Physiologically‐Based Pharmacokinetic Modeling Analysis for Quantitative Prediction of Renal Transporter–Mediated Interactions Between Metformin and Cimetidine

Nishiyama, Keisuke; Toshimoto, Kota; Lee, Wooin; Ishiguro, Naoki; Bister, Bojan; Sugiyama, Yuichi

doi:10.1002/psp4.12398

Cited by 32 publications

(45 citation statements)

References 49 publications

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“…A mechanistic creatinine model that featured bidirectional OCT2 transport was the only model that correctly predicted negligible interaction between ranitidine and creatinine ( Table 3 ). Consideration of the electrochemical gradient driving force for OCT2 transport is consistent with previously reported metformin PBPK model, 16,17 in vitro data demonstrating an effect of membrane potential on creatinine accumulation ( Table ), and in vitro data reporting efflux transport of tetraethylammonium and acetylcholine by OCT2 34,35 . Despite current limited number of perpetrators to test this model, OCT2 transport driving force is seen as an important consideration for complex interactions with dual OCT2/MATE inhibitors, in particular in cases when inhibitor’s intracellular concentration may be higher than in plasma.…”

Section: Discussionsupporting

confidence: 82%

“…These mechanistic models incorporated multiple transporters involved in creatinine renal elimination, assuming either unidirectional or bidirectional OCT2 transport (driven by electrochemical gradient). Accounting for bidirectional transport by OCT2 was previously demonstrated as an important consideration in PBPK simulation of cimetidine‐metformin DDI 16,17 . In a companion paper, 18 technical details of the stepwise development of mechanistic creatinine models and their refinement with clinical creatinine‐trimethoprim interaction data are reported.…”

Section: Figurementioning

confidence: 99%

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Mechanistic Models as Framework for Understanding Biomarker Disposition: Prediction of Creatinine‐Drug Interactions

Scotcher

Arya

Yang

et al. 2020

CPT Pharmacom & Syst Pharma

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Creatinine is widely used as a biomarker of glomerular filtration, and, hence, renal function. However, transporter-mediated secretion also contributes to its renal clearance, albeit to a lesser degree. Inhibition of these transporters causes transient serum creatinine elevation, which can be mistaken as impaired renal function. The current study developed mechanistic models of creatinine kinetics within physiologically based framework accounting for multiple transporters involved in creatinine renal elimination, assuming either unidirectional or bidirectional-OCT2 transport (driven by electrochemical gradient). Robustness of creatinine models was assessed by predicting creatinine-drug interactions with 10 perpetrators; performance evaluation accounted for 5% intra-individual variability in serum creatinine. Models showed comparable predictive performances of the maximum steady-state effect regardless of OCT2 directionality assumptions. However, only the bidirectional-OCT2 model successfully predicted the minimal effect of ranitidine. The dynamic nature of models provides clear advantage to static approaches and most advanced framework for evaluating interplay between multiple processes in creatinine renal disposition.

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Section: Discussionsupporting

confidence: 82%

Section: Figurementioning

confidence: 99%

Mechanistic Models as Framework for Understanding Biomarker Disposition: Prediction of Creatinine‐Drug Interactions

Scotcher

Arya

Yang

et al. 2020

CPT Pharmacom & Syst Pharma

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“…A similar concept can be applied for the prediction of renal clearance (CL R ), integrating process clearances of renal uptake, metabolism, tubular secretion, and back‐flux from intracellular compartments across the basolateral membrane . Combining the predicted renal secretion clearance with glomerular filtration and the reabsorption rate allows the calculation of the in vivo CL R …”

Section: Relevance Of Transporters In Drug Pharmacokineticsmentioning

confidence: 99%

Physiologically‐Based Pharmacokinetic Models for Evaluating Membrane Transporter Mediated Drug–Drug Interactions: Current Capabilities, Case Studies, Future Opportunities, and Recommendations

