufficient by itself and the associated mechanisms need to be identified. It is likely that adaptive immune responses play a broader role in tumor rejection than those strictly related to their antigen-specificity; likely, their primary role is to trigger an acute and tissue-specific inflammatory response at the tumor site that leads to rejection upon recruitment of additional innate and adaptive immune mechanisms. Other candidate systemic and/or tissue-specific biomarkers were recognized that

Emerging concepts in biomarker discovery; The US-Japan workshop on immunological molecular markers in oncology

Directory of Open Access Journals (Sweden)

Rivoltini Licia

2009-06-01

Full Text Available Abstract Supported by the Office of International Affairs, National Cancer Institute (NCI, the "US-Japan Workshop on Immunological Biomarkers in Oncology" was held in March 2009. The workshop was related to a task force launched by the International Society for the Biological Therapy of Cancer (iSBTc and the United States Food and Drug Administration (FDA to identify strategies for biomarker discovery and validation in the field of biotherapy. The effort will culminate on October 28th 2009 in the "iSBTc-FDA-NCI Workshop on Prognostic and Predictive Immunologic Biomarkers in Cancer", which will be held in Washington DC in association with the Annual Meeting. The purposes of the US-Japan workshop were a to discuss novel approaches to enhance the discovery of predictive and/or prognostic markers in cancer immunotherapy; b to define the state of the science in biomarker discovery and validation. The participation of Japanese and US scientists provided the opportunity to identify shared or discordant themes across the distinct immune genetic background and the diverse prevalence of disease between the two Nations. Converging concepts were identified: enhanced knowledge of interferon-related pathways was found to be central to the understanding of immune-mediated tissue-specific destruction (TSD of which tumor rejection is a representative facet. Although the expression of interferon-stimulated genes (ISGs likely mediates the inflammatory process leading to tumor rejection, it is insufficient by itself and the associated mechanisms need to be identified. It is likely that adaptive immune responses play a broader role in tumor rejection than those strictly related to their antigen-specificity; likely, their primary role is to trigger an acute and tissue-specific inflammatory response at the tumor site that leads to rejection upon recruitment of additional innate and adaptive immune mechanisms. Other candidate systemic and/or tissue-specific biomarkers

Discovery and development of new antibacterial drugs: learning from experience?

Science.gov (United States)

Jackson, Nicole; Czaplewski, Lloyd; Piddock, Laura J V

2018-06-01

Antibiotic (antibacterial) resistance is a serious global problem and the need for new treatments is urgent. The current antibiotic discovery model is not delivering new agents at a rate that is sufficient to combat present levels of antibiotic resistance. This has led to fears of the arrival of a 'post-antibiotic era'. Scientific difficulties, an unfavourable regulatory climate, multiple company mergers and the low financial returns associated with antibiotic drug development have led to the withdrawal of many pharmaceutical companies from the field. The regulatory climate has now begun to improve, but major scientific hurdles still impede the discovery and development of novel antibacterial agents. To facilitate discovery activities there must be increased understanding of the scientific problems experienced by pharmaceutical companies. This must be coupled with addressing the current antibiotic resistance crisis so that compounds and ultimately drugs are delivered to treat the most urgent clinical challenges. By understanding the causes of the failures and successes of the pharmaceutical industry's research history, duplication of discovery programmes will be reduced, increasing the productivity of the antibiotic drug discovery pipeline by academia and small companies. The most important scientific issues to address are getting molecules into the Gram-negative bacterial cell and avoiding their efflux. Hence screening programmes should focus their efforts on whole bacterial cells rather than cell-free systems. Despite falling out of favour with pharmaceutical companies, natural product research still holds promise for providing new molecules as a basis for discovery.

Fragment-based drug discovery and protein–protein interactions

Directory of Open Access Journals (Sweden)

Turnbull AP

2014-09-01

Full Text Available Andrew P Turnbull,1 Susan M Boyd,2 Björn Walse31CRT Discovery Laboratories, Department of Biological Sciences, Birkbeck, University of London, London, UK; 2IOTA Pharmaceuticals Ltd, Cambridge, UK; 3SARomics Biostructures AB, Lund, SwedenAbstract: Protein–protein interactions (PPIs are involved in many biological processes, with an estimated 400,000 PPIs within the human proteome. There is significant interest in exploiting the relatively unexplored potential of these interactions in drug discovery, driven by the need to find new therapeutic targets. Compared with classical drug discovery against targets with well-defined binding sites, developing small-molecule inhibitors against PPIs where the contact surfaces are frequently more extensive and comparatively flat, with most of the binding energy localized in “hot spots”, has proven far more challenging. However, despite the difficulties associated with targeting PPIs, important progress has been made in recent years with fragment-based drug discovery playing a pivotal role in improving their tractability. Computational and empirical approaches can be used to identify hot-spot regions and assess the druggability and ligandability of new targets, whilst fragment screening campaigns can detect low-affinity fragments that either directly or indirectly perturb the PPI. Once fragment hits have been identified and confirmed using biochemical and biophysical approaches, three-dimensional structural data derived from nuclear magnetic resonance or X-ray crystallography can be used to drive medicinal chemistry efforts towards the development of more potent inhibitors. A small-scale comparison presented in this review of “standard” fragments with those targeting PPIs has revealed that the latter tend to be larger, be more lipophilic, and contain more polar (acid/base functionality, whereas three-dimensional descriptor data indicate that there is little difference in their three

Structure and organization of drug-target networks: insights from genomic approaches for drug discovery.

Science.gov (United States)

Janga, Sarath Chandra; Tzakos, Andreas

2009-12-01

Recent years have seen an explosion in the amount of "omics" data and the integration of several disciplines, which has influenced all areas of life sciences including that of drug discovery. Several lines of evidence now suggest that the traditional notion of "one drug-one protein" for one disease does not hold any more and that treatment for most complex diseases can best be attempted using polypharmacological approaches. In this review, we formalize the definition of a drug-target network by decomposing it into drug, target and disease spaces and provide an overview of our understanding in recent years about its structure and organizational principles. We discuss advances made in developing promiscuous drugs following the paradigm of polypharmacology and reveal their advantages over traditional drugs for targeting diseases such as cancer. We suggest that drug-target networks can be decomposed to be studied at a variety of levels and argue that such network-based approaches have important implications in understanding disease phenotypes and in accelerating drug discovery. We also discuss the potential and scope network pharmacology promises in harnessing the vast amount of data from high-throughput approaches for therapeutic advantage.

Cloud computing approaches to accelerate drug discovery value chain.

Science.gov (United States)

Garg, Vibhav; Arora, Suchir; Gupta, Chitra

2011-12-01

Continued advancements in the area of technology have helped high throughput screening (HTS) evolve from a linear to parallel approach by performing system level screening. Advanced experimental methods used for HTS at various steps of drug discovery (i.e. target identification, target validation, lead identification and lead validation) can generate data of the order of terabytes. As a consequence, there is pressing need to store, manage, mine and analyze this data to identify informational tags. This need is again posing challenges to computer scientists to offer the matching hardware and software infrastructure, while managing the varying degree of desired computational power. Therefore, the potential of "On-Demand Hardware" and "Software as a Service (SAAS)" delivery mechanisms cannot be denied. This on-demand computing, largely referred to as Cloud Computing, is now transforming the drug discovery research. Also, integration of Cloud computing with parallel computing is certainly expanding its footprint in the life sciences community. The speed, efficiency and cost effectiveness have made cloud computing a 'good to have tool' for researchers, providing them significant flexibility, allowing them to focus on the 'what' of science and not the 'how'. Once reached to its maturity, Discovery-Cloud would fit best to manage drug discovery and clinical development data, generated using advanced HTS techniques, hence supporting the vision of personalized medicine.

Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities.

Science.gov (United States)

Venkatakrishnan, K; Friberg, L E; Ouellet, D; Mettetal, J T; Stein, A; Trocóniz, I F; Bruno, R; Mehrotra, N; Gobburu, J; Mould, D R

2015-01-01

Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety. © 2014 American Society for Clinical Pharmacology and Therapeutics.

Comment on "drug discovery: turning the titanic".

Science.gov (United States)

Lesterhuis, W Joost; Bosco, Anthony; Lake, Richard A

2014-03-26

The pathobiology-based approach to research and development has been the dominant paradigm for successful drug discovery over the last decades. We propose that the molecular and cellular events that govern a resolving, rather than an evolving, disease may reveal new druggable pathways.

Molecular Networking As a Drug Discovery, Drug Metabolism, and Precision Medicine Strategy.

Science.gov (United States)

Quinn, Robert A; Nothias, Louis-Felix; Vining, Oliver; Meehan, Michael; Esquenazi, Eduardo; Dorrestein, Pieter C

2017-02-01

Molecular networking is a tandem mass spectrometry (MS/MS) data organizational approach that has been recently introduced in the drug discovery, metabolomics, and medical fields. The chemistry of molecules dictates how they will be fragmented by MS/MS in the gas phase and, therefore, two related molecules are likely to display similar fragment ion spectra. Molecular networking organizes the MS/MS data as a relational spectral network thereby mapping the chemistry that was detected in an MS/MS-based metabolomics experiment. Although the wider utility of molecular networking is just beginning to be recognized, in this review we highlight the principles behind molecular networking and its use for the discovery of therapeutic leads, monitoring drug metabolism, clinical diagnostics, and emerging applications in precision medicine. Copyright © 2016. Published by Elsevier Ltd.

Pharmacovigilance in oncology: pattern of spontaneous notifications, incidence of adverse drug reactions and under-reporting

Directory of Open Access Journals (Sweden)

Marília Berlofa Visacri

2014-04-01

Full Text Available The high toxicity and narrow therapeutic window of antineoplastic agents makes pharmacovigilance studies essential in oncology. The objectives of the current study were to analyze the pattern of spontaneous notifications of adverse drug reactions (ADRs in oncology patients and to analyze the incidence of ADRs reported by outpatients on antineoplastic treatment in a tertiary care teaching hospital. To compose the pattern of ADR, the notification forms of reactions in oncology patients in 2010 were reviewed, and the reactions were classified based on the drug involved, mechanism, causality, and severity. To evaluate the incidence of reactions, a questionnaire at the time of chemotherapy was included, and the severity was classified based on the Common Terminology Criteria. The profiles of the 10 responses reported to the Pharmacovigilance Sector were type B, severe, possible, and they were primarily related to platinum compounds and taxanes. When the incidence of reactions was analyzed, it was observed that nausea, alopecia, fatigue, diarrhea, and taste disturbance were the most frequently reported reactions by oncology patients, and the grade 3 and 4 reactions were not reported. Based on this analysis, it is proposed that health professionals should be trained regarding notifications and clinical pharmacists should increasingly be brought on board to reduce under-reporting of ADRs.

Fragment-based drug discovery using rational design.

Science.gov (United States)

Jhoti, H

2007-01-01

Fragment-based drug discovery (FBDD) is established as an alternative approach to high-throughput screening for generating novel small molecule drug candidates. In FBDD, relatively small libraries of low molecular weight compounds (or fragments) are screened using sensitive biophysical techniques to detect their binding to the target protein. A lower absolute affinity of binding is expected from fragments, compared to much higher molecular weight hits detected by high-throughput screening, due to their reduced size and complexity. Through the use of iterative cycles of medicinal chemistry, ideally guided by three-dimensional structural data, it is often then relatively straightforward to optimize these weak binding fragment hits into potent and selective lead compounds. As with most other lead discovery methods there are two key components of FBDD; the detection technology and the compound library. In this review I outline the two main approaches used for detecting the binding of low affinity fragments and also some of the key principles that are used to generate a fragment library. In addition, I describe an example of how FBDD has led to the generation of a drug candidate that is now being tested in clinical trials for the treatment of cancer.

Fragment approaches in structure-based drug discovery

International Nuclear Information System (INIS)

Hubbard, Roderick E.

2008-01-01

Fragment-based methods are successfully generating novel and selective drug-like inhibitors of protein targets, with a number of groups reporting compounds entering clinical trials. This paper summarizes the key features of the approach as one of the tools in structure-guided drug discovery. There has been considerable interest recently in what is known as 'fragment-based lead discovery'. The novel feature of the approach is to begin with small low-affinity compounds. The main advantage is that a larger potential chemical diversity can be sampled with fewer compounds, which is particularly important for new target classes. The approach relies on careful design of the fragment library, a method that can detect binding of the fragment to the protein target, determination of the structure of the fragment bound to the target, and the conventional use of structural information to guide compound optimization. In this article the methods are reviewed, and experiences in fragment-based discovery of lead series of compounds against kinases such as PDK1 and ATPases such as Hsp90 are discussed. The examples illustrate some of the key benefits and issues of the approach and also provide anecdotal examples of the patterns seen in selectivity and the binding mode of fragments across different protein targets

Current approaches for the discovery of drugs that deter substance and drug abuse.

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Yasgar, Adam; Simeonov, Anton

2014-11-01

Much has been presented and debated on the topic of drug abuse and its multidimensional nature, including the role of society and its customs and laws, economical factors, and the magnitude and nature of the burden. Given the complex nature of the receptors and pathways implicated in regulation of the cognitive and behavioral processes associated with addiction, a large number of molecular targets have been interrogated during recent years to discover starting points for development of small-molecule interventions. This review describes recent developments in the field of early drug discovery for drug abuse interventions with an emphasis on the advances published during the 2012 - 2014 period. Technologically, the processes/platforms utilized in drug abuse drug discovery are nearly identical to those used in the other disease areas. A key complicating factor in drug abuse research is the enormous biological complexity surrounding the brain processes involved and the associated difficulty in finding 'good' targets and achieving exquisite selectivity of treatment agents. While tremendous progress has been made during recent years to use the power of high-throughput technologies to discover proof-of-principle molecules for many new targets, next-generation models will be especially important in this field. Examples include: seeking advantageous drug-drug combinations, the use of automated whole-animal behavioral screening systems, advancing our understanding of the role of epigenetics in drug addiction and the employment of organoid-level 3D test platforms (also referred to as tissue-chip or organs-on-chip).

Conference Abstracts: Translational Science and Drug Discovery ...

African Journals Online (AJOL)

... and Drug Discovery: Impact on Health, Wellness, Environment and Economics" conference, July 27-29th, 2015, at the Hennessy Park Hotel, Ebène Cybercity, Mauritius. The conference was hosted by the Society for Free radical Research Africa and the International Association of Medical and Biomedical Researchers.

Experiences in fragment-based drug discovery.

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Murray, Christopher W; Verdonk, Marcel L; Rees, David C

2012-05-01

Fragment-based drug discovery (FBDD) has become established in both industry and academia as an alternative approach to high-throughput screening for the generation of chemical leads for drug targets. In FBDD, specialised detection methods are used to identify small chemical compounds (fragments) that bind to the drug target, and structural biology is usually employed to establish their binding mode and to facilitate their optimisation. In this article, we present three recent and successful case histories in FBDD. We then re-examine the key concepts and challenges of FBDD with particular emphasis on recent literature and our own experience from a substantial number of FBDD applications. Our opinion is that careful application of FBDD is living up to its promise of delivering high quality leads with good physical properties and that in future many drug molecules will be derived from fragment-based approaches. Copyright © 2012 Elsevier Ltd. All rights reserved.

New perspectives on innovative drug discovery: an overview.

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Pan, Si Yuan; Pan, Shan; Yu, Zhi-Ling; Ma, Dik-Lung; Chen, Si-Bao; Fong, Wang-Fun; Han, Yi-Fan; Ko, Kam-Ming

2010-01-01

Despite advances in technology, drug discovery is still a lengthy, expensive, difficult, and inefficient process, with a low rate of success. Today, advances in biomedical science have brought about great strides in therapeutic interventions for a wide spectrum of diseases. The advent of biochemical techniques and cutting-edge bio/chemical technologies has made available a plethora of practical approaches to drug screening and design. In 2010, the total sales of the global pharmaceutical market will reach 600 billion US dollars and expand to over 975 billion dollars by 2013. The aim of this review is to summarize available information on contemporary approaches and strategies in the discovery of novel therapeutic agents, especially from the complementary and alternative medicines, including natural products and traditional remedies such as Chinese herbal medicine.

Stem cell technology for drug discovery and development.

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Hook, Lilian A

2012-04-01

Stem cells have enormous potential to revolutionise the drug discovery process at all stages, from target identification through to toxicology studies. Their ability to generate physiologically relevant cells in limitless supply makes them an attractive alternative to currently used recombinant cell lines or primary cells. However, realisation of the full potential of stem cells is currently hampered by the difficulty in routinely directing stem cell differentiation to reproducibly and cost effectively generate pure populations of specific cell types. In this article we discuss how stem cells have already been used in the drug discovery process and how novel technologies, particularly in relation to stem cell differentiation, can be applied to attain widespread adoption of stem cell technology by the pharmaceutical industry. Copyright © 2011 Elsevier Ltd. All rights reserved.

From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

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Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

2017-11-01

Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.

Drug discovery in an academic setting: playing to the strengths.

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Huryn, Donna M

2013-03-14

Drug discovery and medicinal chemistry initiatives in academia provide an opportunity to create a unique environment that is distinct from the traditional industrial model. Two characteristics of a university setting that are not usually associated with pharma are the ability to pursue high-risk projects and a depth of expertise, infrastructure, and capabilities in focused areas. Encouraging, supporting, and fostering drug discovery efforts that take advantage of these and other distinguishing characteristics of an academic setting can lead to novel and innovative therapies that might not be discovered otherwise.

Financing drug discovery for orphan diseases.

Science.gov (United States)

Fagnan, David E; Gromatzky, Austin A; Stein, Roger M; Fernandez, Jose-Maria; Lo, Andrew W

2014-05-01

Recently proposed 'megafund' financing methods for funding translational medicine and drug development require billions of dollars in capital per megafund to de-risk the drug discovery process enough to issue long-term bonds. Here, we demonstrate that the same financing methods can be applied to orphan drug development but, because of the unique nature of orphan diseases and therapeutics (lower development costs, faster FDA approval times, lower failure rates and lower correlation of failures among disease targets) the amount of capital needed to de-risk such portfolios is much lower in this field. Numerical simulations suggest that an orphan disease megafund of only US$575 million can yield double-digit expected rates of return with only 10-20 projects in the portfolio. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Chimeric mice with humanized liver: Application in drug metabolism and pharmacokinetics studies for drug discovery.

Science.gov (United States)

Naritomi, Yoichi; Sanoh, Seigo; Ohta, Shigeru

2018-02-01

Predicting human drug metabolism and pharmacokinetics (PK) is key to drug discovery. In particular, it is important to predict human PK, metabolite profiles and drug-drug interactions (DDIs). Various methods have been used for such predictions, including in vitro metabolic studies using human biological samples, such as hepatic microsomes and hepatocytes, and in vivo studies using experimental animals. However, prediction studies using these methods are often inconclusive due to discrepancies between in vitro and in vivo results, and interspecies differences in drug metabolism. Further, the prediction methods have changed from qualitative to quantitative to solve these issues. Chimeric mice with humanized liver have been developed, in which mouse liver cells are mostly replaced with human hepatocytes. Since human drug metabolizing enzymes are expressed in the liver of these mice, they are regarded as suitable models for mimicking the drug metabolism and PK observed in humans; therefore, these mice are useful for predicting human drug metabolism and PK. In this review, we discuss the current state, issues, and future directions of predicting human drug metabolism and PK using chimeric mice with humanized liver in drug discovery. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

Open Access Could Transform Drug Discovery: A Case Study of JQ1.

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Arshad, Zeeshaan; Smith, James; Roberts, Mackenna; Lee, Wen Hwa; Davies, Ben; Bure, Kim; Hollander, Georg A; Dopson, Sue; Bountra, Chas; Brindley, David

2016-01-01

The cost to develop a new drug from target discovery to market is a staggering $1.8 billion, largely due to the very high attrition rate of drug candidates and the lengthy transition times during development. Open access is an emerging model of open innovation that places no restriction on the use of information and has the potential to accelerate the development of new drugs. To date, no quantitative assessment has yet taken place to determine the effects and viability of open access on the process of drug translation. This need is addressed within this study. The literature and intellectual property landscapes of the drug candidate JQ1, which was made available on an open access basis when discovered, and conventionally developed equivalents that were not are compared using the Web of Science and Thomson Innovation software, respectively. Results demonstrate that openly sharing the JQ1 molecule led to a greater uptake by a wider and more multi-disciplinary research community. A comparative analysis of the patent landscapes for each candidate also found that the broader scientific diaspora of the publically released JQ1 data enhanced innovation, evidenced by a greater number of downstream patents filed in relation to JQ1. The authors' findings counter the notion that open access drug discovery would leak commercial intellectual property. On the contrary, JQ1 serves as a test case to evidence that open access drug discovery can be an economic model that potentially improves efficiency and cost of drug discovery and its subsequent commercialization.

A role for physicians in ethnopharmacology and drug discovery.

Science.gov (United States)

Raza, Mohsin

2006-04-06

Ethnopharmacology investigations classically involved traditional healers, botanists, anthropologists, chemists and pharmacologists. The role of some groups of researchers but not of physician has been highlighted and well defined in ethnopharmacological investigations. Historical data shows that discovery of several important modern drugs of herbal origin owe to the medical knowledge and clinical expertise of physicians. Current trends indicate negligible role of physicians in ethnopharmacological studies. Rising cost of modern drug development is attributed to the lack of classical ethnopharmacological approach. Physicians can play multiple roles in the ethnopharmacological studies to facilitate drug discovery as well as to rescue authentic traditional knowledge of use of medicinal plants. These include: (1) Ethnopharmacological field work which involves interviewing healers, interpreting traditional terminologies into their modern counterparts, examining patients consuming herbal remedies and identifying the disease for which an herbal remedy is used. (2) Interpretation of signs and symptoms mentioned in ancient texts and suggesting proper use of old traditional remedies in the light of modern medicine. (3) Clinical studies on herbs and their interaction with modern medicines. (4) Advising pharmacologists to carryout laboratory studies on herbs observed during field studies. (5) Work in collaboration with local healers to strengthen traditional system of medicine in a community. In conclusion, physician's involvement in ethnopharmacological studies will lead to more reliable information on traditional use of medicinal plants both from field and ancient texts, more focused and cheaper natural product based drug discovery, as well as bridge the gap between traditional and modern medicine.

ACFIS: a web server for fragment-based drug discovery

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Hao, Ge-Fei; Jiang, Wen; Ye, Yuan-Nong; Wu, Feng-Xu; Zhu, Xiao-Lei; Guo, Feng-Biao; Yang, Guang-Fu

2016-01-01

In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown ‘chemical space’ to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for ‘chemical space’, which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/. PMID:27150808

Use of machine learning approaches for novel drug discovery.

Science.gov (United States)

Lima, Angélica Nakagawa; Philot, Eric Allison; Trossini, Gustavo Henrique Goulart; Scott, Luis Paulo Barbour; Maltarollo, Vinícius Gonçalves; Honorio, Kathia Maria

2016-01-01

The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds. This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening. Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.

Preparative Scale Resolution of Enantiomers Enables Accelerated Drug Discovery and Development

Directory of Open Access Journals (Sweden)

Hanna Leek

2017-01-01

Full Text Available The provision of pure enantiomers is of increasing importance not only for the pharmaceutical industry but also for agro-chemistry and biotechnology. In drug discovery and development, the enantiomers of a chiral drug depict unique chemical and pharmacological behaviors in a chiral environment, such as the human body, in which the stereochemistry of the chiral drugs determines their pharmacokinetic, pharmacodynamic and toxicological properties. We present a number of challenging case studies of up-to-kilogram separations of racemic or enriched isomer mixtures using preparative liquid chromatography and super critical fluid chromatography to generate individual enantiomers that have enabled the development of new candidate drugs within AstraZeneca. The combination of chromatography and racemization as well as strategies on when to apply preparative chiral chromatography of enantiomers in a multi-step synthesis of a drug compound can further facilitate accelerated drug discovery and the early clinical evaluation of the drug candidates.

Computer-Aided Drug Design Applied to Marine Drug Discovery: Meridianins as Alzheimer's Disease Therapeutic Agents.

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Llorach-Pares, Laura; Nonell-Canals, Alfons; Sanchez-Martinez, Melchor; Avila, Conxita

2017-11-27

Computer-aided drug discovery/design (CADD) techniques allow the identification of natural products that are capable of modulating protein functions in pathogenesis-related pathways, constituting one of the most promising lines followed in drug discovery. In this paper, we computationally evaluated and reported the inhibitory activity found in meridianins A-G, a group of marine indole alkaloids isolated from the marine tunicate Aplidium , against various protein kinases involved in Alzheimer's disease (AD), a neurodegenerative pathology characterized by the presence of neurofibrillary tangles (NFT). Balance splitting between tau kinase and phosphate activities caused tau hyperphosphorylation and, thereby, its aggregation and NTF formation. Inhibition of specific kinases involved in its phosphorylation pathway could be one of the key strategies to reverse tau hyperphosphorylation and would represent an approach to develop drugs to palliate AD symptoms. Meridianins bind to the adenosine triphosphate (ATP) binding site of certain protein kinases, acting as ATP competitive inhibitors. These compounds show very promising scaffolds to design new drugs against AD, which could act over tau protein kinases Glycogen synthetase kinase-3 Beta (GSK3β) and Casein kinase 1 delta (CK1δ, CK1D or KC1D), and dual specificity kinases as dual specificity tyrosine phosphorylation regulated kinase 1 (DYRK1A) and cdc2-like kinases (CLK1). This work is aimed to highlight the role of CADD techniques in marine drug discovery and to provide precise information regarding the binding mode and strength of meridianins against several protein kinases that could help in the future development of anti-AD drugs.

The University of New Mexico Center for Molecular Discovery

Science.gov (United States)

Edwards, Bruce S.; Gouveia, Kristine; Oprea, Tudor I.; Sklar, Larry A.

2015-01-01

The University of New Mexico Center for Molecular Discovery (UNMCMD) is an academic research center that specializes in discovery using high throughput flow cytometry (HTFC) integrated with virtual screening, as well as knowledge mining and drug informatics. With a primary focus on identifying small molecules that can be used as chemical probes and as leads for drug discovery, it is a central core resource for research and translational activities at UNM that supports implementation and management of funded screening projects as well as “up-front” services such as consulting for project design and implementation, assistance in assay development and generation of preliminary data for pilot projects in support of competitive grant applications. The HTFC platform in current use represents advanced, proprietary technology developed at UNM that is now routinely capable of processing bioassays arrayed in 96-, 384- and 1536-well formats at throughputs of 60,000 or more wells per day. Key programs at UNMCMD include screening of research targets submitted by the international community through NIH’s Molecular Libraries Program; a multi-year effort involving translational partnerships at UNM directed towards drug repurposing - identifying new uses for clinically approved drugs; and a recently established personalized medicine initiative for advancing cancer therapy by the application of “smart” oncology drugs in selected patients based on response patterns of their cancer cells in vitro. UNMCMD discoveries, innovation, and translation have contributed to a wealth of inventions, patents, licenses and publications, as well as startup companies, clinical trials and a multiplicity of domestic and international collaborative partnerships to further the research enterprise. PMID:24409953

An overview of aldehyde oxidase: an enzyme of emerging importance in novel drug discovery.

Science.gov (United States)

Rashidi, Mohammad-Reza; Soltani, Somaieh

2017-03-01

Given the rising trend in medicinal chemistry strategy to reduce cytochrome P450-dependent metabolism, aldehyde oxidase (AOX) has recently gained increased attention in drug discovery programs and the number of drug candidates that are metabolized by AOX is steadily growing. Areas covered: Despite the emerging importance of AOX in drug discovery, there are certain major recognized problems associated with AOX-mediated metabolism of drugs. Intra- and inter-species variations in AOX activity, the lack of reliable and predictive animal models using the common experimental animals, and failure in the predictions of in vivo metabolic activity of AOX using traditional in vitro methods are among these issues that are covered in this article. A comprehensive review of computational human AOX (hAOX) related studies are also provided. Expert opinion: Following the recent progress in the stem cell field, the authors recommend the application of organoids technology as an effective tool to solve the fundamental problems associated with the evaluation of AOX in drug discovery. The recent success in resolving the hAOX crystal structure can too be another valuable data source for the study of AOX-catalyzed metabolism of new drug candidates, using computer-aided drug discovery methods.

Cancer in silico drug discovery: a systems biology tool for identifying candidate drugs to target specific molecular tumor subtypes.

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San Lucas, F Anthony; Fowler, Jerry; Chang, Kyle; Kopetz, Scott; Vilar, Eduardo; Scheet, Paul

2014-12-01

Large-scale cancer datasets such as The Cancer Genome Atlas (TCGA) allow researchers to profile tumors based on a wide range of clinical and molecular characteristics. Subsequently, TCGA-derived gene expression profiles can be analyzed with the Connectivity Map (CMap) to find candidate drugs to target tumors with specific clinical phenotypes or molecular characteristics. This represents a powerful computational approach for candidate drug identification, but due to the complexity of TCGA and technology differences between CMap and TCGA experiments, such analyses are challenging to conduct and reproduce. We present Cancer in silico Drug Discovery (CiDD; scheet.org/software), a computational drug discovery platform that addresses these challenges. CiDD integrates data from TCGA, CMap, and Cancer Cell Line Encyclopedia (CCLE) to perform computational drug discovery experiments, generating hypotheses for the following three general problems: (i) determining whether specific clinical phenotypes or molecular characteristics are associated with unique gene expression signatures; (ii) finding candidate drugs to repress these expression signatures; and (iii) identifying cell lines that resemble the tumors being studied for subsequent in vitro experiments. The primary input to CiDD is a clinical or molecular characteristic. The output is a biologically annotated list of candidate drugs and a list of cell lines for in vitro experimentation. We applied CiDD to identify candidate drugs to treat colorectal cancers harboring mutations in BRAF. CiDD identified EGFR and proteasome inhibitors, while proposing five cell lines for in vitro testing. CiDD facilitates phenotype-driven, systematic drug discovery based on clinical and molecular data from TCGA. ©2014 American Association for Cancer Research.

Providing data science support for systems pharmacology and its implications to drug discovery.

Science.gov (United States)

Hart, Thomas; Xie, Lei

2016-01-01

The conventional one-drug-one-target-one-disease drug discovery process has been less successful in tracking multi-genic, multi-faceted complex diseases. Systems pharmacology has emerged as a new discipline to tackle the current challenges in drug discovery. The goal of systems pharmacology is to transform huge, heterogeneous, and dynamic biological and clinical data into interpretable and actionable mechanistic models for decision making in drug discovery and patient treatment. Thus, big data technology and data science will play an essential role in systems pharmacology. This paper critically reviews the impact of three fundamental concepts of data science on systems pharmacology: similarity inference, overfitting avoidance, and disentangling causality from correlation. The authors then discuss recent advances and future directions in applying the three concepts of data science to drug discovery, with a focus on proteome-wide context-specific quantitative drug target deconvolution and personalized adverse drug reaction prediction. Data science will facilitate reducing the complexity of systems pharmacology modeling, detecting hidden correlations between complex data sets, and distinguishing causation from correlation. The power of data science can only be fully realized when integrated with mechanism-based multi-scale modeling that explicitly takes into account the hierarchical organization of biological systems from nucleic acid to proteins, to molecular interaction networks, to cells, to tissues, to patients, and to populations.

Outsourcing drug discovery to India and China: from surviving to thriving.

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Subramaniam, Swaminathan; Dugar, Sundeep

2012-10-01

Global pharmaceutical companies face an increasingly harsh environment for their primary business of selling medicines. They have to contend with a spiraling decline in the productivity of their R&D programs that is guaranteed to severely diminish their growth prospects. Outsourcing of drug discovery activities to low-cost locations is a growing response to this crisis. However, the upsides to outsourcing are capped by the failure of global pharmaceutical companies to take advantage of the full range of possibilities that this model provides. Companies that radically rethink and transform the way they conduct R&D, such as seeking the benefits of low-cost locations in India and China will be the ones that thrive in this environment. In this article we present our views on how the outsourcing model in drug discovery should go beyond increasing the efficiency of existing drug discovery processes to a fundamental rethink and re-engineering of these processes. Copyright © 2012. Published by Elsevier Ltd.

Lessons from Hot Spot Analysis for Fragment-Based Drug Discovery.

Science.gov (United States)

Hall, David R; Kozakov, Dima; Whitty, Adrian; Vajda, Sandor

2015-11-01

Analysis of binding energy hot spots at protein surfaces can provide crucial insights into the prospects for successful application of fragment-based drug discovery (FBDD), and whether a fragment hit can be advanced into a high-affinity, drug-like ligand. The key factor is the strength of the top ranking hot spot, and how well a given fragment complements it. We show that published data are sufficient to provide a sophisticated and quantitative understanding of how hot spots derive from a protein 3D structure, and how their strength, number, and spatial arrangement govern the potential for a surface site to bind to fragment-sized and larger ligands. This improved understanding provides important guidance for the effective application of FBDD in drug discovery. Copyright © 2015 Elsevier Ltd. All rights reserved.

G-protein-coupled receptors: new approaches to maximise the impact of GPCRS in drug discovery.

Science.gov (United States)

Davey, John

2004-04-01

IBC's Drug Discovery Technology Series is a group of conferences highlighting technological advances and applications in niche areas of the drug discovery pipeline. This 2-day meeting focused on G-protein-coupled receptors (GPCRs), probably the most important and certainly the most valuable class of targets for drug discovery. The meeting was chaired by J Beesley (Vice President, European Business Development for LifeSpan Biosciences, Seattle, USA) and included 17 presentations on various aspects of GPCR activity, drug screens and therapeutic analyses. Keynote Addresses covered two of the emerging areas in GPCR regulation; receptor dimerisation (G Milligan, Professor of Molecular Pharmacology and Biochemistry, University of Glasgow, UK) and proteins that interact with GPCRs (J Bockaert, Laboratory of Functional Genomics, CNRS Montpellier, France). A third Keynote Address from W Thomsen (Director of GPCR Drug Screening, Arena Pharmaceuticals, USA) discussed Arena's general approach to drug discovery and illustrated this with reference to the development of an agonist with potential efficacy in Type II diabetes.

The use of web ontology languages and other semantic web tools in drug discovery.

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Chen, Huajun; Xie, Guotong

2010-05-01

To optimize drug development processes, pharmaceutical companies require principled approaches to integrate disparate data on a unified infrastructure, such as the web. The semantic web, developed on the web technology, provides a common, open framework capable of harmonizing diversified resources to enable networked and collaborative drug discovery. We survey the state of art of utilizing web ontologies and other semantic web technologies to interlink both data and people to support integrated drug discovery across domains and multiple disciplines. Particularly, the survey covers three major application categories including: i) semantic integration and open data linking; ii) semantic web service and scientific collaboration and iii) semantic data mining and integrative network analysis. The reader will gain: i) basic knowledge of the semantic web technologies; ii) an overview of the web ontology landscape for drug discovery and iii) a basic understanding of the values and benefits of utilizing the web ontologies in drug discovery. i) The semantic web enables a network effect for linking open data for integrated drug discovery; ii) The semantic web service technology can support instant ad hoc collaboration to improve pipeline productivity and iii) The semantic web encourages publishing data in a semantic way such as resource description framework attributes and thus helps move away from a reliance on pure textual content analysis toward more efficient semantic data mining.

Fragment-based approaches to anti-HIV drug discovery: state of the art and future opportunities.

Science.gov (United States)

Huang, Boshi; Kang, Dongwei; Zhan, Peng; Liu, Xinyong

2015-12-01

The search for additional drugs to treat HIV infection is a continuing effort due to the emergence and spread of HIV strains resistant to nearly all current drugs. The recent literature reveals that fragment-based drug design/discovery (FBDD) has become an effective alternative to conventional high-throughput screening strategies for drug discovery. In this critical review, the authors describe the state of the art in FBDD strategies for the discovery of anti-HIV drug-like compounds. The article focuses on fragment screening techniques, direct fragment-based design and early hit-to-lead progress. Rapid progress in biophysical detection and in silico techniques has greatly aided the application of FBDD to discover candidate agents directed at a variety of anti-HIV targets. Growing evidence suggests that structural insights on key proteins in the HIV life cycle can be applied in the early phase of drug discovery campaigns, providing valuable information on the binding modes and efficiently prompting fragment hit-to-lead progression. The combination of structural insights with improved methodologies for FBDD, including the privileged fragment-based reconstruction approach, fragment hybridization based on crystallographic overlays, fragment growth exploiting dynamic combinatorial chemistry, and high-speed fragment assembly via diversity-oriented synthesis followed by in situ screening, offers the possibility of more efficient and rapid discovery of novel drugs for HIV-1 prevention or treatment. Though the use of FBDD in anti-HIV drug discovery is still in its infancy, it is anticipated that anti-HIV agents developed via fragment-based strategies will be introduced into the clinic in the future.

