Drug discovery efforts include measurement of physicochemical and biopharmaceutical (ADME) properties at ever earlier stages of lead optimization. Although medicinal chemists understand how to synthetically manipulate lipophilicity, H-bonding, and in some cases intrinsic clearance, in an effort to improve bioavailability (%F) and exposure, identifying the most sensitive properties to focus on is difficult. Physiologically-based pharmacokinetic (PBPK) modeling can be used to predict %F using in silico and/or in vitro compound properties as inputs. Traditionally, PBPK has been used to evaluate a few advanced candidates for each project. Our purpose was to employ PBPK modeling followed by Global Sensitivity Analysis (GSA) to identify the most sensitive properties affecting %F and exposure for an entire lead series to guide lead optimization.We have applied GSA to GastroPlus PBPK simulation results for 2 medchem series: 49 DPP-4 inhibitors collected from the literature and 65 compounds from an in-house kinase inhibitor series. First the PBPK models were optimized for each specific medchem series. For the DPP-4 series, total systemic plasma clearance in rats had been determined. However, metabolic intrinsic clearance from rat liver microsomes was not available. Therefore, CLint was fitted to match the observed %F through the PBPK model for 37 compounds, and a local QSAR was built for CLint of this series. These local CLint predictions, along with in silico biopharmaceutical properties from global QSAR models, were able to predict %F for the remaining 12 compounds, suggesting that in silico inputs can be successfully used for proposed novel compounds. For the in-house kinase inhibitor series, %F predictions were comparable whether based on experimentally measured properties or in silico inputs from global QSARs.We also extracted thousands of small molecules from a drug-like database with ranges of properties consistent with each individual series of compounds. PBPK simulations run on these molecules, followed by GSA, identified the most sensitive properties affecting %F and exposure, suggesting where medicinal chemistry should focus their effort. Our results support the general applicability of PBPK modeling to guide medicinal chemistry in lead optimization.

Predicting drug bioavailability using physiologically-based pharmacokinetic modeling and Global Sensitivity Analysis to identify sensitive parameters

248th National Meeting (2014)

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.