Mechanistic Models as Framework for Understanding Biomarker Disposition: Prediction of Creatinine‐Drug Interactions. (14th May 2020)

• Record Type: Journal Article
• Authors: Scotcher, Daniel; Arya, Vikram; Yang, Xinning; Zhao, Ping; Zhang, Lei; Huang, Shiew‐Mei; Rostami‐Hodjegan, Amin; Galetin, Aleksandra
• Is Part Of: CPT: pharmacometrics & systems pharmacology. Volume 9:Number 5(2020)
• Page Start: 282
• Page End: 293
• Publication Date: 2020-05-14
• Subjects: Pharmacokinetics -- Periodicals; Pharmacology -- Periodicals; Pharmacokinetics; Periodicals; 615.05
• DOI: 10.1002/psp4.12508
• Languages: English
• ISSNs: 2163-8306

Abstract

Abstract : Creatinine is widely used as a biomarker of glomerular filtration, and, hence, renal function. However, transporter‐mediated secretion also contributes to its renal clearance, albeit to a lesser degree. Inhibition of these transporters causes transient serum creatinine elevation, which can be mistaken as impaired renal function. The current study developed mechanistic models of creatinine kinetics within physiologically based framework accounting for multiple transporters involved in creatinine renal elimination, assuming either unidirectional or bidirectional‐OCT2 transport (driven by electrochemical gradient). Robustness of creatinine models was assessed by predicting creatinine‐drug interactions with 10 perpetrators; performance evaluation accounted for 5% intra‐individual variability in serum creatinine. Models showed comparable predictive performances of the maximum steady‐state effect regardless of OCT2 directionality assumptions. However, only the bidirectional‐OCT2 model successfully predicted the minimal effect of ranitidine. The dynamic nature of models provides clear advantage to static approaches and most advanced framework for evaluating interplay between multiple processes in creatinine renal disposition.