Common characteristics of open source software development and applicability for drug discovery: a systematic review.

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Ardal, Christine; Alstadsæter, Annette; Røttingen, John-Arne

2011-09-28

Innovation through an open source model has proven to be successful for software development. This success has led many to speculate if open source can be applied to other industries with similar success. We attempt to provide an understanding of open source software development characteristics for researchers, business leaders and government officials who may be interested in utilizing open source innovation in other contexts and with an emphasis on drug discovery. A systematic review was performed by searching relevant, multidisciplinary databases to extract empirical research regarding the common characteristics and barriers of initiating and maintaining an open source software development project. Common characteristics to open source software development pertinent to open source drug discovery were extracted. The characteristics were then grouped into the areas of participant attraction, management of volunteers, control mechanisms, legal framework and physical constraints. Lastly, their applicability to drug discovery was examined. We believe that the open source model is viable for drug discovery, although it is unlikely that it will exactly follow the form used in software development. Hybrids will likely develop that suit the unique characteristics of drug discovery. We suggest potential motivations for organizations to join an open source drug discovery project. We also examine specific differences between software and medicines, specifically how the need for laboratories and physical goods will impact the model as well as the effect of patents.

Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

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Dobchev, Dimitar; Karelson, Mati

2016-07-01

Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery. In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field. The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.

An integrated dataset for in silico drug discovery

Directory of Open Access Journals (Sweden)

Cockell Simon J

2010-12-01

Full Text Available Drug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.

ACFIS: a web server for fragment-based drug discovery.

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Hao, Ge-Fei; Jiang, Wen; Ye, Yuan-Nong; Wu, Feng-Xu; Zhu, Xiao-Lei; Guo, Feng-Biao; Yang, Guang-Fu

2016-07-08

In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown 'chemical space' to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for 'chemical space', which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Semiconductor technology in protein kinase research and drug discovery: sensing a revolution.

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Bhalla, Nikhil; Di Lorenzo, Mirella; Estrela, Pedro; Pula, Giordano

2017-02-01

Since the discovery of protein kinase activity in 1954, close to 600 kinases have been discovered that have crucial roles in cell physiology. In several pathological conditions, aberrant protein kinase activity leads to abnormal cell and tissue physiology. Therefore, protein kinase inhibitors are investigated as potential treatments for several diseases, including dementia, diabetes, cancer and autoimmune and cardiovascular disease. Modern semiconductor technology has recently been applied to accelerate the discovery of novel protein kinase inhibitors that could become the standard-of-care drugs of tomorrow. Here, we describe current techniques and novel applications of semiconductor technologies in protein kinase inhibitor drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

Computer-aided drug discovery [v1; ref status: indexed, http://f1000r.es/5ij

Directory of Open Access Journals (Sweden)

Jürgen Bajorath

2015-08-01

Full Text Available Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approaches are highlighted that aid in the identification and optimization of new drug candidates. Emphasis is put on the presentation and discussion of computational concepts and methods, rather than case studies or application examples. As such, this contribution aims to provide an overview of the current methodological spectrum of computational drug discovery for a broad audience.

Drug design and discovery: translational biomedical science varies among countries.

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Weaver, Ian N; Weaver, Donald F

2013-10-01

Drug design and discovery is an innovation process that translates the outcomes of fundamental biomedical research into therapeutics that are ultimately made available to people with medical disorders in many countries throughout the world. To identify which nations succeed, exceed, or fail at the drug design/discovery endeavor--more specifically, which countries, within the context of their national size and wealth, are "pulling their weight" when it comes to developing medications targeting the myriad of diseases that afflict humankind--we compiled and analyzed a comprehensive survey of all new drugs (small molecular entities and biologics) approved annually throughout the world over the 20-year period from 1991 to 2010. Based upon this analysis, we have devised prediction algorithms to ascertain which countries are successful (or not) in contributing to the worldwide need for effective new therapeutics. © 2013 Wiley Periodicals, Inc.

Perspectives of biomolecular NMR in drug discovery: the blessing and curse of versatility

International Nuclear Information System (INIS)

Jahnke, Wolfgang

2007-01-01

The versatility of NMR and its broad applicability to several stages in the drug discovery process is well known and generally considered one of the major strengths of NMR (Pellecchia et al., Nature Rev Drug Discov 1:211-219, 2002; Stockman and Dalvit, Prog Nucl Magn Reson Spectrosc 41:187-231, 2002; Lepre et al., Comb Chem High throughput screen 5:583-590, 2002; Wyss et al., Curr Opin Drug Discov Devel 5:630-647, 2002; Jahnke and Widmer, Cell Mol Life Sci 61:580-599, 2004; Huth et al., Methods Enzymol 394:549-571, 2005b; Klages et al., Mol Biosyst 2:318-332, 2006; Takeuchi and Wagner, Curr Opin Struct Biol 16:109-117, 2006; Zartler and Shapiro, Curr Pharm Des 12:3963-3972, 2006). Indeed, NMR is the only biophysical technique which can detect and quantify molecular interactions, and at the same time provide detailed structural information with atomic level resolution. NMR should therefore be ideally suited and widely requested as a tool for drug discovery research, and numerous examples of drug discovery projects which have substantially benefited from NMR contributions or were even driven by NMR have been described in the literature. However, not all pharmaceutical companies have rigorously implemented NMR as integral tool of their research processes. Some companies invest with limited resources, and others do not use biomolecular NMR at all. This discrepancy in assessing the value of a technology is striking, and calls for clarification-under which circumstances can NMR provide added value to the drug discovery process? What kind of contributions can NMR make, and how is it implemented and integrated for maximum impact? This perspectives article suggests key areas of impact for NMR, and a model of integrating NMR with other technologies to realize synergies and maximize their value for drug discovery

BPS Pharmacology 2014 - Drug Discovery Pathways symposium Report

OpenAIRE

Marsh, Andrew

2015-01-01

Report on BPS Pharmacology 2014, BPS Industry Committe and Learned Societies Drug Discovery Pathways Group symposium: "Realizing the potential of new approaches to target identification and validation" by Dr Andrew Marsh Associate Professor Department of Chemistry University of Warwick go.warwick.ac.uk/marshgroup Twitter @marshgroup

Alkaloids as Cyclooxygenase Inhibitors in Anticancer Drug Discovery.

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Hashmi, Muhammad Ali; Khan, Afsar; Farooq, Umar; Khan, Sehroon

2018-01-01

Cancer is the leading cause of death worldwide and anticancer drug discovery is a very hot area of research at present. There are various factors which control and affect cancer, out of which enzymes like cyclooxygenase-2 (COX-2) play a vital role in the growth of tumor cells. Inhibition of this enzyme is a very useful target for the prevention of various types of cancers. Alkaloids are a diverse group of naturally occurring compounds which have shown great COX-2 inhibitory activity both in vitro and in vivo. In this mini-review, we have discussed different alkaloids with COX-2 inhibitory activities and anticancer potential which may act as leads in modern anticancer drug discovery. Different classes of alkaloids including isoquinoline alkaloids, indole alkaloids, piperidine alkaloids, quinazoline alkaloids, and various miscellaneous alkaloids obtained from natural sources have been discussed in detail in this review. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Stem cells: a model for screening, discovery and development of drugs.

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Kitambi, Satish Srinivas; Chandrasekar, Gayathri

2011-01-01

The identification of normal and cancerous stem cells and the recent advances made in isolation and culture of stem cells have rapidly gained attention in the field of drug discovery and regenerative medicine. The prospect of performing screens aimed at proliferation, directed differentiation, and toxicity and efficacy studies using stem cells offers a reliable platform for the drug discovery process. Advances made in the generation of induced pluripotent stem cells from normal or diseased tissue serves as a platform to perform drug screens aimed at developing cell-based therapies against conditions like Parkinson's disease and diabetes. This review discusses the application of stem cells and cancer stem cells in drug screening and their role in complementing, reducing, and replacing animal testing. In addition to this, target identification and major advances in the field of personalized medicine using induced pluripotent cells are also discussed.

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

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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.

Drug target ontology to classify and integrate drug discovery data.

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Lin, Yu; Mehta, Saurabh; Küçük-McGinty, Hande; Turner, John Paul; Vidovic, Dusica; Forlin, Michele; Koleti, Amar; Nguyen, Dac-Trung; Jensen, Lars Juhl; Guha, Rajarshi; Mathias, Stephen L; Ursu, Oleg; Stathias, Vasileios; Duan, Jianbin; Nabizadeh, Nooshin; Chung, Caty; Mader, Christopher; Visser, Ubbo; Yang, Jeremy J; Bologa, Cristian G; Oprea, Tudor I; Schürer, Stephan C

2017-11-09

One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. DTO was built based on the need for a formal semantic

Twenty years on: the impact of fragments on drug discovery.

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Erlanson, Daniel A; Fesik, Stephen W; Hubbard, Roderick E; Jahnke, Wolfgang; Jhoti, Harren

2016-09-01

After 20 years of sometimes quiet growth, fragment-based drug discovery (FBDD) has become mainstream. More than 30 drug candidates derived from fragments have entered the clinic, with two approved and several more in advanced trials. FBDD has been widely applied in both academia and industry, as evidenced by the large number of papers from universities, non-profit research institutions, biotechnology companies and pharmaceutical companies. Moreover, FBDD draws on a diverse range of disciplines, from biochemistry and biophysics to computational and medicinal chemistry. As the promise of FBDD strategies becomes increasingly realized, now is an opportune time to draw lessons and point the way to the future. This Review briefly discusses how to design fragment libraries, how to select screening techniques and how to make the most of information gleaned from them. It also shows how concepts from FBDD have permeated and enhanced drug discovery efforts.

Personalizing oncology treatments by predicting drug efficacy, side-effects, and improved therapy: mathematics, statistics, and their integration.

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Agur, Zvia; Elishmereni, Moran; Kheifetz, Yuri

2014-01-01

Despite its great promise, personalized oncology still faces many hurdles, and it is increasingly clear that targeted drugs and molecular biomarkers alone yield only modest clinical benefit. One reason is the complex relationships between biomarkers and the patient's response to drugs, obscuring the true weight of the biomarkers in the overall patient's response. This complexity can be disentangled by computational models that integrate the effects of personal biomarkers into a simulator of drug-patient dynamic interactions, for predicting the clinical outcomes. Several computational tools have been developed for personalized oncology, notably evidence-based tools for simulating pharmacokinetics, Bayesian-estimated tools for predicting survival, etc. We describe representative statistical and mathematical tools, and discuss their merits, shortcomings and preliminary clinical validation attesting to their potential. Yet, the individualization power of mathematical models alone, or statistical models alone, is limited. More accurate and versatile personalization tools can be constructed by a new application of the statistical/mathematical nonlinear mixed effects modeling (NLMEM) approach, which until recently has been used only in drug development. Using these advanced tools, clinical data from patient populations can be integrated with mechanistic models of disease and physiology, for generating personal mathematical models. Upon a more substantial validation in the clinic, this approach will hopefully be applied in personalized clinical trials, P-trials, hence aiding the establishment of personalized medicine within the main stream of clinical oncology. © 2014 Wiley Periodicals, Inc.

Current status and future prospects for enabling chemistry technology in the drug discovery process.

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Djuric, Stevan W; Hutchins, Charles W; Talaty, Nari N

2016-01-01

This review covers recent advances in the implementation of enabling chemistry technologies into the drug discovery process. Areas covered include parallel synthesis chemistry, high-throughput experimentation, automated synthesis and purification methods, flow chemistry methodology including photochemistry, electrochemistry, and the handling of "dangerous" reagents. Also featured are advances in the "computer-assisted drug design" area and the expanding application of novel mass spectrometry-based techniques to a wide range of drug discovery activities.

DrugQuest - a text mining workflow for drug association discovery.

Science.gov (United States)

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Vizirianakis, Ioannis S; Iliopoulos, Ioannis

2016-06-06

Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .

Timeline of Authorization and Reimbursement for Oncology Drugs in Italy in the Last 3 Years

Directory of Open Access Journals (Sweden)

Mariangela Prada

Full Text Available Introduction The main purpose of this analysis was to quantify the time elapsed between the validation date of European Medicines Agency (EMA centralized procedure and the first purchase of a product by at least 1 Italian health care structure, evaluating different variables that affect the process, the number of products approved by the Committee for Medicinal Products for Human Use (CHMP that are available on the Italian market (July 2016, and the impact of the Cnn class for oncology drugs in Italy. Methods A panel of oncology products has been defined, which considered drugs approved by the EMA between January 2013 and December 2015, and authorized for the treatment of oncology diseases, excluding generics. Data were obtained via the EMA website by the Agenzia Italiana del Farmaco (AIFA; the Italian Medicine Agency meeting reports, by official administrative acts of marketing authorization, and the date of the first purchase (first day of the first handling month. Results The mean time of EMA evaluation for the considered panel of medicines was about 441 days (standard deviation (SD 108; range 266-770; the average approval time for AIFA was about 248 days (SD 131; range 85-688. Interestingly, the mean AIFA evaluation time decreased significantly from 264 days for products submitted to AIFA assessment in 2013-2014 to 219 days for products evaluated in 2015-2016. Focusing on the regional access, both the timing and the number of drugs available for patients were widely different from region to region. Discussion A reduction in the approval time in the last 2 years has been observed in Italy. However, several variables influence the efficiency of the process and need to be addressed to make the access to drugs timely and efficient.

Contributions of computational chemistry and biophysical techniques to fragment-based drug discovery.

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Gozalbes, Rafael; Carbajo, Rodrigo J; Pineda-Lucena, Antonio

2010-01-01

In the last decade, fragment-based drug discovery (FBDD) has evolved from a novel approach in the search of new hits to a valuable alternative to the high-throughput screening (HTS) campaigns of many pharmaceutical companies. The increasing relevance of FBDD in the drug discovery universe has been concomitant with an implementation of the biophysical techniques used for the detection of weak inhibitors, e.g. NMR, X-ray crystallography or surface plasmon resonance (SPR). At the same time, computational approaches have also been progressively incorporated into the FBDD process and nowadays several computational tools are available. These stretch from the filtering of huge chemical databases in order to build fragment-focused libraries comprising compounds with adequate physicochemical properties, to more evolved models based on different in silico methods such as docking, pharmacophore modelling, QSAR and virtual screening. In this paper we will review the parallel evolution and complementarities of biophysical techniques and computational methods, providing some representative examples of drug discovery success stories by using FBDD.

Financing drug discovery via dynamic leverage.

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Montazerhodjat, Vahid; Frishkopf, John J; Lo, Andrew W

2016-03-01

We extend the megafund concept for funding drug discovery to enable dynamic leverage in which the portfolio of candidate therapeutic assets is predominantly financed initially by equity, and debt is introduced gradually as assets mature and begin generating cash flows. Leverage is adjusted so as to maintain an approximately constant level of default risk throughout the life of the fund. Numerical simulations show that applying dynamic leverage to a small portfolio of orphan drug candidates can boost the return on equity almost twofold compared with securitization with a static capital structure. Dynamic leverage can also add significant value to comparable all-equity-financed portfolios, enhancing the return on equity without jeopardizing debt performance or increasing risk to equity investors. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Network-based discovery through mechanistic systems biology. Implications for applications--SMEs and drug discovery: where the action is.

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Benson, Neil

2015-08-01

Phase II attrition remains the most important challenge for drug discovery. Tackling the problem requires improved understanding of the complexity of disease biology. Systems biology approaches to this problem can, in principle, deliver this. This article reviews the reports of the application of mechanistic systems models to drug discovery questions and discusses the added value. Although we are on the journey to the virtual human, the length, path and rate of learning from this remain an open question. Success will be dependent on the will to invest and make the most of the insight generated along the way. Copyright © 2015 Elsevier Ltd. All rights reserved.

Structural Genomics and Drug Discovery for Infectious Diseases

International Nuclear Information System (INIS)

Anderson, W.F.

2009-01-01

The application of structural genomics methods and approaches to proteins from organisms causing infectious diseases is making available the three dimensional structures of many proteins that are potential drug targets and laying the groundwork for structure aided drug discovery efforts. There are a number of structural genomics projects with a focus on pathogens that have been initiated worldwide. The Center for Structural Genomics of Infectious Diseases (CSGID) was recently established to apply state-of-the-art high throughput structural biology technologies to the characterization of proteins from the National Institute for Allergy and Infectious Diseases (NIAID) category A-C pathogens and organisms causing emerging, or re-emerging infectious diseases. The target selection process emphasizes potential biomedical benefits. Selected proteins include known drug targets and their homologs, essential enzymes, virulence factors and vaccine candidates. The Center also provides a structure determination service for the infectious disease scientific community. The ultimate goal is to generate a library of structures that are available to the scientific community and can serve as a starting point for further research and structure aided drug discovery for infectious diseases. To achieve this goal, the CSGID will determine protein crystal structures of 400 proteins and protein-ligand complexes using proven, rapid, highly integrated, and cost-effective methods for such determination, primarily by X-ray crystallography. High throughput crystallographic structure determination is greatly aided by frequent, convenient access to high-performance beamlines at third-generation synchrotron X-ray sources.

Web-based services for drug design and discovery.

Science.gov (United States)

Frey, Jeremy G; Bird, Colin L

2011-09-01

Reviews of the development of drug discovery through the 20(th) century recognised the importance of chemistry and increasingly bioinformatics, but had relatively little to say about the importance of computing and networked computing in particular. However, the design and discovery of new drugs is arguably the most significant single application of bioinformatics and cheminformatics to have benefitted from the increases in the range and power of the computational techniques since the emergence of the World Wide Web, commonly now referred to as simply 'the Web'. Web services have enabled researchers to access shared resources and to deploy standardized calculations in their search for new drugs. This article first considers the fundamental principles of Web services and workflows, and then explores the facilities and resources that have evolved to meet the specific needs of chem- and bio-informatics. This strategy leads to a more detailed examination of the basic components that characterise molecules and the essential predictive techniques, followed by a discussion of the emerging networked services that transcend the basic provisions, and the growing trend towards embracing modern techniques, in particular the Semantic Web. In the opinion of the authors, the issues that require community action are: increasing the amount of chemical data available for open access; validating the data as provided; and developing more efficient links between the worlds of cheminformatics and bioinformatics. The goal is to create ever better drug design services.

Current status and future prospects for enabling chemistry technology in the drug discovery process

Science.gov (United States)

Djuric, Stevan W.; Hutchins, Charles W.; Talaty, Nari N.

2016-01-01

This review covers recent advances in the implementation of enabling chemistry technologies into the drug discovery process. Areas covered include parallel synthesis chemistry, high-throughput experimentation, automated synthesis and purification methods, flow chemistry methodology including photochemistry, electrochemistry, and the handling of “dangerous” reagents. Also featured are advances in the “computer-assisted drug design” area and the expanding application of novel mass spectrometry-based techniques to a wide range of drug discovery activities. PMID:27781094

Phenotypic and genomic comparison of Mycobacterium aurum and surrogate model species to Mycobacterium tuberculosis: implications for drug discovery.

Science.gov (United States)

Namouchi, Amine; Cimino, Mena; Favre-Rochex, Sandrine; Charles, Patricia; Gicquel, Brigitte

2017-07-13

Tuberculosis (TB) is caused by Mycobacterium tuberculosis and represents one of the major challenges facing drug discovery initiatives worldwide. The considerable rise in bacterial drug resistance in recent years has led to the need of new drugs and drug regimens. Model systems are regularly used to speed-up the drug discovery process and circumvent biosafety issues associated with manipulating M. tuberculosis. These include the use of strains such as Mycobacterium smegmatis and Mycobacterium marinum that can be handled in biosafety level 2 facilities, making high-throughput screening feasible. However, each of these model species have their own limitations. We report and describe the first complete genome sequence of Mycobacterium aurum ATCC23366, an environmental mycobacterium that can also grow in the gut of humans and animals as part of the microbiota. This species shows a comparable resistance profile to that of M. tuberculosis for several anti-TB drugs. The aims of this study were to (i) determine the drug resistance profile of a recently proposed model species, Mycobacterium aurum, strain ATCC23366, for anti-TB drug discovery as well as Mycobacterium smegmatis and Mycobacterium marinum (ii) sequence and annotate the complete genome sequence of this species obtained using Pacific Bioscience technology (iii) perform comparative genomics analyses of the various surrogate strains with M. tuberculosis (iv) discuss how the choice of the surrogate model used for drug screening can affect the drug discovery process. We describe the complete genome sequence of M. aurum, a surrogate model for anti-tuberculosis drug discovery. Most of the genes already reported to be associated with drug resistance are shared between all the surrogate strains and M. tuberculosis. We consider that M. aurum might be used in high-throughput screening for tuberculosis drug discovery. We also highly recommend the use of different model species during the drug discovery screening process.

Freely Accessible Chemical Database Resources of Compounds for in Silico Drug Discovery.

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Yang, JingFang; Wang, Di; Jia, Chenyang; Wang, Mengyao; Hao, GeFei; Yang, GuangFu

2018-05-07

In silico drug discovery has been proved to be a solidly established key component in early drug discovery. However, this task is hampered by the limitation of quantity and quality of compound databases for screening. In order to overcome these obstacles, freely accessible database resources of compounds have bloomed in recent years. Nevertheless, how to choose appropriate tools to treat these freely accessible databases are crucial. To the best of our knowledge, this is the first systematic review on this issue. The existed advantages and drawbacks of chemical databases were analyzed and summarized based on the collected six categories of freely accessible chemical databases from literature in this review. Suggestions on how and in which conditions the usage of these databases could be reasonable were provided. Tools and procedures for building 3D structure chemical libraries were also introduced. In this review, we described the freely accessible chemical database resources for in silico drug discovery. In particular, the chemical information for building chemical database appears as attractive resources for drug design to alleviate experimental pressure. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Lessons from hot spot analysis for fragment-based drug discovery

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Hall, David R.; Vajda, Sandor

2015-01-01

Analysis of binding energy hot spots at protein surfaces can provide crucial insights into the prospects for successful application of fragment-based drug discovery (FBDD), and whether a fragment hit can be advanced into a high affinity, druglike ligand. The key factor is the strength of the top ranking hot spot, and how well a given fragment complements it. We show that published data are sufficient to provide a sophisticated and quantitative understanding of how hot spots derive from protein three-dimensional structure, and how their strength, number and spatial arrangement govern the potential for a surface site to bind to fragment-sized and larger ligands. This improved understanding provides important guidance for the effective application of FBDD in drug discovery. PMID:26538314

Stem cells: a model for screening, discovery and development of drugs

Directory of Open Access Journals (Sweden)

Kitambi SS

2011-09-01

Full Text Available Satish Srinivas Kitambi1, Gayathri Chandrasekar21Department of Medical Biochemistry and Biophysics; 2Department of Biosciences, Karolinska Institutet, Stockholm, SwedenAbstract: The identification of normal and cancerous stem cells and the recent advances made in isolation and culture of stem cells have rapidly gained attention in the field of drug discovery and regenerative medicine. The prospect of performing screens aimed at proliferation, directed differentiation, and toxicity and efficacy studies using stem cells offers a reliable platform for the drug discovery process. Advances made in the generation of induced pluripotent stem cells from normal or diseased tissue serves as a platform to perform drug screens aimed at developing cell-based therapies against conditions like Parkinson's disease and diabetes. This review discusses the application of stem cells and cancer stem cells in drug screening and their role in complementing, reducing, and replacing animal testing. In addition to this, target identification and major advances in the field of personalized medicine using induced pluripotent cells are also discussed.Keywords: therapeutics, stem cells, cancer stem cells, screening models, drug development, high throughput screening

The impact of genetics on future drug discovery in schizophrenia.

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Matsumoto, Mitsuyuki; Walton, Noah M; Yamada, Hiroshi; Kondo, Yuji; Marek, Gerard J; Tajinda, Katsunori

2017-07-01

Failures of investigational new drugs (INDs) for schizophrenia have left huge unmet medical needs for patients. Given the recent lackluster results, it is imperative that new drug discovery approaches (and resultant drug candidates) target pathophysiological alterations that are shared in specific, stratified patient populations that are selected based on pre-identified biological signatures. One path to implementing this paradigm is achievable by leveraging recent advances in genetic information and technologies. Genome-wide exome sequencing and meta-analysis of single nucleotide polymorphism (SNP)-based association studies have already revealed rare deleterious variants and SNPs in patient populations. Areas covered: Herein, the authors review the impact that genetics have on the future of schizophrenia drug discovery. The high polygenicity of schizophrenia strongly indicates that this disease is biologically heterogeneous so the identification of unique subgroups (by patient stratification) is becoming increasingly necessary for future investigational new drugs. Expert opinion: The authors propose a pathophysiology-based stratification of genetically-defined subgroups that share deficits in particular biological pathways. Existing tools, including lower-cost genomic sequencing and advanced gene-editing technology render this strategy ever more feasible. Genetically complex psychiatric disorders such as schizophrenia may also benefit from synergistic research with simpler monogenic disorders that share perturbations in similar biological pathways.

Dynamic Docking: A Paradigm Shift in Computational Drug Discovery

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Dario Gioia

2017-11-01

Full Text Available Molecular docking is the methodology of choice for studying in silico protein-ligand binding and for prioritizing compounds to discover new lead candidates. Traditional docking simulations suffer from major limitations, mostly related to the static or semi-flexible treatment of ligands and targets. They also neglect solvation and entropic effects, which strongly limits their predictive power. During the last decade, methods based on full atomistic molecular dynamics (MD have emerged as a valid alternative for simulating macromolecular complexes. In principle, compared to traditional docking, MD allows the full exploration of drug-target recognition and binding from both the mechanistic and energetic points of view (dynamic docking. Binding and unbinding kinetic constants can also be determined. While dynamic docking is still too computationally expensive to be routinely used in fast-paced drug discovery programs, the advent of faster computing architectures and advanced simulation methodologies are changing this scenario. It is feasible that dynamic docking will replace static docking approaches in the near future, leading to a major paradigm shift in in silico drug discovery. Against this background, we review the key achievements that have paved the way for this progress.

A kernel for open source drug discovery in tropical diseases.

Science.gov (United States)

Ortí, Leticia; Carbajo, Rodrigo J; Pieper, Ursula; Eswar, Narayanan; Maurer, Stephen M; Rai, Arti K; Taylor, Ginger; Todd, Matthew H; Pineda-Lucena, Antonio; Sali, Andrej; Marti-Renom, Marc A

2009-01-01

Conventional patent-based drug development incentives work badly for the developing world, where commercial markets are usually small to non-existent. For this reason, the past decade has seen extensive experimentation with alternative R&D institutions ranging from private-public partnerships to development prizes. Despite extensive discussion, however, one of the most promising avenues-open source drug discovery-has remained elusive. We argue that the stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. Historically, open source software collaborations have almost never succeeded without such "kernels". HERE, WE USE A COMPUTATIONAL PIPELINE FOR: (i) comparative structure modeling of target proteins, (ii) predicting the localization of ligand binding sites on their surfaces, and (iii) assessing the similarity of the predicted ligands to known drugs. Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively. The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate. Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other. The TDI kernel, which is being offered under the Creative Commons attribution share-alike license for free and unrestricted use, can be accessed on the World Wide Web at http://www.tropicaldisease.org. We hope that the kernel will facilitate collaborative efforts towards the discovery of new drugs against parasites that cause tropical diseases.

OSIRIS, an entirely in-house developed drug discovery informatics system.

Science.gov (United States)

Sander, Thomas; Freyss, Joel; von Korff, Modest; Reich, Jacqueline Renée; Rufener, Christian

2009-02-01

We present OSIRIS, an entirely in-house developed drug discovery informatics system. Its components cover all information handling aspects from compound synthesis via biological testing to preclinical development. Its design principles are platform and vendor independence, a consistent look and feel, and complete coverage of the drug discovery process by custom tailored applications. These include electronic laboratory notebook applications for biology and chemistry, tools for high-throughput and secondary screening evaluation, chemistry-aware data visualization, physicochemical property prediction, 3D-pharmacophore comparisons, interactive modeling, computing grid based ligand-protein docking, and more. Most applications are developed in Java and are built on top of a Java library layer that provides reusable cheminformatics functionality and GUI components such as chemical editors, structure canonicalization, substructure search, combinatorial enumeration, enhanced stereo perception, force field minimization, and conformation generation.

Medicinal chemistry in drug discovery in big pharma: past, present and future.

Science.gov (United States)

Campbell, Ian B; Macdonald, Simon J F; Procopiou, Panayiotis A

2018-02-01

The changes in synthetic and medicinal chemistry and related drug discovery science as practiced in big pharma over the past few decades are described. These have been predominantly driven by wider changes in society namely the computer, internet and globalisation. Thoughts about the future of medicinal chemistry are also discussed including sharing the risks and costs of drug discovery and the future of outsourcing. The continuing impact of access to substantial computing power and big data, the use of algorithms in data analysis and drug design are also presented. The next generation of medicinal chemists will communicate in ways that reflect social media and the results of constantly being connected to each other and data. Copyright © 2017. Published by Elsevier Ltd.

FLIPR assays of intracellular calcium in GPCR drug discovery

DEFF Research Database (Denmark)

Hansen, Kasper Bø; Bräuner-Osborne, Hans

2009-01-01

Fluorescent dyes sensitive to changes in intracellular calcium have become increasingly popular in G protein-coupled receptor (GPCR) drug discovery for several reasons. First of all, the assays using the dyes are easy to perform and are of low cost compared to other assays. Second, most non...

Bioactive secondary metabolites from marine microbes for drug discovery.

Science.gov (United States)

Nikapitiya, Chamilani

2012-01-01

The isolation and extraction of novel bioactive secondary metabolites from marine microorganisms have a biomedical potential for future drug discovery as the oceans cover 70% of the planet's surface and life on earth originates from sea. Wide range of novel bioactive secondary metabolites exhibiting pharmacodynamic properties has been isolated from marine microorganisms and many to be discovered. The compounds isolated from marine organisms (macro and micro) are important in their natural form and also as templates for synthetic modifications for the treatments for variety of deadly to minor diseases. Many technical issues are yet to overcome before wide-scale bioprospecting of marine microorganisms becomes a reality. This chapter focuses on some novel secondary metabolites having antitumor, antivirus, enzyme inhibitor, and other bioactive properties identified and isolated from marine microorganisms including bacteria, actinomycetes, fungi, and cyanobacteria, which could serve as potentials for drug discovery after their clinical trials. Copyright © 2012 Elsevier Inc. All rights reserved.

SWEETLEAD: an in silico database of approved drugs, regulated chemicals, and herbal isolates for computer-aided drug discovery.

Directory of Open Access Journals (Sweden)

Paul A Novick

Full Text Available In the face of drastically rising drug discovery costs, strategies promising to reduce development timelines and expenditures are being pursued. Computer-aided virtual screening and repurposing approved drugs are two such strategies that have shown recent success. Herein, we report the creation of a highly-curated in silico database of chemical structures representing approved drugs, chemical isolates from traditional medicinal herbs, and regulated chemicals, termed the SWEETLEAD database. The motivation for SWEETLEAD stems from the observance of conflicting information in publicly available chemical databases and the lack of a highly curated database of chemical structures for the globally approved drugs. A consensus building scheme surveying information from several publicly accessible databases was employed to identify the correct structure for each chemical. Resulting structures are filtered for the active pharmaceutical ingredient, standardized, and differing formulations of the same drug were combined in the final database. The publically available release of SWEETLEAD (https://simtk.org/home/sweetlead provides an important tool to enable the successful completion of computer-aided repurposing and drug discovery campaigns.

Drug Discovery Gets a Boost from Data Science.

Science.gov (United States)

Amaro, Rommie E

2016-08-02

In this issue of Structure, Schiebel et al. (2016) describe a workflow-driven approach to high-throughput X-ray crystallographic fragment screening and refinement. In doing so, they extend the applicability of X-ray crystallography as a primary fragment-screening tool and show how data science techniques can favorably impact drug discovery efforts. Copyright © 2016 Elsevier Ltd. All rights reserved.

Competitive intelligence and patent analysis in drug discovery.

Science.gov (United States)

Grandjean, Nicolas; Charpiot, Brigitte; Pena, Carlos Andres; Peitsch, Manuel C

2005-01-01

Patents are a major source of information in drug discovery and, when properly processed and analyzed, can yield a wealth of information on competitors activities, R&D trends, emerging fields, collaborations, among others. This review discusses the current state-of-the-art in textual data analysis and exploration methods as applied to patent analysis.: © 2005 Elsevier Ltd . All rights reserved.

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

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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

Advances in immobilized artificial membrane (IAM) chromatography for novel drug discovery.

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Tsopelas, Fotios; Vallianatou, Theodosia; Tsantili-Kakoulidou, Anna

2016-01-01

The development of immobilized artificial membrane (IAM) chromatography has unfolded new perspectives for the use of chromatographic techniques in drug discovery, combining simulation of the environment of cell membranes with rapid measurements. The present review describes the characteristics of phosphatidylcholine-based stationary phases and analyses the molecular factors governing IAM retention in comparison to n-octanol-water and liposomes partitioning systems as well as to reversed phase chromatography. Other biomimetic stationary phases are also briefly discussed. The potential of IAM chromatography to model permeability through the main physiological barriers and drug membrane interactions is outlined. Further applications to calculate complex pharmacokinetic properties, related to tissue binding, and to screen drug candidates for phospholipidosis, as well as to estimate cell accumulation/retention are surveyed. The ambivalent nature of IAM chromatography, as a border case between passive diffusion and binding, defines its multiple potential applications. However, despite its successful performance in many permeability and drug-membrane interactions studies, IAM chromatography is still used as a supportive and not a stand-alone technique. Further studies looking at IAM chromatography in different biological processes are still required if this technique is to have a more focused and consistent application in drug discovery.

Pharmacokinetic properties and in silico ADME modeling in drug discovery.

Science.gov (United States)

Honório, Kathia M; Moda, Tiago L; Andricopulo, Adriano D

2013-03-01

The discovery and development of a new drug are time-consuming, difficult and expensive. This complex process has evolved from classical methods into an integration of modern technologies and innovative strategies addressed to the design of new chemical entities to treat a variety of diseases. The development of new drug candidates is often limited by initial compounds lacking reasonable chemical and biological properties for further lead optimization. Huge libraries of compounds are frequently selected for biological screening using a variety of techniques and standard models to assess potency, affinity and selectivity. In this context, it is very important to study the pharmacokinetic profile of the compounds under investigation. Recent advances have been made in the collection of data and the development of models to assess and predict pharmacokinetic properties (ADME--absorption, distribution, metabolism and excretion) of bioactive compounds in the early stages of drug discovery projects. This paper provides a brief perspective on the evolution of in silico ADME tools, addressing challenges, limitations, and opportunities in medicinal chemistry.

A Fully Automated High-Throughput Flow Cytometry Screening System Enabling Phenotypic Drug Discovery.

