A Physiologically-Based Pharmacokinetic Model of Trimethoprim for MATE1, OCT1, OCT2, and CYP2C8 Drug–Drug–Gene Interaction Predictions

Abstract

Trimethoprim is a frequently-prescribed antibiotic and therefore likely to be co-administered with other medications, but it is also a potent inhibitor of multidrug and toxin extrusion protein (MATE) and a weak inhibitor of cytochrome P450 (CYP) 2C8. The aim of this work was to develop a physiologically-based pharmacokinetic (PBPK) model of trimethoprim to investigate and predict its drug–drug interactions (DDIs). The model was developed in PK-Sim®, using a large number of clinical studies (66 plasma concentration–time profiles with 36 corresponding fractions excreted in urine) to describe the trimethoprim pharmacokinetics over the entire published dosing range (40 to 960 mg). The key features of the model include intestinal efflux via P-glycoprotein (P-gp), metabolism by CYP3A4, an unspecific hepatic clearance process, and a renal clearance consisting of glomerular filtration and tubular secretion. The DDI performance of this new model was demonstrated by prediction of DDIs and drug–drug–gene interactions (DDGIs) of trimethoprim with metformin, repaglinide, pioglitazone, and rifampicin, with all predicted DDI and DDGI AUClast and Cmax ratios within 1.5-fold of the clinically-observed values. The model will be freely available in the Open Systems Pharmacology model repository, to support DDI studies during drug development.

Physiologically‐based pharmacokinetic modeling of dextromethorphan to investigate interindividual variability within CYP2D6 activity score groups

“…16 Finally, the developed and evaluated PBPK model of dextromethorphan is a useful tool for clinicians to investigate the effect of CYP2D6 DGIs and the associated IIV on the PK of dextromethorphan and its metabolites. The mechanistical model can be extended to be used in other PBPK modeling scenarios, such as the prediction of drugdrug interaction and DGI effects 41 and scaling to special populations, such as pediatrics, 42 geriatrics, 43 or patients with renal or hepatic impairment. 44 Moreover, the modeling approach presented in this study can serve as a blueprint to develop PBPK models of other CYP2D6 substrates.…”

Evaluation for Potential Drug–Drug Interaction of MT921 Using In Vitro Studies and Physiologically–Based Pharmacokinetic Models

“…AMLO, SIMV, and PIO K i values were taken from literature. All of these drugs were assumed to competitively inhibit each other [ 42 ]. Before DDI simulation, drug interaction among chronic disease drugs was investigated.…”

Renal Transporter‐Mediated Drug‐Biomarker Interactions of the Endogenous Substrates Creatinine and N1‐Methylnicotinamide: A PBPK Modeling Approach

“…Creatinine and NMN model performances were evaluated by comparison of predicted to observed plasma concentration‐time and urine profiles as well as by goodness‐of‐fit plots. Quantitative model performance was evaluated by calculating mean relative deviations of predicted plasma concentrations and urinary excretion rates (Ae urine rates) as well as geometric mean fold errors (GMFEs) of predicted area under the concentration‐time curve calculated from the time of compound administration (or first data sampling point) to the time of the last concentration measurement (AUC last ) and maximum plasma concentration ( C max ) values, amounts excreted unchanged in urine (Ae urine ) and renal clearances, as described elsewhere 17,25 . Local sensitivity analyses were performed for the creatinine and NMN models to investigate the impact of single parameter changes on predicted AUC last values.…”

“…The objectives of this study were (1) to develop whole‐body PBPK models of the endogenous biomarkers creatinine and NMN that mechanistically describe their absorption, synthesis, metabolic transformation, and active transport also considering causes of observed diurnal variation, and (2) to test the ability of the newly developed models to adequately describe drug‐biomarker interactions (DBIs) with the potent OCT2 and MATE inhibitors trimethoprim, pyrimethamine, and cimetidine, 24 by coupling the biomarker models to already evaluated and published perpetrator models within a PBPK DDI/DBI modeling network 17,25,26 …”

“…The objectives of this study were (1) to develop whole-body PBPK models of the endogenous biomarkers creatinine and NMN that mechanistically describe their absorption, synthesis, metabolic transformation, and active transport also considering causes of observed diurnal variation, and (2) to test the ability of the newly developed models to adequately describe drug-biomarker interactions (DBIs) with the potent OCT2 and MATE inhibitors trimethoprim, pyrimethamine, and cimetidine, 24 by coupling the biomarker models to already evaluated and published perpetrator models within a PBPK DDI/DBI modeling network. 17,25,26 METHODS Software PBPK models of creatinine and NMN were developed using the PK-Sim and MoBi modeling software suite (Open Systems Pharmacology Suite 9.1, www.open-syste ms-pharm acolo gy.org). Plasma and urine measurements from literature were digitized with Engauge Digitizer 10.12 (M. Mitchell 27 ) according to best practices.…”