A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Drug–Drug–Gene Interaction Model of Metformin and Cimetidine in Healthy Adults and Renally Impaired Individuals

Metformin is an oral antidiabetic that reduces blood glucose levels. It is the first-line therapy for type 2 diabetes mellitus (T2DM) and the fourth most commonly prescribed outpatient medication in the USA, with almost 80 million prescriptions in 2017 [1].

Metformin is a BCS Class III drug of high solubility and very low permeability, positively charged at physiological pH and depends on active transport to cross biological membranes. The metformin rate of absorption is slower than its rate of elimination [2] and the absorption is restricted to the upper intestine [3], leading to incomplete absorption of metformin, an oral bioavailability of 50–60%, and the excretion of approximately 30% of an oral dose, unabsorbed, with the feces [2, 4]. Furthermore, the absorption of metformin is saturable, with higher doses showing decreased dose-normalized plasma concentrations and a decreased fraction excreted to urine [4, 5]. Following its absorption, metformin is not bound to plasma proteins [2, 4, 6], not metabolized [2, 6], and not secreted to bile [2, 4, 7], but excreted unchanged with the urine by passive glomerular filtration and active renal secretion through the sequential action of organic cation transporter 2 (OCT2) and multidrug and toxin extrusion protein 1 (MATE1). Although there are early reports of MATE2-K expression in the human kidney [8, 9], a recent quantitative study found only negligible amounts of MATE2-K compared to MATE1 [10]. Renal clearance is approximately 500 mL/min [11] with a strong correlation between the renal clearances of metformin and creatinine [4]. Patients with renal impairment show a marked increase in metformin exposure, with three- to ten-fold higher plasma trough concentrations in chronic kidney disease (CKD) stages 3A-5 [12]. As a consequence, metformin is contraindicated in patients with a glomerular filtration rate (GFR) < 30 mL/min (i.e., CKD stages 4 and 5) [13, 14], depriving these patients of metformin as a treatment option.

The impact of genetic polymorphisms on the absorption and disposition of metformin (drug–gene interactions or “DGIs”) has been investigated in a multitude of clinical trials, yielding to some extent contradictory results. The transporters of primary interest in these studies were the plasma membrane monoamine transporter (PMAT), OCT1, OCT2, and MATE1, where variations in OCT2 seem to have the largest impact on the plasma concentrations of metformin [15‐19]. The most common polymorphism in the gene encoding for OCT2 is the SLC22A2 808G>T single-nucleotide polymorphism [20], which results in an amino acid exchange from alanine to serine (A270S) and presumably increased function, leading to decreased exposure with ~ 13–20% decreased maximum concentration (C_max) [17, 18, 21].

A third factor that impacts metformin exposure is drug–drug interactions (DDIs). Metformin displays a list of 333 DDIs, with 13 major and 293 moderate interactions [22]. Even though some of these occur on the pharmacodynamic level, pharmacokinetic DDIs are clinically relevant and may call for an adjustment of the co-administration regimen. As metformin is exclusively eliminated by glomerular filtration and secretion through the renal organic cation transport system, co-treatment with a potent inhibitor of this transport pathway, such as cimetidine, decreases the renal clearance of metformin and increases metformin exposure (+ 50% area under the curve [AUC]) [21, 23]. Metformin is recommended by the US Food and Drug Administration as an OCT2/MATE victim drug for clinical DDI studies [24].

The aim of this study was to build and evaluate a whole-body physiologically based pharmacokinetic (PBPK) model of metformin, applicable (1) to describe the impact of the metformin-SLC22A2 808G>T DGI on metformin exposure, (2) to dynamically model the cimetidine-metformin DDI, and (3) to analyze the impact of renal impairment on metformin exposure and generate dose recommendations for different stages of CKD. The newly developed and thoroughly evaluated metformin and cimetidine models will be freely available in the Open Systems Pharmacology PBPK model repository (https://​www.​open-systems-pharmacology.​org), and the Electronic Supplementary Material (ESM) to this article is compiled to serve as a comprehensive and transparent documentation and reference.

Several other PBPK models of metformin have been published previously [41‐44], but our newly developed model is the first to integrate PET-measured human in-vivo metformin kidney concentrations, clinical data of microdose studies, and a mechanistic description of the saturable transporter-dependent absorption of metformin. The limitations of the presented model result from our lack of knowledge regarding the metformin pharmacokinetic processes in the liver and the expression levels of the different transporters throughout the body. As shown in Fig. 3d, the liver concentration–time profile following the intravenous ¹¹C-metformin microdose is not adequately described. The uptake of metformin into the liver is modeled via OCT1, but, in accordance with the literature, no process for metformin metabolism or secretion to bile has been implemented. An unspecific hepatic metabolic clearance was tested, but did not improve the model (causing underestimation of the plasma concentrations in studies with therapeutic doses), supporting the idea that metformin is not metabolized. The plasma concentrations following the oral ¹¹C-metformin microdose, and consequently also the measured tissue concentrations, are underpredicted for the 2 h of the oral PET study (see the ESM). However, the administered microdoses (1.445 µg intravenously and 0.856 µg orally) were more than 300,000 and 500,000 times below the lowest therapeutic dose of 500 mg. Given that the metformin pharmacokinetics are completely governed by saturable transport processes and that the plasma, whole blood, kidney, and muscle concentrations following the intravenous microdose are well described, this underprediction might be caused by a missing process for metformin absorption.

