Single nucleotide polymorphisms in the intergenic region between metformin transporter OCT2 and OCT3 coding genes are associated with short-term response to metformin monotherapy in type 2 diabetes mellitus patients

Genetic polymorphism of organic cation transporter 2 (OCT2) and its effects on the pharmacokinetics and pharmacodynamics of Metformin: a narrative review

Background

Organic cation transporter 2 (OCT2) is a renal carrier transporter protein found in the basolateral membrane of proximal epithelial cells, which facilitates active secretion of Metformin. The genetic polymorphism of OCT2 influences the pharmacodynamic and pharmacokinetic effect of Metformin in type 2 diabetes mellitus (T2DM) patients. This is also mainly associated with frequencies of the associated risk allele in a particular population.

Objective

The purpose of the study is to determine the impact of OCT2 genetic polymorphism on Metformin pharmacodynamics (PD) and pharmacokinetics (PK).

Method of study

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used for performing the research. Following databases were used to conduct the search: PubMed/MEDLINE, Google Scholar, and the Cochrane Library. Relevant studies were retrieved and literatures were appraised for methodology, demographic characteristics, relevant SNPs, genetic intervention trials, and outcomes.

Results

Based on the data collected, 13 OCT2 Single nucleotide polymorphisms (SNPs) were identified across various ethnic groups. There were significant differences between the frequency distribution of shared alleles and impact of thirteen SNPs on Metformin. Among the thirteen OCT2 variants studied, rs316019 variant produced the most diverse responses in population by showing positive and negative impact on PK & PD of Metformin.

Discussion and conclusion

Each population's OCT2 polymorphism had a distinct effect on Metformin responsiveness. The findings of this study could bring significant benefits to patients with OCT2 genetic polymorphism if individualised T2DM therapy is introduced. Patient-centered treatment would improve the Metformin efficacy leading to new research in personalised medicine.

Effects of single-nucleotide polymorphism on the pharmacokinetics and pharmacodynamics of metformin

INTRODUCTION

Metformin has been recognized as the first-choice drug for type 2 diabetes mellitus (T2DM). The potency of metformin in the treatment of type 2 diabetes has always been in the spotlight and shown significant individual differences. Based on previous studies, the efficacy of metformin is related to the single-nucleotide polymorphisms of transporter genes carried by patients, amongst which a variety of gene polymorphisms of transporter and target protein genes affect the effectiveness and adverse repercussion of metformin.

AREAS COVERED

Here, we reviewed the current knowledge about gene polymorphisms impacting metformin efficacy based on transporter and drug target proteins.

EXPERT OPINION

The reason for the difference in clinical drug potency of metformin can be attributed to the gene polymorphism of drug transporters and drug target proteins in the human body. Substantial evidence shows that genetic polymorphisms in transporters such as organic cation transporter 1 (OCT1) and organic cation transporter 2 (OCT2) affect the glucose-lowering effectiveness of metformin. However, optimization of individualized dosing regimens of metformin is necessary to clarify the role of several polymorphisms.

Review of pharmaceutical and therapeutic approaches for type 2 diabetes and related disorders

One of the essential diseases that are increasing in the world is type 2 diabetes (T2D), which many people around the world live with this disease. Various studies have revealed that insulin resistance, lessened insulin production has been associated with T2D, and they also show that this disease can have a genetic origin and is associated with different genes such as KCNQ1, PPAR-γ, calpain-10, ADIPOR2, TCF7L2 that can be utilized as a therapeutic target. Different therapeutic approaches and strategies such as exercise and diet, pharmacological approaches, and utilization of nanoparticles in drug delivery and gene therapy can be effective in the treatment and control of T2D. Glucagon-like peptide 1 (GLP-1) and sodium glucose cotransporter-2 (SGLT2) have both been considered as drug classes in the treatment of T2D and T2D-related diseases such as cardiovascular disease and renal disease, and have considerable influences such as diminished cardiovascular mortality in individuals with T2D, ameliorate postprandial glycaemia, ameliorate fasting glycaemia, and diminish body weight on disease treatment and improvement process. In the present review article, we have made an attempt to explore the risk factors, Genes, and diseases associated with T2D, therapeutic approaches in T2D, the influences of drugs such as Dapagliflozin, Metformin, Acarbose, Januvia (Sitagliptin), and Ertugliflozin on T2D in clinical trials and animal model studies. Research in clinical trials has promising results that support the role of these drug approaches in T2D prophylaxis and ameliorate safety even though additional clinical research is still obligatory.

