IN SILICO QUANTITATIVE STRUCTURE PHARMACOKINETIC RELATIONSHIP STUDIES FOR RENAL CLEARANCE OF ANTIDIABETIC DRUGS

*Sethi Reeta and Paul Yash

ABSTRACT

Renal clearance (CLR), 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 CLR of drugs or drug-like compounds in humans. Human CLR data for 24 antidiabetic 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, net elimination pathways, net elimination clearances, ion status and substrate/inhibitor specificity for renal transporters. The overall predictability was found to be renal clearance (CLR) (R2=0.9337, F=34.96, Q2=0.8527, p<0.001). Moreover, compounds undergoing net reabsorption/extensive net reabsorption predominantly belonged to Biopharmaceutics. In conclusion, constructed parsimonious QSPkR models can be utilized to predict CLR of compounds that, undergo net reabsorption/extensive net reabsorption and are substrates and/or inhibitors of human renal transporters.

Keywords: Quantitative structure pharmacokinetic relationships (QSPkR), Renal clearance, In Silico ADME, antidiabetic compounds.