Population Pharmacokinetic Analyses and Model Validation of Tolvaptan in Subjects With Autosomal Dominant Polycystic Kidney Disease. (7th January 2019)

• Record Type: Journal Article
• Authors: Lanke, Shankar; Shoaf, Susan E.
• Is Part Of: Journal of clinical pharmacology. Volume 59:Number 5(2019)
• Page Start: 763
• Page End: 770
• Publication Date: 2019-01-07
• Subjects: ADPKD -- tolvaptan -- population pharmacokinetics -- model development -- validation -- prediction; Pharmacology -- Periodicals; Pharmacology -- Periodicals; Pharmacology, Clinical -- Periodicals; 615.1
• DOI: 10.1002/jcph.1370
• Languages: English
• ISSNs: 0091-2700

Abstract

Abstract: Tolvaptan is the first approved drug treatment to slow kidney function decline in adults at risk of rapidly progressing autosomal dominant polycystic kidney disease (ADPKD). The objective is to develop (1091 subjects, 7335 observations) and validate (678 subjects, 3012 observations) a population pharmacokinetic model to describe tolvaptan pharmacokinetics in ADPKD subjects. The final model was evaluated with a bootstrapping method. The final model was internally and externally evaluated using visual predictive checks (VPC). Pharmacokinetics was best described by a 1‐compartmental model with 0‐order absorption, nonlinear relative bioavailability (F1), and first‐order elimination. Accounting for changes in F1 significantly improved the model: as the dose increased from 15 mg to 120 mg, F1 decreased by 36%. Population estimates for clearance/F (CL/F), volume of distribution/F (Vd/F), duration of absorption (D1), the highest dose at which F1 is lowest, and the amount of dose at which F1 is 50% were 12.6 L·h ‐1, 110 L, 0.58 hour, 182 mg, and 166 mg, respectively. The interindividual variability was 64% in CL/F, 70% in Vd/F, and 238% in D1. Residual variability was described by a combined‐error model. The VPC (500 data sets simulated) showed that 76% to 92% of the observed data fell within the 90% prediction intervals. The model stability assessed by a 1000‐run bootstrap analysis showed that the mean parameter estimates of data were within 10% of those obtained with the final model. The developed model is robust and stable. Internal and external validation confirmed the model ability to describe the data optimally.