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Joslin, John; Gilligan, James; Anderson, Paul; Garcia, Catherine; Sharif, Orzala; Hampton, Janice; Cohen, Steven; King, Miranda; Zhou, Bin; Jiang, Shumei; Trussell, Christopher; Dunn, Robert; Fathman, John W; Snead, Jennifer L; Boitano, Anthony E; Nguyen, Tommy; Conner, Michael; Cooke, Mike; Harris, Jennifer; Ainscow, Ed; Zhou, Yingyao; Shaw, Chris; Sipes, Dan; Mainquist, James; Lesley, Scott

2018-05-01

The goal of high-throughput screening is to enable screening of compound libraries in an automated manner to identify quality starting points for optimization. This often involves screening a large diversity of compounds in an assay that preserves a connection to the disease pathology. Phenotypic screening is a powerful tool for drug identification, in that assays can be run without prior understanding of the target and with primary cells that closely mimic the therapeutic setting. Advanced automation and high-content imaging have enabled many complex assays, but these are still relatively slow and low throughput. To address this limitation, we have developed an automated workflow that is dedicated to processing complex phenotypic assays for flow cytometry. The system can achieve a throughput of 50,000 wells per day, resulting in a fully automated platform that enables robust phenotypic drug discovery. Over the past 5 years, this screening system has been used for a variety of drug discovery programs, across many disease areas, with many molecules advancing quickly into preclinical development and into the clinic. This report will highlight a diversity of approaches that automated flow cytometry has enabled for phenotypic drug discovery.

[Chapter 2. Transitions in drug-discovery technology and drug-development in Japan (1980-2010)].

Science.gov (United States)

Sakakibara, Noriko; Yoshioka, Ryuzo; Matsumoto, Kazuo

2014-01-01

In 1970s, the material patent system was introduced in Japan. Since then, many Japanese pharmaceutical companies have endeavored to create original in-house products. From 1980s, many of the innovative products were small molecular drugs and were developed using powerful medicinal-chemical technologies. Among them were antibiotics and effective remedies for the digestive organs and circulatory organs. During this period, Japanese companies were able to launch some blockbuster drugs. At the same time, the pharmaceutical market, which had grown rapidly for two decades, was beginning to level off. From the late 1990s, drug development was slowing down due to the lack of expertise in biotechnology such as genetic engineering. In response to the circumstances, the research and development on biotechnology-based drugs such as antibody drugs have become more dynamic and popular at companies than small molecule drugs. In this paper, the writers reviewed in detail the transitions in drug discovery and development between 1980 and 2010.

Open Access Target Validation Is a More Efficient Way to Accelerate Drug Discovery

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Lee, Wen Hwa

2015-01-01

There is a scarcity of novel treatments to address many unmet medical needs. Industry and academia are finally coming to terms with the fact that the prevalent models and incentives for innovation in early stage drug discovery are failing to promote progress quickly enough. Here we will examine how an open model of precompetitive public–private research partnership is enabling efficient derisking and acceleration in the early stages of drug discovery, whilst also widening the range of communities participating in the process, such as patient and disease foundations. PMID:26042736

Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations.

Science.gov (United States)

Cournia, Zoe; Allen, Bryce; Sherman, Woody

2017-12-26

Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives

Protein-Templated Fragment Ligations-From Molecular Recognition to Drug Discovery.

Science.gov (United States)

Jaegle, Mike; Wong, Ee Lin; Tauber, Carolin; Nawrotzky, Eric; Arkona, Christoph; Rademann, Jörg

2017-06-19

Protein-templated fragment ligation is a novel concept to support drug discovery and can help to improve the efficacy of protein ligands. Protein-templated fragment ligations are chemical reactions between small molecules ("fragments") utilizing a protein's surface as a reaction vessel to catalyze the formation of a protein ligand with increased binding affinity. The approach exploits the molecular recognition of reactive small-molecule fragments by proteins both for ligand assembly and for the identification of bioactive fragment combinations. In this way, chemical synthesis and bioassay are integrated in one single step. This Review discusses the biophysical basis of reversible and irreversible fragment ligations and gives an overview of the available methods to detect protein-templated ligation products. The chemical scope and recent applications as well as future potential of the concept in drug discovery are reviewed. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Lack of genotoxicity in medical oncology nurses handling antineoplastic drugs: effect of work environment and protective equipment.

Science.gov (United States)

Gulten, Tuna; Evke, Elif; Ercan, Ilker; Evrensel, Turkkan; Kurt, Ender; Manavoglu, Osman

2011-01-01

In this study we aimed to investigate the genotoxic effects of antineoplastic agents in occupationally exposed oncology nurses. Genotoxic effects mean the disruptive effects in the integrity of DNA and they are associated with cancer development. Biomonitoring of health care workers handling antineoplastic agents is helpful for the evaluation of exposure to cytostatics. The study included an exposed and two control groups. The exposed group (n=9) was comprised of oncology nurses. The first (n=9) and second (n=10) control groups were comprised of subjects who did not come into contact with antineoplastic drugs working respectively in the same department with oncology nurses and in different departments. Genotoxicity evaluation was performed using SCE analysis. After applying culture, harvest and chromosome staining procedures, a total of 25 metaphases were analyzed per person. Kruskal Wallis test was used to perform statistical analysis. A statistically significant difference of sister chromatid exchange frequencies was not observed between the exposed and control groups. Lack of genotoxicity in medical oncology nurses might be due to good working conditions with high standards of technical equipment and improved personal protection.

The current state of GPCR-based drug discovery to treat metabolic disease.

Science.gov (United States)

Sloop, Kyle W; Emmerson, Paul J; Statnick, Michael A; Willard, Francis S

2018-02-02

One approach of modern drug discovery is to identify agents that enhance or diminish signal transduction cascades in various cell types and tissues by modulating the activity of GPCRs. This strategy has resulted in the development of new medicines to treat many conditions, including cardiovascular disease, psychiatric disorders, HIV/AIDS, certain forms of cancer and Type 2 diabetes mellitus (T2DM). These successes justify further pursuit of GPCRs as disease targets and provide key learning that should help guide identifying future therapeutic agents. This report reviews the current landscape of GPCR drug discovery with emphasis on efforts aimed at developing new molecules for treating T2DM and obesity. We analyse historical efforts to generate GPCR-based drugs to treat metabolic disease in terms of causal factors leading to success and failure in this endeavour. © 2018 The British Pharmacological Society.

Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point.

Science.gov (United States)

Rodrigues, Tiago

2017-11-15

Natural products (NPs) present a privileged source of inspiration for chemical probe and drug design. Despite the biological pre-validation of the underlying molecular architectures and their relevance in drug discovery, the poor accessibility to NPs, complexity of the synthetic routes and scarce knowledge of their macromolecular counterparts in phenotypic screens still hinder their broader exploration. Cheminformatics algorithms now provide a powerful means of circumventing the abovementioned challenges and unlocking the full potential of NPs in a drug discovery context. Herein, I discuss recent advances in the computer-assisted design of NP mimics and how artificial intelligence may accelerate future NP-inspired molecular medicine.

Discovery and Development of Therapeutic Drugs against Lethal Human RNA Viruses: a Multidisciplinary Assault.

Science.gov (United States)

1991-07-16

AD-A239 742 AD GRANT NO: DAMD17-89-Z-9021 TITLE: DISCOVERY AND DEVELOPMENT OF THERAPEUTIC DRUGS AGAINST LETHAL HUMAN RNA VIRUSES: A MULTIDISCIPLINARY...62787A871 AB WrJDA317987 11. TITLE (Include Securty Classification) DISCOVERY AND DEVELOPMENT OF THERAPEUTIC DRUGS AGAINST LETHAL HUMAN RNA VIRUSES: A...G. R. Pettit, III, D.-S. Huang, and G. R. Pettit, 23rd Int’l. Horticulture Congress, Italy, 8/27 - 9/1/90. "Bryostatins Define the Role of Protein

RNA Editing and Drug Discovery for Cancer Therapy

Directory of Open Access Journals (Sweden)

Wei-Hsuan Huang

2013-01-01

Full Text Available RNA editing is vital to provide the RNA and protein complexity to regulate the gene expression. Correct RNA editing maintains the cell function and organism development. Imbalance of the RNA editing machinery may lead to diseases and cancers. Recently, RNA editing has been recognized as a target for drug discovery although few studies targeting RNA editing for disease and cancer therapy were reported in the field of natural products. Therefore, RNA editing may be a potential target for therapeutic natural products. In this review, we provide a literature overview of the biological functions of RNA editing on gene expression, diseases, cancers, and drugs. The bioinformatics resources of RNA editing were also summarized.

3D in vitro technology for drug discovery.

Science.gov (United States)

Hosseinkhani, Hossein

2012-02-01

Three-dimensional (3D) in vitro systems that can mimic organ and tissue structure and function in vivo, will be of great benefit for a variety of biological applications from basic biology to toxicity testing and drug discovery. There have been several attempts to generate 3D tissue models but most of these models require costly equipment, and the most serious disadvantage in them is that they are too far from the mature human organs in vivo. Because of these problems, research and development in drug discovery, toxicity testing and biotech industries are highly expensive, and involve sacrifice of countless animals and it takes several years to bring a single drug/product to the market or to find the toxicity or otherwise of chemical entities. Our group has been actively working on several alternative models by merging biomaterials science, nanotechnology and biological principles to generate 3D in vitro living organs, to be called "Human Organs-on-Chip", to mimic natural organ/tissues, in order to reduce animal testing and clinical trials. We have fabricated a novel type of mechanically and biologically bio-mimicking collagen-based hydrogel that would provide for interconnected mini-wells in which 3D cell/organ culture of human samples in a manner similar to human organs with extracellular matrix (ECM) molecules would be possible. These products mimic the physical, chemical, and biological properties of natural organs and tissues at different scales. This paper will review the outcome of our several experiments so far in this direction and the future perspectives.

In silico tools used for compound selection during target-based drug discovery and development.

Science.gov (United States)

Caldwell, Gary W

2015-01-01

The target-based drug discovery process, including target selection, screening, hit-to-lead (H2L) and lead optimization stage gates, is the most common approach used in drug development. The full integration of in vitro and/or in vivo data with in silico tools across the entire process would be beneficial to R&D productivity by developing effective selection criteria and drug-design optimization strategies. This review focuses on understanding the impact and extent in the past 5 years of in silico tools on the various stage gates of the target-based drug discovery approach. There are a large number of in silico tools available for establishing selection criteria and drug-design optimization strategies in the target-based approach. However, the inconsistent use of in vitro and/or in vivo data integrated with predictive in silico multiparameter models throughout the process is contributing to R&D productivity issues. In particular, the lack of reliable in silico tools at the H2L stage gate is contributing to the suboptimal selection of viable lead compounds. It is suggested that further development of in silico multiparameter models and organizing biologists, medicinal and computational chemists into one team with a single accountable objective to expand the utilization of in silico tools in all phases of drug discovery would improve R&D productivity.

The rise of fragment-based drug discovery.

Science.gov (United States)

Murray, Christopher W; Rees, David C

2009-06-01

The search for new drugs is plagued by high attrition rates at all stages in research and development. Chemists have an opportunity to tackle this problem because attrition can be traced back, in part, to the quality of the chemical leads. Fragment-based drug discovery (FBDD) is a new approach, increasingly used in the pharmaceutical industry, for reducing attrition and providing leads for previously intractable biological targets. FBDD identifies low-molecular-weight ligands (∼150 Da) that bind to biologically important macromolecules. The three-dimensional experimental binding mode of these fragments is determined using X-ray crystallography or NMR spectroscopy, and is used to facilitate their optimization into potent molecules with drug-like properties. Compared with high-throughput-screening, the fragment approach requires fewer compounds to be screened, and, despite the lower initial potency of the screening hits, offers more efficient and fruitful optimization campaigns. Here, we review the rise of FBDD, including its application to discovering clinical candidates against targets for which other chemistry approaches have struggled.

Genetics of rheumatoid arthritis conributes to biology and drug discovery

NARCIS (Netherlands)

Okada, Yukinori; Wu, Di; Trynka, Gosia; Raj, Towfique; Terao, Chikashi; Ikari, Katsunori; Kochi, Yuta; Ohmura, Koichiro; Suzuki, A.; Yoshida, S.; Graham, R.R.; Manoharan, A.; Ortmann, W.; Bhangale, T.; Denny, J.C.; Carroll, R.J.; Eyler, A.E.; Greenberg, J.D.; Kremer, J.M.; Pappas, D.A.; Jiang, L.; Yin, L.; Ye, L.; Su, D.F.; Yang, J.; Xie, G.; Keystone, E.; Westra, H.J.; Esko, T.; Metspalu, A.; Zhou, X.; Gupta, N.; Mirel, D.; Stahl, Eli A.; Diogo, D.; Cui, J.; Liao, K.; Guo, M.H.; Myouzen, K.; Kawaguchi, T.; Coenen, M.J.; van Riel, P.L.; van de Laar, Mart A.F.J.; Guchelaar, H.J.; Huizinga, T.W.; Dieudé, P.; Mariette, X.; Louis Bridges Jr, S.; Zhernakova, A.; Toes, R.E.; Tak, P.P.; Miceli-Richard, C.; Bang, S.Y.; Lee, H.S.; Martin, J.; Gonzales-Gay, M.A.; Rodriguez-Rodriguez, L.; Rantapää-Dhlqvist, S.; Arlestig, L.; Choi, H.K.; Kamatani, Y.; Galan, P.; Lathrop, M.; Eyre, S.; Bowes, J.; Barton, A.; de Vries, N.; Moreland, L.W.; Criswell, L.A.; Karlson, E.W.; Taniguchi, A.; Yamada, R; Kubo, M.; Bae, S.C.; Worthington, J.; Padyukov, L.; Klareskog, L.; Gregersen, Peter K.; Raychaudhuri, S.; Stranger, B.E.; de Jager, P.L.; Franke, L.; Visscher, P.M.; Brown, M.A.; Yamanaka, H.; Mimori, T.; Takahashi, A.; Xu, H.; Behrens, T.W.; Siminovitch, K.A.; Momohara, S.; Matsuda, F.; Yamamoto, K.; Plenge, Robert M.

2013-01-01

A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here we performed

Fluorescence lifetime assays: current advances and applications in drug discovery.

Science.gov (United States)

Pritz, Stephan; Doering, Klaus; Woelcke, Julian; Hassiepen, Ulrich

2011-06-01

Fluorescence lifetime assays complement the portfolio of established assay formats available in drug discovery, particularly with the recent advances in microplate readers and the commercial availability of novel fluorescent labels. Fluorescence lifetime assists in lowering complexity of compound screening assays, affording a modular, toolbox-like approach to assay development and yielding robust homogeneous assays. To date, materials and procedures have been reported for biochemical assays on proteases, as well as on protein kinases and phosphatases. This article gives an overview of two assay families, distinguished by the origin of the fluorescence signal modulation. The pharmaceutical industry demands techniques with a robust, integrated compound profiling process and short turnaround times. Fluorescence lifetime assays have already helped the drug discovery field, in this sense, by enhancing productivity during the hit-to-lead and lead optimization phases. Future work will focus on covering other biochemical molecular modifications by investigating the detailed photo-physical mechanisms underlying the fluorescence signal.

Systematic in-vitro evaluation of the NCI/NIH Developmental Therapeutics Program Approved Oncology Drug Set for the identification of a candidate drug repertoire for MLL-rearranged leukemia

Directory of Open Access Journals (Sweden)

Hoeksema KA

2011-09-01

Full Text Available Kimberley A Hoeksema1, Aarthi Jayanthan1, Todd Cooper2, Lia Gore3, Tanya Trippett4, Jessica Boklan6, Robert J Arceci5, Aru Narendran11Division of Pediatric Oncology, Alberta Children's Hospital, Calgary, AB, Canada; 2Aflac Cancer Center and Blood Disorders Service, Children's Healthcare of Atlanta, Emory University, Atlanta, GA, USA; 3Center for Cancer and Blood Disorders, Children's Hospital, University of Colorado Denver, Aurora, CO, USA; 4Memorial Sloan-Kettering Cancer Center, New York, NY, USA; 5Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA; 6Center for Cancer and Blood Disorders, Phoenix Children's Hospital, Phoenix, AZ, USAAbstract: Despite significant progress made in the overall cure rate, the prognosis for relapsed and refractory malignancies in children remains extremely poor. Hence, there is an urgent need for studies that enable the timely selection of appropriate agents for Phase I clinical studies. The Pediatric Oncology Experimental Therapeutics Investigators' Consortium (POETIC is systematically evaluating libraries of known and novel compounds for activity against subsets of high-risk pediatric malignancies with defined molecular aberrations for future clinical development. In this report, we describe the in-vitro activity of a diverse panel of approved oncology drugs against MLL-rearranged pediatric leukemia cell lines. Agents in the Approved Oncology Drug Set II (National Cancer Institute/National Institutes of Health Developmental Therapeutics Program were evaluated by in-vitro cytotoxicity assays in pediatric acute lymphoblastic leukemia and acute myeloid leukemia cell lines with MLL gene rearrangements. Validation studies were carried out with patient leukemia cells in culture. Comparative analysis for toxicity against nonmalignant cells was evaluated in normal bone marrow stromal cells and normal human lymphocytes. Results from this study show that 42 of the 89 agents tested have

Protein crystallography and drug discovery: recollections of knowledge exchange between academia and industry

Directory of Open Access Journals (Sweden)

Tom L. Blundell

2017-07-01

Full Text Available The development of structure-guided drug discovery is a story of knowledge exchange where new ideas originate from all parts of the research ecosystem. Dorothy Crowfoot Hodgkin obtained insulin from Boots Pure Drug Company in the 1930s and insulin crystallization was optimized in the company Novo in the 1950s, allowing the structure to be determined at Oxford University. The structure of renin was developed in academia, on this occasion in London, in response to a need to develop antihypertensives in pharma. The idea of a dimeric aspartic protease came from an international academic team and was discovered in HIV; it eventually led to new HIV antivirals being developed in industry. Structure-guided fragment-based discovery was developed in large pharma and biotechs, but has been exploited in academia for the development of new inhibitors targeting protein–protein interactions and also antimicrobials to combat mycobacterial infections such as tuberculosis. These observations provide a strong argument against the so-called `linear model', where ideas flow only in one direction from academic institutions to industry. Structure-guided drug discovery is a story of applications of protein crystallography and knowledge exhange between academia and industry that has led to new drug approvals for cancer and other common medical conditions by the Food and Drug Administration in the USA, as well as hope for the treatment of rare genetic diseases and infectious diseases that are a particular challenge in the developing world.

Protein crystallography and drug discovery: recollections of knowledge exchange between academia and industry.

Science.gov (United States)

Blundell, Tom L

2017-07-01

The development of structure-guided drug discovery is a story of knowledge exchange where new ideas originate from all parts of the research ecosystem. Dorothy Crowfoot Hodgkin obtained insulin from Boots Pure Drug Company in the 1930s and insulin crystallization was optimized in the company Novo in the 1950s, allowing the structure to be determined at Oxford University. The structure of renin was developed in academia, on this occasion in London, in response to a need to develop antihypertensives in pharma. The idea of a dimeric aspartic protease came from an international academic team and was discovered in HIV; it eventually led to new HIV antivirals being developed in industry. Structure-guided fragment-based discovery was developed in large pharma and biotechs, but has been exploited in academia for the development of new inhibitors targeting protein-protein interactions and also antimicrobials to combat mycobacterial infections such as tuberculosis. These observations provide a strong argument against the so-called 'linear model', where ideas flow only in one direction from academic institutions to industry. Structure-guided drug discovery is a story of applications of protein crystallography and knowledge exhange between academia and industry that has led to new drug approvals for cancer and other common medical conditions by the Food and Drug Administration in the USA, as well as hope for the treatment of rare genetic diseases and infectious diseases that are a particular challenge in the developing world.

The heat is on: thermodynamic analysis in fragment-based drug discovery

NARCIS (Netherlands)

Edink, E.S.; Jansen, C.J.W.; Leurs, R.; De Esch, I.J.

2010-01-01

Thermodynamic analysis provides access to the determinants of binding affinity, enthalpy and entropy. In fragment-based drug discovery (FBDD), thermodynamic analysis provides a powerful tool to discriminate fragments based on their potential for successful optimization. The thermodynamic data

West Nile Virus Drug Discovery

Directory of Open Access Journals (Sweden)

Siew Pheng Lim

2013-12-01

Full Text Available The outbreak of West Nile virus (WNV in 1999 in the USA, and its continued spread throughout the Americas, parts of Europe, the Middle East and Africa, underscored the need for WNV antiviral development. Here, we review the current status of WNV drug discovery. A number of approaches have been used to search for inhibitors of WNV, including viral infection-based screening, enzyme-based screening, structure-based virtual screening, structure-based rationale design, and antibody-based therapy. These efforts have yielded inhibitors of viral or cellular factors that are critical for viral replication. For small molecule inhibitors, no promising preclinical candidate has been developed; most of the inhibitors could not even be advanced to the stage of hit-to-lead optimization due to their poor drug-like properties. However, several inhibitors developed for related members of the family Flaviviridae, such as dengue virus and hepatitis C virus, exhibited cross-inhibition of WNV, suggesting the possibility to re-purpose these antivirals for WNV treatment. Most promisingly, therapeutic antibodies have shown excellent efficacy in mouse model; one of such antibodies has been advanced into clinical trial. The knowledge accumulated during the past fifteen years has provided better rationale for the ongoing WNV and other flavivirus antiviral development.

The Metaboloepigenetic Dimension of Cancer Stem Cells: Evaluating the Market Potential for New Metabostemness-Targeting Oncology Drugs.

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Menendez, Javier A

2015-01-01

The current global portfolio of oncology drugs is unlikely to produce durable disease remission for millions of cancer patients worldwide. This is due, in part, to the existence of so-called cancer stem cells (CSCs), a particularly aggressive type of malignant cell that is capable of indefinite self-replication, is refractory to conventional treatments, and is skilled at spreading and colonizing distant organs. To date, no drugs from big-league Pharma companies are capable of killing CSCs. Why? Quite simply, a classic drug development approach based on mutated genes and pathological protein products cannot efficiently target the plastic, epigenetic proclivity of cancer tissues to generate CSCs. Recent studies have proposed that certain elite metabolites (oncometabolites) and other common metabolites can significantly influence the establishment and maintenance of epigenetic signatures of stemness and cancer. Consequently, cellular metabolism and the core epigenetic codes, DNA methylation and histone modification, can be better viewed as an integrated metaboloepigenetic dimension of CSCs, which we have recently termed cancer metabostemness. By targeting weaknesses in the bridge connecting metabolism and epigenetics, a new generation of metabostemnessspecific drugs can be generated for potent and long-lasting elimination of life-threatening CSCs. Here I evaluate the market potential of re-modeling the oncology drug pipeline by discovering and developing new metabolic approaches able to target the apparently undruggable epigenetic programs that dynamically regulate the plasticity of non-CSC and CSC cellular states.

Trends in GPCR drug discovery: new agents, targets and indications.

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Hauser, Alexander S; Attwood, Misty M; Rask-Andersen, Mathias; Schiöth, Helgi B; Gloriam, David E

2017-12-01

G protein-coupled receptors (GPCRs) are the most intensively studied drug targets, mostly due to their substantial involvement in human pathophysiology and their pharmacological tractability. Here, we report an up-to-date analysis of all GPCR drugs and agents in clinical trials, which reveals current trends across molecule types, drug targets and therapeutic indications, including showing that 475 drugs (~34% of all drugs approved by the US Food and Drug Administration (FDA)) act at 108 unique GPCRs. Approximately 321 agents are currently in clinical trials, of which ~20% target 66 potentially novel GPCR targets without an approved drug, and the number of biological drugs, allosteric modulators and biased agonists has increased. The major disease indications for GPCR modulators show a shift towards diabetes, obesity and Alzheimer disease, although several central nervous system disorders are also highly represented. The 224 (56%) non-olfactory GPCRs that have not yet been explored in clinical trials have broad untapped therapeutic potential, particularly in genetic and immune system disorders. Finally, we provide an interactive online resource to analyse and infer trends in GPCR drug discovery.

Optogenetic Approaches to Drug Discovery in Neuroscience and Beyond.

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Zhang, Hongkang; Cohen, Adam E

2017-07-01

Recent advances in optogenetics have opened new routes to drug discovery, particularly in neuroscience. Physiological cellular assays probe functional phenotypes that connect genomic data to patient health. Optogenetic tools, in particular tools for all-optical electrophysiology, now provide a means to probe cellular disease models with unprecedented throughput and information content. These techniques promise to identify functional phenotypes associated with disease states and to identify compounds that improve cellular function regardless of whether the compound acts directly on a target or through a bypass mechanism. This review discusses opportunities and unresolved challenges in applying optogenetic techniques throughout the discovery pipeline - from target identification and validation, to target-based and phenotypic screens, to clinical trials. Copyright © 2017 Elsevier Ltd. All rights reserved.

ChEMBL web services: streamlining access to drug discovery data and utilities.

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Davies, Mark; Nowotka, Michał; Papadatos, George; Dedman, Nathan; Gaulton, Anna; Atkinson, Francis; Bellis, Louisa; Overington, John P

2015-07-01

ChEMBL is now a well-established resource in the fields of drug discovery and medicinal chemistry research. The ChEMBL database curates and stores standardized bioactivity, molecule, target and drug data extracted from multiple sources, including the primary medicinal chemistry literature. Programmatic access to ChEMBL data has been improved by a recent update to the ChEMBL web services (version 2.0.x, https://www.ebi.ac.uk/chembl/api/data/docs), which exposes significantly more data from the underlying database and introduces new functionality. To complement the data-focused services, a utility service (version 1.0.x, https://www.ebi.ac.uk/chembl/api/utils/docs), which provides RESTful access to commonly used cheminformatics methods, has also been concurrently developed. The ChEMBL web services can be used together or independently to build applications and data processing workflows relevant to drug discovery and chemical biology. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Insights into Integrated Lead Generation and Target Identification in Malaria and Tuberculosis Drug Discovery.

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Okombo, John; Chibale, Kelly

2017-07-18

New, safe and effective drugs are urgently needed to treat and control malaria and tuberculosis, which affect millions of people annually. However, financial return on investment in the poor settings where these diseases are mostly prevalent is very minimal to support market-driven drug discovery and development. Moreover, the imminent loss of therapeutic lifespan of existing therapies due to evolution and spread of drug resistance further compounds the urgency to identify novel effective drugs. However, the advent of new public-private partnerships focused on tropical diseases and the recent release of large data sets by pharmaceutical companies on antimalarial and antituberculosis compounds derived from phenotypic whole cell high throughput screening have spurred renewed interest and opened new frontiers in malaria and tuberculosis drug discovery. This Account recaps the existing challenges facing antimalarial and antituberculosis drug discovery, including limitations associated with experimental animal models as well as biological complexities intrinsic to the causative pathogens. We enlist various highlights from a body of work within our research group aimed at identifying and characterizing new chemical leads, and navigating these challenges to contribute toward the global drug discovery and development pipeline in malaria and tuberculosis. We describe a catalogue of in-house efforts toward deriving safe and efficacious preclinical drug development candidates via cell-based medicinal chemistry optimization of phenotypic whole-cell medium and high throughput screening hits sourced from various small molecule chemical libraries. We also provide an appraisal of target-based screening, as invoked in our laboratory for mechanistic evaluation of the hits generated, with particular focus on the enzymes within the de novo pyrimidine biosynthetic and hemoglobin degradation pathways, the latter constituting a heme detoxification process and an associated cysteine

Hot-spot analysis for drug discovery targeting protein-protein interactions.

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Rosell, Mireia; Fernández-Recio, Juan

2018-04-01

Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.

Assessment of Risk Evaluation and Mitigation Strategies in Oncology: Summary of the Oncology Risk Evaluation and Mitigation Strategies Workshop

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Frame, James N.; Jacobson, Joseph O.; Vogel, Wendy H.; Griffith, Niesha; Wariabharaj, Darshan; Garg, Rekha; Zon, Robin; Stephens, Cyntha L.; Bialecki, Alison M.; Bruinooge, Suanna S.; Allen, Steven L.

2013-01-01

To address oncology community stakeholder concerns regarding implementation of the Risk Evaluation and Mitigation Strategies (REMS) program, ASCO sponsored a workshop to gather REMS experiences from representatives of professional societies, patient organizations, pharmaceutical companies, and the US Food and Drug Administration (FDA). Stakeholder presentations and topical panel discussions addressed REMS program development, implementation processes, and practice experiences, as well as oncology drug safety processes. A draft REMS decision tool prepared by the ASCO REMS Steering Committee was presented for group discussion with facilitated, goal-oriented feedback. The workshop identified several unintended consequences resulting from current oncology REMS: (1) the release of personal health information to drug sponsors as a condition for gaining access to a needed drug; (2) risk information that is not tailored—and therefore not accessible—to all literacy levels; (3) exclusive focus on drug risk, thereby affecting patient-provider treatment discussion; (4) REMS elements that do not consider existing, widely practiced oncology safety standards, professional training, and experience; and (5) administrative burdens that divert the health care team from direct patient care activities and, in some cases, could limit patient access to important therapies. Increased provider and professional society participation should form the basis of ongoing and future REMS standardization discussions with the FDA to work toward overall improvement of risk communication. PMID:23814522

The value of banked samples for oncology drug discovery and development.

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Shaw, Peter M; Patterson, Scott D

2011-01-01

To gain insights into human biology and pathobiology, ready access to banked human tissue samples that encompass a representative cross section of the population is required. For optimal use, the banked human tissue needs to be appropriately consented, collected, annotated, and stored. If any of these elements are missing, the studies using these samples are compromised. These elements are critical whether the research is for academic or pharmaceutical industry purposes. An additional temporal element that adds enormous value to such banked samples is treatment and outcome information from the people who donated the tissue. To achieve these aims, many different groups have to work effectively together, not least of which are the individuals who donate their tissue with appropriate consent. Such research is unlikely to benefit the donors but others who succumb to the same disease. The development of a large accessible human tissue bank resource (National Cancer Institute's Cancer HUman Biobank [caHUB]) that provides an ongoing supply of human tissue for all working toward the common goal of understanding human health and disease has a number of advantages. These include, but are not limited to, access to a broad cross section of healthy and diseased populations beyond what individual collections may achieve for understanding disease pathobiology, therapeutic target discovery, as well as a source of material for diagnostic assay validation. Models will need to be developed to enable fair access to caHUB under terms that enable appropriate intellectual property protection and ultimate data sharing to ensure that the biobank successfully distributes samples to a broad range of researchers.

Opinions on Drug Interaction Sources in Anticancer Treatments and Parameters for an Oncology-Specific Database by Pharmacy Practitioners in Asia

Directory of Open Access Journals (Sweden)

2010-01-01

Full Text Available Cancer patients undergoing chemotherapy are particularly susceptible to drug-drug interactions (DDIs. Practitioners should keep themselves updated with the most current DDI information, particularly involving new anticancer drugs (ACDs. Databases can be useful to obtain up-to-date DDI information in a timely and efficient manner. Our objective was to investigate the DDI information sources of pharmacy practitioners in Asia and their views on the usefulness of an oncology-specific database for ACD interactions. A qualitative, cross-sectional survey was done to collect information on the respondents' practice characteristics, sources of DDI information and parameters useful in an ACD interaction database. Response rate was 49%. Electronic databases (70%, drug interaction textbooks (69% and drug compendia (64% were most commonly used. Majority (93% indicated that a database catering towards ACD interactions was useful. Essential parameters that should be included in the database were the mechanism and severity of the detected interaction, and the presence of a management plan (98% each. This study has improved our understanding on the usefulness of various DDI information sources for ACD interactions among pharmacy practitioners in Asia. An oncology-specific DDI database targeting ACD interactions is definitely attractive for clinical practice.

Application of Combination High-Throughput Phenotypic Screening and Target Identification Methods for the Discovery of Natural Product-Based Combination Drugs.

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Isgut, Monica; Rao, Mukkavilli; Yang, Chunhua; Subrahmanyam, Vangala; Rida, Padmashree C G; Aneja, Ritu

2018-03-01

Modern drug discovery efforts have had mediocre success rates with increasing developmental costs, and this has encouraged pharmaceutical scientists to seek innovative approaches. Recently with the rise of the fields of systems biology and metabolomics, network pharmacology (NP) has begun to emerge as a new paradigm in drug discovery, with a focus on multiple targets and drug combinations for treating disease. Studies on the benefits of drug combinations lay the groundwork for a renewed focus on natural products in drug discovery. Natural products consist of a multitude of constituents that can act on a variety of targets in the body to induce pharmacodynamic responses that may together culminate in an additive or synergistic therapeutic effect. Although natural products cannot be patented, they can be used as starting points in the discovery of potent combination therapeutics. The optimal mix of bioactive ingredients in natural products can be determined via phenotypic screening. The targets and molecular mechanisms of action of these active ingredients can then be determined using chemical proteomics, and by implementing a reverse pharmacokinetics approach. This review article provides evidence supporting the potential benefits of natural product-based combination drugs, and summarizes drug discovery methods that can be applied to this class of drugs. © 2017 Wiley Periodicals, Inc.

Skin too thin? The developing utility of zebrafish skin (neuro)pharmacology for CNS drug discovery research.

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Nguyen, Michael; Poudel, Manoj K; Stewart, Adam Michael; Kalueff, Allan V

2013-09-01

Skin coloration can be affected by many genetic, environmental and pharmacological factors. Zebrafish (Danio rerio) are a useful and versatile model organism in biomedical research due to their genetic tractability, physiological homology to mammals, low cost, reproducibility and high throughput. Zebrafish coloration is mediated by chromatophores - the skin color pigment cells largely controlled by endocrine and neural mechanisms. The characteristic darkening of zebrafish skin is caused by the dispersion (and paling - by aggregation) of melanosomes (pigment-containing organelles), which show high homology to mammalian structures. Various pharmacological agents potently affect zebrafish coloration - the phenotype that often accompanies behavioral effects of the drugs, and may be used for drug discovery. Although zebrafish behavior and skin responses are usually not directly related, they share common regulatory (neural, endocrine) mechanisms, and therefore may be assessed in parallel during psychotropic drug screening. For example, some psychoactive drugs can potently affect zebrafish skin coloration. Can we use this knowledge to refine phenotype-driven psychotropic drug discovery? Here, we present current models using zebrafish skin coloration assays, and discuss how these models may be applied to enhance in vivo CNS drug discovery. Copyright © 2013 Elsevier Inc. All rights reserved.

TCMSP: a database of systems pharmacology for drug discovery from herbal medicines.

Science.gov (United States)

Ru, Jinlong; Li, Peng; Wang, Jinan; Zhou, Wei; Li, Bohui; Huang, Chao; Li, Pidong; Guo, Zihu; Tao, Weiyang; Yang, Yinfeng; Xu, Xue; Li, Yan; Wang, Yonghua; Yang, Ling

2014-01-01

Modern medicine often clashes with traditional medicine such as Chinese herbal medicine because of the little understanding of the underlying mechanisms of action of the herbs. In an effort to promote integration of both sides and to accelerate the drug discovery from herbal medicines, an efficient systems pharmacology platform that represents ideal information convergence of pharmacochemistry, ADME properties, drug-likeness, drug targets, associated diseases and interaction networks, are urgently needed. The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was built based on the framework of systems pharmacology for herbal medicines. It consists of all the 499 Chinese herbs registered in the Chinese pharmacopoeia with 29,384 ingredients, 3,311 targets and 837 associated diseases. Twelve important ADME-related properties like human oral bioavailability, half-life, drug-likeness, Caco-2 permeability, blood-brain barrier and Lipinski's rule of five are provided for drug screening and evaluation. TCMSP also provides drug targets and diseases of each active compound, which can automatically establish the compound-target and target-disease networks that let users view and analyze the drug action mechanisms. It is designed to fuel the development of herbal medicines and to promote integration of modern medicine and traditional medicine for drug discovery and development. The particular strengths of TCMSP are the composition of the large number of herbal entries, and the ability to identify drug-target networks and drug-disease networks, which will help revealing the mechanisms of action of Chinese herbs, uncovering the nature of TCM theory and developing new herb-oriented drugs. TCMSP is freely available at http://sm.nwsuaf.edu.cn/lsp/tcmsp.php.

Lost in translation? Role of metabolomics in solving translational problems in drug discovery and development

NARCIS (Netherlands)

Greef, J. van der; Adourian, A.; Muntendam, P.; McBurney, R.N.

2006-01-01

Too few drug discovery projects generate a marketed drug product, often because preclinical studies fail to predict the clinical experience with a drug candidate. Improving the success of preclinical-to-clinical translation is of paramount importance in optimizing the pharmaceutical value chain.

Binding thermodynamics discriminates fragments from druglike compounds: a thermodynamic description of fragment-based drug discovery.