The effect of the SLC22A2 808G>T polymorphism is difficult to assess from the literature. In-vitro studies report a decreased metformin transport rate [29], equal activity [20], as well as increased transport velocity [17] for the variant OCT2 protein. In-vivo, two studies report decreased clearance by renal secretion in Korean and Chinese 808TT individuals [21, 29], whereof the Chinese study nevertheless shows a non-significantly lower metformin plasma C_max for the 808TT group. However, two different studies report increased clearance by renal secretion in American and European individuals [17, 18], with corresponding decreases in metformin exposure in association with the minor allele. Given that OCT2 and MATE1 are working sequentially to transport metformin through the kidney, it is difficult to distinguish their impacts on renal secretion. Therefore, statements regarding the effect of polymorphisms or co-medications on OCT2 function should not be based on plasma concentrations or renal secretion alone, without concomitant assessment of MATE1 genotype/activity or kidney concentrations. This also holds true for the DDGI results by Wang et al. [21] (Fig. 5f), where the observed lack of cimetidine-metformin DDI in SLC22A2 808TT individuals is difficult to explain, because (1) this DDI is mainly caused by inhibition of MATE1, (2) the MATE1 genotypes were not analyzed in this study, and (3) so far there are no in-vitro results available on the impact of cimetidine on MATE1 variants. Another explanation for the weak effect of cimetidine in the SLC22A2 808TT group might be reduced transport of cimetidine by this OCT2 variant into the kidney and therefore less inhibition of MATE1, as previously proposed [30].

To model the renal impairment, renal secretion was decreased in proportion to the impaired GFR, based on the “intact nephron hypothesis”, which postulates that structurally damaged nephrons stop contributing to both passive renal filtration and active secretion, and that the remaining intact nephrons continue to function in glomerulo-tubular balance with appropriate adaptation to the patient’s needs [35]. This hypothesis has been successfully applied in previous PBPK analyses of renal impairment [36, 42, 45, 46]. The inhibition of liver drug uptake by uremic toxins in renal impairment has been postulated by Zhao et al. [38], based on the fact that the clearance of many nonrenally eliminated drugs is decreased in CKD, and based on their PBPK analysis of repaglinide in CKD4.

We used an empirical approach to model the inhibition of liver and muscle uptake as a function of the degree of renal impairment. The inhibition of the basolateral intestinal permeability/transport in CKD is purely hypothetical, but was essential to describe the shape and elimination phase of the clinically observed data. The induction of OCT2 and MATE1 was demonstrated in hyperuricemic rats [40]. To confirm and refine these hypotheses, in-vitro studies of OCT1 and PMAT inhibition by uremic solutes are needed, to identify the toxins involved and to assess their inhibitory potential; the expression and role of transporters at the basolateral membrane of the intestinal mucosa has to be investigated; and the clinical relevance of OCT2 and MATE1 induction by uric acid in humans needs to be established.

Future applications include the modeling of further DGIs and DDIs, and ultimately, the individualized dose recommendation for real patients with multiple polymorphisms, co-medications, and co-morbidities. Although the effects of some of these interactions do not reach statistical significance in the blood, their impact on kidney or liver concentrations might well be substantial and of therapeutic relevance. The model application with the most immediate medical benefit is the generation of dose recommendations for renally impaired patients with T2DM. Chronic kidney disease is a frequent co-morbidity, but physicians are reluctant to prescribe metformin to patients with reduced renal function because of the contraindication given in most guidelines and fear of lactic acidosis caused by metformin accumulation [47]. These contraindications are based solely on the estimated GFR of the patient, even though for patients with stable renal disease, a dose adjustment based on renal function, with monitoring of the metformin plasma concentrations, would be perfectly feasible. A reduced dose of 500 mg metformin daily was reported to be safe for creatinine clearances as low as 20 mL/min [48] and in a group of CKD4 patients [12], which is in line with the presented model-based recommendation of 200 mg three times daily for CKD4 patients with T2DM.

Mechanistic whole-body PBPK models of metformin and cimetidine have been carefully developed and evaluated to integrate the current pharmacokinetic knowledge on these drugs and to describe the impact of the SLC22A2 808G>T polymorphism, the cimetidine-metformin DDI, and the pathophysiological changes during renal impairment on the exposure of metformin. Both models will be released open-source (https://​www.​open-systems-pharmacology.​org) [49], to support metformin therapy, OCT2/MATE DDI studies during drug development, and to be used as input for pharmacodynamic glucose-homeostasis models [50, 51] and other PBPK/pharmacodynamic analyses. The presented analysis has generated insights into the pharmacokinetics during renal impairment, indicating that the kidneys of patients with severe renal disease might be able to adapt to uremia/hyperuricemia by induction of OCT2 and MATE1, as has been shown for hyperuricemic rats [40].