Metformin Transport Rates Between Plasma and Red Blood Cells in Humans

Background

Metformin has been used for the treatment of type 2 diabetes for over 60 years; however, its mechanism of pharmacological action is not fully clear. Different hypotheses exist regarding metformin distribution and redistribution mechanisms between plasma and erythrocytes/red blood cells (RBCs).

Objective

We aimed to test the hypothesis that the metformin distribution between plasma and RBC occurs via concentration difference-driven passive transport and estimated transport rate coefficient values based on metformin concentration time series in plasma and RBCs from in vivo studies.

Methods

An ordinary differential equation (ODE) system with two compartments was used to describe diffusion-based passive transport between plasma and RBCs. Metformin concentration time series in plasma and RBCs of 35 individuals were used for metformin transport parametrization. Plasma concentration has been approximated by biexponential decline.

Results

A single passive transport coefficient, k = 0.044 ± 0.014 (h^–1), can be applied, describing the uptake and release transport rate versus the linear equation v = k × (M_pl − M_RBC), where M_pl is the metformin concentration in plasma and M_RBC is the metformin concentration in RBCs.

Conclusions

Our research suggests that passive transport can explain metformin distribution dynamics between plasma and RBCs because transport speed is proportional to the metformin concentration difference and independent of the transport direction. Concentration difference-driven passive transport can explain the mechanism of faster metformin distribution to RBCs the first few hours after administration, and faster release and domination of the redistribution transport rate after metformin concentration in plasma becomes smaller than in RBCs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40262-021-01058-2.

A Two-Stage Study Identifies Two Novel Polymorphisms in PRKAG2 Affecting Metformin Response in Chinese Type 2 Diabetes Patients

Objective

Individual differences in glycemic response to metformin in antidiabetic treatment exist widely. Although some associated genetic variations have been discovered, they still cannot accurately predict metformin response. In the current study, we set out to investigate novel genetic variants affecting metformin response in Chinese type 2 diabetes (T2D) patients.

Methods

A two-stage study enrolled 500 T2D patients who received metformin, glibenclamide or a combination of both were recruited from 2009 to 2012 in China. Change of HbA_1c, adjusted by clinical covariates, was used to evaluate glycemic response to metformin. Selected single nucleotide polymorphisms (SNPs) were genotyped using the Infinium iSelect and/or Illumina GoldenGate genotyping platform. A linear regression model was used to evaluate the association between SNPs and response.

Results

A total of 3739 SNPs were screened in Stage 1, of which 50 were associated with drug response. Except for one genetic variant preferred to affect glibenclamide, the remaining SNPs were subsequently verified in Stage 2, and two SNPs were successfully validated. These were PRKAG2 rs2727528 (discovery group: β=−0.212, P=0.046; validation group: β=−0.269, P=0.028) and PRKAG2 rs1105842 (discovery group: β=0.205, P=0.048; validation group: β=0.273, P=0.025). C allele carriers of rs2727528 and C allele carriers of rs1105842 would have a larger difference of HbA_1c level when using metformin.

Conclusion

Two variants rs2727528 and rs1105842 in PRKAG2, encoding γ2 subunit of AMP-activated protein kinase (AMPK), were found to be associated with metformin response in Chinese T2D patients. These findings may provide some novel information for personalized pharmacotherapy of metformin in China.

A Whole-Body Physiologically Based Pharmacokinetic Model Characterizing Interplay of OCTs and MATEs in Intestine, Liver and Kidney to Predict Drug-Drug Interactions of Metformin with Perpetrators

Transmembrane transport of metformin is highly controlled by transporters including organic cation transporters (OCTs), plasma membrane monoamine transporter (PMAT), and multidrug/toxin extrusions (MATEs). Hepatic OCT1, intestinal OCT3, renal OCT2 on tubule basolateral membrane, and MATE1/2-K on tubule apical membrane coordinately work to control metformin disposition. Drug–drug interactions (DDIs) of metformin occur when co-administrated with perpetrators via inhibiting OCTs or MATEs. We aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model characterizing interplay of OCTs and MATEs in the intestine, liver, and kidney to predict metformin DDIs with cimetidine, pyrimethamine, trimethoprim, ondansetron, rabeprazole, and verapamil. Simulations showed that co-administration of perpetrators increased plasma exposures to metformin, which were consistent with clinic observations. Sensitivity analysis demonstrated that contributions of the tested factors to metformin DDI with cimetidine are gastrointestinal transit rate > inhibition of renal OCT2 ≈ inhibition of renal MATEs > inhibition of intestinal OCT3 > intestinal pH > inhibition of hepatic OCT1. Individual contributions of transporters to metformin disposition are renal OCT2 ≈ renal MATEs > intestinal OCT3 > hepatic OCT1 > intestinal PMAT. In conclusion, DDIs of metformin with perpetrators are attributed to integrated effects of inhibitions of these transporters.

Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues

Metformin is the primary drug for type 2 diabetes treatment and a promising candidate for other disease treatment. It has significant deviations between individuals in therapy efficiency and pharmacokinetics, leading to the administration of an unnecessary overdose or an insufficient dose. There is a lack of data regarding the concentration-time profiles in various human tissues that limits the understanding of pharmacokinetics and hinders the development of precision therapies for individual patients. The physiologically based pharmacokinetic (PBPK) model developed in this study is based on humans’ known physiological parameters (blood flow, tissue volume, and others). The missing tissue-specific pharmacokinetics parameters are estimated by developing a PBPK model of metformin in mice where the concentration time series in various tissues have been measured. Some parameters are adapted from human intestine cell culture experiments. The resulting PBPK model for metformin in humans includes 21 tissues and body fluids compartments and can simulate metformin concentration in the stomach, small intestine, liver, kidney, heart, skeletal muscle adipose, and brain depending on the body weight, dose, and administration regimen. Simulations for humans with a bodyweight of 70kg have been analyzed for doses in the range of 500-1500mg. Most tissues have a half-life (T_1/2) similar to plasma (3.7h) except for the liver and intestine with shorter T_1/2 and muscle, kidney, and red blood cells that have longer T_1/2. The highest maximal concentrations (C_max) turned out to be in the intestine (absorption process) and kidney (excretion process), followed by the liver. The developed metformin PBPK model for mice does not have a compartment for red blood cells and consists of 20 compartments. 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 personalized model can be used as a decision support tool for precision therapy development for individuals.

Inhibition of organic cation transporter 3 activity by tyrosine kinase inhibitors

Organic cation transporter (OCT) 3 (SLC22A3) is a widely expressed drug transporter, handling notably metformin and platinum derivatives, as well as endogenous compounds like monoamine neurotransmitters. OCT3 has been shown to be inhibited by a few marketed tyrosine kinase inhibitors (TKIs). The present study was designed to determine whether additional TKIs may interact with OCT3. For this purpose, the effects of 25 TKIs toward OCT3 activity were analyzed using OCT3-overexpressing HEK293 cells. 13/25 TKIs, each used at 10 µM, were found to behave as moderate or strong inhibitors of OCT3 activity, that is, they decreased OCT3-mediated uptake of the fluorescent dye 4-(4-(dimethylamino)styryl)-N-methylpyridinium iodide by at least 50% or 80%, respectively. This OCT3 inhibition was correlated to some molecular descriptors of TKIs, such as the percentage of H atoms and that of cationic forms at pH = 7.4. It was concentration-dependent, notably for brigatinib, ceritinib, and crizotinib, which exhibited low half maximal inhibitory concentration (IC₅₀ ) values in the 28-106 nM range. Clinical concentrations of these three marketed TKIs, as well as those of pacritinib, were next predicted to inhibit in vivo OCT3 activity according to regulatory criteria. Cellular TKI accumulation experiments as well as trans-stimulation assays, however, demonstrated that OCT3 does not transport brigatinib, ceritinib, crizotinib, and pacritinib, thus discarding any implication of OCT3 in the pharmacokinetics of these TKIs. Taken together, these data suggest that some TKIs may act as potent inhibitors of OCT3 activity, which may have consequences in terms of drug-drug interactions and toxicity.

Pharmacogenetics of Type 2 Diabetes-Progress and Prospects

Type 2 diabetes mellitus (T2D) is a chronic metabolic disease resulting from insulin resistance and progressively reduced insulin secretion, which leads to impaired glucose utilization, dyslipidemia and hyperinsulinemia and progressive pancreatic beta cell dysfunction. The incidence of type 2 diabetes mellitus is increasing worldwide and nowadays T2D already became a global epidemic. The well-known interindividual variability of T2D drug actions such as biguanides, sulfonylureas/meglitinides, DPP-4 inhibitors/GLP1R agonists and SGLT-2 inhibitors may be caused, among other things, by genetic factors. Pharmacogenetic findings may aid in identifying new drug targets and obtaining in-depth knowledge of the causes of disease and its physiological processes, thereby, providing an opportunity to elaborate an algorithm for tailor or precision treatment. The aim of this article is to summarize recent progress and discoveries for T2D pharmacogenetics and to discuss the factors which limit the furthering accumulation of genetic variability knowledge in patient response to therapy that will allow improvement the personalized treatment of T2D.

