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The Effect of Famotidine, a MATE1-Selective Inhibitor, on the Pharmacokinetics and Pharmacodynamics of Metformin

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Abstract

Introduction

Pharmacokinetic outcomes of transporter-mediated drug–drug interactions (TMDDIs) are increasingly being evaluated clinically. The goal of our study was to determine the effects of selective inhibition of multidrug and toxin extrusion protein 1 (MATE1), using famotidine, on the pharmacokinetics and pharmacodynamics of metformin in healthy volunteers.

Methods

Volunteers received metformin alone or with famotidine in a crossover design. As a positive control, the longitudinal effects of famotidine on the plasma levels of creatinine (an endogenous substrate of MATE1) were quantified in parallel. Famotidine unbound concentrations in plasma reached 1 µM, thus exceeding the in vitro concentrations that inhibit MATE1 [concentration of drug producing 50 % inhibition (IC50) 0.25 µM]. Based on current regulatory guidance, these concentrations are expected to inhibit MATE1 clinically [i.e. maximum unbound plasma drug concentration (C max,u)/IC50 >0.1].

Results

Consistent with MATE1 inhibition, famotidine administration significantly altered creatinine plasma and urine levels in opposing directions (p < 0.005). Interestingly, famotidine increased the estimated bioavailability of metformin [cumulative amount of unchanged drug excreted in urine from time zero to infinity (A e∞)/dose; p < 0.005] without affecting its systemic exposure [area under the plasma concentration–time curve (AUC) or maximum concentration in plasma (C max)] as a result of a counteracting increase in metformin renal clearance. Moreover, metformin–famotidine co-therapy caused a transient effect on oral glucose tolerance tests [area under the glucose plasma concentration–time curve between time zero and 0.5 h (AUCglu,0.5); p < 0.005].

Conclusions

These results suggest that famotidine may improve the bioavailability and enhance the renal clearance of metformin.

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Acknowledgments

The authors would like to thank the SOPHIE Cohort and Study # 6112 participants for contributing their time. The authors also thank Hector Vizoso, Nurse Manager, and the staff of the CRC, Chav Doherty, Research Study Coordinator, and the staff of the Clinical Laboratory at SFGH for their excellent service and assistance with our clinical study, as well as Dr. Yong Huang, Dr. Howard Horng, and Mr. Nick Massenkoff at the UCSF Drugs Services Unit for access to their bioanalytical facilities and for their technical support.

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    Authors and Affiliations

    1. Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, 1550 4th St, RH 584, Box 2911, San Francisco, CA, 94158, USA

      Jennifer E. Hibma, Arik A. Zur, Richard A. Castro, Matthias B. Wittwer, Ron J. Keizer, Sook Wah Yee, Srijib Goswami, Sophie L. Stocker, Radojka M. Savic & Kathleen M. Giacomini

    2. Department of Clinical Pharmacy, University of California San Francisco, San Francisco, CA, USA

      Jennifer E. Hibma

    3. Optivia Biotechnology Inc., Menlo Park, CA, USA

      Xuexiang Zhang & Yong Huang

    4. Department of Anesthesiology, University of California San Francisco, San Francisco, CA, USA

      Claire M. Brett

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    1. Jennifer E. Hibma
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    Correspondence to Kathleen M. Giacomini.

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    Funding

    This project was funded by the National Institutes of Health (NIH) Grant GM61390, and also by the NIH/National Center for Research Resources (NCRR), University of California, San Francisco (UCSF)—Clinical & Translational Science Institute (CTSI) Grant UL1 RR024131. Jennifer E. Hibma was funded by the National Research Service Award T32 GM07546 from the NIH, and by the Department of Clinical Pharmacy at UCSF. Matthias B. Wittwer was funded by the Swiss National Science Foundation’s grant for prospective researchers (PBBSP3-133384). The authors are solely responsible for the content and do not necessarily represent the official views of the NIH.

    Conflict of interest

    Kathleen M. Giacomini, Sook Wah Yee, Xuexiang Zhang, and Yong Huang have declared the following conflicts of interest that might be relevant to the content of this manuscript: Kathleen M. Giacomini and Sook Wah Yee are co-founders of Apricity Therapeutics, which develops drugs that exploit membrane transporters to enhance their pharmacologic action. Kathleen M. Giacomini receives funds from several pharmaceutical companies (AstraZeneca, Pfizer, Sanofi-Aventis, and GlaxoSmithKline) for research in her laboratory. Xuexiang Zhang and Yong Huang are employees of Optivia Biotechnology Inc., a transporter contract research organization (CRO) company.

