A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations. (22nd 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 6(2020)
• Page Start: 310
• Page End: 321
• Publication Date: 2020-05-22
• Subjects: Pharmacokinetics -- Periodicals; Pharmacology -- Periodicals; Pharmacokinetics; Periodicals; 615.05
• DOI: 10.1002/psp4.12509
• Languages: English
• ISSNs: 2163-8306

Abstract

Abstract : Creatinine is the most common clinical biomarker of renal function. As a substrate for renal transporters, its secretion is susceptible to inhibition by drugs, resulting in transient increase in serum creatinine and false impression of damage to kidney. Novel physiologically based models for creatinine were developed here and (dis)qualified in a stepwise manner until consistency with clinical data. Data from a matrix of studies were integrated, including systems data (common to all models), proteomics‐informed in vitro – in vivo extrapolation of all relevant transporter clearances, exogenous administration of creatinine (to estimate endogenous synthesis rate), and inhibition of different renal transporters (11 perpetrator drugs considered for qualification during creatinine model development and verification on independent data sets). The proteomics‐informed bottom‐up approach resulted in the underprediction of creatinine renal secretion. Subsequently, creatinine‐trimethoprim clinical data were used to inform key model parameters in a reverse translation manner, highlighting best practices and challenges for middle‐out optimization of mechanistic models.