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Clinical Pharmacokinetics

, Volume 52, Issue 5, pp 373–384 | Cite as

Population Pharmacokinetics of Metformin in Healthy Subjects and Patients with Type 2 Diabetes Mellitus: Simulation of Doses According to Renal Function

  • Janna K. Duong
  • Shaun S. Kumar
  • Carl M. Kirkpatrick
  • Louise C. Greenup
  • Manit Arora
  • Toong C. Lee
  • Peter Timmins
  • Garry G. Graham
  • Timothy J. Furlong
  • Jerry R. Greenfield
  • Kenneth M. Williams
  • Richard O. Day
Original Research Article

Abstract

Background and Objective

Metformin is contraindicated in patients with renal impairment; however, there is poor adherence to current dosing guidelines. In addition, the pharmacokinetics of metformin in patients with significant renal impairment are not well described. The aims of this study were to investigate factors influencing the pharmacokinetic variability, including variant transporters, between healthy subjects and patients with type 2 diabetes mellitus (T2DM) and to simulate doses of metformin at varying stages of renal function.

Methods

Plasma concentrations of metformin were pooled from three studies: patients with T2DM (study A; n = 120), healthy Caucasian subjects (study B; n = 16) and healthy Malaysian subjects (study C; n = 169). A population pharmacokinetic model of metformin was developed using NONMEM® version VI for both the immediate-release (IR) formulation and the extended-release (XR) formulation of metformin. Total body weight (TBW), lean body weight (LBW), creatinine clearance (CLCR; estimated using TBW and LBW) and 57 single-nucleotide polymorphisms (SNPs) of metformin transporters (OCT1, OCT2, OCT3, MATE1 and PMAT) were investigated as potential covariates. A nonparametric bootstrap (n = 1,000) was used to evaluate the final model. This model was used to simulate 1,000 concentration–time profiles for doses of metformin at each stage of renal impairment to ensure metformin concentrations do not exceed 5 mg/l, the proposed upper limit.

Results

Creatinine clearance and TBW were clinically and statistically significant covariates with the apparent clearance and volume of distribution of metformin, respectively. None of the 57 SNPs in transporters of metformin were significant covariates. In contrast to previous studies, there was no effect on the pharmacokinetics of metformin in patients carrying the reduced function OCT1 allele (R61C, G401S, 420del or G465R). Dosing simulations revealed that the maximum daily doses in relation to creatinine clearance to prescribe to patients are 500 mg (15 ml/min), 1,000 mg (30 ml/min), 2,000 mg (60 ml/min) and 3,000 mg (120 ml/min), for both the IR and XR formulations.

Conclusion

The population model enabled doses of metformin to be simulated for each stage of renal function, to ensure the concentrations of metformin do not exceed 5 mg/l. However, the plasma concentrations of metformin at these dosage levels are still quite variable and monitoring metformin concentrations may be of value in individualising dosage. This study provides confirmatory data that metformin can be used, with appropriate dosage adjustment, in patients with renal impairment.

Keywords

Metformin Total Body Weight Population Pharmacokinetic Model Lean Body Weight Oral Availability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors would like to thank Prof. Kathleen Giacomini for advice on the selection of SNPs in metformin transporters and Dr. Pavel Bitter for the analyses of SNPs. Funding for this study was provided by the NH&MRC Programme Grant 568612, Australian Research Council Grant LP 0990670, and St Vincent’s Clinic Foundation Sister Mary Bernice Research Grant.

Conflicts of interest

Peter Timmins is a salaried employee of Bristol-Myers Squibb, which is involved in the development and marketing of products containing metformin. Apart from the salary of Dr. Timmins, Bristol-Myers Squibb made no other payments in the support of this work. All other authors declared no conflict of interest.

Supplementary material

40262_2013_46_MOESM1_ESM.pdf (82 kb)
Supplementary material 1 (PDF 81 kb)

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Janna K. Duong
    • 1
    • 2
  • Shaun S. Kumar
    • 1
    • 2
  • Carl M. Kirkpatrick
    • 3
  • Louise C. Greenup
    • 2
  • Manit Arora
    • 1
  • Toong C. Lee
    • 4
  • Peter Timmins
    • 5
  • Garry G. Graham
    • 1
    • 2
  • Timothy J. Furlong
    • 6
  • Jerry R. Greenfield
    • 7
    • 8
  • Kenneth M. Williams
    • 1
    • 2
  • Richard O. Day
    • 1
    • 2
  1. 1.School of Medical SciencesUniversity of New South Wales, KensingtonSydneyAustralia
  2. 2.Department of Clinical Pharmacology and Toxicology, Level 2 Xavier BuildingSt Vincent’s HospitalSydneyAustralia
  3. 3.Centre for Medicine Use and SafetyMonash UniversityMelbourneAustralia
  4. 4.Universiti Sains MalaysiaPenangMalaysia
  5. 5.Bristol-Myers Squibb, Drug Product Sciefnce and TechnologyMerseysideUK
  6. 6.Department of NephrologySt Vincent’s HospitalSydneyAustralia
  7. 7.Department of EndocrinologySt Vincent’s HospitalSydneyAustralia
  8. 8.Diabetes and Obesity Research ProgramGarvan Institute of Medical ResearchSydneyAustralia

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