Biopharmaceutical properties such as solubility, cellular permeability and metabolic stability, among others are important in the successful discovery and development of new drug candidates. Significant progress has been made in recent years towards development and use of computational, structure-based models of these properties and such models are increasingly being used in the lead optimization process. While these biopharmaceutical properties are important individually, the ultimate in vivo performance of a drug candidate is a complex function of all the properties. Physiology-based pharmacokinetic (PB-PK) models using in vitro and physicochemical data can be useful in defining these functional relationships and identifying the important biopharmaceutical characteristics of a lead candidate, helping focus the optimization strategy. Presently, such models do not easily include non-ideal drug behavior such as active transport, for example. However, this can be an advantage in some cases. Significant deviations of experimental observations from prediction can be an alert that such processes are important for a specific lead candidate and needs to be more closely examined. Further, these early generation PB-PK models do not easily accomodate interdependencies of physicochemical properties on multiple biopharmaceutical processes. Generally, these must be considered individually in carrying out a sensitivity analysis query. However, as the models are evolved, these limitations are likely to be addressed and the use of integrated structure-based property models with PB-PK models will find more general applications in the lead optimization process.
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