Clarithromycin, Midazolam, and Digoxin: Application of PBPK Modeling to Gain New Insights into Drug–Drug Interactions and Co-medication Regimens

Abstract

Clarithromycin is a substrate and mechanism-based inhibitor of cytochrome P450 (CYP) 3A4 as well as a substrate and competitive inhibitor of P-glycoprotein (P-gp) and organic anion-transporting polypeptides (OATP) 1B1 and 1B3. Administered concomitantly, clarithromycin causes drug-drug interactions (DDI) with the victim drugs midazolam (CYP3A4 substrate) and digoxin (P-gp substrate). The objective of the presented study was to build a physiologically based pharmacokinetic (PBPK) DDI model for clarithromycin, midazolam, and digoxin and to exemplify dosing adjustments under clarithromycin co-treatment. The PBPK model development included an extensive literature search for representative PK studies and for compound characteristics of clarithromycin, midazolam, and digoxin. Published concentration-time profiles were used for model development (training dataset), and published and unpublished individual profiles were used for model evaluation (evaluation dataset). The developed single-compound PBPK models were linked for DDI predictions. The full clarithromycin DDI model successfully predicted the metabolic (midazolam) and transporter (digoxin) DDI, the acceptance criterion (0.5 ≤ AUC/AUC ≤ 2) was met by all predictions. During co-treatment with 250 or 500 mg clarithromycin (bid), the midazolam and digoxin doses should be reduced by 74 to 88% and by 21 to 22%, respectively, to ensure constant midazolam and digoxin exposures (AUC). With these models, we provide highly mechanistic tools to help researchers understand and characterize the DDI potential of new molecular entities and inform the design of DDI studies with potential CYP3A4 and P-gp substrates.

Drinking Ethanol Has Few Acute Effects on CYP2C9, CYP2C19, NAT2, and P‐Glycoprotein Activities but Somewhat Inhibits CYP1A2, CYP2D6, and Intestinal CYP3A: So What?

“…Thus, reliable quantitative prediction of changes in the pharmacokinetics of therapeutic drugs depending on the same enzyme/transporter would not only require detailed knowledge on the pharmacokinetic properties of the probe drug and the therapeutic drugs, but rather a quantitative description of all their relevant pharmacokinetic processes. Generating physiologically based pharmacokinetic (PBPK) models of all involved drugs would be the method of choice to this end, 38 but the general use of this laborious method is still limited by capacity problems.…”

A physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model of the histone deacetylase (HDAC) inhibitor vorinostat for pediatric and adult patients and its application for dose specification

“…All parameters were varied individually over a wide range of values (via multiplication by factors between 0.1 and 10.0) and the changes in AUC 0-inf , C max , half-life, and T max were documented [49]. If a 10% change of a single parameter led to a > 1% change in AUC 0-inf , C max , half-life, and T max , the model was considered sensitive to this parameter [50]. …”

PBPK Models for CYP3A4 and P‐gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin

“…The presented clarithromycin model is based on the PBPK model published by Moj et al 19. with small modifications.…”

“…18 Furthermore, the model correctly describes the strong food effects for both oral solution and capsule formulation, which is essential for modeling of the reported clinical studies. The presented clarithromycin model is based on the PBPK model published by Moj et al 19 with small modifications. For DDI prediction, different compounds have to be coupled in one and the same individual with a specified expression of enzymes and transporters.…”