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Bioinformatics in translational drug discovery.

ABSTRACT: 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.

SUBMITTER: Wooller SK

PROVIDER: S-EPMC6448364 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Bioinformatics in translational drug discovery.

Wooller Sarah K SK Benstead-Hume Graeme G Chen Xiangrong X Ali Yusuf Y Pearl Frances M G FMG

Bioscience reports 20170707 4

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 wa ...[more]

PMID: 28487472

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Project description:This review advances the case that bidirectional, cross-species translation of findings from experimental animals to and from humans is an important strategy for drug discovery. Animal models of mental disorders require appropriate behavioural or cognitive outcome variables that can be generalized cross-species. One example is the treatment of impulsive behaviour in attention deficit hyperactivity disorder (ADHD) with stimulant drugs. Performance on the stop signal reaction task as an index of impulsivity is improved both in healthy human volunteers and in patients with adult ADHD by stimulant drugs and also by the selective noradrenaline reuptake blocker atomoxetine. Functional neuroimaging evidence suggests a modulation of circuitry including the inferior prefrontal cortex by this drug. Parallel work in rats had shown that atomoxetine improves stop signal performance by affecting possibly homologous regions of the rodent prefrontal cortex. This parallel effect of atomoxetine in rodents and humans could potentially be exploited in other disorders in which impulsivity plays a role, such as stimulant abuse and Parkinson's disease. A contrasting relative lack of involvement of 5-HT mechanisms in the stop signal reaction time task will also be described. Research in humans and experimental animals that demonstrate effects of serotoninergic agents such as the selective serotonin (5-HT) reuptake inhibitor citalopram on probabilistic learning and reversal (upon which atomoxetine has little effect) will also be reviewed, possibly relevant to the treatment of clinical depression, Finally, other promising examples of parallel studies of behavioural effects of CNS-active drugs in animals and humans will also be described. Linked Articles This article is part of a themed section on Pharmacology of Cognition: a Panacea for Neuropsychiatric Disease? To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.19/issuetoc.

Project description:BackgroundWith the surge of translational medicine and computational omics research, complex disease diagnosis is more and more relying on massive omics data-driven molecular signature detection. However, how to detect and prevent possible diagnostic biases in translational bioinformatics remains an unsolved problem despite its importance in the coming era of personalized medicine.MethodsIn this study, we comprehensively investigate the diagnostic bias problem by analyzing benchmark gene array, protein array, RNA-Seq and miRNA-Seq data under the framework of support vector machines for different model selection methods. We further categorize the diagnostic biases into different types by conducting rigorous kernel matrix analysis and provide effective machine learning methods to conquer the diagnostic biases.ResultsIn this study, we comprehensively investigate the diagnostic bias problem by analyzing benchmark gene array, protein array, RNA-Seq and miRNA-Seq data under the framework of support vector machines. We have found that the diagnostic biases happen for data with different distributions and SVM with different kernels. Moreover, we identify total three types of diagnostic biases: overfitting bias, label skewness bias, and underfitting bias in SVM diagnostics, and present corresponding reasons through rigorous analysis. Compared with the overfitting and underfitting biases, the label skewness bias is more challenging to detect and conquer because it can be easily confused as a normal diagnostic case from its deceptive accuracy. To tackle this problem, we propose a derivative component analysis based support vector machines to conquer the label skewness bias by achieving the rivaling clinical diagnostic results.ConclusionsOur studies demonstrate that the diagnostic biases are mainly caused by the three major factors, i.e. kernel selection, signal amplification mechanism in high-throughput profiling, and training data label distribution. Moreover, the proposed DCA-SVM diagnosis provides a generic solution for the label skewness bias overcome due to the powerful feature extraction capability from derivative component analysis. Our work identifies and solves an important but less addressed problem in translational research. It also has a positive impact on machine learning for adding new results to kernel-based learning for omics data.

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Bioinformatics in translational drug discovery.

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Bioinformatics in translational drug discovery.

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