Quantitative analysis of elevation of serum creatinine via renal transporter inhibition by trimethoprim in healthy subjects using physiologically-based pharmacokinetic model

Nakada, Tomohisa; Kudo, Toshiyuki; Kume, Toshiyuki; Kusuhara, Hiroyuki; K, Ito

doi:10.1016/j.dmpk.2017.11.314

Cited by 23 publications

(29 citation statements)

References 49 publications

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“…Physiologically based kidney models for creatinine were developed and accounted for the roles of transporters and passive permeability via transcellular and paracellular routes in the proximal tubule 18 . In contrast to previous modeling attempts, 14,15,31 mechanistic creatinine models developed here explicitly defined the proximal tubule cell compartment, including membrane localization of OAT2 and OCT2 (basolateral) and MATE1 and MATE2‐K (apical) transporters ( Figure ). In addition, the role of the resting membrane potential on transport rate and direction was considered for OCT2, as described previously for metformin 17,32 …”

Section: Resultsmentioning

confidence: 99%

“…The data for trimethoprim were not included in model performance evaluation to separate the “model development” and “verification” data sets and ensure robust evaluation. Three models were considered in this analysis, namely the uptake‐OCT2 and bidirectional‐OCT2 mechanistic creatinine models as described above, and a mechanistic static model reported in the literature (“Nakada model”) 14 …”

Section: Resultsmentioning

confidence: 99%

“…Nakada et al . (2018) reported a static model for describing trimethoprim‐creatinine interaction and related changes in serum creatinine concentrations and creatinine CL R 14 . This one‐compartment model accounted for processes of filtration, secretion, and re‐absorption, but lacked physiological complexity of the models in the current study.…”

Section: Methodsmentioning

confidence: 99%

“…PBPK models for DDI predictions rely upon quantitative translation of in vitro data through use of in vitro‐in vivo extrapolation (IVIVE). Examples of creatinine models and their application to predict effects following administration of different drugs have been reported in the literature 14,15 . In contrast to those examples, the current study developed physiologically based creatinine models accounting for its synthesis and mechanistic description of processes occurring in the renal proximal tubule.…”

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: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

See 2 more Smart Citations

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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“…[11][12][13][14][15] Creatinine clearance serves as an index of kidney function. 16 On the other hand, drugs known to be inhibitors of OCT2 and MATEs have been shown to cause reversible elevation of serum creatinine without kidney injury, 17,18 such as cimetidine, trimethoprim, and pyrimethamine that also reduce the renal clearance of metformin at their therapeutic doses. [19][20][21][22] The 1-NMN, an endogenous metabolite of nicotinamide (also known as vitamin B3 or niacin) formed by N-mehtylation, 23 is also known to be secreted into the urine by renal organic cation transporters.…”

Section: How Might This Change Clinical Pharma-cology or Translationamentioning

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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Multidrug and Toxin Extrusion Proteins

2022

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Quantitative analysis of elevation of serum creatinine via renal transporter inhibition by trimethoprim in healthy subjects using physiologically-based pharmacokinetic model

Cited by 23 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

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

Multidrug and Toxin Extrusion Proteins

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