In silico classification of major clearance pathways of drugs with their physiochemical parameters

Predicting major clearance pathways of drugs is important in understanding their pharmacokinetic properties in clinical use, such as drug-drug interactions and genetic polymorphisms, and their subsequent pharmacological/toxicological effects. In this study, we established an in silico classification method to predict the major clearance pathways of drugs by identifying the boundaries of physicochemical parameters in empirical decisions for each clearance pathway. It requires only four physicochemical parameters [charge, molecular weight (MW), lipophilicity (log D), and protein unbound fraction in plasma (f(up))] that were predicted from their molecular structures without performing any benchwork experiments. The training dataset consisted of 141 approved drugs whose major clearance pathways were determined to be metabolism by CYP3A4, CYP2C9, and CYP2D6, hepatic uptake by OATPs, or renal excretion in an unchanged form. After grouping by charge, each drug was plotted in a three-dimensional space according to three axes of MW, log D, and f(up). Then, rectangular boxes for each clearance pathway were drawn mathematically under the criterion of "maximizing F value (harmonic mean of precision and recall) with minimum volume," yielding to a precision of 88%, which was confirmed through two types of validation: leave-one-out method and validation using a new dataset. With further modification toward multiple pathways and/or other pathways, not only would this in silico classification system be useful for industrial scientists at the early stage of drug development, which can lead to the selection of candidate compounds with optimal pharmacokinetic properties, but also for regulators in evaluating new drugs and giving regulatory requirements that are pharmacokinetically reasonable.