Disciplined approach to drug discovery and early development

Plenge, Robert M.

doi:10.1126/scitranslmed.aaf2608

Cited by 84 publications

(57 citation statements)

References 28 publications

(27 reference statements)

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“…Successful AD drug development is an unmet need and more disciplined approaches to drug development will assist in reducing the current high rate of negative trials . The lessons learned from trials with negative outcomes can inform drug development.…”

Section: Discussionmentioning

confidence: 99%

Lessons Learned from Alzheimer Disease: Clinical Trials with Negative Outcomes

Cummings

2017

Clinical Translational Sci

237

209

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Section: Discussionmentioning

confidence: 99%

Lessons Learned from Alzheimer Disease: Clinical Trials with Negative Outcomes

Cummings

2017

Clinical Translational Sci

237

209

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“…However, analysis of progress through development pipelines has highlighted that lack of efficacy is a major cause of failure, particularly in the later, more expensive, clinical stages (3,4). The implication is that the link between the target and its influence on physiology and disease was not well enough established, and that better assessment of the evidence behind the role of the target in disease might improve success rates and/or allow early termination of implausible development programs (5). …”

Section: Introductionmentioning

confidence: 99%

Open Targets: a platform for therapeutic target identification and validation

et al. 2016

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We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.

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“…Efficacy failures, in turn, are most often due to a poor linkage between the therapeutic drug target and the disease of interest or the lack of a well validated animal model of the disease [4]. Hence, the selection of the right target for the right disease in early discovery phases is a key decision to maximise the chances of success in the clinic and ensure a sustainable business in the longer term [5]. …”

Section: Introductionmentioning

confidence: 99%

In silico prediction of novel therapeutic targets using gene–disease association data

2017

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BackgroundTarget identification and validation is a pressing challenge in the pharmaceutical industry, with many of the programmes that fail for efficacy reasons showing poor association between the drug target and the disease. Computational prediction of successful targets could have a considerable impact on attrition rates in the drug discovery pipeline by significantly reducing the initial search space. Here, we explore whether gene–disease association data from the Open Targets platform is sufficient to predict therapeutic targets that are actively being pursued by pharmaceutical companies or are already on the market.MethodsTo test our hypothesis, we train four different classifiers (a random forest, a support vector machine, a neural network and a gradient boosting machine) on partially labelled data and evaluate their performance using nested cross-validation and testing on an independent set. We then select the best performing model and use it to make predictions on more than 15,000 genes. Finally, we validate our predictions by mining the scientific literature for proposed therapeutic targets.ResultsWe observe that the data types with the best predictive power are animal models showing a disease-relevant phenotype, differential expression in diseased tissue and genetic association with the disease under investigation. On a test set, the neural network classifier achieves over 71% accuracy with an AUC of 0.76 when predicting therapeutic targets in a semi-supervised learning setting. We use this model to gain insights into current and failed programmes and to predict 1431 novel targets, of which a highly significant proportion has been independently proposed in the literature.ConclusionsOur in silico approach shows that data linking genes and diseases is sufficient to predict novel therapeutic targets effectively and confirms that this type of evidence is essential for formulating or strengthening hypotheses in the target discovery process. Ultimately, more rapid and automated target prioritisation holds the potential to reduce both the costs and the development times associated with bringing new medicines to patients.Electronic supplementary materialThe online version of this article (doi:10.1186/s12967-017-1285-6) contains supplementary material, which is available to authorized users.

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Disciplined approach to drug discovery and early development

Cited by 84 publications

References 28 publications

Lessons Learned from Alzheimer Disease: Clinical Trials with Negative Outcomes

Lessons Learned from Alzheimer Disease: Clinical Trials with Negative Outcomes

Open Targets: a platform for therapeutic target identification and validation

In silico prediction of novel therapeutic targets using gene–disease association data

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