Predicting complex kidney drug handling using a physiologically‐based pharmacokinetic model informed by biomarker‐estimated secretory clearance and blood flow

Abstract

Kidney function‐adjusted drug dosing is currently based solely on the estimated glomerular filtration rate (eGFR), however, kidney drug handling is accomplished by a combination of filtration, tubular secretion, and reabsorption. Mechanistic physiologically‐based pharmacokinetic (PBPK) models recapitulate anatomic compartments to predict elimination from estimated perfusion, filtration, secretion, and reabsorption, but clinical applications are limited by a lack of empiric individual‐level measurements of these functions. We adapted and validated a PBPK model to predict drug clearance from individual biomarker‐based estimates of kidney perfusion and secretory clearance. We estimated organic anion transporter‐mediated secretion via kynurenic acid clearance and kidney blood flow via isovalerylglycine clearance in human participants, incorporating these measurements with GFR into the model to predict kidney drug clearance. We compared measured and model‐predicted clearances of administered tenofovir and oseltamivir, which are cleared by both filtration and secretion. There were 27 outpatients (age 55±15years, mean iGFR 76±31ml/min/1.73m2) in this drug clearance study. The mean observed and mechanistic model‐predicted tenofovir clearances were 169+102ml/min and 163+80ml/min, respectively; estimated mean error of the mechanistic model was 37.1ml/min [95% CI:24‐52.9], compared to a mean error of 41.8ml/min [25‐61.6] from regression model. The mean observed and model‐predicted oseltamivir carboxylate clearances were 183+104ml/min and 179+89ml/min, respectively; estimated mean error of the mechanistic model was 42.9ml/min [29.7‐56.4], vs. error of 48.1ml/min [31.2‐67.3] from regression model. Individualized estimates of tubular secretion and kidney blood flow improved the accuracy of PBPK model‐predicted tenofovir and oseltamivir kidney clearances, suggesting the potential for biomarker‐informed measures of kidney function to refine personalized drug dosing.