Physiologically‐Based Pharmacokinetic Modelling of Creatinine‐Drug Interactions in the Chronic Kidney Disease Population

Abstract

Elevated serum creatinine (S Cr) caused by the inhibition of renal transporter(s) may be misinterpreted as kidney injury. The interpretation is more complicated in chronic kidney disease (CKD) patients due to altered disposition of creatinine and renal transporter inhibitors. A clinical study was conducted in 17 CKD patients (estimated glomerular filtration rate 15-59 mL/min/1.73m 2); changes in S Cr were monitored during trimethoprim treatment (100-200 mg/day), administered to prevent recurrent urinary infection, relative to the baseline level. Additional S Cr-interaction data with trimethoprim, cimetidine, and famotidine in CKD patients were collated from the literature. Our published physiologically-based creatinine model was extended to predict the effect of the CKD on S Cr and creatinine-drug interaction. The creatinine-CKD model incorporated age/sex-related differences in creatinine synthesis, CKD-related glomerular filtration deterioration; change in transporter activity either proportional or disproportional to GFR decline were explored. Optimized models successfully recovered baseline S Cr from 64 CKD patients (geometric mean fold-error of 1.1). Combined with pharmacokinetic models of inhibitors, the creatinine model was used to simulate transporter-mediated creatinine-drug interactions. Use of inhibitor unbound plasma concentrations resulted in 66% of simulated S Cr interaction data within the prediction limits, with cimetidine interaction significantly underestimated. Assuming that transporter activity deteriorates disproportional to GFR decline resulted in higher predicted sensitivity to transporter inhibition in CKD patients relative to healthy, consistent with sparse clinical data. For the first time, this novel modelling approach enables quantitative prediction of S Cr in CKD and delineation of the effect of disease and renal transporter inhibition in this patient population.

Impact of Hepatic CYP3A4 Ontogeny Functions on Drug–Drug Interaction Risk in Pediatric Physiologically‐Based Pharmacokinetic/Pharmacodynamic Modeling: Critical Literature Review and Ivabradine Case Study

“…2 In later stages, PBPK model predictive performance can be improved by refining input parameters with clinical data (middle-out approach / reverse translation [3][4][5] ). This results in greater confidence in model extrapolation to untested scenarios (i.e., drug-drug interactions (DDIs)) and/or special populations (e.g., pediatrics, organ impairment 6 ).…”

PBPK Simulation-Based Evaluation of Ganciclovir Crystalluria Risk Factors: Effect of Renal Impairment, Old Age, and Low Fluid Intake

“…in line with the 'intact nephron hypothesis' (INH, [21]) or non-proportional to corresponding changes in glomerular filtration rate. The consistency of these various plausible mechanisms (and their extent) in explaining clinical PK data in CKD patients has been explored using both empirical and mechanistic/PBPK models [14,20,[22][23][24][25][26]. In addition to plasma concentrations, the structural complexity of mechanistic kidney models enables simulation of local (intra-tubular) drug exposure and regional tubular differences and assessment of certain scenarios (e.g.…”

Population pharmacokinetic modeling and simulation to support qualification of pyridoxic acid as endogenous biomarker of OAT1/3 renal transporters

“…14 Modeling and simulation has increasingly been used as a tool to gain mechanistic insight into disposition of transporter biomarkers. 3,14,[19][20][21][22][23][24] These techniques are helpful in understanding the formation, disposition, and elimination process of endogenous biomarkers to facilitate their qualification and inform optimal design of subsequent interaction studies. Most of the work so far has been reported for endogenous biomarkers of hepatic transporters (organic anion polypeptides OATP1B1 and OATP1B3), specifically coproporphyrin I (CPI).…”

“…Modeling and simulation has increasingly been used as a tool to gain mechanistic insight into disposition of transporter biomarkers 3,14,19‐24 . These techniques are helpful in understanding the formation, disposition, and elimination process of endogenous biomarkers to facilitate their qualification and inform optimal design of subsequent interaction studies.…”