    Jennifer E. Hibma, Arik A. Zur, Richard A. Castro, Matthias B. Wittwer, Ron J. Keizer, Srijib Goswami, Sophie L. Stocker, Claire M. Brett, and Radojka M. Savic have no conflicts of interest that might be relevant to the content of this study.

    Ethical approval

    This analysis was approved by the Committee on Human Research (CHR), which is the Institutional Review Board (IRB) at UCSF, approval # 10-02578.

    Additional information

    J. E. Hibma and A. A. Zur contributed equally to this work.

    Electronic supplementary material

    Below is the link to the electronic supplementary material.

    Supplementary material 1 (DOCX 28 kb)

    40262_2015_346_MOESM2_ESM.tif

    Supplementary material 2: Fig. 1 Famotidine inhibition curves against metformin uptake in HEK293 cell lines stably expressing various transporters; the clinical concentration of famotidine is shaded grey (0-1 µM). Data represent mean ± SEM (n=3). Curves were used to generate half-maximal inhibitory potencies (IC50 values) (TIFF 1521 kb)

    40262_2015_346_MOESM3_ESM.tif

    Supplementary material 3: Fig. 2 a) Famotidine plasma concentration-time curves from 0-24h following two doses of metformin in healthy volunteers (n=12). Doses of famotidine were administered orally as follows: 200 mg at t=-24h and 160 mg at t=-9, -5, -1, 3 and 7h in relation to the second metformin dose (t=0h). Dashed lines represent the half-maximal inhibitory concentrations (IC50), or multiples of the IC50, for MATE1 and MATE2. b) Ratio of maximal unbound concentration of famotidine in plasma and respective IC50 values, i.e., ([I]/IC50), for organic cation transporters hMATE1 (square), hMATE2 (triangle), hOCT1 (diamond) and hOCT2 (circle). The dashed line represents [I]/IC50 = 0.1. Data represent mean ± SEM (TIFF 1521 kb)

    40262_2015_346_MOESM4_ESM.tiff

    Supplementary material 4: Fig. 3 Metformin fractional renal clearance (CLR) from 0-2, 2-4, 4-6, 6-8, 8-12, 12-24h after metformin treatment alone (black bars) and during co-administration with famotidine (checkered bars). *P < 0.005. Data represent mean ± SEM (TIFF 20342 kb)

    40262_2015_346_MOESM5_ESM.tiff

    Supplementary material 5: Fig. 4 Famotidine uptake in cell lines over expressing hOCT1, hOCT2, hMATE1, hMATE2 (black bars) and empty vector (EV) transfected cells (white bars). Famotidine uptake was tested alone (black bars) or together with a specific inhibitor for the relevant transporter (gray bars) (i.e., pyrimethamine (10 µM) for hMATE1/2, cimetidine (500 µM) for hOCT2 and spironolactone (100 µM) for hOCT1). Radioactive counts are normalized per protein amounts in each well and presented as mean ± SEM (n=3) (TIFF 10662 kb)

    40262_2015_346_MOESM6_ESM.tiff

    Supplementary material 6: Fig. 5 Visual predictive check of final model for population pharmacokinetics of famotidine in plasma in healthy volunteers (n=12). The dashed lines represent the 2.5th percentile, median and 97.5th percentile of the observed data and the shaded area represents the 95% confidence intervals for the 2.5th percentile, median and 97.5th percentile of the simulated data (TIFF 1877 kb)

    40262_2015_346_MOESM7_ESM.tiff

    Supplementary material 7: Fig. 6 Visual predictive check of final model for population pharmacokinetics of creatinine in plasma in healthy volunteers (n=12). The dashed lines represent the 5th percentile, median and 95th percentile of the observed data and the shaded area represents the 90% confidence intervals for the 5th percentile, median and 95th percentile of the simulated data. (TIFF 1877 kb)

    40262_2015_346_MOESM8_ESM.tiff

    Supplementary material 8: Fig. 7 Metformin uptake (30uM) in a cell lines transiently expressing hPMAT/ENT4 or mock-transfected cells were compared to uptake in the presence of known ENT4 inhibitor: decynium-22 (100uM) or famotidine (2mM). Data represent mean ± SEM (n=3) (TIFF 6023 kb)

    Supplementary material 9 (DOCX 46 kb)

    Supplementary material 10 (DOCX 54 kb)

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    Hibma, J.E., Zur, A.A., Castro, R.A. et al. The Effect of Famotidine, a MATE1-Selective Inhibitor, on the Pharmacokinetics and Pharmacodynamics of Metformin. Clin Pharmacokinet 55, 711–721 (2016). https://doi.org/10.1007/s40262-015-0346-3

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