Evaluation of Renal Impairment Influence on Metabolic Drug Clearance using a Modelling Approach

Background and Objective Chronic kidney disease (CKD) may alter drug renal elimination but is also known for interacting with hepatic metabolism via multiple uremic components. However, few global models, considering the five major cytochromes, have been published, and none specifically address the decrease in cytochrome P450 (CYP450) activity. The aim of our study was to estimate the possibility of quantifying residual cytochrome activity as a function of filtration rate, according to the data available in the literature. Methods For each drug in the DDI-predictor database, we collected available pharmacokinetic data comparing drug exposition in the healthy patient and in various stages of CKD, before building a model capable of predicting the variation of exposure according to the degree of renal damage. We followed an In vivo Mechanistic Static Model (IMSM) approach, previously validated for predicting change in liver clearance. We estimated the remaining fraction parameters at glomerular filtration rate (GFR) = 0 and the alpha value of GFR to 50% impairment for the 5 major cytochromes using a non-linear constrained regression using Matlab software. Results Thirty-one compounds had usable pharmacokinetic data, with 51 AUC ratios between healthy and renal impaired patients. The remaining CYP3A4 activity was estimated to be 0.4 when CYP2D6, 2C9, 2C19 and 1A2 activity was estimated to be 0.43; 1; 0.73 and 0.7, respectively. The alpha value was estimated to be at 6.62; 25; 9.8; 1.38 and 11.04 for each cytochrome. In comparison with published data, all estimates but one were correctly predicted in the range of 0.5–2. Conclusion Our approach was able to describe the impact of CKD on metabolic elimination. Modelling this process makes it possible to anticipate changes in clearance and drug exposure in CKD patients, with the advantage of greater simplicity than approaches based on physiologically-based pharmacokinetic modelling. However, a precise estimation of the impact of renal failure is not possible with an IMSM approach due to the large variability of the published data, and thus should rely on specific pharmacokinetic modelling for narrow therapeutic margin drugs.