Evaluation of Exposure Change of Nonrenally Eliminated Drugs in Patients With Chronic Kidney Disease Using Physiologically Based Pharmacokinetic Modeling and Simulation

• Published in: Journal of clinical pharmacology 2012-01, Vol.52 (S1), p.91S-108S
• Main Authors: Zhao, Ping, Vieira, Manuela de L. T., Grillo, Joseph A., Song, Pengfei, Wu, Ta-Chen, Zheng, Jenny H., Arya, Vikram, Berglund, Eva Gil, Atkinson Jr, Arthur J., Sugiyama, Yuichi, Pang, K. Sandy, Reynolds, Kellie S., Abernethy, Darrell R., Zhang, Lei, Lesko, Lawrence J., Huang, Shiew-Mei
• Format: Article
• Language: English
• Subjects: Area Under Curve; Carbamates - blood; Carbamates - pharmacokinetics; Chronic Disease; combined intrinsic and extrinsic factors; Computer Simulation; Drug Interactions; drug-drug interaction; Drugs; Humans; Ketolides - blood; Ketolides - pharmacokinetics; Kidney diseases; Kidney Diseases - metabolism; Models, Biological; PBPK; Piperazines - blood; Piperazines - pharmacokinetics; Piperidines - blood; Piperidines - pharmacokinetics; Purines - blood; Purines - pharmacokinetics; renal impairment; Sexual disorders; Sildenafil Citrate; Simulation; Sulfones - blood; Sulfones - pharmacokinetics
• ISSN: 0091-2700; 1552-4604
• DOI: 10.1177/0091270011415528

Chronic kidney disease, or renal impairment (RI) can increase plasma levels for drugs that are primarily renally cleared and for some drugs whose renal elimination is not a major pathway. We constructed physiologically based pharmacokinetic (PBPK) models for 3 nonrenally eliminated drugs (sildenafil, repaglinide, and telithromycin). These models integrate drugdependent parameters derived from in vitro, in silico, and in vivo data, and system‐dependent parameters that are independent of the test drugs. Plasma pharmacokinetic profiles of test drugs were simulated in subjects with severe RI and normal renal function, respectively. The simulated versus observed areas under the concentration versus time curve changes (AUCR, severe RI/normal) were comparable for sildenafil (2.2 vs 2.0) and telithromycin (1.6 vs 1.9). For repaglinide, the initial, simulated AUCR was lower than that observed (1.2 vs 3.0). The underestimation was corrected once the estimated changes in transporter activity were incorporated into the model. The simulated AUCR values were confirmed using a static, clearance concept model. The PBPK models were further used to evaluate the changes in pharmacokinetic profiles of sildenafil metabolite by RI and of telithromycin by RI and co‐administration with ketoconazole. The simulations demonstrate the utility and challenges of the PBPK approach in evaluating the pharmacokinetics of nonrenally cleared drugs in subjects with RI.