Focus on success: using a probabilistic approach to achieve an optimal balance of compound properties in drug discovery

Abstract

The success of any drug will depend on how closely it achieves an ideal combination of potency, selectivity, pharmacokinetics and safety. The key to achieving this success efficiently is to consider the overall balance of molecular properties of compounds against the ideal profile for the therapeutic indication from the earliest stages of a drug discovery project. The use of insilico predictive models of absorption, distribution, metabolism and elimination (ADME) and physicochemical properties is a major aid in this exercise, as it enables virtual molecules to be assessed across a broad range of properties from initial library generation, through to candidate selection. Of course, no measurement, whether insilico, invitro or invivo, is perfect and the uncertainties in any data should be explicitly taken into account when basing conclusions on test results. In addition, in the early stages of drug discovery, when designing a library that is lead seeking or building compound structureactivity relationships, the quality of any set of molecules should also be balanced against the chemical diversity covered. Here, a scheme is presented for achieving these goals based on a suite of predictive ADME models, probabilistic scoring and multiobjective optimisation for library design. The use of this platform for applications in lead identification and optimisation is illustrated.

Keywords::

• ADME
• in silico
• in vitro
• molecule design
• parallel optimisation
• target product profile
• uncertainty

Acknowledgements

The authors thank K Frankcombe and R Hutchings for their contributions to the ideas expressed in this article, and EChampness for providing the graphics used in Figure 3.