Avoiding Missed Opportunities by Analyzing the Sensitivity of Our Decisions.

Drug discovery is a multiparameter optimization process in which the goal of a project is to identify compounds that meet multiple property criteria required to achieve a therapeutic objective. However, once a profile of property criteria has been chosen, the impact of these criteria on the decisions made regarding progression of compounds or chemical series should be carefully considered. In some cases the decision is very sensitive to a specific property criterion, and such a criterion may artificially distort the direction of the project; any uncertainty in the "correct" value or the importance of this criterion may lead to valuable opportunities being missed. In this paper, we describe a method for analyzing the sensitivity of the prioritization of compounds to a multiparameter profile of property criteria. We show how the results can be easily interpreted and illustrate how this analysis can highlight new avenues for exploration.

Finding the rules for successful drug optimisation.

Drug discovery is a process of multiparameter optimisation, with the objective of finding compounds that achieve multiple, project-specific property criteria. These criteria are often based on the subjective opinion of the project team, but analysis of historical data can help to find the most appropriate profile. Computational 'rule induction' approaches enable an objective analysis of complex data to identify interpretable, multiparameter rules that distinguish compounds with the greatest likelihood of success for a project. Each property criterion highlights the most critical data that enable effective compound prioritisation decisions. We illustrate this with two applications: determining rules for simple, drug-like properties; and exploring experimental target inhibition data to find rules to reduce the risk of toxicity.

Considering the impact drug-like properties have on the chance of success.

Many definitions of 'drug-like' compound properties have been published; based on the analysis of simple molecular properties of successful drugs. These are typically presented as rules that define acceptable boundaries for these properties. When a compound does not 'fit' within these boundaries then its properties differ from those of the majority of drugs, which could indicate a higher risk of poor pharmacokinetics or safety outcomes in vivo. Here, we review the strengths and weaknesses of these rules and note, in particular, that the overly rigid application of strict cut-off points can introduce artificial distinctions between similar compounds, running the risk of missing valuable opportunities. Alternatively, compounds can be ranked according to their similarity to marketed drugs using a continuous measure of drug-likeness. However, being similar to known drugs does not necessarily mean that a compound is more likely to become a drug and we demonstrate how a new approach, employing Bayesian methods, can be used to compare a set of successful drugs with a set of non-drug compounds to identify those properties that give the greatest distinction between the two sets, and hence the greatest increase in the likelihood of a compound becoming a successful drug. This analysis further illustrates that guidelines for drug-likeness might not be generally applicable across all compound and target classes or therapeutic indications. Therefore, it might be more appropriate to consider specific guidelines for drug-likeness that are project specific.