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Early prediction of clearance mechanisms allows for the rapid progression of drug discovery and development programs, and facilitates risk assessment of the pharmacokinetic variability associated with drug interactions and pharmacogenomics. Here we propose a scientific framework--Extended Clearance Classification System (ECCS)--which can be used to predict the predominant clearance mechanism (rate-determining process) based on physicochemical properties and passive membrane permeability. Compounds are classified as: Class 1A--metabolism as primary systemic clearance mechanism (high permeability acids/zwitterions with molecular weight (MW) ≤400 Da), Class 1B--transporter-mediated hepatic uptake as primary systemic clearance mechanism (high permeability acids/zwitterions with MW >400 Da), Class 2--metabolism as primary clearance mechanism (high permeability bases/neutrals), Class 3A--renal clearance (low permeability acids/zwitterions with MW ≤400 Da), Class 3B--transporter mediated hepatic uptake or renal clearance (low permeability acids/zwitterions with MW >400 Da), and Class 4--renal clearance (low permeability bases/neutrals). The performance of the ECCS framework was validated using 307 compounds with single clearance mechanism contributing to ≥70% of systemic clearance. The apparent permeability across clonal cell line of Madin - Darby canine kidney cells, selected for low endogenous efflux transporter expression, with a cut-off of 5 × 10(-6) cm/s was used for permeability classification, and the ionization (at pH7) was assigned based on calculated pKa. The proposed scheme correctly predicted the rate-determining clearance mechanism to be either metabolism, hepatic uptake or renal for ~92% of total compounds. We discuss the general characteristics of each ECCS class, as well as compare and contrast the framework with the biopharmaceutics classification system (BCS) and the biopharmaceutics drug disposition classification system (BDDCS). Collectively, the ECCS framework is valuable in early prediction of clearance mechanism and can aid in choosing the right preclinical tool kit and strategy for optimizing drug exposure and evaluating clinical risk of pharmacokinetic variability caused by drug interactions and pharmacogenomics.

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Oral bioavailability (F) is a product of fraction absorbed (Fa), fraction escaping gut-wall elimination (Fg), and fraction escaping hepatic elimination (Fh). In this study, using a database comprised of Fa, Fg, Fh, and F values for 309 drugs in humans, an analysis of the interrelation of physicochemical properties and the individual parameters was carried out in order to define the physicochemical space for optimum human oral bioavailability. Trend analysis clearly indicated molecular weight (MW), ionization state, lipophilicity, polar descriptors, and free rotatable bonds (RB) influence bioavailability. These trends were due to a combination of effects of the properties on Fa and first-pass elimination (Fg and Fh). Higher MW significantly impacted Fa, while Fg and Fh decreased with increasing lipophilicity. Parabolic trends were observed for bioavailability with polar descriptors. Interestingly, RB has a negative effect on all three parameters, leading to its pronounced effect on bioavailability. In conclusion, physicochemical properties influence bioavailability with typically opposing effects on Fa and first-pass elimination. This analysis may provide a rational judgment on the physicochemical space to optimize oral bioavailability.