Epigenetic markers associated with metformin response and intolerance in drug-naïve patients with type 2 diabetes

Metformin is the first-line pharmacotherapy for managing type 2 diabetes (T2D). However, many patients with T2D do not respond to or tolerate metformin well. Currently, there are no phenotypes that successfully predict glycemic response to, or tolerance of, metformin. We explored whether blood-based epigenetic markers could discriminate metformin response and tolerance by analyzing genome-wide DNA methylation in drug-naïve patients with T2D at the time of their diagnosis. DNA methylation of 11 and 4 sites differed between glycemic responders/nonresponders and metformin-tolerant/intolerant patients, respectively, in discovery and replication cohorts. Greater methylation at these sites associated with a higher risk of not responding to or not tolerating metformin with odds ratios between 1.43 and 3.09 per 1-SD methylation increase. Methylation risk scores (MRSs) of the 11 identified sites differed between glycemic responders and nonresponders with areas under the curve (AUCs) of 0.80 to 0.98. MRSs of the 4 sites associated with future metformin intolerance generated AUCs of 0.85 to 0.93. Some of these blood-based methylation markers mirrored the epigenetic pattern in adipose tissue, a key tissue in diabetes pathogenesis, and genes to which these markers were annotated to had biological functions in hepatocytes that altered metformin-related phenotypes. Overall, we could discriminate between glycemic responders/nonresponders and participants tolerant/intolerant to metformin at diagnosis by measuring blood-based epigenetic markers in drug-naïve patients with T2D. This epigenetics-based tool may be further developed to help patients with T2D receive optimal therapy.

The Impact of Genetic Polymorphisms in Organic Cation Transporters on Renal Drug Disposition

A considerable number of drugs and/or their metabolites are excreted by the kidneys through glomerular filtration and active renal tubule secretion via transporter proteins. Uptake transporters in the proximal tubule are part of the solute carrier (SLC) superfamily, and include the organic cation transporters (OCTs). Several studies have shown that specific genetic polymorphisms in OCTs alter drug disposition and may lead to nephrotoxicity. Multiple single nucleotide polymorphisms (SNPs) have been reported for the OCT genes (SLC22A1, SLC22A2 and SLC22A3), which can influence the proteins’ structure and expression levels and affect their transport function. A gain-in-function mutation may lead to accumulation of drugs in renal proximal tubule cells, eventually leading to nephrotoxicity. This review illustrates the impact of genetic polymorphisms in OCTs on renal drug disposition and kidney injury, the clinical significances and how to personalize therapies to minimize the risk of drug toxicity.

Emerging neuroprotective effect of metformin in Parkinson's disease: A molecular crosstalk

Parkinson's disease (PD) is a devastating neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and Lewy pathology. PD is a major concern of today's aging population and has emerged as a global health burden. Despite the rapid advances in PD research over the past decades, the gold standard therapy provides only symptomatic relief and fails to halt disease progression. Therefore, exploring novel disease-modifying therapeutic strategies is highly demanded. Metformin, which is currently used as a first-line therapy for type 2 diabetes mellitus (T2DM), has recently demonstrated to exert a neuroprotective role in several neurodegenerative disorders including PD, both in vitro and in vivo. In this review, we explore the neuroprotective potential of metformin based on emerging evidence from pre-clinical and clinical studies. Regarding the underlying molecular mechanisms, metformin has been shown to inhibit α-synuclein (SNCA) phosphorylation and aggregation, prevent mitochondrial dysfunction, attenuate oxidative stress, modulate autophagy mainly via AMP-activated protein kinase (AMPK) activation, as well as prevent neurodegeneration and neuroinflammation. Overall, the neuroprotective effects of metformin in PD pathogenesis present a novel promising therapeutic strategy that might overcome the limitations of current PD treatment.

Molecular and cellular physiology of organic cation transporter 2

Organic cation transporters play a critical role in mediating the distribution of cationic pharmaceuticals. Indeed, organic cation transporter (OCT)2 is the initial step in the renal secretion of organic cations and consequently plays a defining role in establishing the pharmacokinetics of many cationic drugs. Although a hallmark of OCTs is their broad selectivity, this characteristic also makes them targets for unwanted, adverse drug-drug interactions (DDIs), making them a focus for efforts to develop models of ligand interaction that could predict and preempt these adverse interactions. This review discusses the molecular characteristics of these transporters as well as the evidence that established the OCTs as key players in the distribution of organic cations. However, the primary focus is the present understanding of the complexity of ligand interaction with OCTs, particularly OCT2, including evidence for the presence of multiple ligand-binding sites and the influence of substrate structure on the affinity of the transporter for inhibitory ligands. This leads to a discussion of the complexities associated with the development of protocols for assessing the inhibitory potential of new molecular entities to perpetrate unwanted DDIs, the criteria that should be considered in the interpretation of the results of such protocols, and the challenges associated with development of models capable of predicting unwanted DDIs.

