Integrated Lead Optimization: Translational Models as We Advance Toward the Clinic

Abstract

Drug discovery requires the convergence of molecular attributes including magnitude and duration of exposure, tissue distribution, target engagement, and pharmacological action. To this end, during lead optimization, discovery scientists must leverage integrated data sets and translatable models to offer projections of clinical performance and thereby make informed decisions on the merits of individual molecules. This chapter presents methodologies to predict human clearance, drug-drug interaction (DDI) risk, and penetration of the blood-brain barrier (BBB) and exposure to the central nervous system during various stages of discovery with emphasis on immediate preclinical stages. By focusing on current state and best practices of the contemporary lead optimization scientist, we discuss the use of human-derived model systems and multiparameter optimization to drive the discovery of clinical candidates with favorable human ADME/PK properties in mind. We present strategies to predict and mitigate DDIs at different stages of drug discovery and development by evaluating CYP involvement in metabolism as well as achieving an assessment of a DDI’s clinical significance. We introduce concepts related to brain penetration from the perspective of small molecule drug discovery and discuss how to effectively address BBB issues in lead optimization. Emphasis is given to creation and application of preclinical data and methodologies that provide a mechanistic understanding of drug disposition leading to translatable models to predict clinical outcomes, assess developability risk, and help address simple to complex “what-if” scenarios. Predictive models of clearance, CNS penetration, and DDIs will be presented and discussed including comprehensive case studies to highlight integrated approaches used to discover drug candidates suitable for the safe exploration of clinical hypotheses.