A physiologically-based pharmacokinetic modeling approach to assess the impact of chronic kidney disease
Eduard Schmulenson (1, 2), Jan-Frederik Schlender (1, 2), Sebastian Frechen (2), Ulrich Jaehde (1)
(1) Institute of Pharmacy, Clinical Pharmacy, University of Bonn, Bonn, Germany, (2) Systems Pharmacology & Medicine, Bayer AG, Leverkusen, Germany
Objectives: Chronic kidney disease (CKD) is a general term for various irreversible disorders of kidney structure or kidney function. It has an impact on the pharmacokinetics of numerous drugs and consequently, concepts of dose adjustments according to the renal function of CKD patients have been established. The progression of CKD affects nearly all organs and body systems, revealing the need to characterize its systemic nature by applying a quantitative systems pharmacologic approach. The aim of the study was to develop a physiologically-based pharmacokinetic (PBPK) modeling approach to understand and predict drug exposure in patients suffering from CKD of different stages.
Methods: A systematic literature search was conducted to identify and to inform pathological conditions of CKD patients which possibly have an impact on the pharmacokinetics of drugs. Within the Open Systems Pharmacology Suite [1] the parametrization of the physiological changes was performed according to the CKD classification [2] by calculating fractional changes along the staging system. An incorporated aging database [3] was used to distinguish between age- and disease-related alterations. The uncertainty of each parameter was described by calculating a Taylor series expansion.
In order to qualify the parametrization, PBPK models of four paradigm compounds (gentamicin, amikacin, gadodiamide, zanamivir) which are eliminated solely by glomerular filtration were built. The mean prediction error (ME) and the root mean squared prediction error (RMSE) were calculated to assess the predictive performance of the diseased-informed fractional changes compared to uninformed simulations in which solely the glomerular filtration rate was adjusted.
Results: The identified parameters included pathophysiological alterations of the kidney blood flow, kidney volume, hematocrit, gastric emptying time and concentrations of drug binding plasma proteins. The re-parametrization was applied to describe the exposure of the four paradigm compounds. The calculations of ME did not indicate a bias except for two simulations of gentamicin in CKD stage 5 or end-stage renal disease (ESRD) patients. Disease-informed simulations of patients with CKD stages 2, 3 and 4 were more precise than the uninformed ones (relative improvements of 15% to 22%) except for one simulation of patients with CKD stage 3 after administration of zanamivir. The precision of the prediction of ESRD patients receiving gadodiamide improved by 54%, whereas the simulations of gentamicin and amikacin in ESRD patients did not indicate an improved predictive performance comparing to the respective uninformed simulations.
Conclusions: An accurate and precise prediction of drug exposure for paradigm compounds eliminated by glomerular filtration for different stages of CKD was successfully conducted. The lack of improvement of predictive performance of simulations in ESRD patients suggests that the potential involvement of dialysis and the progression of uremia may require an extension of the model. However, this PBPK modeling approach provides support for specific considerations regarding clinical trial design and pharmacotherapy for patients suffering from CKD.
References:
[1] Open Systems Pharmacology. Available at: http://www.open-systems-pharmacology.org/, last access on February 10, 2018.
[2] Kidney Disease Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 2013; 3: 1–150.
[3] Schlender J-F, Meyer M, Thelen K, Krauss M, Willmann S, Eissing T, Jaehde U. Development of a Whole-Body Physiologically Based Pharmacokinetic Approach to Assess the Pharmacokinetics of Drugs in Elderly Individuals. Clin Pharmacokinet 2016; 55: 1573–1589.
