Computational Models Supporting Lead Optimization in Drug Discovery

Design of successful drug development candidates requires balancing a number of different characteristics simultaneously including intrinsic activity, biopharmaceutical properties, synthesis, stability, and many others. Independently, each of these can be considered a barrier to drug performance or development success (Kerns and Di, 2003). Among the most important of these processes determining in vivo performance are absorption, distribution, metabolism and excretion, collectively referred to as ADME. The structure and physicochemical characteristics of the drug are important determinants of these processes, as are the characteristics of the physiological mechanisms. Further complicating the issue is the interrelationship of many of these processes, frequently in antagonistic ways. Increasing solute lipophilicity, for example, can decrease aqueous solubility, which frequently compromises oral absorption. Also, it can increase metabolic clearance, thus making difficult sustaining the pharmacologically relevant systemic exposure. In contrast, permeability, in many cases, increases with increasing lipophilicity, favoring absorption (Conradi et al., 1996; Hansch et al., 2004). The actual result will be determined by the relative contributions of these two competing phenomena.