Abstract

Renal clearance (CL_R), a major route of elimination for many drugs and drug metabolites, represents the net result of glomerular filtration, active secretion and reabsorption, and passive reabsorption. The aim of this study was to develop quantitative structure-pharmacokinetic relationships (QSPKR) to predict CL_R of drugs or drug-like compounds in humans. Human CL_R data for 382 compounds were obtained from the literature. Step-wise multiple linear regression was used to construct QSPKR models for training sets and their predictive performance was evaluated using internal validation (leave-one-out method). All qualified models were validated externally using test sets. QSPKR models were also constructed for compounds in accordance with their 1) net elimination pathways (net secretion, extensive net secretion, net reabsorption, and extensive net reabsorption), 2) net elimination clearances (net secretion clearance, CL_SEC; or net reabsorption clearance, CL_REAB), 3) ion status, and 4) substrate/inhibitor specificity for renal transporters. We were able to predict 1) CL_REAB (Q² = 0.77) of all compounds undergoing net reabsorption; 2) CL_REAB (Q² = 0.81) of all compounds undergoing extensive net reabsorption; and 3) CL_R for substrates and/or inhibitors of OAT1/3 (Q² = 0.81), OCT2 (Q² = 0.85), MRP2/4 (Q² = 0.78), P-gp (Q² = 0.71), and MATE1/2K (Q² = 0.81). Moreover, compounds undergoing net reabsorption/extensive net reabsorption predominantly belonged to Biopharmaceutics Drug Disposition Classification System classes 1 and 2. In conclusion, constructed parsimonious QSPKR models can be used to predict CL_R of compounds that 1) undergo net reabsorption/extensive net reabsorption and 2) are substrates and/or inhibitors of human renal transporters.