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Williams, Glyn; Ferenczy, György G; Ulander, Johan; Keserű, György M

2017-04-01

Small is beautiful - reducing the size and complexity of chemical starting points for drug design allows better sampling of chemical space, reveals the most energetically important interactions within protein-binding sites and can lead to improvements in the physicochemical properties of the final drug. The impact of fragment-based drug discovery (FBDD) on recent drug discovery projects and our improved knowledge of the structural and thermodynamic details of ligand binding has prompted us to explore the relationships between ligand-binding thermodynamics and FBDD. Information on binding thermodynamics can give insights into the contributions to protein-ligand interactions and could therefore be used to prioritise compounds with a high degree of specificity in forming key interactions. Copyright © 2016 Elsevier Ltd. All rights reserved.

In vivo brain microdialysis: advances in neuropsychopharmacology and drug discovery.

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Darvesh, Altaf S; Carroll, Richard T; Geldenhuys, Werner J; Gudelsky, Gary A; Klein, Jochen; Meshul, Charles K; Van der Schyf, Cornelis J

2011-02-01

INTRODUCTION: Microdialysis is an important in vivo sampling technique, useful in the assay of extracellular tissue fluid. The technique has both pre-clinical and clinical applications but is most widely used in neuroscience. The in vivo microdialysis technique allows measurement of neurotransmitters such as acetycholine (ACh), the biogenic amines including dopamine (DA), norepinephrine (NE) and serotonin (5-HT), amino acids such as glutamate (Glu) and gamma aminobutyric acid (GABA), as well as the metabolites of the aforementioned neurotransmitters, and neuropeptides in neuronal extracellular fluid in discrete brain regions of laboratory animals such as rodents and non-human primates. AREAS COVERED: In this review we present a brief overview of the principles and procedures related to in vivo microdialysis and detail the use of this technique in the pre-clinical measurement of drugs designed to be used in the treatment of chemical addiction, neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD) and as well as psychiatric disorders such as attention-deficit/hyperactivity disorder (ADHD) and schizophrenia. This review offers insight into the tremendous utility and versatility of this technique in pursuing neuropharmacological investigations as well its significant potential in rational drug discovery. EXPERT OPINION: In vivo microdialysis is an extremely versatile technique, routinely used in the neuropharmacological investigation of drugs used for the treatment of neurological disorders. This technique has been a boon in the elucidation of the neurochemical profile and mechanism of action of several classes of drugs especially their effects on neurotransmitter systems. The exploitation and development of this technique for drug discovery in the near future will enable investigational new drug candidates to be rapidly moved into the clinical trial stages and to market thus providing new successful therapies for neurological diseases

Leveraging the contribution of thermodynamics in drug discovery with the help of fluorescence-based thermal shift assays.

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Hau, Jean Christophe; Fontana, Patrizia; Zimmermann, Catherine; De Pover, Alain; Erdmann, Dirk; Chène, Patrick

2011-06-01

The development of new drugs with better pharmacological and safety properties mandates the optimization of several parameters. Today, potency is often used as the sole biochemical parameter to identify and select new molecules. Surprisingly, thermodynamics, which is at the core of any interaction, is rarely used in drug discovery, even though it has been suggested that the selection of scaffolds according to thermodynamic criteria may be a valuable strategy. This poor integration of thermodynamics in drug discovery might be due to difficulties in implementing calorimetry experiments despite recent technological progress in this area. In this report, the authors show that fluorescence-based thermal shift assays could be used as prescreening methods to identify compounds with different thermodynamic profiles. This approach allows a reduction in the number of compounds to be tested in calorimetry experiments, thus favoring greater integration of thermodynamics in drug discovery.

Utility of Glioblastoma Patient-Derived Orthotopic Xenografts in Drug Discovery and Personalized Therapy

Directory of Open Access Journals (Sweden)

Michele Patrizii

2018-02-01

Full Text Available Despite substantial effort and resources dedicated to drug discovery and development, new anticancer agents often fail in clinical trials. Among many reasons, the lack of reliable predictive preclinical cancer models is a fundamental one. For decades, immortalized cancer cell cultures have been used to lay the groundwork for cancer biology and the quest for therapeutic responses. However, cell lines do not usually recapitulate cancer heterogeneity or reveal therapeutic resistance cues. With the rapidly evolving exploration of cancer “omics,” the scientific community is increasingly investigating whether the employment of short-term patient-derived tumor cell cultures (two- and three-dimensional and/or patient-derived xenograft models might provide a more representative delineation of the cancer core and its therapeutic response. Patient-derived cancer models allow the integration of genomic with drug sensitivity data on a personalized basis and currently represent the ultimate approach for preclinical drug development and biomarker discovery. The proper use of these patient-derived cancer models might soon influence clinical outcomes and allow the implementation of tailored personalized therapy. When assessing drug efficacy for the treatment of glioblastoma multiforme (GBM, currently, the most reliable models are generated through direct injection of patient-derived cells or more frequently the isolation of glioblastoma cells endowed with stem-like features and orthotopically injecting these cells into the cerebrum of immunodeficient mice. Herein, we present the key strengths, weaknesses, and potential applications of cell- and animal-based models of GBM, highlighting our experience with the glioblastoma stem-like patient cell-derived xenograft model and its utility in drug discovery.

Screening the Medicines for Malaria Venture Pathogen Box across Multiple Pathogens Reclassifies Starting Points for Open-Source Drug Discovery.

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Duffy, Sandra; Sykes, Melissa L; Jones, Amy J; Shelper, Todd B; Simpson, Moana; Lang, Rebecca; Poulsen, Sally-Ann; Sleebs, Brad E; Avery, Vicky M

2017-09-01

Open-access drug discovery provides a substantial resource for diseases primarily affecting the poor and disadvantaged. The open-access Pathogen Box collection is comprised of compounds with demonstrated biological activity against specific pathogenic organisms. The supply of this resource by the Medicines for Malaria Venture has the potential to provide new chemical starting points for a number of tropical and neglected diseases, through repurposing of these compounds for use in drug discovery campaigns for these additional pathogens. We tested the Pathogen Box against kinetoplastid parasites and malaria life cycle stages in vitro Consequently, chemical starting points for malaria, human African trypanosomiasis, Chagas disease, and leishmaniasis drug discovery efforts have been identified. Inclusive of this in vitro biological evaluation, outcomes from extensive literature reviews and database searches are provided. This information encompasses commercial availability, literature reference citations, other aliases and ChEMBL number with associated biological activity, where available. The release of this new data for the Pathogen Box collection into the public domain will aid the open-source model of drug discovery. Importantly, this will provide novel chemical starting points for drug discovery and target identification in tropical disease research. Copyright © 2017 Duffy et al.

[Frontiers in Live Bone Imaging Researches. Novel drug discovery by means of intravital bone imaging technology].

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Ishii, Masaru

2015-06-01

Recent advances in intravital bone imaging technology has enabled us to grasp the real cellular behaviors and functions in vivo , revolutionizing the field of drug discovery for novel therapeutics against intractable bone diseases. In this chapter, I introduce various updated information on pharmacological actions of several antibone resorptive agents, which could only be derived from advanced imaging techniques, and also discuss the future perspectives of this new trend in drug discovery.

Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery.

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Heifetz, Alexander; Southey, Michelle; Morao, Inaki; Townsend-Nicholson, Andrea; Bodkin, Mike J

2018-01-01

GPCR modeling approaches are widely used in the hit-to-lead (H2L) and lead optimization (LO) stages of drug discovery. The aims of these modeling approaches are to predict the 3D structures of the receptor-ligand complexes, to explore the key interactions between the receptor and the ligand and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this book chapter, we present a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in hit-to-lead (H2L) and in lead optimization (LO) stages of structure-based drug discovery (SBDD). We outline the differences in modeling strategies used in H2L and LO of SBDD and illustrate how these tools have been applied in three drug discovery projects.

Recent advances in combinatorial biosynthesis for drug discovery

Directory of Open Access Journals (Sweden)

Sun H

2015-02-01

Full Text Available Huihua Sun,1,* Zihe Liu,1,* Huimin Zhao,1,2 Ee Lui Ang1 1Metabolic Engineering Research Laboratory, Institute of Chemical and Engineering Sciences, Agency for Science, Technology and Research, Singapore; 2Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA *These authors contributed equally to this work Abstract: Because of extraordinary structural diversity and broad biological activities, natural products have played a significant role in drug discovery. These therapeutically important secondary metabolites are assembled and modified by dedicated biosynthetic pathways in their host living organisms. Traditionally, chemists have attempted to synthesize natural product analogs that are important sources of new drugs. However, the extraordinary structural complexity of natural products sometimes makes it challenging for traditional chemical synthesis, which usually involves multiple steps, harsh conditions, toxic organic solvents, and byproduct wastes. In contrast, combinatorial biosynthesis exploits substrate promiscuity and employs engineered enzymes and pathways to produce novel “unnatural” natural products, substantially expanding the structural diversity of natural products with potential pharmaceutical value. Thus, combinatorial biosynthesis provides an environmentally friendly way to produce natural product analogs. Efficient expression of the combinatorial biosynthetic pathway in genetically tractable heterologous hosts can increase the titer of the compound, eventually resulting in less expensive drugs. In this review, we will discuss three major strategies for combinatorial biosynthesis: 1 precursor-directed biosynthesis; 2 enzyme-level modification, which includes swapping of the entire domains, modules and subunits, site-specific mutagenesis, and directed evolution; 3 pathway-level recombination. Recent examples of combinatorial biosynthesis employing these

The medical applications of the discoveries of Marie Sklodowska-Curie

International Nuclear Information System (INIS)

Krawczyk, M.

2011-01-01

In this work, the author indicates what have been the applications of the discoveries of Marie Curie in the field of medicine and how these discoveries have contributed in particular to the development of oncologic radiotherapy. (O.M.)

The future of drug discovery: enabling technologies for enhancing lead characterization and profiling therapeutic potential.

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Janero, David R

2014-08-01

Technology often serves as a handmaiden and catalyst of invention. The discovery of safe, effective medications depends critically upon experimental approaches capable of providing high-impact information on the biological effects of drug candidates early in the discovery pipeline. This information can enable reliable lead identification, pharmacological compound differentiation and successful translation of research output into clinically useful therapeutics. The shallow preclinical profiling of candidate compounds promulgates a minimalistic understanding of their biological effects and undermines the level of value creation necessary for finding quality leads worth moving forward within the development pipeline with efficiency and prognostic reliability sufficient to help remediate the current pharma-industry productivity drought. Three specific technologies discussed herein, in addition to experimental areas intimately associated with contemporary drug discovery, appear to hold particular promise for strengthening the preclinical valuation of drug candidates by deepening lead characterization. These are: i) hydrogen-deuterium exchange mass spectrometry for characterizing structural and ligand-interaction dynamics of disease-relevant proteins; ii) activity-based chemoproteomics for profiling the functional diversity of mammalian proteomes; and iii) nuclease-mediated precision gene editing for developing more translatable cellular and in vivo models of human diseases. When applied in an informed manner congruent with the clinical understanding of disease processes, technologies such as these that span levels of biological organization can serve as valuable enablers of drug discovery and potentially contribute to reducing the current, unacceptably high rates of compound clinical failure.

Laboratory informatics tools integration strategies for drug discovery: integration of LIMS, ELN, CDS, and SDMS.

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Machina, Hari K; Wild, David J

2013-04-01

There are technologies on the horizon that could dramatically change how informatics organizations design, develop, deliver, and support applications and data infrastructures to deliver maximum value to drug discovery organizations. Effective integration of data and laboratory informatics tools promises the ability of organizations to make better informed decisions about resource allocation during the drug discovery and development process and for more informed decisions to be made with respect to the market opportunity for compounds. We propose in this article a new integration model called ELN-centric laboratory informatics tools integration.

Key drivers of biomedical innovation in cancer drug discovery

OpenAIRE

Huber, Margit A; Kraut, Norbert

2014-01-01

Discovery and translational research has led to the identification of a series of ?cancer drivers??genes that, when mutated or otherwise misregulated, can drive malignancy. An increasing number of drugs that directly target such drivers have demonstrated activity in clinical trials and are shaping a new landscape for molecularly targeted cancer therapies. Such therapies rely on molecular and genetic diagnostic tests to detect the presence of a biomarker that predicts response. Here, we highli...

Big Data in Drug Discovery.

Science.gov (United States)

Brown, Nathan; Cambruzzi, Jean; Cox, Peter J; Davies, Mark; Dunbar, James; Plumbley, Dean; Sellwood, Matthew A; Sim, Aaron; Williams-Jones, Bryn I; Zwierzyna, Magdalena; Sheppard, David W

2018-01-01

Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation. © 2018 Elsevier B.V. All rights reserved.

Precision oncology: origins, optimism, and potential.

Science.gov (United States)

Prasad, Vinay; Fojo, Tito; Brada, Michael

2016-02-01

Imatinib, the first and arguably the best targeted therapy, became the springboard for developing drugs aimed at molecular targets deemed crucial to tumours. As this development unfolded, a revolution in the speed and cost of genetic sequencing occurred. The result--an armamentarium of drugs and an array of molecular targets--set the stage for precision oncology, a hypothesis that cancer treatment could be markedly improved if therapies were guided by a tumour's genomic alterations. Drawing lessons from the biological basis of cancer and recent empirical investigations, we take a more measured view of precision oncology's promise. Ultimately, the promise is not our concern, but the threshold at which we declare success. We review reports of precision oncology alongside those of precision diagnostics and novel radiotherapy approaches. Although confirmatory evidence is scarce, these interventions have been widely endorsed. We conclude that the current path will probably not be successful or, at a minimum, will have to undergo substantive adjustments before it can be successful. For the sake of patients with cancer, we hope one form of precision oncology will deliver on its promise. However, until confirmatory studies are completed, precision oncology remains unproven, and as such, a hypothesis in need of rigorous testing. Copyright © 2016 Elsevier Ltd. All rights reserved.

Discovery of potent, reversible MetAP2 inhibitors via fragment based drug discovery and structure based drug design-Part 2.

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McBride, Christopher; Cheruvallath, Zacharia; Komandla, Mallareddy; Tang, Mingnam; Farrell, Pamela; Lawson, J David; Vanderpool, Darin; Wu, Yiqin; Dougan, Douglas R; Plonowski, Artur; Holub, Corine; Larson, Chris

2016-06-15

Methionine aminopeptidase-2 (MetAP2) is an enzyme that cleaves an N-terminal methionine residue from a number of newly synthesized proteins. This step is required before they will fold or function correctly. Pre-clinical and clinical studies with a MetAP2 inhibitor suggest that they could be used as a novel treatment for obesity. Herein we describe the discovery of a series of pyrazolo[4,3-b]indoles as reversible MetAP2 inhibitors. A fragment-based drug discovery (FBDD) approach was used, beginning with the screening of fragment libraries to generate hits with high ligand-efficiency (LE). An indazole core was selected for further elaboration, guided by structural information. SAR from the indazole series led to the design of a pyrazolo[4,3-b]indole core and accelerated knowledge-based fragment growth resulted in potent and efficient MetAP2 inhibitors, which have shown robust and sustainable body weight loss in DIO mice when dosed orally. Copyright © 2016 Elsevier Ltd. All rights reserved.

Residual Complexity Does Impact Organic Chemistry and Drug Discovery: The Case of Rufomyazine and Rufomycin.

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Choules, Mary P; Klein, Larry L; Lankin, David C; McAlpine, James B; Cho, Sang-Hyun; Cheng, Jinhua; Lee, Hanki; Suh, Joo-Won; Jaki, Birgit U; Franzblau, Scott G; Pauli, Guido F

2018-05-24

Residual complexity (RC) involves the impact of subtle but critical structural and biological features on drug lead validation, including unexplained effects related to unidentified impurities. RC commonly plagues drug discovery efforts due to the inherent imperfections of chromatographic separation methods. The new diketopiperazine, rufomyazine (6), and the previously known antibiotic, rufomycin (7), represent a prototypical case of RC that (almost) resulted in the misassignment of biological activity. The case exemplifies that impurities well below the natural abundance of 13 C (1.1%) can be highly relevant and calls for advanced analytical characterization of drug leads with extended molar dynamic ranges of >1:1,000 using qNMR and LC-MS. Isolated from an actinomycete strain, 6 was originally found to be active against Mycobacterium tuberculosis with a minimum inhibitory concentration (MIC) of 2 μg/mL and high selectivity. As a part of lead validation, the dipeptide was synthesized and surprisingly found to be inactive. The initially observed activity was eventually attributed to a very minor contamination (0.24% [m/m]) with a highly active cyclic peptide (MIC ∼ 0.02 μM), subsequently identified as an analogue of 7. This study illustrates the serious implications RC can exert on organic chemistry and drug discovery, and what efforts are vital to improve lead validation and efficiency, especially in NP-related drug discovery programs.

From Protein Structure to Small-Molecules: Recent Advances and Applications to Fragment-Based Drug Discovery.

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Ferreira, Leonardo G; Andricopulo, Adriano D

2017-01-01

Fragment-based drug discovery (FBDD) is a broadly used strategy in structure-guided ligand design, whereby low-molecular weight hits move from lead-like to drug-like compounds. Over the past 15 years, an increasingly important role of the integration of these strategies into industrial and academic research platforms has been successfully established, allowing outstanding contributions to drug discovery. One important factor for the current prominence of FBDD is the better coverage of the chemical space provided by fragment-like libraries. The development of the field relies on two features: (i) the growing number of structurally characterized drug targets and (ii) the enormous chemical diversity available for experimental and virtual screenings. Indeed, fragment-based campaigns have contributed to address major challenges in lead optimization, such as the appropriate physicochemical profile of clinical candidates. This perspective paper outlines the usefulness and applications of FBDD approaches in medicinal chemistry and drug design. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project.

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Verbist, Bie; Klambauer, Günter; Vervoort, Liesbet; Talloen, Willem; Shkedy, Ziv; Thas, Olivier; Bender, Andreas; Göhlmann, Hinrich W H; Hochreiter, Sepp

2015-05-01

The pharmaceutical industry is faced with steadily declining R&D efficiency which results in fewer drugs reaching the market despite increased investment. A major cause for this low efficiency is the failure of drug candidates in late-stage development owing to safety issues or previously undiscovered side-effects. We analyzed to what extent gene expression data can help to de-risk drug development in early phases by detecting the biological effects of compounds across disease areas, targets and scaffolds. For eight drug discovery projects within a global pharmaceutical company, gene expression data were informative and able to support go/no-go decisions. Our studies show that gene expression profiling can detect adverse effects of compounds, and is a valuable tool in early-stage drug discovery decision making. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

The principle of safety evaluation in medicinal drug - how can toxicology contribute to drug discovery and development as a multidisciplinary science?

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Horii, Ikuo

2016-01-01

Pharmaceutical (drug) safety assessment covers a diverse science-field in the drug discovery and development including the post-approval and post-marketing phases in order to evaluate safety and risk management. The principle in toxicological science is to be placed on both of pure and applied sciences that are derived from past/present scientific knowledge and coming new science and technology. In general, adverse drug reactions are presented as "biological responses to foreign substances." This is the basic concept of thinking about the manifestation of adverse drug reactions. Whether or not toxic expressions are extensions of the pharmacological effect, adverse drug reactions as seen from molecular targets are captured in the category of "on-target" or "off-target", and are normally expressed as a biological defense reaction. Accordingly, reactions induced by pharmaceuticals can be broadly said to be defensive reactions. Recent molecular biological conception is in line with the new, remarkable scientific and technological developments in the medical and pharmaceutical areas, and the viewpoints in the field of toxicology have shown that they are approaching toward the same direction as well. This paper refers to the basic concept of pharmaceutical toxicology, the differences for safety assessment in each stage of drug discovery and development, regulatory submission, and the concept of scientific considerations for risk assessment and management from the viewpoint of "how can multidisciplinary toxicology contribute to innovative drug discovery and development?" And also realistic translational research from preclinical to clinical application is required to have a significant risk management in post market by utilizing whole scientific data derived from basic and applied scientific research works. In addition, the significance for employing the systems toxicology based on AOP (Adverse Outcome Pathway) analysis is introduced, and coming challenges on precision

Inositol Polyphosphate Kinases, Fungal Virulence and Drug Discovery

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Cecilia Li

2016-09-01

Full Text Available Opportunistic fungi are a major cause of morbidity and mortality world-wide, particularly in immunocompromised individuals. Developing new treatments to combat invasive fungal disease is challenging given that fungal and mammalian host cells are eukaryotic, with similar organization and physiology. Even therapies targeting unique fungal cell features have limitations and drug resistance is emerging. New approaches to the development of antifungal drugs are therefore needed urgently. Cryptococcus neoformans, the commonest cause of fungal meningitis worldwide, is an accepted model for studying fungal pathogenicity and driving drug discovery. We recently characterized a phospholipase C (Plc1-dependent pathway in C. neoformans comprising of sequentially-acting inositol polyphosphate kinases (IPK, which are involved in synthesizing inositol polyphosphates (IP. We also showed that the pathway is essential for fungal cellular function and pathogenicity. The IP products of the pathway are structurally diverse, each consisting of an inositol ring, with phosphate (P and pyrophosphate (PP groups covalently attached at different positions. This review focuses on (1 the characterization of the Plc1/IPK pathway in C. neoformans; (2 the identification of PP-IP5 (IP7 as the most crucial IP species for fungal fitness and virulence in a mouse model of fungal infection; and (3 why IPK enzymes represent suitable candidates for drug development.

Open Innovation Drug Discovery (OIDD): a potential path to novel therapeutic chemical space.

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Alvim-Gaston, Maria; Grese, Timothy; Mahoui, Abdelaziz; Palkowitz, Alan D; Pineiro-Nunez, Marta; Watson, Ian

2014-01-01

The continued development of computational and synthetic methods has enabled the enumeration or preparation of a nearly endless universe of chemical structures. Nevertheless, the ability of this chemical universe to deliver small molecules that can both modulate biological targets and have drug-like physicochemical properties continues to be a topic of interest to the pharmaceutical industry and academic researchers alike. The chemical space described by public, commercial, in-house and virtual compound collections has been interrogated by multiple approaches including biochemical, cellular and virtual screening, diversity analysis, and in-silico profiling. However, current drugs and known chemical probes derived from these efforts are contained within a remarkably small volume of the predicted chemical space. Access to more diverse classes of chemical scaffolds that maintain the properties relevant for drug discovery is certainly needed to meet the increasing demands for pharmaceutical innovation. The Lilly Open Innovation Drug Discovery platform (OIDD) was designed to tackle barriers to innovation through the identification of novel molecules active in relevant disease biology models. In this article we will discuss several computational approaches towards describing novel, biologically active, drug-like chemical space and illustrate how the OIDD program may facilitate access to previously untapped molecules that may aid in the search for innovative pharmaceuticals.

Public and private sector contributions to the discovery and development of "impact" drugs.

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Reichert, Janice M; Milne, Christopher-Paul

2002-01-01

Recently, well-publicized reports by Public Citizen and the Joint Economic Committee (JEC) of the US Congress questioned the role of the drug industry in the discovery and development of therapeutically important drugs. To gain a better understanding of the relative roles of the public and private sectors in pharmaceutic innovation, the Tufts Center for the Study of Drug Development evaluated the underlying National Institutes of Health (NIH) and academic research cited in the Public Citizen and JEC reports and performed its own assessment of the relationship between the private and public sectors in drug discovery and development of 21 "impact" drugs. We found that, ultimately, any attempt to measure the relative contribution of the public and private sectors to the research and development (R&D) of therapeutically important drugs by output alone, such as counting publications or even product approvals, is flawed. Several key factors (eg, degree of uncertainty, expected market value, potential social benefit) affect investment decisions and determine whether public or private sector funds, or both, are most appropriate. Because of the competitiveness and complexity of today's R&D environment, both sectors are increasingly challenged to show returns on their investment and the traditional boundaries separating the roles of the private and public research spheres have become increasingly blurred. What remains clear, however, is that the process still starts with good science and ends with good medicine.

Recent Advances in Drug Discovery from South African Marine Invertebrates

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Michael T. Davies-Coleman

2015-10-01

Full Text Available Recent developments in marine drug discovery from three South African marine invertebrates, the tube worm Cephalodiscus gilchristi, the ascidian Lissoclinum sp. and the sponge Topsentia pachastrelloides, are presented. Recent reports of the bioactivity and synthesis of the anti-cancer secondary metabolites cephalostatin and mandelalides (from C. gilchristi and Lissoclinum sp., respectively and various analogues are presented. The threat of drug-resistant pathogens, e.g., methicillin-resistant Staphylococcus aureus (MRSA, is assuming greater global significance, and medicinal chemistry strategies to exploit the potent MRSA PK inhibition, first revealed by two marine secondary metabolites, cis-3,4-dihydrohamacanthin B and bromodeoxytopsentin from T. pachastrelloides, are compared.

Machine-learning techniques applied to antibacterial drug discovery.

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Durrant, Jacob D; Amaro, Rommie E

2015-01-01

The emergence of drug-resistant bacteria threatens to revert humanity back to the preantibiotic era. Even now, multidrug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the pipeline. Traditional high-throughput screens and lead-optimization efforts are expensive and labor intensive. Computer-aided drug-discovery techniques, which are cheaper and faster, can accelerate the identification of novel antibiotics, leading to improved hit rates and faster transitions to preclinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field. © 2015 John Wiley & Sons A/S.

Molecular property diagnostic suite (MPDS): Development of disease-specific open source web portals for drug discovery.

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Nagamani, S; Gaur, A S; Tanneeru, K; Muneeswaran, G; Madugula, S S; Consortium, Mpds; Druzhilovskiy, D; Poroikov, V V; Sastry, G N

2017-11-01

Molecular property diagnostic suite (MPDS) is a Galaxy-based open source drug discovery and development platform. MPDS web portals are designed for several diseases, such as tuberculosis, diabetes mellitus, and other metabolic disorders, specifically aimed to evaluate and estimate the drug-likeness of a given molecule. MPDS consists of three modules, namely data libraries, data processing, and data analysis tools which are configured and interconnected to assist drug discovery for specific diseases. The data library module encompasses vast information on chemical space, wherein the MPDS compound library comprises 110.31 million unique molecules generated from public domain databases. Every molecule is assigned with a unique ID and card, which provides complete information for the molecule. Some of the modules in the MPDS are specific to the diseases, while others are non-specific. Importantly, a suitably altered protocol can be effectively generated for another disease-specific MPDS web portal by modifying some of the modules. Thus, the MPDS suite of web portals shows great promise to emerge as disease-specific portals of great value, integrating chemoinformatics, bioinformatics, molecular modelling, and structure- and analogue-based drug discovery approaches.

Patient-derived stem cells: pathways to drug discovery for brain diseases

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Alan eMackay-Sim

2013-03-01

Full Text Available The concept of drug discovery through stem cell biology is based on technological developments whose genesis is now coincident. The first is automated cell microscopy with concurrent advances in image acquisition and analysis, known as high content screening (HCS. The second is patient-derived stem cells for modelling the cell biology of brain diseases. HCS has developed from the requirements of the pharmaceutical industry for high throughput assays to screen thousands of chemical compounds in the search for new drugs. HCS combines new fluorescent probes with automated microscopy and computational power to quantify the effects of compounds on cell functions. Stem cell biology has advanced greatly since the discovery of genetic reprogramming of somatic cells into induced pluripotent stem cells (iPSCs. There is now a rush of papers describing their generation from patients with various diseases of the nervous system. Although the majority of these have been genetic diseases, iPSCs have been generated from patients with complex diseases (schizophrenia and sporadic Parkinson’s disease. Some genetic diseases are also modelled in embryonic stem cells generated from blastocysts rejected during in vitro fertilisation. Neural stem cells have been isolated from post-mortem brain of Alzheimer’s patients and neural stem cells generated from biopsies of the olfactory organ of patients is another approach. These olfactory neurosphere-derived cells demonstrate robust disease-specific phenotypes in patients with schizophrenia and Parkinson’s disease. High content screening is already in use to find small molecules for the generation and differentiation of embryonic stem cells and induced pluripotent stem cells. The challenges for using stem cells for drug discovery are to develop robust stem cell culture methods that meet the rigorous requirements for repeatable, consistent quantities of defined cell types at the industrial scale necessary for high

Utilization of the Bridging Strategy for the Development of New Drugs in Oncology to Avoid Drug Lag.

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Kogure, Seiji; Koyama, Nobuyuki; Hidaka, Shinji

2017-11-01

Global trial (GT) strategy and bridging (BG) strategy are currently the main clinical development strategies of oncology drugs in Japan, but the relationship between development style and drug lag and how the bridging strategy has contributed to the solution of drug lag have not been clear. We investigated the potential factors that influenced submission lag (SL), and also compared the differences in SL among early-initiation BG strategy, late-initiation BG strategy, and GT strategy. A stepwise linear regression analysis identified the potential factors that shorten SL: development start lag and development style. Comparison of the differences in SL among the strategies also indicated that the SL in the GT strategy and that in the early-initiation BG strategy were significantly shorter than that in the late-initiation BG strategy. The findings in our study suggest that the late-initiation BG strategy may not contribute to shortening drug lag. Because the number of late-initiation BG studies has not decreased, we propose first that pharmaceutical companies should initiate clinical development as early as possible in Japan so that they can choose the GT strategy as a first option at the next step, and second when they cannot choose the GT strategy after investigating differences in exposure between Japanese and non-Japanese in a phase 1 study, they should select the early BG strategy to avoid future drug lag. It is also important for the regulatory authorities to provide reasonable guidance to have a positive impact on strategic decisions, even for foreign-capital companies. © 2017, The American College of Clinical Pharmacology.

[Quality assurance in head and neck medical oncology].

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Digue, Laurence; Pedeboscq, Stéphane

2014-05-01

In medical oncology, how can we be sure that the right drug is being administered to the right patient at the right time? The implementation of quality assurance criteria is important in medical oncology, in order to ensure that the patient receives the best treatment safely. There is very little literature about quality assurance in medical oncology, as opposed to radiotherapy or cancer surgery. Quality assurance must cover the entire patient care process, from the diagnosis, to the therapeutic decision and drug distribution, including its selection, its preparation and its delivery to the patient (administration and dosage), and finally the potential side effects and their management. The dose-intensity respect is crucial, and its reduction can negatively affect overall survival rates, as shown in breast and testis cancers for example. In head and neck medical oncology, it is essential to respect the few well-standardized recommendations and the dose-intensity, in a population with numerous comorbidities. We will first review quality assurance criteria for the general medical oncology organization and then focus on head and neck medical oncology. We will then describe administration specificities of head and neck treatments (chemoradiation, radiation plus cetuximab, postoperative chemoradiation, induction and palliative chemotherapy) as well as their follow-up. Lastly, we will offer some recommendations to improve quality assurance in head and neck medical oncology.

The case for open-source software in drug discovery.

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DeLano, Warren L

2005-02-01

Widespread adoption of open-source software for network infrastructure, web servers, code development, and operating systems leads one to ask how far it can go. Will "open source" spread broadly, or will it be restricted to niches frequented by hopeful hobbyists and midnight hackers? Here we identify reasons for the success of open-source software and predict how consumers in drug discovery will benefit from new open-source products that address their needs with increased flexibility and in ways complementary to proprietary options.

Current status and future prospects for enabling chemistry technology in the drug discovery process [version 1; referees: 2 approved

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Stevan W. Djuric

2016-09-01

Full Text Available This review covers recent advances in the implementation of enabling chemistry technologies into the drug discovery process. Areas covered include parallel synthesis chemistry, high-throughput experimentation, automated synthesis and purification methods, flow chemistry methodology including photochemistry, electrochemistry, and the handling of “dangerous” reagents. Also featured are advances in the “computer-assisted drug design” area and the expanding application of novel mass spectrometry-based techniques to a wide range of drug discovery activities.

Cardiovascular drug discovery in the academic setting: building infrastructure, harnessing strengths, and seeking synergies.

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Gardell, Stephen J; Roth, Gregory P; Kelly, Daniel P

2010-10-01

The flow of innovative, effective, and safe new drugs from pharmaceutical laboratories for the treatment and prevention of cardiovascular disease has slowed to a trickle. While the need for breakthrough cardiovascular disease drugs is still paramount, the incentive to develop these agents has been blunted by burgeoning clinical development costs coupled with a heightened risk of failure due to the unprecedented nature of the emerging drug targets and increasingly challenging regulatory environment. A fuller understanding of the drug targets and employing novel biomarker strategies in clinical trials should serve to mitigate the risk. In any event, these current challenges have evoked changing trends in the pharmaceutical industry, which have created an opportunity for non-profit biomedical research institutions to play a pivotal partnering role in early stage drug discovery. The obvious strengths of academic research institutions is the breadth of their scientific programs and the ability and motivation to "go deep" to identify and characterize new target pathways that unlock the specific mysteries of cardiovascular diseases--leading to a bounty of novel therapeutic targets and prescient biomarkers. However, success in the drug discovery arena within the academic environment is contingent upon assembling the requisite infrastructure, annexing the talent to interrogate and validate the drug targets, and building translational bridges with pharmaceutical organizations and patient-oriented researchers.

[From the discovery of antibiotics to emerging highly drug-resistant bacteria].

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Meunier, Olivier

2015-01-01

The discovery of antibiotics has enabled serious infections to be treated. However, bacteria resistant to several families of antibiotics and the emergence of new highly drug-resistant bacteria constitute a public health issue in France and across the world. Actions to prevent their transmission are being put in place. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

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

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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.

Biomarker-guided repurposing of chemotherapeutic drugs for cancer therapy: a novel strategy in drug development

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Jan eStenvang

2013-12-01

Full Text Available Cancer is a leading cause of mortality worldwide and matters are only set to worsen as its incidence continues to rise. Traditional approaches to combat cancer include improved prevention, early diagnosis, optimized surgery, development of novel drugs and honing regimens of existing anti-cancer drugs. Although discovery and development of novel and effective anti-cancer drugs is a major research area, it is well known that oncology drug development is a lengthy process, extremely costly and with high attrition rates. Furthermore, those drugs that do make it through the drug development mill are often quite expensive, laden with severe side-effects and, unfortunately, to date, have only demonstrated minimal increases in overall survival. Therefore, a strong interest has emerged to identify approved non-cancer drugs that possess anti-cancer activity, thus shortcutting the development process. This research strategy is commonly known as drug repurposing or drug repositioning and provides a faster path to the clinics. We have developed and implemented a modification of the standard drug repurposing strategy that we review here; rather than investigating target-promiscuous non-cancer drugs for possible anti-cancer activity, we focus on the discovery of novel cancer indications for already approved chemotherapeutic anti-cancer drugs. Clinical implementation of this strategy is normally commenced at clinical phase II trials and includes pre-treated patients. As the response rates to any non-standard chemotherapeutic drug will be relatively low in such a patient cohort it is a pre-requisite that such testing is based on predictive biomarkers. This review describes our strategy of biomarker-guided repurposing of chemotherapeutic drugs for cancer therapy, taking the repurposing of topoisomerase I inhibitors and topoisomerase I as a potential predictive biomarker as case in point.

Open source drug discovery in practice: a case study.

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Årdal, Christine; Røttingen, John-Arne

2012-01-01

Open source drug discovery offers potential for developing new and inexpensive drugs to combat diseases that disproportionally affect the poor. The concept borrows two principle aspects from open source computing (i.e., collaboration and open access) and applies them to pharmaceutical innovation. By opening a project to external contributors, its research capacity may increase significantly. To date there are only a handful of open source R&D projects focusing on neglected diseases. We wanted to learn from these first movers, their successes and failures, in order to generate a better understanding of how a much-discussed theoretical concept works in practice and may be implemented. A descriptive case study was performed, evaluating two specific R&D projects focused on neglected diseases. CSIR Team India Consortium's Open Source Drug Discovery project (CSIR OSDD) and The Synaptic Leap's Schistosomiasis project (TSLS). Data were gathered from four sources: interviews of participating members (n = 14), a survey of potential members (n = 61), an analysis of the websites and a literature review. Both cases have made significant achievements; however, they have done so in very different ways. CSIR OSDD encourages international collaboration, but its process facilitates contributions from mostly Indian researchers and students. Its processes are formal with each task being reviewed by a mentor (almost always offline) before a result is made public. TSLS, on the other hand, has attracted contributors internationally, albeit significantly fewer than CSIR OSDD. Both have obtained funding used to pay for access to facilities, physical resources and, at times, labor costs. TSLS releases its results into the public domain, whereas CSIR OSDD asserts ownership over its results. Technically TSLS is an open source project, whereas CSIR OSDD is a crowdsourced project. However, both have enabled high quality research at low cost. The critical success factors appear to be clearly

Weak affinity chromatography for evaluation of stereoisomers in early drug discovery.

