Strategies to optimize drug half-life in lead candidate identification

The PK optimization of drug candidates is one of the most resource-intensive tasks in pharmaceutical research and development. With the increasing availability of in silico, in vitro and mechanistic in vivo ADME models, drug discovery scientists have progressively learned to recognize common SAR patterns and engineer data-driven strategies to accelerate the resolution of ADME issues in lead optimization. Many of these strategies gravitate toward the concept of drug-likeness, which defines a number of optimal holistic physicochemical parameters (such as lipophilicity) that idealized oral drugs possess. Areas covered: Herein, the authors discuss the interplay of lipophilicity with in vitro and in vivo ADME data in order to refine existing thought around drug half-life optimization. Strategies to prolong the half-life of oral drugs via formulation are beyond the scope of this review. Expert opinion: Optimizing active properties such as potency, selectivity, and intrinsic metabolic clearance is an unambiguously beneficial strategy for small molecules within or beyond the Lipinski rule of five chemical space. The data that we present in this work suggests that emphasis should be primarily placed on optimizing active properties such as potency, selectivity, and metabolic stability.