Skip to main content
SpringerLink
Log in

Physiologically Based Pharmacokinetic Modelling and Prediction of Metformin Pharmacokinetics in Renal/Hepatic-Impaired Young Adults and Elderly Populations

  • Original Research Article
  • Published:
European Journal of Drug Metabolism and Pharmacokinetics Aims and scope Submit manuscript

Abstract

Background and objectives

Physiologically based pharmacokinetic (PBPK) modelling and simulation enable researchers to overcome practical limitations for clinical trials on special populations. This study was conducted to investigate how the PBPK model describes the pharmacokinetics of metformin in young adult and elderly populations and to predict the pharmacokinetics of metformin in patients with renal or hepatic impairment in both populations.

Methods

A first-order absorption/PBPK model for metformin was built in the Simcyp simulator version 14 release 1. A full PBPK model was constructed for metformin based on physicochemical properties and clinical observations. The model was refined and validated using clinical plasma concentration data obtained in healthy young adults and elderly after the oral administration of metformin. Metformin pharmacokinetics in patients with renal or hepatic impairment were then investigated and compared by simulation.

Results

The PBPK model reasonably predicted the pharmacokinetic profiles of metformin for both young adults and the elderly. The predicted pharmacokinetic parameters, including maximum concentration, area under the time–concentration curve, and apparent oral clearance values, were within 1.5-fold of the observed data of metformin. In the simulation results, the systemic exposure of metformin was expected to be markedly increased not only with a decrease in renal function but also with severe hepatic impairments.

Conclusions

The PBPK model adequately characterised the pharmacokinetics of metformin in both young adult and elderly populations. PBPK modelling and simulation can be used as a useful tool to investigate and compare the pharmacokinetics in geriatric populations incorporating various disease conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Rowland M, Peck C, Tucker G. Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol. 2011;51:45–73. doi:10.1146/annurev-pharmtox-010510-100540.

    Article  CAS  PubMed  Google Scholar 

  2. Jones HM, Chen Y, Gibson C, Heimbach T, Parrott N, Peters SA, et al. Physiologically based pharmacokinetic modeling in drug discovery and development: a pharmaceutical industry perspective. Clin Pharmacol Ther. 2015;97(3):247–62. doi:10.1002/cpt.37.

    Article  CAS  PubMed  Google Scholar 

  3. American Diabetes A. 7. Approaches to Glycemic Treatment. Diabetes Care. 2016;39(Suppl 1):S52–9. doi:10.2337/dc16-S010.

    Google Scholar 

  4. Graham GG, Punt J, Arora M, Day RO, Doogue MP, Duong JK, et al. Clinical pharmacokinetics of metformin. Clin Pharmacokinet. 2011;50(2):81–98. doi:10.2165/11534750-000000000-00000.

    Article  CAS  PubMed  Google Scholar 

  5. Kimura N, Okuda M, Inui K. Metformin transport by renal basolateral organic cation transporter hOCT2. Pharm Res. 2005;22(2):255–9.

    Article  CAS  PubMed  Google Scholar 

  6. Tanihara Y, Masuda S, Sato T, Katsura T, Ogawa O, Inui K. Substrate specificity of MATE1 and MATE2-K, human multidrug and toxin extrusions/H(+)-organic cation antiporters. Biochem Pharmacol. 2007;74(2):359–71. doi:10.1016/j.bcp.2007.04.010.

    Article  CAS  PubMed  Google Scholar 

  7. Oh J, Chung H, Park SI, Yi SJ, Jang K, Kim AH, et al. Inhibition of the multidrug and toxin extrusion (MATE) transporter by pyrimethamine increases the plasma concentration of metformin but does not increase antihyperglycaemic activity in humans. Diabetes Obes Metab. 2016;18(1):104–8. doi:10.1111/dom.12577.

