A GFR-Based Method to Predict the Effect of Renal Impairment on the Exposure or Clearance of Renally Excreted Drugs: A Comparative Study Between a Simple GFR Method and a Physiologically Based Pharmacokinetic Model

Abstract

Objective

The objective of this study was to compare the predictive performances of a glomerular filtration rate (GFR) model with a physiologically based pharmacokinetic (PBPK) model to predict total or renal clearance or area under the curve of renally excreted drugs in subjects with varying degrees of renal impairment.

Methods

From the literature, 11 studies were randomly selected in which total or renal clearance or area under the curve of drugs in subjects with different degrees of renal impairment were predicted by PBPK models. In these published studies, drugs were given to subjects intravenously or orally. The PBPK model was generally a whole-body model whereas the GFR model was as follows: Predicted total clearance (CL _T) = CL _T in healthy subjects × (GFR in RI/GFR in H), Predicted AUC = AUC in healthy subjects × (GFR in H/GFR in RI), where H is the healthy subjects and RI is renal impairment. The predicted clearance or area under the curve values using PBPK and GFR models were compared with the observed (experimental pharmacokinetic) values. The acceptable prediction error was within the 0.5- to 2-fold or 0.5- to 1.5-fold prediction error.

Results

There were 33 drugs with a total number of 101 observations (area under the curve, total and renal clearance in subjects with mild, moderate, and severe renal impairment). From PBPK and GFR models, out of 101 observations, 94 (93.1%) and 96 (95.0%) observations were within the 0.5- to 2-fold prediction error, respectively.

Conclusions

This study indicates that the predictive power of a simple GFR model is similar to a PBPK model for the prediction of clearance or area under the curve in subjects with renal impairment. The GFR method is simple, robust, and reliable and can replace complex empirical PBPK models.