Tudor I. Oprea M.D., Ph.D. University of New Mexico School of Medicine
John P. Overington Ph.D. European Bioinformatics Institute
Insightful Guidance Culled from Personal Experience and the Literature
A wide and increasing number of approaches and strategies for drug repositioning have been proposed, ranging from text mining or in silico screening, via in vitro/ex vivo screening, study in animal disease models, and finally to observational studies from human trials. In contrast to first-in-class agents, where the need to validate both the drug safety and the causal role of the targeted mechanism, there is less cost and risk for repositioning studies, akin to follow-up drugs. The costs and speed of performing these types of studies range from very low and fast for computational studies to expensive and slow for postapproval observational studies. In this article, we use our personal experience to outline various issues that can have large impact on the success of drug repurposing, repositioning, and re-patenting, and map these onto a number of examples from the literature. Specifically, we address the general case where there is no a priori hypothesis for drug reuse, no assumption over target mechanism, and where a drug library is tested, somehow, against either a single or panel of screens. In most cases, we argue that drug repositioning is vastly more complicated than typically imagined and currently implemented. Via more rigorous experimental design, data preparation, and prior art curation, more effective and successful drug repositioning could be accomplished. We illustrate these points with reference to some recent examples of the screening of drug collections in the neglected diseases therapeutic area.
The basic workflow for many drug repositioning projects is to assemble a set of known drugs—either as physical samples or, in the case of computational methods, reliable 2D structures, and then “screen” these in a relevant system (either a physical assay, or in silico)—Figure 1. It is generally assumed that experimental bioassays are more reliable and predictive than computational assays; however, experimental conditions, number of concentrations tested, as well as number of replicates can influence the accuracy of experiment. In contrast to reliable experimental data, the far lower cost and lower barrier to entry have made computational approaches of high interest and effort in the research community.
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Drug Repurposing, Rescue, and Repositioning, published by Mary Ann Liebert, Inc., is a new peer-reviewed journal, presents techniques and tools for finding new uses for approved drugs – particularly for disorders where no animal model, physiologic abnormality, biochemical pathway, or molecular target has been identified. The above article was first published in the March 2015 issue of Drug Repurposing, Rescue, and Repositioning with the title “Computational and Practical Aspects of Drug Repositioning”. The views expressed here are those of the authors and are not necessarily those of Drug Repurposing, Rescue, and Repositioning, Mary Ann Liebert, Inc., publishers, or their affiliates. No endorsement of any entity or technology is implied.