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Duong-Thi, Minh-Dao; Bergström, Maria; Fex, Tomas; Svensson, Susanne; Ohlson, Sten; Isaksson, Roland

2013-07-01

In early drug discovery (e.g., in fragment screening), recognition of stereoisomeric structures is valuable and guides medicinal chemists to focus only on useful configurations. In this work, we concurrently screened mixtures of stereoisomers and estimated their affinities to a protein target (thrombin) using weak affinity chromatography-mass spectrometry (WAC-MS). Affinity determinations by WAC showed that minor changes in stereoisomeric configuration could have a major impact on affinity. The ability of WAC-MS to provide instant information about stereoselectivity and binding affinities directly from analyte mixtures is a great advantage in fragment library screening and drug lead development.

The evolution of the matrix metalloproteinase inhibitor drug discovery program at abbott laboratories.

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Wada, Carol K

2004-01-01

Matrix metalloproteinases (MMPs) have been implicated in several pathologies. At Abbott Laboratories, the matrix metalloproteinases inhibitor drug discovery program has focused on the discovery of a potent, selective, orally bioavailable MMP inhibitor for the treatment of cancer. The program evolved from early succinate-based inhibitors to utilizing in-house technology such as SAR by NMR to develop a novel class of biaryl hydroxamate MMP inhibitors. The metabolic instability of the biaryl hydroxamates led to the discovery of a new class of N-formylhydroxylamine (retrohydroxamate) biaryl ethers, exemplified by ABT-770 (16). Toxicity issues with this pre-clinical candidate led to the discovery of another novel class of retrohydroxamate MMP inhibitors, the phenoxyphenyl sulfones such as ABT-518 (19j). ABT-518 is a potent, orally bioavailable, selective inhibitor of MMP-2 and 9 over MMP-1 that has been evaluated in Phase I clinical trials in cancer patients.

Open Science Meets Stem Cells: A New Drug Discovery Approach for Neurodegenerative Disorders.

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Han, Chanshuai; Chaineau, Mathilde; Chen, Carol X-Q; Beitel, Lenore K; Durcan, Thomas M

2018-01-01

Neurodegenerative diseases are a challenge for drug discovery, as the biological mechanisms are complex and poorly understood, with a paucity of models that faithfully recapitulate these disorders. Recent advances in stem cell technology have provided a paradigm shift, providing researchers with tools to generate human induced pluripotent stem cells (iPSCs) from patient cells. With the potential to generate any human cell type, we can now generate human neurons and develop "first-of-their-kind" disease-relevant assays for small molecule screening. Now that the tools are in place, it is imperative that we accelerate discoveries from the bench to the clinic. Using traditional closed-door research systems raises barriers to discovery, by restricting access to cells, data and other research findings. Thus, a new strategy is required, and the Montreal Neurological Institute (MNI) and its partners are piloting an "Open Science" model. One signature initiative will be that the MNI biorepository will curate and disseminate patient samples in a more accessible manner through open transfer agreements. This feeds into the MNI open drug discovery platform, focused on developing industry-standard assays with iPSC-derived neurons. All cell lines, reagents and assay findings developed in this open fashion will be made available to academia and industry. By removing the obstacles many universities and companies face in distributing patient samples and assay results, our goal is to accelerate translational medical research and the development of new therapies for devastating neurodegenerative disorders.

Open Science Meets Stem Cells: A New Drug Discovery Approach for Neurodegenerative Disorders

Directory of Open Access Journals (Sweden)

Chanshuai Han

2018-02-01

Full Text Available Neurodegenerative diseases are a challenge for drug discovery, as the biological mechanisms are complex and poorly understood, with a paucity of models that faithfully recapitulate these disorders. Recent advances in stem cell technology have provided a paradigm shift, providing researchers with tools to generate human induced pluripotent stem cells (iPSCs from patient cells. With the potential to generate any human cell type, we can now generate human neurons and develop “first-of-their-kind” disease-relevant assays for small molecule screening. Now that the tools are in place, it is imperative that we accelerate discoveries from the bench to the clinic. Using traditional closed-door research systems raises barriers to discovery, by restricting access to cells, data and other research findings. Thus, a new strategy is required, and the Montreal Neurological Institute (MNI and its partners are piloting an “Open Science” model. One signature initiative will be that the MNI biorepository will curate and disseminate patient samples in a more accessible manner through open transfer agreements. This feeds into the MNI open drug discovery platform, focused on developing industry-standard assays with iPSC-derived neurons. All cell lines, reagents and assay findings developed in this open fashion will be made available to academia and industry. By removing the obstacles many universities and companies face in distributing patient samples and assay results, our goal is to accelerate translational medical research and the development of new therapies for devastating neurodegenerative disorders.

An In Vivo Platform for Rapid High-Throughput Antitubercular Drug Discovery

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Kevin Takaki

2012-07-01

Full Text Available Treatment of tuberculosis, like other infectious diseases, is increasingly hindered by the emergence of drug resistance. Drug discovery efforts would be facilitated by facile screening tools that incorporate the complexities of human disease. Mycobacterium marinum-infected zebrafish larvae recapitulate key aspects of tuberculosis pathogenesis and drug treatment. Here, we develop a model for rapid in vivo drug screening using fluorescence-based methods for serial quantitative assessment of drug efficacy and toxicity. We provide proof-of-concept that both traditional bacterial-targeting antitubercular drugs and newly identified host-targeting drugs would be discovered through the use of this model. We demonstrate the model’s utility for the identification of synergistic combinations of antibacterial drugs and demonstrate synergy between bacterial- and host-targeting compounds. Thus, the platform can be used to identify new antibacterial agents and entirely new classes of drugs that thwart infection by targeting host pathways. The methods developed here should be widely applicable to small-molecule screens for other infectious and noninfectious diseases.

Biomarkers for personalized oncology: recent advances and future challenges.

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Kalia, Madhu

2015-03-01

Cancer is a group of diseases characterized by the uncontrolled growth and spread of abnormal cells and oncology is a branch of medicine that deals with tumors. The last decade has seen significant advances in the development of biomarkers in oncology that play a critical role in understanding molecular and cellular mechanisms which drive tumor initiation, maintenance and progression. Clinical molecular diagnostics and biomarker discoveries in oncology are advancing rapidly as we begin to understand the complex mechanisms that transform a normal cell into an abnormal one. These discoveries have fueled the development of novel drug targets and new treatment strategies. The standard of care for patients with advanced-stage cancers has shifted away from an empirical treatment strategy based on the clinical-pathological profile to one where a biomarker driven treatment algorithm based on the molecular profile of the tumor is used. Recent advances in multiplex genotyping technologies and high-throughput genomic profiling by next-generation sequencing make possible the rapid and comprehensive analysis of the cancer genome of individual patients even from very little tumor biopsy material. Predictive (diagnostic) biomarkers are helpful in matching targeted therapies with patients and in preventing toxicity of standard (systemic) therapies. Prognostic biomarkers identify somatic germ line mutations, changes in DNA methylation, elevated levels of microRNA (miRNA) and circulating tumor cells (CTC) in blood. Predictive biomarkers using molecular diagnostics are currently in use in clinical practice of personalized oncotherapy for the treatment of five diseases: chronic myeloid leukemia, colon, breast, lung cancer and melanoma and these biomarkers are being used successfully to evaluate benefits that can be achieved through targeted therapy. Examples of these molecularly targeted biomarker therapies are: tyrosine kinase inhibitors in chronic myeloid leukemia and

Native Mass Spectrometry in Fragment-Based Drug Discovery

Directory of Open Access Journals (Sweden)

Liliana Pedro

2016-07-01

Full Text Available The advent of native mass spectrometry (MS in 1990 led to the development of new mass spectrometry instrumentation and methodologies for the analysis of noncovalent protein–ligand complexes. Native MS has matured to become a fast, simple, highly sensitive and automatable technique with well-established utility for fragment-based drug discovery (FBDD. Native MS has the capability to directly detect weak ligand binding to proteins, to determine stoichiometry, relative or absolute binding affinities and specificities. Native MS can be used to delineate ligand-binding sites, to elucidate mechanisms of cooperativity and to study the thermodynamics of binding. This review highlights key attributes of native MS for FBDD campaigns.

Native Mass Spectrometry in Fragment-Based Drug Discovery.

Science.gov (United States)

Pedro, Liliana; Quinn, Ronald J

2016-07-28

The advent of native mass spectrometry (MS) in 1990 led to the development of new mass spectrometry instrumentation and methodologies for the analysis of noncovalent protein-ligand complexes. Native MS has matured to become a fast, simple, highly sensitive and automatable technique with well-established utility for fragment-based drug discovery (FBDD). Native MS has the capability to directly detect weak ligand binding to proteins, to determine stoichiometry, relative or absolute binding affinities and specificities. Native MS can be used to delineate ligand-binding sites, to elucidate mechanisms of cooperativity and to study the thermodynamics of binding. This review highlights key attributes of native MS for FBDD campaigns.

Nanomedicine in veterinary oncology.

Science.gov (United States)

Lin, Tzu-Yin; Rodriguez, Carlos O; Li, Yuanpei

2015-08-01

Nanomedicine is an interdisciplinary field that combines medicine, engineering, chemistry, biology and material sciences to improve disease management and can be especially valuable in oncology. Nanoparticle-based agents that possess functions such as tumor targeting, imaging and therapy are currently under intensive investigation. This review introduces the basic concept of nanomedicine and the classification of nanoparticles. Because of their favorable pharmacokinetics, tumor targeting properties, and resulting superior efficacy and toxicity profiles, nanoparticle-based agents can overcome several limitations associated with conventional diagnostic and therapeutic protocols in veterinary oncology. The two most important tumor targeting mechanisms (passive and active tumor targeting) and their dominating factors (i.e. shape, charge, size and nanoparticle surface display) are discussed. The review summarizes published clinical and preclinical studies that utilize different nanoformulations in veterinary oncology, as well as the application of nanoparticles for cancer diagnosis and imaging. The toxicology of various nanoformulations is also considered. Given the benefits of nanoformulations demonstrated in human medicine, nanoformulated drugs are likely to gain more traction in veterinary oncology. Published by Elsevier Ltd.

A Multimodal Data Analysis Approach for Targeted Drug Discovery Involving Topological Data Analysis (TDA).

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Alagappan, Muthuraman; Jiang, Dadi; Denko, Nicholas; Koong, Albert C

In silico drug discovery refers to a combination of computational techniques that augment our ability to discover drug compounds from compound libraries. Many such techniques exist, including virtual high-throughput screening (vHTS), high-throughput screening (HTS), and mechanisms for data storage and querying. However, presently these tools are often used independent of one another. In this chapter, we describe a new multimodal in silico technique for the hit identification and lead generation phases of traditional drug discovery. Our technique leverages the benefits of three independent methods-virtual high-throughput screening, high-throughput screening, and structural fingerprint analysis-by using a fourth technique called topological data analysis (TDA). We describe how a compound library can be independently tested with vHTS, HTS, and fingerprint analysis, and how the results can be transformed into a topological data analysis network to identify compounds from a diverse group of structural families. This process of using TDA or similar clustering methods to identify drug leads is advantageous because it provides a mechanism for choosing structurally diverse compounds while maintaining the unique advantages of already established techniques such as vHTS and HTS.

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

Fragment-Based Drug Discovery in the Bromodomain and Extra-Terminal Domain Family.

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Radwan, Mostafa; Serya, Rabah

2017-08-01

Bromodomain and extra-terminal domain (BET) inhibition has emerged recently as a potential therapeutic target for the treatment of many human disorders such as atherosclerosis, inflammatory disorders, chronic obstructive pulmonary disease (COPD), some viral infections, and cancer. Since the discovery of the two potent inhibitors, I-BET762 and JQ1, different research groups have used different techniques to develop novel potent and selective inhibitors. In this review, we will be concerned with the trials that used fragment-based drug discovery (FBDD) approaches to discover or optimize BET inhibitors, also showing fragments that can be further optimized in future projects to reach novel potent BET inhibitors. © 2017 Deutsche Pharmazeutische Gesellschaft.

Feature Issue Introduction: Bio-Optics in Clinical Applications, Nanotechnology, and Drug Discovery

OpenAIRE

Nordstrom, Robert J.; Almutairi, Adah; Hillman, Elizabeth M.C.

2010-01-01

The editors introduce the Biomedical Optics Express feature issue, “Bio-Optics in Clinical Applications, Nanotechnology, and Drug Discovery,” which combines three technical areas from the 2010 Optical Society of America (OSA), Biomedical Optics (BIOMED) Topical Meeting held on 11–14 April in Miami, FL and includes contributions from conference attendees.

Identifying and quantifying heterogeneity in high content analysis: application of heterogeneity indices to drug discovery.

Directory of Open Access Journals (Sweden)

Albert H Gough

Full Text Available One of the greatest challenges in biomedical research, drug discovery and diagnostics is understanding how seemingly identical cells can respond differently to perturbagens including drugs for disease treatment. Although heterogeneity has become an accepted characteristic of a population of cells, in drug discovery it is not routinely evaluated or reported. The standard practice for cell-based, high content assays has been to assume a normal distribution and to report a well-to-well average value with a standard deviation. To address this important issue we sought to define a method that could be readily implemented to identify, quantify and characterize heterogeneity in cellular and small organism assays to guide decisions during drug discovery and experimental cell/tissue profiling. Our study revealed that heterogeneity can be effectively identified and quantified with three indices that indicate diversity, non-normality and percent outliers. The indices were evaluated using the induction and inhibition of STAT3 activation in five cell lines where the systems response including sample preparation and instrument performance were well characterized and controlled. These heterogeneity indices provide a standardized method that can easily be integrated into small and large scale screening or profiling projects to guide interpretation of the biology, as well as the development of therapeutics and diagnostics. Understanding the heterogeneity in the response to perturbagens will become a critical factor in designing strategies for the development of therapeutics including targeted polypharmacology.

Incorporation of rapid thermodynamic data in fragment-based drug discovery.

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Kobe, Akihiro; Caaveiro, Jose M M; Tashiro, Shinya; Kajihara, Daisuke; Kikkawa, Masato; Mitani, Tomoya; Tsumoto, Kouhei

2013-03-14

Fragment-based drug discovery (FBDD) has enjoyed increasing popularity in recent years. We introduce SITE (single-injection thermal extinction), a novel thermodynamic methodology that selects high-quality hits early in FBDD. SITE is a fast calorimetric competitive assay suitable for automation that captures the essence of isothermal titration calorimetry but using significantly fewer resources. We describe the principles of SITE and identify a novel family of fragment inhibitors of the enzyme ketosteroid isomerase displaying high values of enthalpic efficiency.

Factors influencing oncology nurses' use of hazardous drug safe-handling precautions.

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Polovich, Martha; Clark, Patricia C

2012-05-01

To examine relationships among factors affecting nurses' use of hazardous drug (HD) safe-handling precautions, identify factors that promote or interfere with HD precaution use, and determine managers' perspectives on the use of HD safe-handling precautions. Cross-sectional, mixed methods; mailed survey to nurses who handle chemotherapy and telephone interviews with managers. Mailed invitation to oncology centers across the United States. 165 nurses who reported handling chemotherapy and 20 managers of nurses handling chemotherapy. Instruments measured the use of HD precautions and individual and organizational factors believed to influence precaution use. Data analysis included descriptive statistics and hierarchical regression. Manager interview data were analyzed using content analysis. Chemotherapy exposure knowledge, self-efficacy, perceived barriers, perceived risk, interpersonal influences, and workplace safety climate. Nurses were well educated, experienced, and certified in oncology nursing. The majority worked in outpatient settings and administered chemotherapy to an average of 6.8 patients per day. Exposure knowledge, self-efficacy for using personal protective equipment, and perceived risk of harm from HD exposure were high; total precaution use was low. Nurse characteristics did not predict HD precaution use. Fewer barriers, better workplace safety climate, and fewer patients per day were independent predictors of higher HD precaution use. HD handling policies were present, but many did not reflect current recommendations. Few managers formally monitored nurses' HD precaution use. Circumstances in the workplace interfere with nurses' use of HD precautions. Interventions should include fostering a positive workplace safety climate, reducing barriers, and providing appropriate nurse-patient ratios.

A drug's life: the pathway to drug approval.

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Keng, Michael K; Wenzell, Candice M; Sekeres, Mikkael A

2013-10-01

In the United States, drugs and medical devices are regulated by the US Food and Drug Administration (FDA). A drug must undergo rigorous testing prior to marketing to and medical use by the general public. The FDA grants marketing approval for drug products based on a comprehensive review of safety and efficacy data. This review article explains the history behind the establishment of the FDA and examines the historical legislation and approval processes for drugs, specifically in the fields of medical oncology and hematology. The agents imatinib (Gleevec, Novartis) and decitabine (Dacogen, Eisai) are used to illustrate both the current FDA regulatory process-specifically the orphan drug designation and accelerated approval process-and why decitabine failed to gain an indication for acute myeloid leukemia. The purpose and construct of the Oncologic Drugs Advisory Committee are also discussed, along with examples of 2 renal cell cancer drugs-axitinib (Inlyta, Pfizer) and tivozanib-that used progression-free survival as an endpoint. Regulatory approval of oncology drugs is the cornerstone of the development of new treatment agents and modalities, which lead to improvements in the standard of cancer care. The future landscape of drug development and regulatory approval will be influenced by the new breakthrough therapy designation, and choice of drug will be guided by genomic insights.

Exploring Chemical Space for Drug Discovery Using the Chemical Universe Database

Science.gov (United States)

2012-01-01

Herein we review our recent efforts in searching for bioactive ligands by enumeration and virtual screening of the unknown chemical space of small molecules. Enumeration from first principles shows that almost all small molecules (>99.9%) have never been synthesized and are still available to be prepared and tested. We discuss open access sources of molecules, the classification and representation of chemical space using molecular quantum numbers (MQN), its exhaustive enumeration in form of the chemical universe generated databases (GDB), and examples of using these databases for prospective drug discovery. MQN-searchable GDB, PubChem, and DrugBank are freely accessible at www.gdb.unibe.ch. PMID:23019491

Structural biology contributions to the discovery of drugs to treat chronic myelogenous leukaemia

International Nuclear Information System (INIS)

Cowan-Jacob, Sandra W.; Fendrich, Gabriele; Floersheimer, Andreas; Furet, Pascal; Liebetanz, Janis; Rummel, Gabriele; Rheinberger, Paul; Centeleghe, Mario; Fabbro, Doriano; Manley, Paul W.

2006-01-01

A case study showing how the determination of multiple cocrystal structures of the protein tyrosine kinase c-Abl was used to support drug discovery, resulting in a compound effective in the treatment of chronic myelogenous leukaemia. Chronic myelogenous leukaemia (CML) results from the Bcr-Abl oncoprotein, which has a constitutively activated Abl tyrosine kinase domain. Although most chronic phase CML patients treated with imatinib as first-line therapy maintain excellent durable responses, patients who have progressed to advanced-stage CML frequently fail to respond or lose their response to therapy owing to the emergence of drug-resistant mutants of the protein. More than 40 such point mutations have been observed in imatinib-resistant patients. The crystal structures of wild-type and mutant Abl kinase in complex with imatinib and other small-molecule Abl inhibitors were determined, with the aim of understanding the molecular basis of resistance and to aid in the design and optimization of inhibitors active against the resistance mutants. These results are presented in a way which illustrates the approaches used to generate multiple structures, the type of information that can be gained and the way that this information is used to support drug discovery

Omics-based nanomedicine: the future of personalized oncology.

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Rosenblum, Daniel; Peer, Dan

2014-09-28

The traditional "one treatment fits all" paradigm disregards the heterogeneity between cancer patients, and within a particular tumor, thus limit the success of common treatments. Moreover, current treatment lacks specificity and therefore most of the anticancer drugs induce severe adverse effects. Personalized medicine aims to individualize therapeutic interventions, based on the growing knowledge of the human multiple '-oms' (e.g. genome, epigenome, transcriptome, proteome and metabolome), which has led to the discovery of various biomarkers that can be used to detect early stage cancers and predict tumor progression, drug response, and clinical outcome. Nanomedicine, the application of nanotechnology to healthcare, holds great promise for revolutionizing disease management such as drug delivery, molecular imaging, reduced adverse effects and the ability to contain both therapeutic and diagnostic modalities simultaneously termed theranostics. Personalizednanomedicine has the power of combining nanomedicine with clinical and molecular biomarkers ("OMICS" data) achieving improve prognosis and disease management as well as individualized drug selection and dosage profiling to ensure maximal efficacy and safety. Tumor's heterogeneity sets a countless challenge for future personalized therapy in cancer, however the use of multi-parameter 'omic's data for specific molecular biomarkers recognition together with versatile drug delivery nanocarriers, which could target concomitantly and specifically tumor cells subpopulations, might heralds a brighter future for personalized cancer management. In this review, we present the current leading technologies available for personalized oncology. We discusses the immense potential of combining the best of these two worlds, nanomedicine and high throughput OMICS technologies to pave the way towards cancer personalized medicine. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Drug Development for Metastasis Prevention.

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Fontebasso, Yari; Dubinett, Steven M

2015-01-01

Metastatic disease is responsible for 90% of death from solid tumors. However, only a minority of metastasis-specific targets has been exploited therapeutically, and effective prevention and suppression of metastatic disease is still an elusive goal. In this review, we will first summarize the current state of knowledge about the molecular features of the disease, with particular focus on steps and targets potentially amenable to therapeutic intervention. We will then discuss the reasons underlying the paucity of metastatic drugs in the current oncological arsenal and potential ways to overcome this therapeutic gap. We reason that the discovery of novel promising targets, an increased understanding of the molecular features of the disease, the effect of disruptive technologies, and a shift in the current preclinical and clinical settings have the potential to create more successful drug development endeavors.

Establishing MALDI-TOF as Versatile Drug Discovery Readout to Dissect the PTP1B Enzymatic Reaction.

Science.gov (United States)

Winter, Martin; Bretschneider, Tom; Kleiner, Carola; Ries, Robert; Hehn, Jörg P; Redemann, Norbert; Luippold, Andreas H; Bischoff, Daniel; Büttner, Frank H

2018-07-01

Label-free, mass spectrometric (MS) detection is an emerging technology in the field of drug discovery. Unbiased deciphering of enzymatic reactions is a proficient advantage over conventional label-based readouts suffering from compound interference and intricate generation of tailored signal mediators. Significant evolvements of matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS, as well as associated liquid handling instrumentation, triggered extensive efforts in the drug discovery community to integrate the comprehensive MS readout into the high-throughput screening (HTS) portfolio. Providing speed, sensitivity, and accuracy comparable to those of conventional, label-based readouts, combined with merits of MS-based technologies, such as label-free parallelized measurement of multiple physiological components, emphasizes the advantages of MALDI-TOF for HTS approaches. Here we describe the assay development for the identification of protein tyrosine phosphatase 1B (PTP1B) inhibitors. In the context of this precious drug target, MALDI-TOF was integrated into the HTS environment and cross-compared with the well-established AlphaScreen technology. We demonstrate robust and accurate IC 50 determination with high accordance to data generated by AlphaScreen. Additionally, a tailored MALDI-TOF assay was developed to monitor compound-dependent, irreversible modification of the active cysteine of PTP1B. Overall, the presented data proves the promising perspective for the integration of MALDI-TOF into drug discovery campaigns.

A Bioinorganic Approach to Fragment-Based Drug Discovery Targeting Metalloenzymes.

Science.gov (United States)

Cohen, Seth M

2017-08-15

Metal-dependent enzymes (i.e., metalloenzymes) make up a large fraction of all enzymes and are critically important in a wide range of biological processes, including DNA modification, protein homeostasis, antibiotic resistance, and many others. Consequently, metalloenzymes represent a vast and largely untapped space for drug development. The discovery of effective therapeutics that target metalloenzymes lies squarely at the interface of bioinorganic and medicinal chemistry and requires expertise, methods, and strategies from both fields to mount an effective campaign. In this Account, our research program that brings together the principles and methods of bioinorganic and medicinal chemistry are described, in an effort to bridge the gap between these fields and address an important class of medicinal targets. Fragment-based drug discovery (FBDD) is an important drug discovery approach that is particularly well suited for metalloenzyme inhibitor development. FBDD uses relatively small but diverse chemical structures that allow for the assembly of privileged molecular collections that focus on a specific feature of the target enzyme. For metalloenzyme inhibition, the specific feature is rather obvious, namely, a metal-dependent active site. Surprisingly, prior to our work, the exploration of diverse molecular fragments for binding the metal active sites of metalloenzymes was largely unexplored. By assembling a modest library of metal-binding pharmacophores (MBPs), we have been able to find lead hits for many metalloenzymes and, from these hits, develop inhibitors that act via novel mechanisms of action. A specific case study on the use of this strategy to identify a first-in-class inhibitor of zinc-dependent Rpn11 (a component of the proteasome) is highlighted. The application of FBDD for the development of metalloenzyme inhibitors has raised several other compelling questions, such as how the metalloenzyme active site influences the coordination chemistry of bound

Overcoming the challenges of drug discovery for neglected tropical diseases: the A·WOL experience.

Science.gov (United States)

Johnston, Kelly L; Ford, Louise; Taylor, Mark J

2014-03-01

Neglected tropical diseases (NTDs) are a group of 17 diseases that typically affect poor people in tropical countries. Each has been neglected for decades in terms of funding, research, and policy, but the recent grouping of them into one unit, which can be targeted using integrated control measures, together with increased advocacy has helped to place them on the global health agenda. The World Health Organization has set ambitious goals to control or eliminate 10 NTDs by 2020 and launched a roadmap in January 2012 to guide this global plan. The result of the launch meeting, which brought together representatives from the pharmaceutical industry, donors, and politicians, was the London Declaration: a series of commitments to provide more drugs, research, and funds to achieve the 2020 goals. Drug discovery and development for these diseases are extremely challenging, and this article highlights these challenges in the context of the London Declaration, before focusing on an example of a drug discovery and development program for the NTDs onchocerciasis and lymphatic filariasis (the anti-Wolbachia consortium, A·WOL).

Pharmacogenetics in diverse ethnic populations--implications for drug discovery and development.

Science.gov (United States)

McCarthy, Linda C; Davies, Kirstie J; Campbell, David A

2002-07-01

It is widely acknowledged that the vast quantities of data now publicly available as a result of the human genome initiative have the potential to revolutionize the pharmaceutical industry. More tangibly to the drug development business, the dawn of the pharmacogenetics era has the potential to impact not only the discovery of new medicines but also the safety and efficacy of pharmaceutical agents. Coincident with these scientific advances is the emergence of new markets for pharmaceutical agents. Japan, which represents the world's second biggest market, is a good example. With the ICH E5 agreement in 1998 and a rapid change in the drug registration process in Japan, there are increasing opportunities to improve access to more medicines in all parts of the world. However, it is increasingly clear that significant genetic variation still exists between populations, with a host of data on interethnic variation in drug metabolizing enzyme and drug transporter activity. Evidence suggesting that this genetic variation may play an important role in defining some of the interethnic variation in drug response to currently marketed compounds is reviewed here, and future possibilities of using such information to better streamline the drug development process are discussed.

Anticancer drug discovery and pharmaceutical chemistry: a history.

Science.gov (United States)

Braña, Miguel F; Sánchez-Migallón, Ana

2006-10-01

There are several procedures for the chemical discovery and design of new drugs from the point of view of the pharmaceutical or medicinal chemistry. They range from classical methods to the very new ones, such as molecular modeling or high throughput screening. In this review, we will consider some historical approaches based on the screening of natural products, the chances for luck, the systematic screening of new chemical entities and serendipity. Another group comprises rational design, as in the case of metabolic pathways, conformation versus configuration and, finally, a brief description on available new targets to be carried out. In each approach, the structure of some examples of clinical interest will be shown.

Identification of a Novel Topoisomerase Inhibitor Effective in Cells Overexpressing Drug Efflux Transporters

OpenAIRE

Fayad, Walid; Frykn?s, M?rten; Brnjic, Slavica; Olofsson, Maria H?gg; Larsson, Rolf; Linder, Stig

2009-01-01

BACKGROUND: Natural product structures have high chemical diversity and are attractive as lead structures for discovery of new drugs. One of the disease areas where natural products are most frequently used as therapeutics is oncology. METHOD AND FINDINGS: A library of natural products (NCI Natural Product set) was screened for compounds that induce apoptosis of HCT116 colon carcinoma cells using an assay that measures an endogenous caspase-cleavage product. One of the apoptosis-inducing comp...

Software Infrastructure for Computer-aided Drug Discovery and Development, a Practical Example with Guidelines.

Science.gov (United States)

Moretti, Loris; Sartori, Luca

2016-09-01

In the field of Computer-Aided Drug Discovery and Development (CADDD) the proper software infrastructure is essential for everyday investigations. The creation of such an environment should be carefully planned and implemented with certain features in order to be productive and efficient. Here we describe a solution to integrate standard computational services into a functional unit that empowers modelling applications for drug discovery. This system allows users with various level of expertise to run in silico experiments automatically and without the burden of file formatting for different software, managing the actual computation, keeping track of the activities and graphical rendering of the structural outcomes. To showcase the potential of this approach, performances of five different docking programs on an Hiv-1 protease test set are presented. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Stem cells: a model for screening, discovery and development of drugs

OpenAIRE

Kitambi, Satish Srinivas; Chandrasekar, Gayathri

2011-01-01

Satish Srinivas Kitambi1, Gayathri Chandrasekar21Department of Medical Biochemistry and Biophysics; 2Department of Biosciences, Karolinska Institutet, Stockholm, SwedenAbstract: The identification of normal and cancerous stem cells and the recent advances made in isolation and culture of stem cells have rapidly gained attention in the field of drug discovery and regenerative medicine. The prospect of performing screens aimed at proliferation, directed differentiation, and toxicity and efficac...

Designing an intuitive web application for drug discovery scientists.

Science.gov (United States)

Karamanis, Nikiforos; Pignatelli, Miguel; Carvalho-Silva, Denise; Rowland, Francis; Cham, Jennifer A; Dunham, Ian

2018-01-11

We discuss how we designed the Open Targets Platform (www.targetvalidation.org), an intuitive application for bench scientists working in early drug discovery. To meet the needs of our users, we applied lean user experience (UX) design methods: we started engaging with users very early and carried out research, design and evaluation activities within an iterative development process. We also emphasize the collaborative nature of applying lean UX design, which we believe is a foundation for success in this and many other scientific projects. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Drug Discovery of Host CLK1 Inhibitors for Influenza Treatment

Directory of Open Access Journals (Sweden)

Mian Zu

2015-11-01

Full Text Available The rapid evolution of influenza virus makes antiviral drugs less effective, which is considered to be a major bottleneck in antiviral therapy. The key proteins in the host cells, which are related with the replication cycle of influenza virus, are regarded as potential drug targets due to their distinct advantage of lack of evolution and drug resistance. Cdc2-like kinase 1 (CLK1 in the host cells is responsible for alternative splicing of the M2 gene of influenza virus during influenza infection and replication. In this study, we carried out baculovirus-mediated expression and purification of CLK1 and established a reliable screening assay for CLK1 inhibitors. After a virtual screening of CLK1 inhibitors was performed, the activities of the selected compounds were evaluated. Finally, several compounds with strong inhibitory activity against CLK1 were discovered and their in vitro anti-influenza virus activities were validated using a cytopathic effect (CPE reduction assay. The assay results showed that clypearin, corilagin, and pinosylvine were the most potential anti-influenza virus compounds as CLK1 inhibitors among the compounds tested. These findings will provide important information for new drug design and development in influenza treatment, and CLK1 may be a potent drug target for anti-influenza drug screening and discovery.

Pharmacovigilance training for specialist oncology nurses-a two way evaluation

NARCIS (Netherlands)

Schutte, T.; Van Eekeren, R.; Richir, M.; Van Staveren, J.; Van Puijenbroek, E.P.; Tichelaar, J.; Van Agtmael, M.A.

2017-01-01

Background: In a new prescribing qualifcation course for specialist oncology nurses, we thought it important to emphasize pharma-covigilance and adverse drug reaction (ADR)-reporting. To this end, our aim was to develop and evaluate an ADR reporting assignment for specialist oncology nurses.

Therapeutic approaches to genetic ion channelopathies and perspectives in drug discovery

Directory of Open Access Journals (Sweden)

Paola eImbrici

2016-05-01

Full Text Available In the human genome more than 400 genes encode ion channels, which are transmembrane proteins mediating ion fluxes across membranes. Being expressed in all cell types, they are involved in almost all physiological processes, including sense perception, neurotransmission, muscle contraction, secretion, immune response, cell proliferation and differentiation. Due to the widespread tissue distribution of ion channels and their physiological functions, mutations in genes encoding ion channel subunits, or their interacting proteins, are responsible for inherited ion channelopathies. These diseases can range from common to very rare disorders and their severity can be mild, disabling, or life-threatening. In spite of this, ion channels are the primary target of only about 5% of the marketed drugs suggesting their potential in drug discovery. The current review summarizes the therapeutic management of the principal ion channelopathies of central and peripheral nervous system, heart, kidney, bone, skeletal muscle and pancreas, resulting from mutations in calcium, sodium, potassium and chloride ion channels. For most channelopathies the therapy is mainly empirical and symptomatic, often limited by lack of efficacy and tolerability for a significant number of patients. Other channelopathies can exploit ion channel targeted drugs, such as marketed sodium channel blockers. Developing new and more specific therapeutic approaches is therefore required. To this aim, a major advancement in the pharmacotherapy of channelopathies has been the discovery that ion channel mutations lead to change in biophysics that can in turn specifically modify the sensitivity to drugs: this opens the way to a pharmacogenetics strategy, allowing the development of a personalized therapy with increased efficacy and reduced side effects. In addition, the identification of disease modifiers in ion channelopathies appears an alternative strategy to discover novel druggable targets.

Oncology PET imaging

International Nuclear Information System (INIS)

Inubushi, Masayuki

2014-01-01

At the beginning of this article, likening medical images to 'Where is Waldo?' I indicate the concept of diagnostic process of PET/CT imaging, so that medical physics specialists could understand the role of each imaging modality and infer our distress for image diagnosis. Then, I state the present situation of PET imaging and the basics (e.g. health insurance coverage, clinical significance, principle, protocol, and pitfall) of oncology FDG-PET imaging which accounts for more than 99% of all clinical PET examinations in Japan. Finally, I would like to give a wishful prospect of oncology PET that will expand to be more cancer-specific in order to assess therapeutic effects of emerging molecular targeted drugs targeting the 'hallmarks of cancer'. (author)

Open Source Drug Discovery in Practice: A Case Study

Science.gov (United States)

Årdal, Christine; Røttingen, John-Arne

2012-01-01

Background Open source drug discovery offers potential for developing new and inexpensive drugs to combat diseases that disproportionally affect the poor. The concept borrows two principle aspects from open source computing (i.e., collaboration and open access) and applies them to pharmaceutical innovation. By opening a project to external contributors, its research capacity may increase significantly. To date there are only a handful of open source R&D projects focusing on neglected diseases. We wanted to learn from these first movers, their successes and failures, in order to generate a better understanding of how a much-discussed theoretical concept works in practice and may be implemented. Methodology/Principal Findings A descriptive case study was performed, evaluating two specific R&D projects focused on neglected diseases. CSIR Team India Consortium's Open Source Drug Discovery project (CSIR OSDD) and The Synaptic Leap's Schistosomiasis project (TSLS). Data were gathered from four sources: interviews of participating members (n = 14), a survey of potential members (n = 61), an analysis of the websites and a literature review. Both cases have made significant achievements; however, they have done so in very different ways. CSIR OSDD encourages international collaboration, but its process facilitates contributions from mostly Indian researchers and students. Its processes are formal with each task being reviewed by a mentor (almost always offline) before a result is made public. TSLS, on the other hand, has attracted contributors internationally, albeit significantly fewer than CSIR OSDD. Both have obtained funding used to pay for access to facilities, physical resources and, at times, labor costs. TSLS releases its results into the public domain, whereas CSIR OSDD asserts ownership over its results. Conclusions/Significance Technically TSLS is an open source project, whereas CSIR OSDD is a crowdsourced project. However, both have enabled high quality

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs.

