Mathematical model of pharmacokinetics for personalized optimization of metformin therapy (2018 – 2020)
Project No. lzp-2018/2-0088
Metformin is the main drug for type 2 diabetes treatment and a promising candidate for other disease treatment. It has big deviations between individuals in therapy efficiency and pharmacokinetics leading to the administration of an unnecessary overdose or an insufficient dose. In addition to that, there is a lack of data regarding the concentration-time profiles in different human tissues that limits the understanding of pharmacokinetics and hinders the development of precision therapies for individual patients.
The aim of the project is to develop a dynamic mechanistic model of metformin pharmacokinetics to
1) determine the range of therapeutic concentrations in relevant tissues and
2) parameterize model for the patient to develop suggestions for minimization of dose and optimization of metformin administration frequency.
The popular drug metformin is used for the treatment of more than 120 million patients with Type 2 Diabetes mellitus and other diseases. Transport of metformin through tissues and body fluids is characterized by high variation among individuals due to genetic variants found in metformin transporters coding genes (OCT1, OCT2, OCT3, PMAT, MATE1 and MATE2). As a result, metformin concentration in target tissues does not correspond to required therapeutical levels leading to overprescription of the drug. It is necessary to investigate the relation between particular genetic variants and the peculiarities of metformin transport in the human body in order to improve current drug prescription algorithm.
The project idea is to apply more than ten years of medical and pharmacological expertise of pharmacogeneticists with more than ten years of experience of computer scientists and information technologists with ordinary differential equation systems based dynamic modelling of bioprocesses. Already obtained experimental data from metformin pharmacokinetics study in combination with genetic information will serve for parametrisation of the personalized dynamic mechanistic model. The model will enable to develop suggestions for minimisation of dose and optimisation of metformin administration to reach the best effect possible for each genetic profile investigated.
This approach of systemic mathematical modelling can be extended to studies of the pharmacokinetics of other pharmaceutical compounds as well.
The physiologically based pharmacokinetic (PBPK) model developed in this study is based on the known physiological parameters of humans (blood flow, tissue volume and others). The missing tissue-specific parameters of pharmacokinetics are estimated by the development of a PBPK model of metformin in mice where the concentration-time series in different tissues have been measured. Some parameters are adapted from human intestine cell culture experiments.
The developed human model can be personalized by adapting measurable values (tissue volumes, blood flow) and measuring metformin concentration time-course in blood and urine after a single dose of metformin. The personalised model can be used as a decision support tool for precision therapy development for individuals.
Kurlovics J., Zake D.M., Zaharenko L., Berzins K., Janis Klovins J., Stalidzans E. (2021) Metformin transport rates between plasma and red blood cells in humans. Clinical Pharmacokinetics. https://doi.org/10.1007/s40262-021-01058-2
Zake D.M., Kurlovics J., Zaharenko L., Komasilovs V., Klovins J., Stalidzans E. (2021) Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues, PLOS ONE, 16 (4). Article number: e0249594. https://doi.org/10.1371/journal.pone.0249594
Stalidzans, E., Zanin, M., Tieri, P., Castiglione, F., Polster, A., Scheiner, S., Pahle, J., Stres, B., List, M., Baumbach, J., Lautizi, M., VanSteen, K., Schmidt, H.H.H.W. (2020) Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice. Network and Systems Medicine, 3.1, pp. 36–56. https://doi.org/10.1089/nsm.2020.0002