Taskar

Reddy

Burt

et al. 2019

Clin Pharma and Therapeutics

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104

103

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Physiologically-based pharmacokinetic (PBPK) modeling has been extensively used to quantitatively translate in vitro data and evaluate temporal effects from drug-drug interactions (DDIs), arising due to reversible enzyme and transporter inhibition, irreversible time-dependent inhibition, enzyme induction, and/or suppression. PBPK modeling has now gained reasonable acceptance with the regulatory authorities for the cytochrome-P450-mediated DDIs and is routinely used. However, the application of PBPK for transporter-mediated DDIs (tDDI) in drug development is relatively uncommon. Because the predictive performance of PBPK models for tDDI is not well established, here, we represent and discuss examples of PBPK analyses included in regulatory submission (the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Pharmaceuticals and Medical Devices Agency (PMDA)) across various tDDIs. The goal of this collaborative effort (involving scientists representing 17 pharmaceutical companies in the Consortium and from academia) is to reflect on the use of current databases and models to address tDDIs. This challenges the common perceptions on applications of PBPK for tDDIs and further delves into the requirements to improve such PBPK predictions. This review provides a reflection on the current trends in PBPK modeling for tDDIs and provides a framework to promote continuous use, verification, and improvement in industrialization of the transporter PBPK modeling. ) † Venkatesh Pilla Reddy and Kunal S. Taskar equally contributed to this article and are joint first authors. REVIEW(1)) CL H,int = (PS inf ,act + PS inf,pas ) * (CL int,met + CL int,sec ) PS ef f,act + PS ef f,pas + CL int,met + CL int,sec (3) CL H,int = PS inf * REVIEW

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“…41 A PBPK model for metformin has already been constructed and it is able to describe the metformin-cimetidine DDI involving MATE1 inhibition. 43 Likewise, the construction of PBPK models for 1-NMN and m 1 A will advance the prediction of OCT2 and MATE1/2-Kmediated DDI in drug development.…”

Section: Articlementioning

confidence: 99%

Identification of Appropriate Endogenous Biomarker for Risk Assessment of Multidrug and Toxin Extrusion Protein‐Mediated Drug‐Drug Interactions in Healthy Volunteers

Miyake

Kimoto

Luo

et al. 2020

Clin Pharma and Therapeutics

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Endogenous biomarkers are emerging to advance clinical drug-drug interaction (DDI) risk assessment in drug development. Twelve healthy subjects received a multidrug and toxin exclusion protein (MATE) inhibitor (pyrimethamine, 10, 25, and 75 mg) in a crossover fashion to identify an appropriate endogenous biomarker to assess MATE1/2-K-mediated DDI in the kidneys. Metformin (500 mg) was also given as reference probe drug for MATE1/2-K. In addition to the previously reported endogenous biomarker candidates (creatinine and N 1methylnicotinamide (1-NMN)), N 1-methyladenosine (m 1 A) was included as novel biomarkers. 1-NMN and m 1 A presented as superior MATE1/2-K biomarkers since changes in their renal clearance (CL r) along with pyrimethamine dose were well-correlated with metformin CL r changes. The CL r of creatinine was reduced by pyrimethamine, however, its changes poorly correlated with metformin CL r changes. Nonlinear regression analysis (CL r vs. mean total concentration of pyrimethamine in plasma) yielded an estimate of the inhibition constant (K i) of pyrimethamine and the fraction of the clearance pathway sensitive to pyrimethamine. The in vivo K i value thus obtained was further converted to unbound Ki using plasma unbound fraction of pyrimethamine, which was comparable to the in vitro K i for MATE1 (1-NMN) and MATE2-K (1-NMN and m 1 A). It is concluded that 1-NMN and m 1 A CL r can be leveraged as quantitative MATE1/2-K biomarkers for DDI risk assessment in healthy volunteers.

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Physiologically‐Based Pharmacokinetic Modeling Analysis for Quantitative Prediction of Renal Transporter–Mediated Interactions Between Metformin and Cimetidine

Cited by 32 publications

References 49 publications

Mechanistic Models as Framework for Understanding Biomarker Disposition: Prediction of Creatinine‐Drug Interactions

Mechanistic Models as Framework for Understanding Biomarker Disposition: Prediction of Creatinine‐Drug Interactions

Physiologically‐Based Pharmacokinetic Models for Evaluating Membrane Transporter Mediated Drug–Drug Interactions: Current Capabilities, Case Studies, Future Opportunities, and Recommendations

Identification of Appropriate Endogenous Biomarker for Risk Assessment of Multidrug and Toxin Extrusion Protein‐Mediated Drug‐Drug Interactions in Healthy Volunteers

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