Science.gov (United States)

Wagner, Jeffrey R; Lee, Christopher T; Durrant, Jacob D; Malmstrom, Robert D; Feher, Victoria A; Amaro, Rommie E

2016-06-08

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

Optimizing Oral Bioavailability in Drug Discovery: An Overview of Design and Testing Strategies and Formulation Options.

Science.gov (United States)

Aungst, Bruce J

2017-04-01

For discovery teams working toward new, orally administered therapeutic agents, one requirement is to attain adequate systemic exposure after oral dosing, which is best accomplished when oral bioavailability is optimized. This report summarizes the bioavailability challenges currently faced in drug discovery, and the design and testing methods and strategies currently utilized to address the challenges. Profiling of discovery compounds usually includes separate assessments of solubility, permeability, and susceptibility to first-pass metabolism, which are the 3 most likely contributors to incomplete oral bioavailability. An initial assessment of absorption potential may be made computationally, and high throughput in vitro assays are typically performed to prioritize compounds for in vivo studies. The initial pharmacokinetic study is a critical decision point in compound evaluation, and the importance of the effect the dosing vehicle or formulation can have on oral bioavailability, especially for poorly water soluble compounds, is emphasized. Dosing vehicles and bioavailability-enabling formulations that can be used for discovery and preclinical studies are described. Optimizing oral bioavailability within a chemical series or for a lead compound requires identification of the barrier limiting bioavailability, and methods used for this purpose are outlined. Finally, a few key guidelines are offered for consideration when facing the challenges of optimizing oral bioavailability in drug discovery. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Medical Device Recalls in Radiation Oncology: Analysis of US Food and Drug Administration Data, 2002-2015

International Nuclear Information System (INIS)

Connor, Michael J.; Tringale, Kathryn; Moiseenko, Vitali; Marshall, Deborah C.; Moore, Kevin; Cervino, Laura; Atwood, Todd; Brown, Derek; Mundt, Arno J.; Pawlicki, Todd; Recht, Abram; Hattangadi-Gluth, Jona A.

2017-01-01

Purpose: To analyze all recalls involving radiation oncology devices (RODs) from the US Food and Drug Administration (FDA)'s recall database, comparing these with non–radiation oncology device recalls to identify discipline-specific trends that may inform improvements in device safety. Methods and Materials: Recall data on RODs from 2002 to 2015 were sorted into 4 product categories (external beam, brachytherapy, planning systems, and simulation systems). Outcomes included determined cause of recall, recall class (severity), quantity in commerce, time until recall termination (date FDA determines recall is complete), and time since 510(k) approval. Descriptive statistics were performed with linear regression of time-series data. Results for RODs were compared with those for other devices by Pearson χ"2 test for categorical data and 2-sample Kolmogorov-Smirnov test for distributions. Results: There were 502 ROD recalls and 9534 other class II device recalls during 2002 to 2015. Most recalls were for external beam devices (66.7%) and planning systems (22.9%), and recall events peaked in 2011. Radiation oncology devices differed significantly from other devices in all recall outcomes (P≤.04). Recall cause was commonly software related (49% vs 10% for other devices). Recall severity was more often moderate among RODs (97.6% vs 87.2%) instead of severe (0.2% vs 4.4%; P<.001). Time from 510(k) market approval to recall was shorter among RODs (P<.001) and progressively shortened over time. Radiation oncology devices had fewer recalled devices in commerce than other devices (P<.001). Conclusions: Compared with other class II devices, RODs experience recalls sooner after market approval and are trending sooner still. Most of these recalls were moderate in severity, and software issues are prevalent. Comprehensive analysis of recall data can identify areas for device improvement, such as better system design among RODs.

Medical Device Recalls in Radiation Oncology: Analysis of US Food and Drug Administration Data, 2002-2015

Energy Technology Data Exchange (ETDEWEB)

Connor, Michael J. [Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California (United States); University of California Irvine School of Medicine, Irvine, California (United States); Tringale, Kathryn; Moiseenko, Vitali; Marshall, Deborah C.; Moore, Kevin; Cervino, Laura; Atwood, Todd; Brown, Derek; Mundt, Arno J.; Pawlicki, Todd [Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California (United States); Recht, Abram [Department of Radiation Oncology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts (United States); Hattangadi-Gluth, Jona A., E-mail: jhattangadi@ucsd.edu [Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California (United States)

2017-06-01

Purpose: To analyze all recalls involving radiation oncology devices (RODs) from the US Food and Drug Administration (FDA)'s recall database, comparing these with non–radiation oncology device recalls to identify discipline-specific trends that may inform improvements in device safety. Methods and Materials: Recall data on RODs from 2002 to 2015 were sorted into 4 product categories (external beam, brachytherapy, planning systems, and simulation systems). Outcomes included determined cause of recall, recall class (severity), quantity in commerce, time until recall termination (date FDA determines recall is complete), and time since 510(k) approval. Descriptive statistics were performed with linear regression of time-series data. Results for RODs were compared with those for other devices by Pearson χ{sup 2} test for categorical data and 2-sample Kolmogorov-Smirnov test for distributions. Results: There were 502 ROD recalls and 9534 other class II device recalls during 2002 to 2015. Most recalls were for external beam devices (66.7%) and planning systems (22.9%), and recall events peaked in 2011. Radiation oncology devices differed significantly from other devices in all recall outcomes (P≤.04). Recall cause was commonly software related (49% vs 10% for other devices). Recall severity was more often moderate among RODs (97.6% vs 87.2%) instead of severe (0.2% vs 4.4%; P<.001). Time from 510(k) market approval to recall was shorter among RODs (P<.001) and progressively shortened over time. Radiation oncology devices had fewer recalled devices in commerce than other devices (P<.001). Conclusions: Compared with other class II devices, RODs experience recalls sooner after market approval and are trending sooner still. Most of these recalls were moderate in severity, and software issues are prevalent. Comprehensive analysis of recall data can identify areas for device improvement, such as better system design among RODs.

Open-access public-private partnerships to enable drug discovery--new approaches.

Science.gov (United States)

Müller, Susanne; Weigelt, Johan

2010-03-01

The productivity of the pharmaceutical industry, as assessed by the number of NMEs produced per US dollar spent in R&D, has been in steady decline during the past 40 years. This decline in productivity not only poses a significant challenge to the pharmaceutical industry, but also to society because of the importance of developing drugs for the treatment of unmet medical needs. The major challenge in progressing a new drug to the market is the successful completion of clinical trials. However, the failure rate of drugs entering trials has not decreased, despite various technological and scientific breakthroughs in recent decades, and despite intense target validation efforts. This lack of success suggests limitations in the fundamental understanding of target biology and human pharmacology. One contributing factor may be the traditional secrecy of the pharmaceutical sector, a characteristic that does not promote scientific discovery in an optimal manner. Access to broader knowledge relating to target biology and human pharmacology is difficult to obtain because interactions between researchers in industry and academia are typically restricted to closed collaborations in which the knowledge gained is confidential.However, open-access collaborative partnerships are gaining momentum in industry, and are also favored by funding agencies. Such open-access collaborations may be a powerful alternative to closed collaborations; the sharing of early-stage research data is expected to enable scientific discovery by engaging a broader section of the scientific community in the exploration of new findings. Potentially, the sharing of data could contribute to an increased understanding of biological processes and a decrease in the attrition of clinical programs.

Drug waste minimisation and cost-containment in Medical Oncology: Two-year results of a feasibility study

Directory of Open Access Journals (Sweden)

Mansutti Mauro

2008-04-01

Full Text Available Abstract Background Cost-containment strategies are required to face the challenge of rising drug expenditures in Oncology. Drug wastage leads to economic loss, but little is known about the size of the problem in this field. Methods Starting January 2005 we introduced a day-to-day monitoring of drug wastage and an accurate assessment of its costs. An internal protocol for waste minimisation was developed, consisting of four corrective measures: 1. A rational, per pathology distribution of chemotherapy sessions over the week. 2. The use of multi-dose vials. 3. A reasonable rounding of drug dosages. 4. The selection of the most convenient vial size, depending on drug unit pricing. Results Baseline analysis focused on 29 drugs over one year. Considering their unit price and waste amount, a major impact on expense was found to be attributable to six drugs: cetuximab, docetaxel, gemcitabine, oxaliplatin, pemetrexed and trastuzumab. The economic loss due to their waste equaled 4.8% of the annual drug expenditure. After the study protocol was started, the expense due to unused drugs showed a meaningful 45% reduction throughout 2006. Conclusion Our experience confirms the economic relevance of waste minimisation and may represent a feasible model in addressing this issue. A centralised unit of drug processing, the availability of a computerised physician order entry system and an active involvement of the staff play a key role in allowing waste reduction and a consequent, substantial cost-saving.

Applications of fiber-optics-based nanosensors to drug discovery.

Science.gov (United States)

Vo-Dinh, Tuan; Scaffidi, Jonathan; Gregas, Molly; Zhang, Yan; Seewaldt, Victoria

2009-08-01

Fiber-optic nanosensors are fabricated by heating and pulling optical fibers to yield sub-micron diameter tips and have been used for in vitro analysis of individual living mammalian cells. Immobilization of bioreceptors (e.g., antibodies, peptides, DNA) selective to targeting analyte molecules of interest provides molecular specificity. Excitation light can be launched into the fiber, and the resulting evanescent field at the tip of the nanofiber can be used to excite target molecules bound to the bioreceptor molecules. The fluorescence or surface-enhanced Raman scattering produced by the analyte molecules is detected using an ultra-sensitive photodetector. This article provides an overview of the development and application of fiber-optic nanosensors for drug discovery. The nanosensors provide minimally invasive tools to probe subcellular compartments inside single living cells for health effect studies (e.g., detection of benzopyrene adducts) and medical applications (e.g., monitoring of apoptosis in cells treated with anticancer drugs).

The impact of assay technology as applied to safety assessment in reducing compound attrition in drug discovery.

Science.gov (United States)

Thomas, Craig E; Will, Yvonne

2012-02-01

Attrition in the drug industry due to safety findings remains high and requires a shift in the current safety testing paradigm. Many companies are now positioning safety assessment at each stage of the drug development process, including discovery, where an early perspective on potential safety issues is sought, often at chemical scaffold level, using a variety of emerging technologies. Given the lengthy development time frames of drugs in the pharmaceutical industry, the authors believe that the impact of new technologies on attrition is best measured as a function of the quality and timeliness of candidate compounds entering development. The authors provide an overview of in silico and in vitro models, as well as more complex approaches such as 'omics,' and where they are best positioned within the drug discovery process. It is important to take away that not all technologies should be applied to all projects. Technologies vary widely in their validation state, throughput and cost. A thoughtful combination of validated and emerging technologies is crucial in identifying the most promising candidates to move to proof-of-concept testing in humans. In spite of the challenges inherent in applying new technologies to drug discovery, the successes and recognition that we cannot continue to rely on safety assessment practices used for decades have led to rather dramatic strategy shifts and fostered partnerships across government agencies and industry. We are optimistic that these efforts will ultimately benefit patients by delivering effective and safe medications in a timely fashion.

When fragments link : a bibliometric perspective on the development of fragment-based drug discovery

NARCIS (Netherlands)

Romasanta, A.K.S.; van der Sijde, P.C.; Hellsten, I.; Hubbard, Roderick E.; Keseru, Gyorgy M.; van Muijlwijk-Koezen, Jacqueline E.; de Esch, I.J.P.

2018-01-01

Fragment-based drug discovery (FBDD) is a highly interdisciplinary field, rich in ideas integrated from pharmaceutical sciences, chemistry, biology, and physics, among others. To enrich our understanding of the development of the field, we used bibliometric techniques to analyze 3642 publications in

Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond

Science.gov (United States)

Van Voorhis, Wesley C.; Adams, John H.; Adelfio, Roberto; Ahyong, Vida; Akabas, Myles H.; Alano, Pietro; Alday, Aintzane; Alemán Resto, Yesmalie; Alsibaee, Aishah; Alzualde, Ainhoa; Andrews, Katherine T.; Avery, Simon V.; Avery, Vicky M.; Ayong, Lawrence; Baker, Mark; Baker, Stephen; Ben Mamoun, Choukri; Bhatia, Sangeeta; Bickle, Quentin; Bounaadja, Lotfi; Bowling, Tana; Bosch, Jürgen; Boucher, Lauren E.; Boyom, Fabrice F.; Brea, Jose; Brennan, Marian; Burton, Audrey; Caffrey, Conor R.; Camarda, Grazia; Carrasquilla, Manuela; Carter, Dee; Belen Cassera, Maria; Chih-Chien Cheng, Ken; Chindaudomsate, Worathad; Chubb, Anthony; Colon, Beatrice L.; Colón-López, Daisy D.; Corbett, Yolanda; Crowther, Gregory J.; Cowan, Noemi; D’Alessandro, Sarah; Le Dang, Na; Delves, Michael; Du, Alan Y.; Duffy, Sandra; Abd El-Salam El-Sayed, Shimaa; Ferdig, Michael T.; Fernández Robledo, José A.; Fidock, David A.; Florent, Isabelle; Fokou, Patrick V. T.; Galstian, Ani; Gamo, Francisco Javier; Gold, Ben; Golub, Todd; Goldgof, Gregory M.; Guha, Rajarshi; Guiguemde, W. Armand; Gural, Nil; Guy, R. Kiplin; Hansen, Michael A. E.; Hanson, Kirsten K.; Hemphill, Andrew; Hooft van Huijsduijnen, Rob; Horii, Takaaki; Horrocks, Paul; Hughes, Tyler B.; Huston, Christopher; Igarashi, Ikuo; Ingram-Sieber, Katrin; Itoe, Maurice A.; Jadhav, Ajit; Naranuntarat Jensen, Amornrat; Jensen, Laran T.; Jiang, Rays H. Y.; Kaiser, Annette; Keiser, Jennifer; Ketas, Thomas; Kicka, Sebastien; Kim, Sunyoung; Kirk, Kiaran; Kumar, Vidya P.; Kyle, Dennis E.; Lafuente, Maria Jose; Landfear, Scott; Lee, Nathan; Lee, Sukjun; Lehane, Adele M.; Li, Fengwu; Little, David; Liu, Liqiong; Llinás, Manuel; Loza, Maria I.; Lubar, Aristea; Lucantoni, Leonardo; Lucet, Isabelle; Maes, Louis; Mancama, Dalu; Mansour, Nuha R.; March, Sandra; McGowan, Sheena; Medina Vera, Iset; Meister, Stephan; Mercer, Luke; Mestres, Jordi; Mfopa, Alvine N.; Misra, Raj N.; Moon, Seunghyun; Moore, John P.; Morais Rodrigues da Costa, Francielly; Müller, Joachim; Muriana, Arantza; Nakazawa Hewitt, Stephen; Nare, Bakela; Nathan, Carl; Narraidoo, Nathalie; Nawaratna, Sujeevi; Ojo, Kayode K.; Ortiz, Diana; Panic, Gordana; Papadatos, George; Parapini, Silvia; Patra, Kailash; Pham, Ngoc; Prats, Sarah; Plouffe, David M.; Poulsen, Sally-Ann; Pradhan, Anupam; Quevedo, Celia; Quinn, Ronald J.; Rice, Christopher A.; Abdo Rizk, Mohamed; Ruecker, Andrea; St. Onge, Robert; Salgado Ferreira, Rafaela; Samra, Jasmeet; Robinett, Natalie G.; Schlecht, Ulrich; Schmitt, Marjorie; Silva Villela, Filipe; Silvestrini, Francesco; Sinden, Robert; Smith, Dennis A.; Soldati, Thierry; Spitzmüller, Andreas; Stamm, Serge Maximilian; Sullivan, David J.; Sullivan, William; Suresh, Sundari; Suzuki, Brian M.; Suzuki, Yo; Swamidass, S. Joshua; Taramelli, Donatella; Tchokouaha, Lauve R. Y.; Theron, Anjo; Thomas, David; Tonissen, Kathryn F.; Townson, Simon; Tripathi, Abhai K.; Trofimov, Valentin; Udenze, Kenneth O.; Ullah, Imran; Vallieres, Cindy; Vigil, Edgar; Vinetz, Joseph M.; Voong Vinh, Phat; Vu, Hoan; Watanabe, Nao-aki; Weatherby, Kate; White, Pamela M.; Wilks, Andrew F.; Winzeler, Elizabeth A.; Wojcik, Edward; Wree, Melanie; Wu, Wesley; Yokoyama, Naoaki; Zollo, Paul H. A.; Abla, Nada; Blasco, Benjamin; Burrows, Jeremy; Laleu, Benoît; Leroy, Didier; Spangenberg, Thomas; Wells, Timothy; Willis, Paul A.

2016-01-01

A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34%) of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments and medicinal

Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond.

Directory of Open Access Journals (Sweden)

Wesley C Van Voorhis

2016-07-01

Full Text Available A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34% of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments

Test systems in drug discovery for hazard identification and risk assessment of human drug-induced liver injury.

Science.gov (United States)

Weaver, Richard J; Betts, Catherine; Blomme, Eric A G; Gerets, Helga H J; Gjervig Jensen, Klaus; Hewitt, Philip G; Juhila, Satu; Labbe, Gilles; Liguori, Michael J; Mesens, Natalie; Ogese, Monday O; Persson, Mikael; Snoeys, Jan; Stevens, James L; Walker, Tracy; Park, B Kevin

2017-07-01

The liver is an important target for drug-induced toxicities. Early detection of hepatotoxic drugs requires use of well-characterized test systems, yet current knowledge, gaps and limitations of tests employed remains an important issue for drug development. Areas Covered: The current state of the science, understanding and application of test systems in use for the detection of drug-induced cytotoxicity, mitochondrial toxicity, cholestasis and inflammation is summarized. The test systems highlighted herein cover mostly in vitro and some in vivo models and endpoint measurements used in the assessment of small molecule toxic liabilities. Opportunities for research efforts in areas necessitating the development of specific tests and improved mechanistic understanding are highlighted. Expert Opinion: Use of in vitro test systems for safety optimization will remain a core activity in drug discovery. Substantial inroads have been made with a number of assays established for human Drug-induced Liver Injury. There nevertheless remain significant gaps with a need for improved in vitro tools and novel tests to address specific mechanisms of human Drug-Induced Liver Injury. Progress in these areas will necessitate not only models fit for application, but also mechanistic understanding of how chemical insult on the liver occurs in order to identify translational and quantifiable readouts for decision-making.

Marine Microorganism-Invertebrate Assemblages: Perspectives to Solve the “Supply Problem” in the Initial Steps of Drug Discovery

Science.gov (United States)

Leal, Miguel Costa; Sheridan, Christopher; Osinga, Ronald; Dionísio, Gisela; Rocha, Rui Jorge Miranda; Silva, Bruna; Rosa, Rui; Calado, Ricardo

2014-01-01

The chemical diversity associated with marine natural products (MNP) is unanimously acknowledged as the “blue gold” in the urgent quest for new drugs. Consequently, a significant increase in the discovery of MNP published in the literature has been observed in the past decades, particularly from marine invertebrates. However, it remains unclear whether target metabolites originate from the marine invertebrates themselves or from their microbial symbionts. This issue underlines critical challenges associated with the lack of biomass required to supply the early stages of the drug discovery pipeline. The present review discusses potential solutions for such challenges, with particular emphasis on innovative approaches to culture invertebrate holobionts (microorganism-invertebrate assemblages) through in toto aquaculture, together with methods for the discovery and initial production of bioactive compounds from these microbial symbionts. PMID:24983638

Marine Microorganism-Invertebrate Assemblages: Perspectives to Solve the “Supply Problem” in the Initial Steps of Drug Discovery

Directory of Open Access Journals (Sweden)

Miguel Costa Leal

2014-06-01

Full Text Available The chemical diversity associated with marine natural products (MNP is unanimously acknowledged as the “blue gold” in the urgent quest for new drugs. Consequently, a significant increase in the discovery of MNP published in the literature has been observed in the past decades, particularly from marine invertebrates. However, it remains unclear whether target metabolites originate from the marine invertebrates themselves or from their microbial symbionts. This issue underlines critical challenges associated with the lack of biomass required to supply the early stages of the drug discovery pipeline. The present review discusses potential solutions for such challenges, with particular emphasis on innovative approaches to culture invertebrate holobionts (microorganism-invertebrate assemblages through in toto aquaculture, together with methods for the discovery and initial production of bioactive compounds from these microbial symbionts.

Medical Device Recalls in Radiation Oncology: Analysis of US Food and Drug Administration Data, 2002-2015.

Science.gov (United States)

Connor, Michael J; Tringale, Kathryn; Moiseenko, Vitali; Marshall, Deborah C; Moore, Kevin; Cervino, Laura; Atwood, Todd; Brown, Derek; Mundt, Arno J; Pawlicki, Todd; Recht, Abram; Hattangadi-Gluth, Jona A

2017-06-01

To analyze all recalls involving radiation oncology devices (RODs) from the US Food and Drug Administration (FDA)'s recall database, comparing these with non-radiation oncology device recalls to identify discipline-specific trends that may inform improvements in device safety. Recall data on RODs from 2002 to 2015 were sorted into 4 product categories (external beam, brachytherapy, planning systems, and simulation systems). Outcomes included determined cause of recall, recall class (severity), quantity in commerce, time until recall termination (date FDA determines recall is complete), and time since 510(k) approval. Descriptive statistics were performed with linear regression of time-series data. Results for RODs were compared with those for other devices by Pearson χ 2 test for categorical data and 2-sample Kolmogorov-Smirnov test for distributions. There were 502 ROD recalls and 9534 other class II device recalls during 2002 to 2015. Most recalls were for external beam devices (66.7%) and planning systems (22.9%), and recall events peaked in 2011. Radiation oncology devices differed significantly from other devices in all recall outcomes (P≤.04). Recall cause was commonly software related (49% vs 10% for other devices). Recall severity was more often moderate among RODs (97.6% vs 87.2%) instead of severe (0.2% vs 4.4%; Panalysis of recall data can identify areas for device improvement, such as better system design among RODs. Copyright © 2017 Elsevier Inc. All rights reserved.

Cunninghamella Biotransformation--Similarities to Human Drug Metabolism and Its Relevance for the Drug Discovery Process.

Science.gov (United States)

Piska, Kamil; Żelaszczyk, Dorota; Jamrozik, Marek; Kubowicz-Kwaśny, Paulina; Pękala, Elżbieta

2016-01-01

Studies of drug metabolism are one of the most significant issues in the process of drug development, its introduction to the market and also in treatment. Even the most promising molecule may show undesirable metabolic properties that would disqualify it as a potential drug. Therefore, such studies are conducted in the early phases of drug discovery and development process. Cunninghamella is a filamentous fungus known for its catalytic properties, which mimics mammalian drug metabolism. It has been proven that C. elegans carries at least one gene coding for a CYP enzyme closely related to the CYP51 family. The transformation profile of xenobiotics in Cunninghamella spp. spans a number of reactions catalyzed by different mammalian CYP isoforms. This paper presents detailed data on similar biotransformation drug products in humans and Cunninghamella spp. and covers the most important aspects of preparative biosynthesis of metabolites, since this model allows to obtain metabolites in sufficient quantities to conduct the further detailed investigations, as quantification, structure analysis and pharmacological activity and toxicity testing. The metabolic activity of three mostly used Cunninghamella species in obtaining hydroxylated, dealkylated and oxidated metabolites of different drugs confirmed its convergence with human biotransformation. Though it cannot replace the standard methods, it can provide support in the field of biotransformation and identifying metabolic soft spots of new chemicals and in predicting possible metabolic pathways. Another aspect is the biosynthesis of metabolites. In this respect, techniques using Cunninghamella spp. seem to be competitive to the chemical methods currently used.

2013 Philip S. Portoghese Medicinal Chemistry Lectureship: Drug Discovery Targeting Allosteric Sites†

Science.gov (United States)

2015-01-01

The identification of sites on receptors topographically distinct from the orthosteric sites, so-called allosteric sites, has heralded novel approaches and modes of pharmacology for target modulation. Over the past 20 years, our understanding of allosteric modulation has grown significantly, and numerous advantages, as well as caveats (e.g., flat structure–activity relationships, species differences, “molecular switches”), have been identified. For multiple receptors and proteins, numerous examples have been described where unprecedented levels of selectivity are achieved along with improved physiochemical properties. While not a panacea, these novel approaches represent exciting opportunities for tool compound development to probe the pharmacology and therapeutic potential of discrete molecular targets, as well as new medicines. In this Perspective, in commemoration of the 2013 Philip S. Portoghese Medicinal Chemistry Lectureship (LindsleyC. W.Adventures in allosteric drug discovery. Presented at the 246th National Meeting of the American Chemical Society, Indianapolis, IN, September 10, 2013; The 2013 Portoghese Lectureship), several vignettes of drug discovery campaigns targeting novel allosteric mechanisms will be recounted, along with lessons learned and guidelines that have emerged for successful lead optimization. PMID:25180768

High throughput electrophysiology: new perspectives for ion channel drug discovery

DEFF Research Database (Denmark)

Willumsen, Niels J; Bech, Morten; Olesen, Søren-Peter

2003-01-01

Proper function of ion channels is crucial for all living cells. Ion channel dysfunction may lead to a number of diseases, so-called channelopathies, and a number of common diseases, including epilepsy, arrhythmia, and type II diabetes, are primarily treated by drugs that modulate ion channels....... A cornerstone in current drug discovery is high throughput screening assays which allow examination of the activity of specific ion channels though only to a limited extent. Conventional patch clamp remains the sole technique with sufficiently high time resolution and sensitivity required for precise and direct...... characterization of ion channel properties. However, patch clamp is a slow, labor-intensive, and thus expensive, technique. New techniques combining the reliability and high information content of patch clamping with the virtues of high throughput philosophy are emerging and predicted to make a number of ion...

21 CFR 17.23 - Discovery.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 1 2010-04-01 2010-04-01 false Discovery. 17.23 Section 17.23 Food and Drugs FOOD... HEARINGS § 17.23 Discovery. (a) No later than 60 days prior to the hearing, unless otherwise ordered by the..., depositions, and any forms of discovery, other than those permitted under paragraphs (a) and (e) of this...

Novel Technology for Protein-Protein Interaction-based Targeted Drug Discovery

Directory of Open Access Journals (Sweden)

Jung Me Hwang

2011-12-01

Full Text Available We have developed a simple but highly efficient in-cell protein-protein interaction (PPI discovery system based on the translocation properties of protein kinase C- and its C1a domain in live cells. This system allows the visual detection of trimeric and dimeric protein interactions including cytosolic, nuclear, and/or membrane proteins with their cognate ligands. In addition, this system can be used to identify pharmacological small compounds that inhibit specific PPIs. These properties make this PPI system an attractive tool for screening drug candidates and mapping the protein interactome.

Design of diversity and focused combinatorial libraries in drug discovery.

Science.gov (United States)

Young, S Stanley; Ge, Nanxiang

2004-05-01

Using well-characterized chemical reactions and readily available monomers, chemists are able to create sets of compounds, termed libraries, which are useful in drug discovery processes. The design of combinatorial chemical libraries can be complex and there has been much information recently published offering suggestions on how the design process can be carried out. This review focuses on literature with the goal of organizing current thinking. At this point in time, it is clear that benchmarking of current suggested methods is required as opposed to further new methods.

Engineering and physical sciences in oncology: challenges and opportunities.

Science.gov (United States)

Mitchell, Michael J; Jain, Rakesh K; Langer, Robert

2017-11-01

The principles of engineering and physics have been applied to oncology for nearly 50 years. Engineers and physical scientists have made contributions to all aspects of cancer biology, from quantitative understanding of tumour growth and progression to improved detection and treatment of cancer. Many early efforts focused on experimental and computational modelling of drug distribution, cell cycle kinetics and tumour growth dynamics. In the past decade, we have witnessed exponential growth at the interface of engineering, physics and oncology that has been fuelled by advances in fields including materials science, microfabrication, nanomedicine, microfluidics, imaging, and catalysed by new programmes at the National Institutes of Health (NIH), including the National Institute of Biomedical Imaging and Bioengineering (NIBIB), Physical Sciences in Oncology, and the National Cancer Institute (NCI) Alliance for Nanotechnology. Here, we review the advances made at the interface of engineering and physical sciences and oncology in four important areas: the physical microenvironment of the tumour and technological advances in drug delivery; cellular and molecular imaging; and microfluidics and microfabrication. We discussthe research advances, opportunities and challenges for integrating engineering and physical sciences with oncology to develop new methods to study, detect and treat cancer, and we also describe the future outlook for these emerging areas.

Comparative psychology and the grand challenge of drug discovery in psychiatry and neurodegeneration.

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Brunner, Dani; Balcı, Fuat; Ludvig, Elliot A

2012-02-01

Drug discovery for brain disorders is undergoing a period of upheaval. Faced with an empty drug pipeline and numerous failures of potential new drugs in clinical trials, many large pharmaceutical companies have been shrinking or even closing down their research divisions that focus on central nervous system (CNS) disorders. In this paper, we argue that many of the difficulties facing CNS drug discovery stem from a lack of robustness in pre-clinical (i.e., non-human animal) testing. There are two main sources for this lack of robustness. First, there is the lack of replicability of many results from the pre-clinical stage, which we argue is driven by a combination of publication bias and inappropriate selection of statistical and experimental designs. Second, there is the frequent failure to translate results in non-human animals to parallel results in humans in the clinic. This limitation can only be overcome by developing new behavioral tests for non-human animals that have predictive, construct, and etiological validity. Here, we present these translational difficulties as a "grand challenge" to researchers from comparative cognition, who are well positioned to provide new methods for testing behavior and cognition in non-human animals. These new experimental protocols will need to be both statistically robust and target behavioral and cognitive processes that allow for better connection with human CNS disorders. Our hope is that this downturn in industrial research may represent an opportunity to develop new protocols that will re-kindle the search for more effective and safer drugs for CNS disorders. Copyright © 2011 Elsevier B.V. All rights reserved.

Investigation of the possible use of Arglabin, new antitumor medicine to prevent oncological diseases after radiation

International Nuclear Information System (INIS)

Shaikenov, T.; Karabalin, B.; Rakhimov, K.; Adekenov, S.

1996-01-01

Background: One of the heaviest effects of radiation exposure and its pollutants to the environment is a growth of oncologic diseases as a sequence of breakage of genetic body substance and transmission of Genetic load to the next generation. People of Kazakstan are anxious of the present situation with harmful effect of remaining radiation doses.Purpose of research: the examination of molecular mechanisms of action for a new drug 'Arglabin' was done with the purpose of preventing the malignant tumor occurrence for those people living on the territory of higher risk (in high radiation locations). Research work of the mechanisms of action for 'Arglabin' drug: - examination of the drug effectiveness to enzymes of prenylated proteins; - examination of the drug effectiveness on functioning the oncogene products; - examination of drug metabolism. Research work of the possibility for using 'Arglabin' to prevent oncology diseases includes: - establishment of experimental model of increased risk of oncologic disease on different groups of laboratory animals using the low-dosage radiation sphere; - examination of the drug effectiveness in oncology diseases prevention; - determination of mechanisms and the prevention's results on cancer

Exploiting pluripotent stem cell technology for drug discovery, screening, safety, and toxicology assessments.

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McGivern, Jered V; Ebert, Allison D

2014-04-01

In order for the pharmaceutical industry to maintain a constant flow of novel drugs and therapeutics into the clinic, compounds must be thoroughly validated for safety and efficacy in multiple biological and biochemical systems. Pluripotent stem cells, because of their ability to develop into any cell type in the body and recapitulate human disease, may be an important cellular system to add to the drug development repertoire. This review will discuss some of the benefits of using pluripotent stem cells for drug discovery and safety studies as well as some of the recent applications of stem cells in drug screening studies. We will also address some of the hurdles that need to be overcome in order to make stem cell-based approaches an efficient and effective tool in the quest to produce clinically successful drug compounds. Copyright © 2013 Elsevier B.V. All rights reserved.

Role of Open Source Tools and Resources in Virtual Screening for Drug Discovery.

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Karthikeyan, Muthukumarasamy; Vyas, Renu

2015-01-01

Advancement in chemoinformatics research in parallel with availability of high performance computing platform has made handling of large scale multi-dimensional scientific data for high throughput drug discovery easier. In this study we have explored publicly available molecular databases with the help of open-source based integrated in-house molecular informatics tools for virtual screening. The virtual screening literature for past decade has been extensively investigated and thoroughly analyzed to reveal interesting patterns with respect to the drug, target, scaffold and disease space. The review also focuses on the integrated chemoinformatics tools that are capable of harvesting chemical data from textual literature information and transform them into truly computable chemical structures, identification of unique fragments and scaffolds from a class of compounds, automatic generation of focused virtual libraries, computation of molecular descriptors for structure-activity relationship studies, application of conventional filters used in lead discovery along with in-house developed exhaustive PTC (Pharmacophore, Toxicophores and Chemophores) filters and machine learning tools for the design of potential disease specific inhibitors. A case study on kinase inhibitors is provided as an example.

Human embryonic stem cell technologies and drug discovery.

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Jensen, Janne; Hyllner, Johan; Björquist, Petter

2009-06-01

Development of new drugs is costly and takes huge resources into consideration. The big pharmaceutical companies are currently facing increasing developmental costs and a lower success-rate of bringing new compounds to the market. Therefore, it is now of outmost importance that the drug-hunting companies minimize late attritions due to sub-optimal pharmacokinetic properties or unexpected toxicity when entering the clinical programs. To achieve this, a strong need to test new candidate drugs in assays of high human relevance in vitro as early as possible has been identified. The traditionally used cell systems are however remarkably limited in this sense, and new improved technologies are of greatest importance. The human embryonic stem cells (hESC) is one of the most powerful cell types known. They have not only the possibility to divide indefinitely; these cells can also differentiate into all mature cell types of the human body. This makes them potentially very valuable for pharmaceutical development, spanning from use as tools in early target studies, DMPK or safety assessment, as screening models to find new chemical entities modulating adult stem cell fate, or as the direct use in cell therapies. This review illustrates the use of hESC in the drug discovery process, today, as well as in a future perspective. This will specifically be exemplified with the most important cell type for pharmaceutical development-the hepatocyte. We discuss how hESC-derived hepatocyte-like cells could improve this process, and how these cells should be cultured if optimized functionality and usefulness should be achieved. J. Cell. Physiol. 219: 513-519, 2009. (c) 2009 Wiley-Liss, Inc.

Report on the 10th anniversary of international drug discovery science and technology conference, 8 - 10 november 2012, nanjing, china.

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Everett, Jeremy R

2013-03-01

The 10th Anniversary of International Drug Discovery Science and Technology (IDDST) Conference was held in Nanjing, China from 8 to 10 November 2012. The conference ran in parallel with the 2nd Annual Symposium of Drug Delivery Systems. Over 400 delegates from both conferences came together for the Opening Ceremony and Keynote Addresses but otherwise pursued separate paths in the huge facilities of the Nanjing International Expo Centre. The IDDST was arranged into 19 separate Chapters covering drug discovery biology, target validation, chemistry, rational drug design, pharmacology and toxicology, drug screening technology, 'omics' technologies, analytical, automation and enabling technologies, informatics, stem cells and regenerative medicine, bioprocessing, generics, biosimilars and biologicals and seven disease areas: cancer, CNS, respiratory and inflammation, autoimmune, emerging infectious, bone and orphan diseases. There were also two sessions of a 'Bench to Bedside to Business' Program and a Chinese Scientist programme. In each period of the IDDST conference, up to seven sessions were running in parallel. This Meeting Highlight samples just a fraction of the content of this large meeting. The talks included have as a link, the use of new approaches to drug discovery. Many other excellent talks could have been highlighted and the author has necessarily had to be selective.

