I. Yusof, F. Shah, T. Hashimoto, M. D. Segall, N. Greene, Drug Discovery Today (2014) 19(5) pp. 680-687
In this drug optimisation article, coauthored with Pfizer we discuss new ‘rule induction’ methods. These explore complex data to find interpretable, multi-parameter rules, tailored to any drug discovery objective, and are useful in identifying compounds with a higher chance of success. We illustrate this with applications to simple ‘drug like’ properties for oral drugs and to the exploration of experimental target inhibition data to find rules for selecting compounds with a low risk of cardio- and hepatotoxicity.
Drug discovery is widely recognised as a process of multi-parameter optimisation. The aim? Finding compounds that meet a profile of many property criteria. These criteria depend on the ultimate therapeutic goal of the project and are typically chosen based on the subjective opinion of the project team. However, analysing historical data can help guide the determination of the most appropriate profile.
In this article we discuss computational approaches, described as rule induction, which enable an objective analysis of complex data to identify multi-parameter rules that distinguish compounds likely to be successful for a project’s goal. The resulting rules are interpretable and modifiable. This allows experts to understand and adjust them based on their knowledge of the underlying biology and chemistry. Furthermore, the importance of each property criterion can be identified, allowing scientists to focus experimental resources on generating the most critical data necessary to make effective compound prioritisation decisions. We illustrate this method with two applications. Firstly, determining rules for simple, calculated properties for orally administered drugs, and comparing these with previously published measures of ‘drug-likeness’. Next, exploring experimental target inhibition data to find rules to reduce the risk of hepatotoxicity and cardiotoxicity.
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