Simulation-Based Analysis of the Impact of Renal Impairment on the Pharmacokinetics of Highly Metabolized Compounds

Abstract

Renal impairment (RI) is a highly prevalent disease which can alter the pharmacokinetics (PK) of xenobiotics, including those that are predominately metabolized. The expression and activity of drug metabolizing enzymes (DMEs) and protein binding of compounds has been demonstrated to be affected in RI. A simulation based approach allows for the characterization of the impact of changes in these factors on the PK of compounds which are highly metabolized and allows for improved prediction of PK in RI. Simulations with physiologically based pharmacokinetic (PBPK) modeling was utilized to define the impact of these factors in PK in RI for a model substrate, nifedipine. Changes in fraction unbound and DME expression/activity had profound effects on PK in RI. Increasing fraction unbound and DME expression resulted in a reduction in exposure of nifedipine, while the reduction of DME activity resulted in an increase in exposure. In vitro and preclinical data were utilized to inform simulations for nifedipine, sildenafil and zidovudine. Increasing fraction unbound and changes in the expression/activity of DMEs led to improved predictions of PK. Further characterization of the impact of RI on these factors is warranted in order to better inform a priori predictions of PK in RI.

Physiologically‐Based Pharmacokinetic Modeling in Renal and Hepatic Impairment Populations: A Pharmaceutical Industry Perspective

“…The performance of the PBPK models was validated by the contributing companies by simulating relevant clinical trials such as single ascending dose and multiple ascending dose, or DDI studies in either healthy volunteers or patients. While this IQ white paper does not intend to recommend specifics about model verification, such as to provide recommendations for parameter sensitivity analyses, the selection of parameters and associate ranges for the analyses should be based on the specific ADME properties of the study compounds, particularly the pathologic factors that can significantly impact the variability in renal and hepatic impairment populations, such as metabolic enzyme expression level and plasma protein binding 27,28 . When applying the PBPK model in HI/RI patients for a DDI prediction, the degree of induction and inhibition can be impacted by the metabolic enzyme expression levels in the corresponding HI/RI patient group, and thereby, parameter sensitivity approaches to investigate their global relationships before starting to simulate a specific study scenario 27 .…”

“…While this IQ white paper does not intend to recommend specifics about model verification, such as to provide recommendations for parameter sensitivity analyses, the selection of parameters and associate ranges for the analyses should be based on the specific ADME properties of the study compounds, particularly the pathologic factors that can significantly impact the variability in renal and hepatic impairment populations, such as metabolic enzyme expression level and plasma protein binding 27,28 . When applying the PBPK model in HI/RI patients for a DDI prediction, the degree of induction and inhibition can be impacted by the metabolic enzyme expression levels in the corresponding HI/RI patient group, and thereby, parameter sensitivity approaches to investigate their global relationships before starting to simulate a specific study scenario 27 . In addition, the scenario‐based simulation can investigate different hypothetical conditions when a modeling compound undergoes multiple metabolic or excretive pathways simultaneously (e.g., dual substrate of CYP enzymes and transporters) or key input parameters were not certain when performing the simulation 28 .…”

“… 27 , 28 When applying the PBPK model in HI/RI patients for a DDI prediction, the degree of induction and inhibition can be impacted by the metabolic enzyme expression levels in the corresponding HI/RI patient group, and thereby, parameter sensitivity approaches to investigate their global relationships before starting to simulate a specific study scenario. 27 In addition, the scenario‐based simulation can investigate different hypothetical conditions when a modeling compound undergoes multiple metabolic or excretive pathways simultaneously (e.g., dual substrate of CYP enzymes and transporters) or key input parameters were not certain when performing the simulation. 28 Such an approach can typically provide more useful insights in optimization of PBPK models when modelers have to evaluate the risk of study outcome by understanding the “worst‐case” scenario.”…”

Changes in the Pharmacokinetics and Pharmacodynamics of Sildenafil in Cigarette and Cannabis Smokers

“…Pharmaceutics 2021, 13, 876 2 of 15 Sildenafil is mainly metabolized by the cytochrome P450 hepatic isoenzymes 3A4 (a major pathway) and 2C9 (a minor route) [5]. CYP450 converts sildenafil to an active form of N-desmethyl metabolite, which has been shown to contribute to approximately half of the potency and activity of sildenafil.…”

Interaction of Sildenafil With Herbal Supplements: A Review

“…SILD is rapidly absorbed after oral administration 6 and is mainly metabolized by the cytochrome P450 (CYP) hepatic isoenzymes 3A4 (a major pathway) and 2C9 (a minor route) 7 . CYP450 transforms SILD into an active form of N-desmethyl metabolite which has been found to account for around half of SILD potency and activity.…”