In vitro membrane binding and protein binding (IAM MB/PB technology to estimate in vivo distribution: applications in early drug discovery

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Klara Livia Valko

2017-03-01

Full Text Available The drug discovery process can be accelerated by chromatographic profiling of the analogs to model in vivo distribution and the major non-specific binding. A balanced potency and chromatographically determined membrane and protein binding (IAM MB/PB data enable selecting drug discovery compounds for further analysis that have the highest probability to show the desired in vivo distribution behavior for efficacy and reduced chance for toxicity. Although the basic principles of the technology have already appeared in numerous publications, the lack of standardized procedures limited its widespread applications especially in academia and small drug discovery biotech companies. In this paper, the standardized procedures are described that has been trademarked as Regis IAM MB/PB Technology®. Comparison between the Drug Efficiency Index (DEI=pIC50-logVdu+2 and generally used Ligand Lipophilicity Efficiency (LLE has been made, demonstrating the advantage of measured IAM and HSA binding over calculated log P. The power of the proposed chromatographic technology is demonstrated using the data of marketed drugs.

Traditional Medicine Collection Tracking System (TM-CTS): a database for ethnobotanically driven drug-discovery programs.

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Harris, Eric S J; Erickson, Sean D; Tolopko, Andrew N; Cao, Shugeng; Craycroft, Jane A; Scholten, Robert; Fu, Yanling; Wang, Wenquan; Liu, Yong; Zhao, Zhongzhen; Clardy, Jon; Shamu, Caroline E; Eisenberg, David M

2011-05-17

Ethnobotanically driven drug-discovery programs include data related to many aspects of the preparation of botanical medicines, from initial plant collection to chemical extraction and fractionation. The Traditional Medicine Collection Tracking System (TM-CTS) was created to organize and store data of this type for an international collaborative project involving the systematic evaluation of commonly used Traditional Chinese Medicinal plants. The system was developed using domain-driven design techniques, and is implemented using Java, Hibernate, PostgreSQL, Business Intelligence and Reporting Tools (BIRT), and Apache Tomcat. The TM-CTS relational database schema contains over 70 data types, comprising over 500 data fields. The system incorporates a number of unique features that are useful in the context of ethnobotanical projects such as support for information about botanical collection, method of processing, quality tests for plants with existing pharmacopoeia standards, chemical extraction and fractionation, and historical uses of the plants. The database also accommodates data provided in multiple languages and integration with a database system built to support high throughput screening based drug discovery efforts. It is accessed via a web-based application that provides extensive, multi-format reporting capabilities. This new database system was designed to support a project evaluating the bioactivity of Chinese medicinal plants. The software used to create the database is open source, freely available, and could potentially be applied to other ethnobotanically driven natural product collection and drug-discovery programs. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Traditional Medicine Collection Tracking System (TM-CTS): A Database for Ethnobotanically-Driven Drug-Discovery Programs

Science.gov (United States)

Harris, Eric S. J.; Erickson, Sean D.; Tolopko, Andrew N.; Cao, Shugeng; Craycroft, Jane A.; Scholten, Robert; Fu, Yanling; Wang, Wenquan; Liu, Yong; Zhao, Zhongzhen; Clardy, Jon; Shamu, Caroline E.; Eisenberg, David M.

2011-01-01

Aim of the study. Ethnobotanically-driven drug-discovery programs include data related to many aspects of the preparation of botanical medicines, from initial plant collection to chemical extraction and fractionation. The Traditional Medicine-Collection Tracking System (TM-CTS) was created to organize and store data of this type for an international collaborative project involving the systematic evaluation of commonly used Traditional Chinese Medicinal plants. Materials and Methods. The system was developed using domain-driven design techniques, and is implemented using Java, Hibernate, PostgreSQL, Business Intelligence and Reporting Tools (BIRT), and Apache Tomcat. Results. The TM-CTS relational database schema contains over 70 data types, comprising over 500 data fields. The system incorporates a number of unique features that are useful in the context of ethnobotanical projects such as support for information about botanical collection, method of processing, quality tests for plants with existing pharmacopoeia standards, chemical extraction and fractionation, and historical uses of the plants. The database also accommodates data provided in multiple languages and integration with a database system built to support high throughput screening based drug discovery efforts. It is accessed via a web-based application that provides extensive, multi-format reporting capabilities. Conclusions. This new database system was designed to support a project evaluating the bioactivity of Chinese medicinal plants. The software used to create the database is open source, freely available, and could potentially be applied to other ethnobotanically-driven natural product collection and drug-discovery programs. PMID:21420479

Designing multi-targeted agents: An emerging anticancer drug discovery paradigm.

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Fu, Rong-Geng; Sun, Yuan; Sheng, Wen-Bing; Liao, Duan-Fang

2017-08-18

The dominant paradigm in drug discovery is to design ligands with maximum selectivity to act on individual drug targets. With the target-based approach, many new chemical entities have been discovered, developed, and further approved as drugs. However, there are a large number of complex diseases such as cancer that cannot be effectively treated or cured only with one medicine to modulate the biological function of a single target. As simultaneous intervention of two (or multiple) cancer progression relevant targets has shown improved therapeutic efficacy, the innovation of multi-targeted drugs has become a promising and prevailing research topic and numerous multi-targeted anticancer agents are currently at various developmental stages. However, most multi-pharmacophore scaffolds are usually discovered by serendipity or screening, while rational design by combining existing pharmacophore scaffolds remains an enormous challenge. In this review, four types of multi-pharmacophore modes are discussed, and the examples from literature will be used to introduce attractive lead compounds with the capability of simultaneously interfering with different enzyme or signaling pathway of cancer progression, which will reveal the trends and insights to help the design of the next generation multi-targeted anticancer agents. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Small Molecule Drug Discovery at the Glucagon-Like Peptide-1 Receptor

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Francis S. Willard

2012-01-01

Full Text Available The therapeutic success of peptide glucagon-like peptide-1 (GLP-1 receptor agonists for the treatment of type 2 diabetes mellitus has inspired discovery efforts aimed at developing orally available small molecule GLP-1 receptor agonists. Although the GLP-1 receptor is a member of the structurally complex class B1 family of GPCRs, in recent years, a diverse array of orthosteric and allosteric nonpeptide ligands has been reported. These compounds include antagonists, agonists, and positive allosteric modulators with intrinsic efficacy. In this paper, a comprehensive review of currently disclosed small molecule GLP-1 receptor ligands is presented. In addition, examples of “ligand bias” and “probe dependency” for the GLP-1 receptor are discussed; these emerging concepts may influence further optimization of known molecules or persuade designs of expanded screening strategies to identify novel chemical starting points for GLP-1 receptor drug discovery.

Current Landscape of Antiviral Drug Discovery [version 1; referees: 2 approved

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Wade Blair

2016-02-01

Full Text Available Continued discovery and development of new antiviral medications are paramount for global human health, particularly as new pathogens emerge and old ones evolve to evade current therapeutic agents. Great success has been achieved in developing effective therapies to suppress human immunodeficiency virus (HIV and hepatitis B virus (HBV; however, the therapies are not curative and therefore current efforts in HIV and HBV drug discovery are directed toward longer-acting therapies and/or developing new mechanisms of action that could potentially lead to cure, or eradication, of the virus. Recently, exciting early clinical data have been reported for novel antivirals targeting respiratory syncytial virus (RSV and influenza (flu. Preclinical data suggest that these new approaches may be effective in treating high-risk patients afflicted with serious RSV or flu infections. In this review, we highlight new directions in antiviral approaches for HIV, HBV, and acute respiratory virus infections.

Open innovation for phenotypic drug discovery: The PD2 assay panel.

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Lee, Jonathan A; Chu, Shaoyou; Willard, Francis S; Cox, Karen L; Sells Galvin, Rachelle J; Peery, Robert B; Oliver, Sarah E; Oler, Jennifer; Meredith, Tamika D; Heidler, Steven A; Gough, Wendy H; Husain, Saba; Palkowitz, Alan D; Moxham, Christopher M

2011-07-01

Phenotypic lead generation strategies seek to identify compounds that modulate complex, physiologically relevant systems, an approach that is complementary to traditional, target-directed strategies. Unlike gene-specific assays, phenotypic assays interrogate multiple molecular targets and signaling pathways in a target "agnostic" fashion, which may reveal novel functions for well-studied proteins and discover new pathways of therapeutic value. Significantly, existing compound libraries may not have sufficient chemical diversity to fully leverage a phenotypic strategy. To address this issue, Eli Lilly and Company launched the Phenotypic Drug Discovery Initiative (PD(2)), a model of open innovation whereby external research groups can submit compounds for testing in a panel of Lilly phenotypic assays. This communication describes the statistical validation, operations, and initial screening results from the first PD(2) assay panel. Analysis of PD(2) submissions indicates that chemical diversity from open source collaborations complements internal sources. Screening results for the first 4691 compounds submitted to PD(2) have confirmed hit rates from 1.6% to 10%, with the majority of active compounds exhibiting acceptable potency and selectivity. Phenotypic lead generation strategies, in conjunction with novel chemical diversity obtained via open-source initiatives such as PD(2), may provide a means to identify compounds that modulate biology by novel mechanisms and expand the innovation potential of drug discovery.

Simulated drug discovery process to conduct a synoptic assessment of pharmacy students.

Science.gov (United States)

Richardson, Alan; Curtis, Anthony D M; Moss, Gary P; Pearson, Russell J; White, Simon; Rutten, Frank J M; Perumal, Dhaya; Maddock, Katie

2014-03-12

OBJECTIVE. To implement and assess a task-based learning exercise that prompts pharmacy students to integrate their understanding of different disciplines. DESIGN. Master of pharmacy (MPharm degree) students were provided with simulated information from several preclinical science and from clinical trials and asked to synthesize this into a marketing authorization application for a new drug. Students made a link to pharmacy practice by creating an advice leaflet for pharmacists. ASSESSMENT. Students' ability to integrate information from different disciplines was evaluated by oral examination. In 2 successive academic years, 96% and 82% of students demonstrated an integrated understanding of their proposed new drug. Students indicated in a survey that their understanding of the links between different subjects improved. CONCLUSION. Simulated drug discovery provides a learning environment that emphasizes the connectivity of the preclinical sciences with each other and the practice of pharmacy.

The mass-action law based algorithm for cost-effective approach for cancer drug discovery and development.

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Chou, Ting-Chao

2011-01-01

The mass-action law based system analysis via mathematical induction and deduction lead to the generalized theory and algorithm that allows computerized simulation of dose-effect dynamics with small size experiments using a small number of data points in vitro, in animals, and in humans. The median-effect equation of the mass-action law deduced from over 300 mechanism specific-equations has been shown to be the unified theory that serves as the common-link for complicated biomedical systems. After using the median-effect principle as the common denominator, its applications are mechanism-independent, drug unit-independent, and dynamic order-independent; and can be used generally for single drug analysis or for multiple drug combinations in constant-ratio or non-constant ratios. Since the "median" is the common link and universal reference point in biological systems, these general enabling lead to computerized quantitative bio-informatics for econo-green bio-research in broad disciplines. Specific applications of the theory, especially relevant to drug discovery, drug combination, and clinical trials, have been cited or illustrated in terms of algorithms, experimental design and computerized simulation for data analysis. Lessons learned from cancer research during the past fifty years provide a valuable opportunity to reflect, and to improve the conventional divergent approach and to introduce a new convergent avenue, based on the mass-action law principle, for the efficient cancer drug discovery and the low-cost drug development.

Brivaracetam: a rational drug discovery success story

Science.gov (United States)

Rogawski, M A

2008-01-01

Levetiracetam, the α-ethyl analogue of the nootropic piracetam, is a widely used antiepileptic drug (AED) that provides protection against partial seizures and is also effective in the treatment of primary generalized seizure syndromes including juvenile myoclonic epilepsy. Levetiracetam was discovered in 1992 through screening in audiogenic seizure susceptible mice and, 3 years later, was reported to exhibit saturable, stereospecific binding in brain to a ∼90 kDa protein, later identified as the ubiquitous synaptic vesicle glycoprotein SV2A. A large-scale screening effort to optimize binding affinity identified the 4-n-propyl analogue, brivaracetam, as having greater potency and a broadened spectrum of activity in animal seizure models. Recent phase II clinical trials demonstrating that brivaracetam is efficacious and well tolerated in the treatment of partial onset seizures have validated the strategy of the discovery programme. Brivaracetam is among the first clinically effective AEDs to be discovered by optimization of pharmacodynamic activity at a molecular target. PMID:18552880

Phenotypic Screening Approaches to Develop Aurora Kinase Inhibitors: Drug Discovery Perspectives.

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Marugán, Carlos; Torres, Raquel; Lallena, María José

2015-01-01

Targeting mitotic regulators as a strategy to fight cancer implies the development of drugs against key proteins, such as Aurora-A and -B. Current drugs, which target mitosis through a general mechanism of action (stabilization/destabilization of microtubules), have several side effects (neutropenia, alopecia, and emesis). Pharmaceutical companies aim at avoiding these unwanted effects by generating improved and selective drugs that increase the quality of life of the patients. However, the development of these drugs is an ambitious task that involves testing thousands of compounds through biochemical and cell-based assays. In addition, molecules usually target complex biological processes, involving several proteins and different molecular pathways, further emphasizing the need for high-throughput screening techniques and multiplexing technologies in order to identify drugs with the desired phenotype. We will briefly describe two multiplexing technologies [high-content imaging (HCI) and flow cytometry] and two key processes for drug discovery research (assay development and validation) following our own published industry quality standards. We will further focus on HCI as a useful tool for phenotypic screening and will provide a concrete example of HCI assay to detect Aurora-A or -B selective inhibitors discriminating the off-target effects related to the inhibition of other cell cycle or non-cell cycle key regulators. Finally, we will describe other assays that can help to characterize the in vitro pharmacology of the inhibitors.

Sistema integrado de prevención de errores en el proceso de utilización de medicamentos en oncología Integrated system for error prevention in process of drugs used in Oncology

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Jorge L. Soriano García

2007-08-01

indirectos. Conclusiones: Constituye el primer reporte de un sistema integrado de prevención de errores en los antineoplásicos en países con recursos limitados y puede, por su sencillez y factibilidad, ser aplicado en cualquiera de estos países.Justification: Medication mistakes in case of chemotherapy or adjuvant treatment used in any stage of drug application process: prescription, transcription, preparation, dispense or administration, are a frequent cause of side effects of antineoplastic drugs. Methods: Main sourses of information were meetings to analyze application of quality guarantee program in Oncology in services and the revision of medical literature published in from 1995 to January 2006, appeared in MEDLINE. Searching strategy was performed under headings “mediction errors” and “chemotherapy”. Additional searches were performed under headings “safety patient”, “antineoplastic drugs”, “preventing medications errors”, and were combined each other. Results: Experience of Oncology Service from “Hermanos Ameijeiras” Clinical Surgical Hospital was exposed as for application of work strategy related to error prevention in administration of drugs in above service from year 2000. To date, some novel features has been applied: forms for chemotherapeutic treatment, standardized suggestions in pre-printed sheets, drugs dilution tables, new organizing nursing systems, and report sheets in case of patient toxicity. Application of above strategy involves antineoplastic chemotherapy, and global survival of patients. It contributes to reduct direct health expenses (decrease in complications and treatment derived from it, real time of staff in different phases of drug use process, and consumption of cytostatic sera and indirect charges. Conclusions: This is the first report from aa integrated system of error prevention in antineoplastic drugs in countries with limites resources, and by its simplicity and feasibility, may be applied in any of these

Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.

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Jing, Yankang; Bian, Yuemin; Hu, Ziheng; Wang, Lirong; Xie, Xiang-Qun Sean

2018-03-30

Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.

Drug Repositioning Discovery for Early- and Late-Stage Non-Small-Cell Lung Cancer

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Chien-Hung Huang

2014-01-01

Full Text Available Drug repositioning is a popular approach in the pharmaceutical industry for identifying potential new uses for existing drugs and accelerating the development time. Non-small-cell lung cancer (NSCLC is one of the leading causes of death worldwide. To reduce the biological heterogeneity effects among different individuals, both normal and cancer tissues were taken from the same patient, hence allowing pairwise testing. By comparing early- and late-stage cancer patients, we can identify stage-specific NSCLC genes. Differentially expressed genes are clustered separately to form up- and downregulated communities that are used as queries to perform enrichment analysis. The results suggest that pathways for early- and late-stage cancers are different. Sets of up- and downregulated genes were submitted to the cMap web resource to identify potential drugs. To achieve high confidence drug prediction, multiple microarray experimental results were merged by performing meta-analysis. The results of a few drug findings are supported by MTT assay or clonogenic assay data. In conclusion, we have been able to assess the potential existing drugs to identify novel anticancer drugs, which may be helpful in drug repositioning discovery for NSCLC.

Combining NMR and X-ray crystallography in fragment-based drug discovery: discovery of highly potent and selective BACE-1 inhibitors.

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Wyss, Daniel F; Wang, Yu-Sen; Eaton, Hugh L; Strickland, Corey; Voigt, Johannes H; Zhu, Zhaoning; Stamford, Andrew W

2012-01-01

Fragment-based drug discovery (FBDD) has become increasingly popular over the last decade. We review here how we have used highly structure-driven fragment-based approaches to complement more traditional lead discovery to tackle high priority targets and those struggling for leads. Combining biomolecular nuclear magnetic resonance (NMR), X-ray crystallography, and molecular modeling with structure-assisted chemistry and innovative biology as an integrated approach for FBDD can solve very difficult problems, as illustrated in this chapter. Here, a successful FBDD campaign is described that has allowed the development of a clinical candidate for BACE-1, a challenging CNS drug target. Crucial to this achievement were the initial identification of a ligand-efficient isothiourea fragment through target-based NMR screening and the determination of its X-ray crystal structure in complex with BACE-1, which revealed an extensive H-bond network with the two active site aspartate residues. This detailed 3D structural information then enabled the design and validation of novel, chemically stable and accessible heterocyclic acylguanidines as aspartic acid protease inhibitor cores. Structure-assisted fragment hit-to-lead optimization yielded iminoheterocyclic BACE-1 inhibitors that possess desirable molecular properties as potential therapeutic agents to test the amyloid hypothesis of Alzheimer's disease in a clinical setting.

Computational medicinal chemistry in fragment-based drug discovery: what, how and when.

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Rabal, Obdulia; Urbano-Cuadrado, Manuel; Oyarzabal, Julen

2011-01-01

The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure-activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario - what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.

Measuring the Value of New Drugs: Validity and Reliability of 4 Value Assessment Frameworks in the Oncology Setting.

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Bentley, Tanya G K; Cohen, Joshua T; Elkin, Elena B; Huynh, Julie; Mukherjea, Arnab; Neville, Thanh H; Mei, Matthew; Copher, Ronda; Knoth, Russell; Popescu, Ioana; Lee, Jackie; Zambrano, Jenelle M; Broder, Michael S

2017-06-01

Several organizations have developed frameworks to systematically assess the value of new drugs. To evaluate the convergent validity and interrater reliability of 4 value frameworks to understand the extent to which these tools can facilitate value-based treatment decisions in oncology. Eight panelists used the American Society of Clinical Oncology (ASCO), European Society for Medical Oncology (ESMO), Institute for Clinical and Economic Review (ICER), and National Comprehensive Cancer Network (NCCN) frameworks to conduct value assessments of 15 drugs for advanced lung and breast cancers and castration-refractory prostate cancer. Panelists received instructions and published clinical data required to complete the assessments, assigning each drug a numeric or letter score. Kendall's Coefficient of Concordance for Ranks (Kendall's W) was used to measure convergent validity by cancer type among the 4 frameworks. Intraclass correlation coefficients (ICCs) were used to measure interrater reliability for each framework across cancers. Panelists were surveyed on their experiences. Kendall's W across all 4 frameworks for breast, lung, and prostate cancer drugs was 0.560 (P= 0.010), 0.562 (P = 0.010), and 0.920 (P fair to excellent, increasing with clinical benefit subdomain concordance and simplicity of drug trial data. Interrater reliability, highest for ASCO and ESMO, improved with clarity of instructions and specificity of score definitions. Continued use, analyses, and refinements of these frameworks will bring us closer to the ultimate goal of using value-based treatment decisions to improve patient care and outcomes. This work was funded by Eisai Inc. Copher and Knoth are employees of Eisai Inc. Bentley, Lee, Zambrano, and Broder are employees of Partnership for Health Analytic Research, a health services research company paid by Eisai Inc. to conduct this research. For this study, Cohen, Huynh, and Neville report fees from Partnership for Health Analytic Research

FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects.

Science.gov (United States)

Lagorce, David; Sperandio, Olivier; Galons, Hervé; Miteva, Maria A; Villoutreix, Bruno O

2008-09-24

Drug discovery and chemical biology are exceedingly complex and demanding enterprises. In recent years there are been increasing awareness about the importance of predicting/optimizing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small chemical compounds along the search process rather than at the final stages. Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses) and as such, compound collections' property profiling can be performed in silico. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making. This paper presents FAF-Drugs2, a free adaptable tool for ADMET filtering of electronic compound collections. FAF-Drugs2 is a command line utility program (e.g., written in Python) based on the open source chemistry toolkit OpenBabel, which performs various physicochemical calculations, identifies key functional groups, some toxic and unstable molecules/functional groups. In addition to filtered collections, FAF-Drugs2 can provide, via Gnuplot, several distribution diagrams of major physicochemical properties of the screened compound libraries. We have developed FAF-Drugs2 to facilitate compound collection preparation, prior to (or after) experimental screening or virtual screening computations. Users can select to apply various filtering thresholds and add rules as needed for a given project. As it stands, FAF-Drugs2 implements numerous filtering rules (23 physicochemical rules and 204 substructure searching rules) that can be easily tuned.

Stem cells in drug discovery, tissue engineering, and regenerative medicine: emerging opportunities and challenges.

Science.gov (United States)

Nirmalanandhan, Victor Sanjit; Sittampalam, G Sitta

2009-08-01

Stem cells, irrespective of their origin, have emerged as valuable reagents or tools in human health in the past 2 decades. Initially, a research tool to study fundamental aspects of developmental biology is now the central focus of generating transgenic animals, drug discovery, and regenerative medicine to address degenerative diseases of multiple organ systems. This is because stem cells are pluripotent or multipotent cells that can recapitulate developmental paths to repair damaged tissues. However, it is becoming clear that stem cell therapy alone may not be adequate to reverse tissue and organ damage in degenerative diseases. Existing small-molecule drugs and biologicals may be needed as "molecular adjuvants" or enhancers of stem cells administered in therapy or adult stem cells in the diseased tissues. Hence, a combination of stem cell-based, high-throughput screening and 3D tissue engineering approaches is necessary to advance the next wave of tools in preclinical drug discovery. In this review, the authors have attempted to provide a basic account of various stem cells types, as well as their biology and signaling, in the context of research in regenerative medicine. An attempt is made to link stem cells as reagents, pharmacology, and tissue engineering as converging fields of research for the next decade.

The birth of the subspecialty of medical oncology and examples of its early scientific foundations.

Science.gov (United States)

Band, Pierre R

2010-08-01

"Passion is not accepting defeat."--Emil Frei III. In the early 1950s, an experimental and clinical program characterized by unique cross-fertilization was developed. The clinical importance of experimental animal models in drug screening and in establishing key chemotherapy concepts and the role of the pioneers of medical oncology in the design of the various phases of drug trials, using childhood acute leukemia and breast cancer as models, are discussed. Over a short time and with only a few drugs, principles of chemotherapy were laid out, which led to cures in such diseases as childhood acute leukemia and Hodgkin's disease and to improved disease-free survival in breast cancer. It is these and other achievements that paved the way to medical oncology. At the instigation of the American Society of Clinical Oncology (ASCO), the American Board of Internal Medicine made inquiries about a subspecialty in oncology. ASCO and B. J. Kennedy, MD, played key roles in the events leading to the official recognition of medical oncology as a new subspecialty of internal medicine in 1972.

The adverse drug reaction reporting assignment for specialist oncology nurses: a preliminary evaluation of quality, relevance and educational value in a prospective cohort study.

Science.gov (United States)

Schutte, Tim; van Eekeren, Rike; Richir, Milan; van Staveren, Jojanneke; van Puijenbroek, Eugène; Tichelaar, Jelle; van Agtmael, Michiel

2018-01-01

In a new prescribing qualification course for specialist oncology nurses, we thought that it is important to emphasize pharmacovigilance and adverse drug reaction (ADR) reporting. We aimed to develop and evaluate an ADR reporting assignment for specialist oncology nurses. The quality of report documentation was assessed with the "Clinical Documentation tool to assess Individual Case Safety Reports" (ClinDoc). The relevance of the reports was evaluated in terms of ADR seriousness, the listing for additional monitoring of the drug by European Medicines Agency (EMA), and lack of labelling information about the ADR. Nurses' opinions of the assignment were evaluated using an E-survey. Thirty-three ADRs were reported, 32 (97%) of which were well documented according to ClinDoc. Thirteen ADRs (39%) were "serious" according to CIOMS criteria. In five cases (15%), the suspect drugs were listed for additional monitoring by EMA and in seven cases (21%), the ADR was not mentioned in the Summary of Product Characteristics. Twenty-five (78.1%) of the 32 enrolled nurses completed the E-survey. Most were > 45 years of age (68%), female (92%) and had extensive clinical experience (6-33 years). All agreed or completely agreed that the reporting assignment was useful, that it fitted in daily practice and that it increased their attention for medication/patient safety. A large majority (84.0%) agreed the assignment changed how they dealt with ADRs. Specialist oncology nurses are capable of reporting ADRs, and they considered the assignment useful. The assignment yielded valuable, relevant, and well-documented ADR reports for pharmacovigilance practice.

Discovery of inhibitors of bacterial histidine kinases

NARCIS (Netherlands)

Velikova, N.R.

2014-01-01

Discovery of Inhibitors of Bacterial Histidine Kinases Summary

The thesis is on novel antibacterial drug discovery (http://youtu.be/NRMWOGgeysM). Using structure-based and fragment-based drug discovery approach, we have identified small-molecule histidine-kinase

Companion diagnostics and molecular imaging-enhanced approaches for oncology clinical trials.

Science.gov (United States)

Van Heertum, Ronald L; Scarimbolo, Robert; Ford, Robert; Berdougo, Eli; O'Neal, Michael

2015-01-01

In the era of personalized medicine, diagnostic approaches are helping pharmaceutical and biotechnology sponsors streamline the clinical trial process. Molecular assays and diagnostic imaging are routinely being used to stratify patients for treatment, monitor disease, and provide reliable early clinical phase assessments. The importance of diagnostic approaches in drug development is highlighted by the rapidly expanding global cancer diagnostics market and the emergent attention of regulatory agencies worldwide, who are beginning to offer more structured platforms and guidance for this area. In this paper, we highlight the key benefits of using companion diagnostics and diagnostic imaging with a focus on oncology clinical trials. Nuclear imaging using widely available radiopharmaceuticals in conjunction with molecular imaging of oncology targets has opened the door to more accurate disease assessment and the modernization of standard criteria for the evaluation, staging, and treatment responses of cancer patients. Furthermore, the introduction and validation of quantitative molecular imaging continues to drive and optimize the field of oncology diagnostics. Given their pivotal role in disease assessment and treatment, the validation and commercialization of diagnostic tools will continue to advance oncology clinical trials, support new oncology drugs, and promote better patient outcomes.

Drug discovery and development tomorrow -- changing the mindset.

Science.gov (United States)

Coleman, Robert A

2009-09-01

Today's drug discovery and development paradigm is not working, and something needs to be done about it. There is good reason to believe that a move away from reliance on animal surrogates for human subjects in the Pharma Industry's R&D programmes could provide an important step forward. However, no serious move will be made in that direction until there is some hard evidence that it will be rewarded with improved productivity outcomes. The Safer Medicines Trust are proposing that a study be undertaken, involving a range of drugs that have been approved for human use, but have subsequently proved to have limitations in terms of safety and/or efficacy. The aim is to determine the efficiency of a battery of human-based test methods to identify a compound's safety and efficacy profiles, and to compare this with that of the more traditional, largely animal-based methods that were employed in their original development. Should such an approach prove more reliable, the authorities will be faced with important decisions relating to the role of human biological test data in regulatory submissions, while the Pharma Industry will be faced with the key logistical issue of how to acquire the human biomaterials necessary to make possible the routine application of such test methods. 2009 FRAME.

Have there been improvements in Alzheimer's disease drug discovery over the past 5 years?

Science.gov (United States)

Cacabelos, Ramón

2018-06-01

Alzheimer's disease (AD) is the most important neurodegenerative disorder with a global cost worldwide of over $700 billion. Pharmacological treatment accounts for 10-20% of direct costs; no new drugs have been approved during the past 15 years; and the available medications are not cost-effective. Areas covered: A massive scrutiny of AD-related PubMed publications (ps)(2013-2017) identified 42,053ps of which 8,380 (19.60%) were associated with AD treatments. The most prevalent pharmacological categories included neurotransmitter enhancers (11.38%), multi-target drugs (2.45%), anti-Amyloid agents (13.30%), anti-Tau agents (2.03%), natural products and derivatives (25.58%), novel drugs (8.13%), novel targets (5.66%), other (old) drugs (11.77%), anti-inflammatory drugs (1.20%), neuroprotective peptides (1.25%), stem cell therapy (1.85%), nanocarriers/nanotherapeutics (1.52%), and others (discovery programs, (vi) the updating of regulatory requirements, (vii) the introduction of pharmacogenomics in drug development and personalized treatments, and (viii) the implementation of preventive programs.

Female Representation in the Academic Oncology Physician Workforce: Radiation Oncology Losing Ground to Hematology Oncology

Energy Technology Data Exchange (ETDEWEB)

Ahmed, Awad A. [Sylvester Comprehensive Cancer Center University of Miami Health System, Miami, Florida (United States); Hwang, Wei-Ting [Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania (United States); Holliday, Emma B. [Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Chapman, Christina H.; Jagsi, Reshma [Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan (United States); Thomas, Charles R. [Department of Radiation Medicine, Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon (United States); Deville, Curtiland, E-mail: cdeville@jhmi.edu [Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland (United States)

2017-05-01

Purpose: Our purpose was to assess comparative female representation trends for trainees and full-time faculty in the academic radiation oncology and hematology oncology workforce of the United States over 3 decades. Methods and Materials: Simple linear regression models with year as the independent variable were used to determine changes in female percentage representation per year and associated 95% confidence intervals for trainees and full-time faculty in each specialty. Results: Peak representation was 48.4% (801/1654) in 2013 for hematology oncology trainees, 39.0% (585/1499) in 2014 for hematology oncology full-time faculty, 34.8% (202/581) in 2007 for radiation oncology trainees, and 27.7% (439/1584) in 2015 for radiation oncology full-time faculty. Representation significantly increased for trainees and full-time faculty in both specialties at approximately 1% per year for hematology oncology trainees and full-time faculty and 0.3% per year for radiation oncology trainees and full-time faculty. Compared with radiation oncology, the rates were 3.84 and 2.94 times greater for hematology oncology trainees and full-time faculty, respectively. Conclusion: Despite increased female trainee and full-time faculty representation over time in the academic oncology physician workforce, radiation oncology is lagging behind hematology oncology, with trainees declining in recent years in radiation oncology; this suggests a de facto ceiling in female representation. Whether such issues as delayed or insufficient exposure, inadequate mentorship, or specialty competitiveness disparately affect female representation in radiation oncology compared to hematology oncology are underexplored and require continued investigation to ensure that the future oncologic physician workforce reflects the diversity of the population it serves.

Female Representation in the Academic Oncology Physician Workforce: Radiation Oncology Losing Ground to Hematology Oncology

International Nuclear Information System (INIS)

Ahmed, Awad A.; Hwang, Wei-Ting; Holliday, Emma B.; Chapman, Christina H.; Jagsi, Reshma; Thomas, Charles R.; Deville, Curtiland

2017-01-01

Purpose: Our purpose was to assess comparative female representation trends for trainees and full-time faculty in the academic radiation oncology and hematology oncology workforce of the United States over 3 decades. Methods and Materials: Simple linear regression models with year as the independent variable were used to determine changes in female percentage representation per year and associated 95% confidence intervals for trainees and full-time faculty in each specialty. Results: Peak representation was 48.4% (801/1654) in 2013 for hematology oncology trainees, 39.0% (585/1499) in 2014 for hematology oncology full-time faculty, 34.8% (202/581) in 2007 for radiation oncology trainees, and 27.7% (439/1584) in 2015 for radiation oncology full-time faculty. Representation significantly increased for trainees and full-time faculty in both specialties at approximately 1% per year for hematology oncology trainees and full-time faculty and 0.3% per year for radiation oncology trainees and full-time faculty. Compared with radiation oncology, the rates were 3.84 and 2.94 times greater for hematology oncology trainees and full-time faculty, respectively. Conclusion: Despite increased female trainee and full-time faculty representation over time in the academic oncology physician workforce, radiation oncology is lagging behind hematology oncology, with trainees declining in recent years in radiation oncology; this suggests a de facto ceiling in female representation. Whether such issues as delayed or insufficient exposure, inadequate mentorship, or specialty competitiveness disparately affect female representation in radiation oncology compared to hematology oncology are underexplored and require continued investigation to ensure that the future oncologic physician workforce reflects the diversity of the population it serves.

Female Representation in the Academic Oncology Physician Workforce: Radiation Oncology Losing Ground to Hematology Oncology.

Science.gov (United States)

Ahmed, Awad A; Hwang, Wei-Ting; Holliday, Emma B; Chapman, Christina H; Jagsi, Reshma; Thomas, Charles R; Deville, Curtiland

2017-05-01

Our purpose was to assess comparative female representation trends for trainees and full-time faculty in the academic radiation oncology and hematology oncology workforce of the United States over 3 decades. Simple linear regression models with year as the independent variable were used to determine changes in female percentage representation per year and associated 95% confidence intervals for trainees and full-time faculty in each specialty. Peak representation was 48.4% (801/1654) in 2013 for hematology oncology trainees, 39.0% (585/1499) in 2014 for hematology oncology full-time faculty, 34.8% (202/581) in 2007 for radiation oncology trainees, and 27.7% (439/1584) in 2015 for radiation oncology full-time faculty. Representation significantly increased for trainees and full-time faculty in both specialties at approximately 1% per year for hematology oncology trainees and full-time faculty and 0.3% per year for radiation oncology trainees and full-time faculty. Compared with radiation oncology, the rates were 3.84 and 2.94 times greater for hematology oncology trainees and full-time faculty, respectively. Despite increased female trainee and full-time faculty representation over time in the academic oncology physician workforce, radiation oncology is lagging behind hematology oncology, with trainees declining in recent years in radiation oncology; this suggests a de facto ceiling in female representation. Whether such issues as delayed or insufficient exposure, inadequate mentorship, or specialty competitiveness disparately affect female representation in radiation oncology compared to hematology oncology are underexplored and require continued investigation to ensure that the future oncologic physician workforce reflects the diversity of the population it serves. Copyright © 2017 Elsevier Inc. All rights reserved.

Live Cell in Vitro and in Vivo Imaging Applications: Accelerating Drug Discovery

Directory of Open Access Journals (Sweden)

Neil O Carragher

2011-04-01

Full Text Available Dynamic regulation of specific molecular processes and cellular phenotypes in live cell systems reveal unique insights into cell fate and drug pharmacology that are not gained from traditional fixed endpoint assays. Recent advances in microscopic imaging platform technology combined with the development of novel optical biosensors and sophisticated image analysis solutions have increased the scope of live cell imaging applications in drug discovery. We highlight recent literature examples where live cell imaging has uncovered novel insight into biological mechanism or drug mode-of-action. We survey distinct types of optical biosensors and associated analytical methods for monitoring molecular dynamics, in vitro and in vivo. We describe the recent expansion of live cell imaging into automated target validation and drug screening activities through the development of dedicated brightfield and fluorescence kinetic imaging platforms. We provide specific examples of how temporal profiling of phenotypic response signatures using such kinetic imaging platforms can increase the value of in vitro high-content screening. Finally, we offer a prospective view of how further application and development of live cell imaging technology and reagents can accelerate preclinical lead optimization cycles and enhance the in vitro to in vivo translation of drug candidates.