    Article  CAS  PubMed  Google Scholar 

  8. Grun B, Kiessling MK, Burhenne J, Riedel KD, Weiss J, Rauch G, et al. Trimethoprim–metformin interaction and its genetic modulation by OCT2 and MATE1 transporters. Br J Clin Pharmacol. 2013;76(5):787–96. doi:10.1111/bcp.12079.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Wang ZJ, Yin OQ, Tomlinson B, Chow MS. OCT2 polymorphisms and in vivo renal functional consequence: studies with metformin and cimetidine. Pharmacogenet Genom. 2008;18(7):637–45. doi:10.1097/FPC.0b013e328302cd41.

    Article  CAS  Google Scholar 

  10. FDA Drug Safety Communication: FDA revises warnings regarding use of the diabetes medicine metformin in certain patients with reduced kidney function. US Food and Drug Administration. 2016. http://www.fda.gov/Drugs/DrugSafety/ucm493244.htm. Accessed 13 Dec 2016.

  11. Sambol NC, Chiang J, Lin ET, Goodman AM, Liu CY, Benet LZ, et al. Kidney function and age are both predictors of pharmacokinetics of metformin. J Clin Pharmacol. 1995;35(11):1094–102.

    Article  CAS  PubMed  Google Scholar 

  12. Jang K, Chung H, Yoon JS, Moon SJ, Yoon SH, Yu KS, et al. Pharmacokinetics, safety, and tolerability of metformin in healthy elderly subjects. J Clin Pharmacol. 2016;56(9):1104–10. doi:10.1002/jcph.699.

    Article  CAS  PubMed  Google Scholar 

  13. Li J, Guo HF, Liu C, Zhong Z, Liu L, Liu XD. Prediction of drug disposition in diabetic patients by means of a physiologically based pharmacokinetic model. Clin Pharmacokinet. 2015;54(2):179–93. doi:10.1007/s40262-014-0192-8.

    Article  CAS  PubMed  Google Scholar 

  14. Xia B, Heimbach T, Gollen R, Nanavati C, He H. A simplified PBPK modeling approach for prediction of pharmacokinetics of four primarily renally excreted and CYP3A metabolized compounds during pregnancy. AAPS J. 2013;15(4):1012–24. doi:10.1208/s12248-013-9505-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Burt HJ, Neuhoff S, Almond L, Gaohua L, Harwood MD, Jamei M, et al. Metformin and cimetidine: physiologically based pharmacokinetic modelling to investigate transporter mediated drug-drug interactions. Eur J Pharm Sci. 2016;88:70–82. doi:10.1016/j.ejps.2016.03.020.

    Article  CAS  PubMed  Google Scholar 

  16. Pentikainen PJ, Neuvonen PJ, Penttila A. Pharmacokinetics of metformin after intravenous and oral administration to man. Eur J Clin Pharmacol. 1979;16(3):195–202.

    Article  CAS  PubMed  Google Scholar 

  17. Tucker GT, Casey C, Phillips PJ, Connor H, Ward JD, Woods HF. Metformin kinetics in healthy subjects and in patients with diabetes mellitus. Br J Clin Pharmacol. 1981;12(2):235–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Jamei M, Bajot F, Neuhoff S, Barter Z, Yang J, Rostami-Hodjegan A, et al. A mechanistic framework for in vitro-in vivo extrapolation of liver membrane transporters: prediction of drug–drug interaction between rosuvastatin and cyclosporine. Clin Pharmacokinet. 2014;53(1):73–87. doi:10.1007/s40262-013-0097-y.

    Article  CAS  PubMed  Google Scholar 

  19. Neuhoff S, Yeo KR, Barter Z, Jamei M, Turner DB, Rostami-Hodjegan A. Application of permeability-limited physiologically-based pharmacokinetic models: part I-digoxin pharmacokinetics incorporating P-glycoprotein-mediated efflux. J Pharm Sci. 2013;102(9):3145–60. doi:10.1002/jps.23594.

    Article  CAS  PubMed  Google Scholar 

  20. Neuhoff S, Gaohua L, Burt H, Jamei M, Li L, Tucker GT, et al. Accounting for transporters in renal clearance: towards a mechanistic kidney model (Mech KiM). In: Sugiyama Y, Steffansen B, editors. Transporters in drug development: discovery, optimization, clinical study and regulation. New York: Springer New York; 2013. p. 155–77.

