Assessing the impact of choosing different estimated glomerular filtration rate (eGFR) equations on pharmacokinetic models: A proof-of-concept study with illustrative examples
(1, 2) Thao-Nguyen Pham, (2) Yu-Wei Lin, (2) S. Y. Amy Cheung
(1) Normandie Univ, UNICAEN, CNRS, ISTCT, GIP CYCERON, 14000, Caen, France (2) Certara, Princeton, NJ, USA
Objectives:
Renal function is assessed using the glomerular filtration rate (GFR), determined by measuring the elimination of exogenous biomarkers that are exclusively excreted by the kidneys. Although accurate, this method is complex, costly, and impractical for routine clinical use. As an alternative, renal function can be estimated with endogenous biomarkers, such as serum creatinine, to calculate the estimated GFR (eGFR). Over time, a variety of equations for calculating renal function have been developed, incorporating factors like age, gender, body surface area, and weight. Additionally, specific equations have been tailored and tested among different population groups, including Asian population [1]. Our study aims to evaluate the interchangeability of various renal function estimation equations within the framework of PK assessment.
Methods:
Our research started with an extensive literature review aimed at identifying existing equations for determining renal function and population pharmacokinetic (PopPK) models that use estimated GFR (eGFR) as a covariate. For practical application, we focused on exenatide and remdesivir as exemplars, given that both are renally eliminated drugs with PK closely tied to eGFR levels [2,3]. We explored the influence of different eGFR equations on derivation of drug exposures based on the developed PopPK models, including but not limited to the chronic kidney disease epidemiology collaboration (CKD-EPI) formulas, both with and without cystatin C as a covariate, and the modification of diet in renal disease (MDRD) equations, also evaluated with and without the inclusion of cystatin C [4,5].
By leveraging data from the national health and nutrition examination survey (NHANES), we generated a virtual population of 1500 individuals. This virtual population was created based on the reported individual levels of serum creatinine, serum cystatin C, and basic demographic details such as age, race, and gender. We then compared the estimated PK exposure metrics, including the area under the curve (AUC0-tau,ss) at steady state and the maximum concentration (Cmax,ss) at steady state, across different eGFR equations. Our analysis also involved quantifying and categorising the correlations between these exposure metrics and the renal function equations applied.
Results:
We identified 15 equations for evaluating renal function via eGFR, including nine tailored for Asian populations. The eGFR values derived from these equations showed a high correlation with each other (R > 0.8, p-value < 0.01). However, we observed discrepancies in the calculated eGFR values at higher levels, particularly when the estimate exceeded 150 mL/min/1.73m2.
These discrepancies in eGFR values led to variations in the calculated exposure of exenatide and remdesivir, as measured by AUC and Cmax. Notably, significant differences in AUC0-tau,ss and Cmax,ss were observed at high eGFR values above 150mL/min/1.73m2. Conversely, in populations with renal failure or in population without renal failure and an eGFR lower than 150 mL/min/1.73m2 (eGFR within 90-150 mL/min/1.73m2), no statistically significant differences were found in the exposure estimations of exenatide and remdesivir calculated using different eGFR equations (p-value < 0.05, under ANOVA test).
Conclusions:
The selection of a specific equation for estimating renal function appears to have a notable effect on exposure derivation for drugs that are eliminated via the kidneys. It underscores the necessity for additional research to investigate the influence of different renal function equations on exposure estimation across various subpopulations, including pediatric patients and individuals with conditions such as diabetes or cancer.
References:
[1] Teo BW, Zhang L, Guh JY, et al. Glomerular Filtration Rates in Asians. Adv Chronic Kidney Dis. 2018;25(1):41-48. doi:10.1053/j.ackd.2017.10.005
[2] Cirincione B, Mager DE. Population pharmacokinetics of exenatide. Br J Clin Pharmacol. 2017;83(3):517-526. doi:10.1111/bcp.13135
[3] Sukeishi A, Itohara K, Yonezawa A, et al. Population pharmacokinetic modeling of GS-441524, the active metabolite of remdesivir, in Japanese COVID-19 patients with renal dysfunction. CPT Pharmacometrics Syst Pharmacol. 2022;11(1):94-103. doi:10.1002/psp4.12736
[4] Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate [published correction appears in Ann Intern Med. 2011 Sep 20;155(6):408]. Ann Intern Med. 2009;150(9):604-612. doi:10.7326/0003-4819-150-9-200905050-00006
[5] Inker LA, Eneanya ND, Coresh J, et al. New Creatinine- and Cystatin C-Based Equations to Estimate GFR without Race. N Engl J Med. 2021;385(19):1737-1749. doi:10.1056/NEJMoa2102953