Assessment of Dengue virus helicase and methyltransferase as targets for fragment-based drug discovery.

Science.gov (United States)

Coutard, Bruno; Decroly, Etienne; Li, Changqing; Sharff, Andrew; Lescar, Julien; Bricogne, Gérard; Barral, Karine

2014-06-01

Seasonal and pandemic flaviviruses continue to be leading global health concerns. With the view to help drug discovery against Dengue virus (DENV), a fragment-based experimental approach was applied to identify small molecule ligands targeting two main components of the flavivirus replication complex: the NS3 helicase (Hel) and the NS5 mRNA methyltransferase (MTase) domains. A library of 500 drug-like fragments was first screened by thermal-shift assay (TSA) leading to the identification of 36 and 32 fragment hits binding Hel and MTase from DENV, respectively. In a second stage, we set up a fragment-based X-ray crystallographic screening (FBS-X) in order to provide both validated fragment hits and structural binding information. No fragment hit was confirmed for DENV Hel. In contrast, a total of seven fragments were identified as DENV MTase binders and structures of MTase-fragment hit complexes were solved at resolution at least 2.0Å or better. All fragment hits identified contain either a five- or six-membered aromatic ring or both, and three novel binding sites were located on the MTase. To further characterize the fragment hits identified by TSA and FBS-X, we performed enzymatic assays to assess their inhibition effect on the N7- and 2'-O-MTase enzymatic activities: five of these fragment hits inhibit at least one of the two activities with IC50 ranging from 180μM to 9mM. This work validates the FBS-X strategy for identifying new anti-flaviviral hits targeting MTase, while Hel might not be an amenable target for fragment-based drug discovery (FBDD). This approach proved to be a fast and efficient screening method for FBDD target validation and discovery of starting hits for the development of higher affinity molecules that bind to novel allosteric sites. Copyright © 2014 Elsevier B.V. All rights reserved.

Renaissance in Antibiotic Discovery: Some Novel Approaches for Finding Drugs to Treat Bad Bugs.

Science.gov (United States)

Gadakh, Bharat; Van Aerschot, Arthur

2015-01-01

With the alarming resistance to currently used antibiotics, there is a serious worldwide threat to public health. Therefore, there is an urgent need to search for new antibiotics or new cellular targets which are essential for survival of the pathogens. However, during the past 50 years, only two new classes of antibiotics (oxazolidinone and lipopeptides) have reached the clinic. This suggests that the success rate in discovering new/novel antibiotics using conventional approaches is limited and that we must reconsider our antibiotic discovery approaches. While many new strategies are being pursued lately, this review primarily focuses only on a few of these novel/new approaches for antibiotic discovery. These include structure-based drug design (SBDD), the genomic approach, anti-virulence strategy, targeting nonmultiplying bacteria and the use of bacteriophages. In general, recent advancements in nuclear magnetic resonance, Xcrystallography, and genomic evolution have significant impact on antibacterial drug research. This review therefore aims to discuss recent strategies in searching new antibacterial agents making use of these technical novelties, their advantages, disadvantages and limitations.

Oncology Advanced Practitioners Bring Advanced Community Oncology Care.

Science.gov (United States)

Vogel, Wendy H

2016-01-01

Oncology care is becoming increasingly complex. The interprofessional team concept of care is necessary to meet projected oncology professional shortages, as well as to provide superior oncology care. The oncology advanced practitioner (AP) is a licensed health care professional who has completed advanced training in nursing or pharmacy or has completed training as a physician assistant. Oncology APs increase practice productivity and efficiency. Proven to be cost effective, APs may perform varied roles in an oncology practice. Integrating an AP into an oncology practice requires forethought given to the type of collaborative model desired, role expectations, scheduling, training, and mentoring.

Phenotypic screening approaches to develop Aurora kinase inhibitors: Drug Discovery perspectives

Directory of Open Access Journals (Sweden)

Carlos eMarugán

2016-01-01

Full Text Available Targeting mitotic regulators as a strategy to fight cancer implies the development of drugs against key proteins such as Aurora A and B. Current drugs which target mitosis through a general mechanism of action (stabilization/destabilization of microtubules, have several side effects (neutropenia, alopecia, emesis. Pharmaceutical companies aim at avoiding these unwanted effects by generating improved and selective drugs that increase the quality of life of the patients. However, the development of these drugs is an ambitious task that involves testing thousands of compounds through biochemical and cell-based assays. In addition, molecules usually target complex biological processes, involving several proteins and different molecular pathways, further emphasizing the need for high-throughput screening techniques and multiplexing technologies in order to identify drugs with the desired phenotype.We will briefly describe two multiplexing technologies (high-content imaging, microarrays and flow cytometry and two key processes for drug discovery research (assay development and validation following our own published industry quality standards. We will further focus on high-content imaging as a useful tool for phenotypic screening and will provide a concrete example of high-content imaging assay to detect Aurora A or B selective inhibitors discriminating the off-target effects related to inhibition of other cell cycle or non-cell cycle key regulators. Finally, we will describe other assays that can help to characterize the in vitro pharmacology of the inhibitors.

The Roles of Water in the Protein Matrix: A Largely Untapped Resource for Drug Discovery.

Science.gov (United States)

Spyrakis, Francesca; Ahmed, Mostafa H; Bayden, Alexander S; Cozzini, Pietro; Mozzarelli, Andrea; Kellogg, Glen E

2017-08-24

The value of thoroughly understanding the thermodynamics specific to a drug discovery/design study is well known. Over the past decade, the crucial roles of water molecules in protein structure, function, and dynamics have also become increasingly appreciated. This Perspective explores water in the biological environment by adopting its point of view in such phenomena. The prevailing thermodynamic models of the past, where water was seen largely in terms of an entropic gain after its displacement by a ligand, are now known to be much too simplistic. We adopt a set of terminology that describes water molecules as being "hot" and "cold", which we have defined as being easy and difficult to displace, respectively. The basis of these designations, which involve both enthalpic and entropic water contributions, are explored in several classes of biomolecules and structural motifs. The hallmarks for characterizing water molecules are examined, and computational tools for evaluating water-centric thermodynamics are reviewed. This Perspective's summary features guidelines for exploiting water molecules in drug discovery.

View and review on viral oncology research

Directory of Open Access Journals (Sweden)

Parolin Cristina

2010-05-01

Full Text Available Abstract To date, almost one and a half million cases of cancer are diagnosed every year in the US and nearly 560,000 Americans are expected to die of cancer in the current year, more than 1,500 people a day (data from the American Cancer Society at http://www.cancer.org/. According to the World Health Organization (WHO, roughly 20% of all cancers worldwide results from chronic infections; in particular, up to 15% of human cancers is characterized by a viral aetiology with higher incidence in Developing Countries. The link between viruses and cancer was one of the pivotal discoveries in cancer research during the past Century. Indeed, the infectious nature of specific tumors has important implications in terms of their prevention, diagnosis, and therapy. In the 21st Century, the research on viral oncology field continues to be vigorous, with new significant and original studies on viral oncogenesis and translational research from basic virology to treatment of cancer. This review will cover different viral oncology aspects, starting from the history of viral oncology and moving to the peculiar features of oncogenic RNA and DNA viruses, with a special focus on human pathogens.

Seeking Ligand Bias: Assessing GPCR Coupling to Beta-Arrestins for Drug Discovery

OpenAIRE

Bohn, Laura M.; McDonald, Patricia H.

2010-01-01

G protein-coupled receptors (GPCR) are the major site of action for endogenous hormones and neurotransmitters. Early drug discovery efforts focused on determining whether ligands could engage G protein coupling and subsequently activate or inhibit cognate “second messengers.” Gone are those simple days as we now realize that receptors can also couple βarrestins. As we delve into the complexity of ligand-directed signaling and receptosome scaffolds, we are faced with what may seem like endless...

Ebola virus: A gap in drug design and discovery - experimental and computational perspective.

Science.gov (United States)

Balmith, Marissa; Faya, Mbuso; Soliman, Mahmoud E S

2017-03-01

The Ebola virus, formally known as the Ebola hemorrhagic fever, is an acute viral syndrome causing sporadic outbreaks that have ravaged West Africa. Due to its extreme virulence and highly transmissible nature, Ebola has been classified as a category A bioweapon organism. Only recently have vaccine or drug regimens for the Ebola virus been developed, including Zmapp and peptides. In addition, existing drugs which have been repurposed toward anti-Ebola virus activity have been re-examined and are seen to be promising candidates toward combating Ebola. Drug development involving computational tools has been widely employed toward target-based drug design. Screening large libraries have greatly stimulated research toward effective anti-Ebola virus drug regimens. Current emphasis has been placed on the investigation of host proteins and druggable viral targets. There is a huge gap in the literature regarding guidelines in the discovery of Ebola virus inhibitors, which may be due to the lack of information on the Ebola drug targets, binding sites, and mechanism of action of the virus. This review focuses on Ebola virus inhibitors, drugs which could be repurposed to combat the Ebola virus, computational methods which study drug-target interactions as well as providing further insight into the mode of action of the Ebola virus. © 2016 John Wiley & Sons A/S.

Applicability of bioanalysis of multiple analytes in drug discovery and development: review of select case studies including assay development considerations.

Science.gov (United States)

Srinivas, Nuggehally R

2006-05-01

The development of sound bioanalytical method(s) is of paramount importance during the process of drug discovery and development culminating in a marketing approval. Although the bioanalytical procedure(s) originally developed during the discovery stage may not necessarily be fit to support the drug development scenario, they may be suitably modified and validated, as deemed necessary. Several reviews have appeared over the years describing analytical approaches including various techniques, detection systems, automation tools that are available for an effective separation, enhanced selectivity and sensitivity for quantitation of many analytes. The intention of this review is to cover various key areas where analytical method development becomes necessary during different stages of drug discovery research and development process. The key areas covered in this article with relevant case studies include: (a) simultaneous assay for parent compound and metabolites that are purported to display pharmacological activity; (b) bioanalytical procedures for determination of multiple drugs in combating a disease; (c) analytical measurement of chirality aspects in the pharmacokinetics, metabolism and biotransformation investigations; (d) drug monitoring for therapeutic benefits and/or occupational hazard; (e) analysis of drugs from complex and/or less frequently used matrices; (f) analytical determination during in vitro experiments (metabolism and permeability related) and in situ intestinal perfusion experiments; (g) determination of a major metabolite as a surrogate for the parent molecule; (h) analytical approaches for universal determination of CYP450 probe substrates and metabolites; (i) analytical applicability to prodrug evaluations-simultaneous determination of prodrug, parent and metabolites; (j) quantitative determination of parent compound and/or phase II metabolite(s) via direct or indirect approaches; (k) applicability in analysis of multiple compounds in select

FAF-Drugs2: Free ADME/tox filtering tool to assist drug discovery and chemical biology projects

Directory of Open Access Journals (Sweden)

Miteva Maria A

2008-09-01

Full Text Available Abstract Background Drug discovery and chemical biology are exceedingly complex and demanding enterprises. In recent years there are been increasing awareness about the importance of predicting/optimizing the absorption, distribution, metabolism, excretion and toxicity (ADMET properties of small chemical compounds along the search process rather than at the final stages. Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses and as such, compound collections' property profiling can be performed in silico. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making. Results This paper presents FAF-Drugs2, a free adaptable tool for ADMET filtering of electronic compound collections. FAF-Drugs2 is a command line utility program (e.g., written in Python based on the open source chemistry toolkit OpenBabel, which performs various physicochemical calculations, identifies key functional groups, some toxic and unstable molecules/functional groups. In addition to filtered collections, FAF-Drugs2 can provide, via Gnuplot, several distribution diagrams of major physicochemical properties of the screened compound libraries. Conclusion We have developed FAF-Drugs2 to facilitate compound collection preparation, prior to (or after experimental screening or virtual screening computations. Users can select to apply various filtering thresholds and add rules as needed for a given project. As it stands, FAF-Drugs2 implements numerous filtering rules (23 physicochemical rules and 204 substructure searching rules that can be easily tuned.

The application of molecular topology for ulcerative colitis drug discovery.

Science.gov (United States)

Bellera, Carolina L; Di Ianni, Mauricio E; Talevi, Alan

2018-01-01

Although the therapeutic arsenal against ulcerative colitis has greatly expanded (including the revolutionary advent of biologics), there remain patients who are refractory to current medications while the safety of the available therapeutics could also be improved. Molecular topology provides a theoretic framework for the discovery of new therapeutic agents in a very efficient manner, and its applications in the field of ulcerative colitis have slowly begun to flourish. Areas covered: After discussing the basics of molecular topology, the authors review QSAR models focusing on validated targets for the treatment of ulcerative colitis, entirely or partially based on topological descriptors. Expert opinion: The application of molecular topology to ulcerative colitis drug discovery is still very limited, and many of the existing reports seem to be strictly theoretic, with no experimental validation or practical applications. Interestingly, mechanism-independent models based on phenotypic responses have recently been reported. Such models are in agreement with the recent interest raised by network pharmacology as a potential solution for complex disorders. These and other similar studies applying molecular topology suggest that some therapeutic categories may present a 'topological pattern' that goes beyond a specific mechanism of action.

Drug discovery and the impact of the safe harbor provision of the Hatch- Waxman Act.

Science.gov (United States)

Goodson, Susanne H

2010-01-01

Many facets of drug discovery involve the use of patented materials and methods, subjecting the researcher to potential liability from infringement of the underlying patents. Enacted in 1984, the Hatch-Waxman Act established a “safe harbor” for activities that would otherwise constitute infringement of a patented invention, if those activities were “solely for uses reasonably related to the development and submission of information under a Federal law which regulates the manufacture, use, or sale of drugs or veterinary biological products”. This article examines the major court decisions interpreting the scope of the safe harbor and their application to various activities in drug development.

ASM Inaugural Lecture 2010: Single crystal X-ray structural determination: A powerful technique for natural products research and drug discovery

International Nuclear Information System (INIS)

Fun Hoong Kun; Chantrapromma, S.; Boonnak, N.; Lee, V.S.

2010-01-01

Drug discovery from natural products resources have been extensively studied worldwide because natural products with their great structural diversity have traditionally provided most of the drugs in use. They offer major opportunities for finding novel low molecular weight leading-structures that are active against a wide range of assay targets. The most important step in the discovery process is the identification of compounds with interesting biological activity. Single crystal X-ray structure determination is a powerful technique for natural products research and drug discovery. The detailed three-dimensional structures that emerge can be co-related to the activities to these structures. In this article the following is presented: (i) co-crystal and disorder structures; (ii) determination of absolute configuration and (iii) the ability to distinguish between whether a natural product compound is a natural product or a natural product artifact. All these three properties are unique to the technique of single crystal X-ray structure determination. Case (iii) was demonstrated with a compound containing a chromene ring, namely macluraxanthone (which was isolated from Cratoxylum formosum subsp. pruniflorum, a Thai medicinal plant). (author)

Cardiac Arrhythmia: In vivo screening in the zebrafish to overcome complexity in drug discovery.

Science.gov (United States)

Macrae, Calum A

2010-07-01

IMPORTANCE OF THE FIELD: Cardiac arrhythmias remain a major challenge for modern drug discovery. Clinical events are paroxysmal, often rare and may be asymptomatic until a highly morbid complication. Target selection is often based on limited information and though highly specific agents are identified in screening, the final efficacy is often compromised by unanticipated systemic responses, a narrow therapeutic index and substantial toxicities. AREAS COVERED IN THIS REVIEW: Our understanding of complexity of arrhythmogenesis has grown dramatically over the last two decades, and the range of potential disease mechanisms now includes pathways previously thought only tangentially involved in arrhythmia. This review surveys the literature on arrhythmia mechanisms from 1965 to the present day, outlines the complex biology underlying potentially each and every rhythm disturbance, and highlights the problems for rational target identification. The rationale for in vivo screening is described and the utility of the zebrafish for this approach and for complementary work in functional genomics is discussed. Current limitations of the model in this setting and the need for careful validation in new disease areas are also described. WHAT THE READER WILL GAIN: An overview of the complex mechanisms underlying most clinical arrhythmias, and insight into the limits of ion channel conductances as drug targets. An introduction to the zebrafish as a model organism, in particular for cardiovascular biology. Potential approaches to overcoming the hurdles to drug discovery in the face of complex biology including in vivo screening of zebrafish genetic disease models. TAKE HOME MESSAGE: In vivo screening in faithful disease models allows the effects of drugs on integrative physiology and disease biology to be captured during the screening process, in a manner agnostic to potential drug target or targets. This systematic strategy bypasses current gaps in our understanding of disease

The Drug Discovery and Development Industry in India-Two Decades of Proprietary Small-Molecule R&D.

Science.gov (United States)

Differding, Edmond

2017-06-07

This review provides a comprehensive survey of proprietary drug discovery and development efforts performed by Indian companies between 1994 and mid-2016. It is based on the identification and detailed analysis of pharmaceutical, biotechnology, and contract research companies active in proprietary new chemical entity (NCE) research and development (R&D) in India. Information on preclinical and clinical development compounds was collected by company, therapeutic indication, mode of action, target class, and development status. The analysis focuses on the overall pipeline and its evolution over two decades, contributions by type of company, therapeutic focus, attrition rates, and contribution to Western pharmaceutical pipelines through licensing agreements. This comprehensive analysis is the first of its kind, and, in our view, represents a significant contribution to the understanding of the current state of the drug discovery and development industry in India. © 2017 The Author. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Pharmacogenetics in the oncological clinical practice

International Nuclear Information System (INIS)

Gruber, S.

2004-01-01

The genetic control of drug metabolism allows new insights into the bioavailability, toxicity, and efficacy of chemotherapy. In addition, molecular expression profiles of tumors offers the potential for targeted therapy to be directed more specifically to the biologic behavior of the cancer. Together these strategies are likely to change the practice of clinical oncology. However, appropriate clinical trials will be required to demonstrate the utility of these approaches before they are broadly implemented the biologic behavior of the cancer. Together these strategies are likely to change the practice of clinical oncology. However, appropriate clinical trials will be required to demonstrate the utility of these approaches before they are broadly implemented

pH-Dependent solubility and permeability criteria for provisional biopharmaceutics classification (BCS and BDDCS) in early drug discovery.

Science.gov (United States)

Varma, Manthena V; Gardner, Iain; Steyn, Stefanus J; Nkansah, Paul; Rotter, Charles J; Whitney-Pickett, Carrie; Zhang, Hui; Di, Li; Cram, Michael; Fenner, Katherine S; El-Kattan, Ayman F

2012-05-07

The Biopharmaceutics Classification System (BCS) is a scientific framework that provides a basis for predicting the oral absorption of drugs. These concepts have been extended in the Biopharmaceutics Drug Disposition Classification System (BDDCS) to explain the potential mechanism of drug clearance and understand the effects of uptake and efflux transporters on absorption, distribution, metabolism, and elimination. The objective of present work is to establish criteria for provisional biopharmaceutics classification using pH-dependent passive permeability and aqueous solubility data generated from high throughput screening methodologies in drug discovery settings. The apparent permeability across monolayers of clonal cell line of Madin-Darby canine kidney cells, selected for low endogenous efflux transporter expression, was measured for a set of 105 drugs, with known BCS and BDDCS class. The permeability at apical pH 6.5 for acidic drugs and at pH 7.4 for nonacidic drugs showed a good correlation with the fraction absorbed in human (Fa). Receiver operating characteristic (ROC) curve analysis was utilized to define the permeability class boundary. At permeability ≥ 5 × 10(-6) cm/s, the accuracy of predicting Fa of ≥ 0.90 was 87%. Also, this cutoff showed more than 80% sensitivity and specificity in predicting the literature permeability classes (BCS), and the metabolism classes (BDDCS). The equilibrium solubility of a subset of 49 drugs was measured in pH 1.2 medium, pH 6.5 phosphate buffer, and in FaSSIF medium (pH 6.5). Although dose was not considered, good concordance of the measured solubility with BCS and BDDCS solubility class was achieved, when solubility at pH 1.2 was used for acidic compounds and FaSSIF solubility was used for basic, neutral, and zwitterionic compounds. Using a cutoff of 200 μg/mL, the data set suggested a 93% sensitivity and 86% specificity in predicting both the BCS and BDDCS solubility classes. In conclusion, this study identified

Allosteric modulation of endogenous metabolites as an avenue for drug discovery.

Science.gov (United States)

Wootten, Denise; Savage, Emilia E; Valant, Celine; May, Lauren T; Sloop, Kyle W; Ficorilli, James; Showalter, Aaron D; Willard, Francis S; Christopoulos, Arthur; Sexton, Patrick M

2012-08-01

G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors and a key drug target class. Recently, allosteric drugs that can co-bind with and modulate the activity of the endogenous ligand(s) for the receptor have become a major focus of the pharmaceutical and biotechnology industry for the development of novel GPCR therapeutic agents. This class of drugs has distinct properties compared with drugs targeting the endogenous (orthosteric) ligand-binding site that include the ability to sculpt cellular signaling and to respond differently in the presence of discrete orthosteric ligands, a behavior termed "probe dependence." Here, using cell signaling assays combined with ex vivo and in vivo studies of insulin secretion, we demonstrate that allosteric ligands can cause marked potentiation of previously "inert" metabolic products of neurotransmitters and peptide hormones, a novel consequence of the phenomenon of probe dependence. Indeed, at the muscarinic M(2) receptor and glucagon-like peptide 1 (GLP-1) receptor, allosteric potentiation of the metabolites, choline and GLP-1(9-36)NH(2), respectively, was ~100-fold and up to 200-fold greater than that seen with the physiological signaling molecules acetylcholine and GLP-1(7-36)NH(2). Modulation of GLP-1(9-36)NH(2) was also demonstrated in ex vivo and in vivo assays of insulin secretion. This work opens up new avenues for allosteric drug discovery by directly targeting modulation of metabolites, but it also identifies a behavior that could contribute to unexpected clinical outcomes if interaction of allosteric drugs with metabolites is not part of their preclinical assessment.

How Phenotypic Screening Influenced Drug Discovery: Lessons from Five Years of Practice.

Science.gov (United States)

Haasen, Dorothea; Schopfer, Ulrich; Antczak, Christophe; Guy, Chantale; Fuchs, Florian; Selzer, Paul

Since 2011, phenotypic screening has been a trend in the pharmaceutical industry as well as in academia. This renaissance was triggered by analyses that suggested that phenotypic screening is a superior strategy to discover first-in-class drugs. Despite these promises and considerable investments, pharmaceutical research organizations have encountered considerable challenges with the approach. Few success stories have emerged in the past 5 years and companies are questioning their investment in this area. In this contribution, we outline what we have learned about success factors and challenges of phenotypic screening. We then describe how our efforts in phenotypic screening have influenced our approach to drug discovery in general. We predict that concepts from phenotypic screening will be incorporated into target-based approaches and will thus remain influential beyond the current trend.

Toward Omics-Based, Systems Biomedicine, and Path and Drug Discovery Methodologies for Depression-Inflammation Research.

Science.gov (United States)

Maes, Michael; Nowak, Gabriel; Caso, Javier R; Leza, Juan Carlos; Song, Cai; Kubera, Marta; Klein, Hans; Galecki, Piotr; Noto, Cristiano; Glaab, Enrico; Balling, Rudi; Berk, Michael

2016-07-01

Meta-analyses confirm that depression is accompanied by signs of inflammation including increased levels of acute phase proteins, e.g., C-reactive protein, and pro-inflammatory cytokines, e.g., interleukin-6. Supporting the translational significance of this, a meta-analysis showed that anti-inflammatory drugs may have antidepressant effects. Here, we argue that inflammation and depression research needs to get onto a new track. Firstly, the choice of inflammatory biomarkers in depression research was often too selective and did not consider the broader pathways. Secondly, although mild inflammatory responses are present in depression, other immune-related pathways cannot be disregarded as new drug targets, e.g., activation of cell-mediated immunity, oxidative and nitrosative stress (O&NS) pathways, autoimmune responses, bacterial translocation, and activation of the toll-like receptor and neuroprogressive pathways. Thirdly, anti-inflammatory treatments are sometimes used without full understanding of their effects on the broader pathways underpinning depression. Since many of the activated immune-inflammatory pathways in depression actually confer protection against an overzealous inflammatory response, targeting these pathways may result in unpredictable and unwanted results. Furthermore, this paper discusses the required improvements in research strategy, i.e., path and drug discovery processes, omics-based techniques, and systems biomedicine methodologies. Firstly, novel methods should be employed to examine the intracellular networks that control and modulate the immune, O&NS and neuroprogressive pathways using omics-based assays, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, immunoproteomics and metagenomics. Secondly, systems biomedicine analyses are essential to unravel the complex interactions between these cellular networks, pathways, and the multifactorial trigger factors and to delineate new drug targets in the cellular

Epigenetics and cancer: implications for drug discovery and safety assessment

International Nuclear Information System (INIS)

Moggs, Jonathan G.; Goodman, Jay I.; Trosko, James E.; Roberts, Ruth A.

2004-01-01

It is necessary to determine whether chemicals or drugs have the potential to pose a threat to human health. Research conducted over the last two decades has led to the paradigm that chemicals can cause cancer either by damaging DNA or by altering cellular growth, probably via receptor-mediated changes in gene expression. However, recent evidence suggests that gene expression can be altered markedly via several diverse epigenetic mechanisms that can lead to permanent or reversible changes in cellular behavior. Key molecular events underlying these mechanisms include the alteration of DNA methylation and chromatin, and changes in the function of cell surface molecules. Thus, for example, DNA methyltransferase enzymes together with chromatin-associated proteins such as histone modifying enzymes and remodelling factors can modify the genetic code and contribute to the establishment and maintenance of altered epigenetic states. This is relevant to many types of toxicity including but not limited to cancer. In this paper, we describe the potential for interplay between genetic alteration and epigenetic changes in cell growth regulation and discuss the implications for drug discovery and safety assessment

Small Molecules from Nature Targeting G-Protein Coupled Cannabinoid Receptors: Potential Leads for Drug Discovery and Development

Directory of Open Access Journals (Sweden)

Charu Sharma

2015-01-01

Full Text Available The cannabinoid molecules are derived from Cannabis sativa plant which acts on the cannabinoid receptors types 1 and 2 (CB1 and CB2 which have been explored as potential therapeutic targets for drug discovery and development. Currently, there are numerous cannabinoid based synthetic drugs used in clinical practice like the popular ones such as nabilone, dronabinol, and Δ9-tetrahydrocannabinol mediates its action through CB1/CB2 receptors. However, these synthetic based Cannabis derived compounds are known to exert adverse psychiatric effect and have also been exploited for drug abuse. This encourages us to find out an alternative and safe drug with the least psychiatric adverse effects. In recent years, many phytocannabinoids have been isolated from plants other than Cannabis. Several studies have shown that these phytocannabinoids show affinity, potency, selectivity, and efficacy towards cannabinoid receptors and inhibit endocannabinoid metabolizing enzymes, thus reducing hyperactivity of endocannabinoid systems. Also, these naturally derived molecules possess the least adverse effects opposed to the synthetically derived cannabinoids. Therefore, the plant based cannabinoid molecules proved to be promising and emerging therapeutic alternative. The present review provides an overview of therapeutic potential of ligands and plants modulating cannabinoid receptors that may be of interest to pharmaceutical industry in search of new and safer drug discovery and development for future therapeutics.

A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing

Science.gov (United States)

Guerrero, Ginés D.; Imbernón, Baldomero; García, José M.

2014-01-01

Bioinformatics is an interdisciplinary research field that develops tools for the analysis of large biological databases, and, thus, the use of high performance computing (HPC) platforms is mandatory for the generation of useful biological knowledge. The latest generation of graphics processing units (GPUs) has democratized the use of HPC as they push desktop computers to cluster-level performance. Many applications within this field have been developed to leverage these powerful and low-cost architectures. However, these applications still need to scale to larger GPU-based systems to enable remarkable advances in the fields of healthcare, drug discovery, genome research, etc. The inclusion of GPUs in HPC systems exacerbates power and temperature issues, increasing the total cost of ownership (TCO). This paper explores the benefits of volunteer computing to scale bioinformatics applications as an alternative to own large GPU-based local infrastructures. We use as a benchmark a GPU-based drug discovery application called BINDSURF that their computational requirements go beyond a single desktop machine. Volunteer computing is presented as a cheap and valid HPC system for those bioinformatics applications that need to process huge amounts of data and where the response time is not a critical factor. PMID:25025055

Report on the international colloquium on cardio-oncology (rome, 12-14 march 2014)

NARCIS (Netherlands)

Ewer, Michael; Gianni, Luca; Pane, Fabrizio; Sandri, Maria Teresa; Steiner, Rudolf K.; Wojnowski, Leszek; Yeh, Edward T.; Carver, Joseph R.; Lipshultz, Steven E.; Minotti, Giorgio; Armstrong, Gregory T.; Cardinale, Daniela; Colan, Steven D.; Darby, Sarah C.; Force, Thomas L.; Kremer, Leontien C. M.; Lenihan, Daniel J.; Sallan, Stephen E.; Sawyer, Douglas B.; Suter, Thomas M.; Swain, Sandra M.; van Leeuwen, Flora E.

2014-01-01

Cardio-oncology is a relatively new discipline that focuses on the cardiovascular sequelae of anti-tumour drugs. As any other young adolescent discipline, cardio-oncology struggles to define its scientific boundaries and to identify best standards of care for cancer patients or survivors at risk of

Cancer Patients and Oncology Nursing: Perspectives of Oncology ...

African Journals Online (AJOL)

Background and Aim: Burnout and exhaustion is a frequent problem in oncology nursing. The aim of this study is to evaluate the aspects of oncology nurses about their profession in order to enhance the standards of oncology nursing. Materials and Methods: This survey was conducted with 70 oncology nurses working at ...

Adverse Events Involving Radiation Oncology Medical Devices: Comprehensive Analysis of US Food and Drug Administration Data, 1991 to 2015

International Nuclear Information System (INIS)

Connor, Michael J.; Marshall, Deborah C.; Moiseenko, Vitali; Moore, Kevin; Cervino, Laura; Atwood, Todd; Sanghvi, Parag; Mundt, Arno J.; Pawlicki, Todd; Recht, Abram; Hattangadi-Gluth, Jona A.

2017-01-01

Purpose: Radiation oncology relies on rapidly evolving technology and highly complex processes. The US Food and Drug Administration collects reports of adverse events related to medical devices. We sought to characterize all events involving radiation oncology devices (RODs) from the US Food and Drug Administration's postmarket surveillance Manufacturer and User Facility Device Experience (MAUDE) database, comparing these with non–radiation oncology devices. Methods and Materials: MAUDE data on RODs from 1991 to 2015 were sorted into 4 product categories (external beam, brachytherapy, planning systems, and simulation systems) and 5 device problem categories (software, mechanical, electrical, user error, and dose delivery impact). Outcomes included whether the device was evaluated by the manufacturer, adverse event type, remedial action, problem code, device age, and time since 510(k) approval. Descriptive statistics were performed with linear regression of time-series data. Results for RODs were compared with those for other devices by the Pearson χ"2 test for categorical data and 2-sample Kolmogorov-Smirnov test for distributions. Results: There were 4234 ROD and 4,985,698 other device adverse event reports. Adverse event reports increased over time, and events involving RODs peaked in 2011. Most ROD reports involved external beam therapy (50.8%), followed by brachytherapy (24.9%) and treatment planning systems (21.6%). The top problem types were software (30.4%), mechanical (20.9%), and user error (20.4%). RODs differed significantly from other devices in each outcome (P<.001). RODs were more likely to be evaluated by the manufacturer after an event (46.9% vs 33.0%) but less likely to be recalled (10.5% vs 37.9%) (P<.001). Device age and time since 510(k) approval were shorter among RODs (P<.001). Conclusions: Compared with other devices, RODs may experience adverse events sooner after manufacture and market approval. Close postmarket surveillance, improved

Adverse Events Involving Radiation Oncology Medical Devices: Comprehensive Analysis of US Food and Drug Administration Data, 1991 to 2015

Energy Technology Data Exchange (ETDEWEB)

Connor, Michael J. [Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California (United States); Department of Radiation Oncology, University of California Irvine School of Medicine, Irvine, California (United States); Marshall, Deborah C.; Moiseenko, Vitali; Moore, Kevin; Cervino, Laura; Atwood, Todd; Sanghvi, Parag; Mundt, Arno J.; Pawlicki, Todd [Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California (United States); Recht, Abram [Department of Radiation Oncology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts (United States); Hattangadi-Gluth, Jona A., E-mail: jhattangadi@ucsd.edu [Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California (United States)

2017-01-01

Purpose: Radiation oncology relies on rapidly evolving technology and highly complex processes. The US Food and Drug Administration collects reports of adverse events related to medical devices. We sought to characterize all events involving radiation oncology devices (RODs) from the US Food and Drug Administration's postmarket surveillance Manufacturer and User Facility Device Experience (MAUDE) database, comparing these with non–radiation oncology devices. Methods and Materials: MAUDE data on RODs from 1991 to 2015 were sorted into 4 product categories (external beam, brachytherapy, planning systems, and simulation systems) and 5 device problem categories (software, mechanical, electrical, user error, and dose delivery impact). Outcomes included whether the device was evaluated by the manufacturer, adverse event type, remedial action, problem code, device age, and time since 510(k) approval. Descriptive statistics were performed with linear regression of time-series data. Results for RODs were compared with those for other devices by the Pearson χ{sup 2} test for categorical data and 2-sample Kolmogorov-Smirnov test for distributions. Results: There were 4234 ROD and 4,985,698 other device adverse event reports. Adverse event reports increased over time, and events involving RODs peaked in 2011. Most ROD reports involved external beam therapy (50.8%), followed by brachytherapy (24.9%) and treatment planning systems (21.6%). The top problem types were software (30.4%), mechanical (20.9%), and user error (20.4%). RODs differed significantly from other devices in each outcome (P<.001). RODs were more likely to be evaluated by the manufacturer after an event (46.9% vs 33.0%) but less likely to be recalled (10.5% vs 37.9%) (P<.001). Device age and time since 510(k) approval were shorter among RODs (P<.001). Conclusions: Compared with other devices, RODs may experience adverse events sooner after manufacture and market approval. Close postmarket surveillance

Combining automatic table classification and relationship extraction in extracting anticancer drug-side effect pairs from full-text articles.

Science.gov (United States)

Xu, Rong; Wang, QuanQiu

2015-02-01

Anticancer drug-associated side effect knowledge often exists in multiple heterogeneous and complementary data sources. A comprehensive anticancer drug-side effect (drug-SE) relationship knowledge base is important for computation-based drug target discovery, drug toxicity predication and drug repositioning. In this study, we present a two-step approach by combining table classification and relationship extraction to extract drug-SE pairs from a large number of high-profile oncological full-text articles. The data consists of 31,255 tables downloaded from the Journal of Oncology (JCO). We first trained a statistical classifier to classify tables into SE-related and -unrelated categories. We then extracted drug-SE pairs from SE-related tables. We compared drug side effect knowledge extracted from JCO tables to that derived from FDA drug labels. Finally, we systematically analyzed relationships between anti-cancer drug-associated side effects and drug-associated gene targets, metabolism genes, and disease indications. The statistical table classifier is effective in classifying tables into SE-related and -unrelated (precision: 0.711; recall: 0.941; F1: 0.810). We extracted a total of 26,918 drug-SE pairs from SE-related tables with a precision of 0.605, a recall of 0.460, and a F1 of 0.520. Drug-SE pairs extracted from JCO tables is largely complementary to those derived from FDA drug labels; as many as 84.7% of the pairs extracted from JCO tables have not been included a side effect database constructed from FDA drug labels. Side effects associated with anticancer drugs positively correlate with drug target genes, drug metabolism genes, and disease indications. Copyright © 2014 Elsevier Inc. All rights reserved.