    Chapter  Google Scholar 

  21. Rowland Yeo K, Aarabi M, Jamei M, Rostami-Hodjegan A. Modeling and predicting drug pharmacokinetics in patients with renal impairment. Expert Rev Clin Pharm. 2011;4(2):261–74. doi:10.1586/ecp.10.143.

    Article  Google Scholar 

  22. Johnson TN, Boussery K, Rowland-Yeo K, Tucker GT, Rostami-Hodjegan A. A semi-mechanistic model to predict the effects of liver cirrhosis on drug clearance. Clin Pharmacokinet. 2010;49(3):189–206. doi:10.2165/11318160-000000000-00000.

    Article  CAS  PubMed  Google Scholar 

  23. Glucophage® Label. http://www.accessdata.fda.gov/drugsatfda_docs/label/2008/020357s031,021202s016lbl.pdf. Accessed 11 Dec 2016.

  24. Woitas RP, Flommersfeld S, Stoffel-Wagner B, Schiedermaier P, Brensing KA, Spengler U, et al. Renal functional reserve is well maintained in patients with advanced liver cirrhosis and ascites. Scand J Gastroenterol. 2002;37(11):1321–7.

    Article  CAS  PubMed  Google Scholar 

  25. Huang W, Lee SL, Yu LX. Mechanistic approaches to predicting oral drug absorption. AAPS J. 2009;11(2):217–24. doi:10.1208/s12248-009-9098-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Marathe PH, Wen Y, Norton J, Greene DS, Barbhaiya RH, Wilding IR. Effect of altered gastric emptying and gastrointestinal motility on metformin absorption. Br J Clin Pharmacol. 2000;50(4):325–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ren J, Zhou Y, Zhang G, Zhou L, Zhao J, Wei Y, et al. Role of age-related decrease of renal organic cation transporter 2 in the effect of atenolol on renal excretion of metformin in rats. Eur J Drug Metab Pharmacokinet. 2015;40(3):349–54. doi:10.1007/s13318-014-0214-9.

    Article  CAS  PubMed  Google Scholar 

  28. Gaowa A, Motohashi H, Katsura T, Inui K. Effects of metabolic acidosis on expression levels of renal drug transporters. Pharm Res. 2011;28(5):1023–30. doi:10.1007/s11095-010-0348-7.

    Article  CAS  PubMed  Google Scholar 

  29. Komazawa H, Yamaguchi H, Hidaka K, Ogura J, Kobayashi M, Iseki K. Renal uptake of substrates for organic anion transporters Oat1 and Oat3 and organic cation transporters Oct1 and Oct2 is altered in rats with adenine-induced chronic renal failure. J Pharm Sci. 2013;102(3):1086–94. doi:10.1002/jps.23433.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (Grant No. HI14C2770).

Author information

Author notes

  1. Su-jin Rhee and Hyewon Chung contributed equally to this work.

Authors and Affiliations

  1. Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul, Korea

    Su-jin Rhee, Hyewon Chung, SoJeong Yi & Kyung-Sang Yu

  2. Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Bundang Hospital, Seongnam, Korea

    Jae-Yong Chung

Authors
  1. Su-jin Rhee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hyewon Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. SoJeong Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kyung-Sang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jae-Yong Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jae-Yong Chung.

Ethics declarations

Funding

No funding was received for this study.

Conflict of interest

SJR, HC, SJY, KSY and JYC declare no conflicts of interest.

Ethical approval

Not applicable.

Informed consent

Not applicable.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 262 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rhee, Sj., Chung, H., Yi, S. et al. Physiologically Based Pharmacokinetic Modelling and Prediction of Metformin Pharmacokinetics in Renal/Hepatic-Impaired Young Adults and Elderly Populations. Eur J Drug Metab Pharmacokinet 42, 973–980 (2017). https://doi.org/10.1007/s13318-017-0418-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13318-017-0418-x

Search

Navigation