The Role of Histamine in the Pathophysiology of Asthma and the Clinical Efficacy of Antihistamines in Asthma Therapy

Mast cells play a critical role in the pathogenesis of allergic asthma. Histamine is a central mediator released from mast cells through allergic reactions. Histamine plays a role in airway obstruction via smooth muscle contraction, bronchial secretion, and airway mucosal edema. However, previous clinical trials of H1 receptor antagonists (H1RAs) as a treatment for asthma were not successful. In recent years, type 2 innate immunity has been demonstrated to be involved in allergic airway inflammation. Allergic asthma is defined by IgE antibody-mediated mast cell degranulation, while group 2 innate lymphoid cells (ILC2) induce eosinophilic inflammation in nonallergic asthma without allergen-specific IgE. Anti-IgE therapy has demonstrated prominent efficacy in the treatment of severe allergic asthmatics sensitized with specific perennial allergens. Furthermore, recent trials of specific cytokine antagonists indicated that these antagonists were effective in only some subtypes of asthma. Accordingly, H1RAs may show significant clinical efficacy for some subtypes of allergic asthma in which histamine is deeply associated with the pathophysiology.

Pharmacogenetics of type 2 diabetes mellitus, the route toward tailored medicine

Type 2 diabetes mellitus (T2DM) is a chronic disease that has reached the levels of a global epidemic. In order to achieve optimal glucose control, it is often necessary to rely on combination therapy of multiple drugs or insulin because uncontrolled glucose levels result in T2DM progression and enhanced risk of complications and mortality. Several antihyperglycemic agents have been developed over time, and T2DM pharmacotherapy should be prescribed based on suitability for the individual patient's characteristics. Pharmacogenetics is the branch of genetics that investigates how our genome influences individual responses to drugs, therapeutic outcomes, and incidence of adverse effects. In this review, we evaluated the pharmacogenetic evidences currently available in the literature, and we identified the top informative genetic variants associated with response to the most common anti-diabetic drugs: metformin, DPP-4 inhibitors/GLP1R agonists, thiazolidinediones, and sulfonylureas/meglitinides. Overall, we found 40 polymorphisms for each drug class in a total of 71 loci, and we examined the possibility of encouraging genetic screening of these variants/loci in order to critically implement decision-making about the therapeutic approach through precision medicine strategies. It is possible then to anticipate that when the clinical practice will take advantage of the genetic information of the diabetic patients, this will provide a useful resource for the prevention of T2DM progression, enabling the identification of the precise drug that is most likely to be effective and safe for each patient and the reduction of the economic impact on a global scale.

Metformin transporter pharmacogenomics: insights into drug disposition-where are we now?

INTRODUCTION

Metformin is recommended as first-line treatment for type 2 diabetes (T2D) by all major diabetes guidelines. With appropriate usage it is safe and effective overall, but its efficacy and tolerability show considerable variation between individuals. It is a substrate for several drug transporters and polymorphisms in these transporter genes have shown effects on metformin pharmacokinetics and pharmacodynamics. Areas covered: This article provides a review of the current status of the influence of transporter pharmacogenomics on metformin efficacy and tolerability. The transporter variants identified to have an important influence on the absorption, distribution, and elimination of metformin, particularly those in organic cation transporter 1 (OCT1, gene SLC22A1), are reviewed. Expert opinion: Candidate gene studies have shown that genetic variations in SLC22A1 and other drug transporters influence the pharmacokinetics, glycemic responses, and gastrointestinal intolerance to metformin, although results are somewhat discordant. Conversely, genome-wide association studies of metformin response have identified signals in the pharmacodynamic pathways rather than the transporters involved in metformin disposition. Currently, pharmacogenomic testing to predict metformin response and tolerability may not have a clinical role, but with additional data from larger studies and availability of safe and effective alternative antidiabetic agents, this is likely to change.

Pharmacogenetics of oral antidiabetic therapy

Type 2 diabetes prevalence is still on the rise worldwide. Antidiabetic drugs are widely prescribed to patients with Type 2 diabetes. Most patients start with metformin which is mostly well tolerated. However, a high percentage of patients fail to achieve glycemic control. The effectiveness of metformin as well as most other antidiabetic drugs depends among other factors on interindividual genetic differences that are up to now ignored in the treatment of Type 2 diabetes. Interestingly, many genes influencing the effectiveness of antidiabetic drugs are Type 2 diabetes risk genes making matters worse. Here, we shed light on these interindividual genetic differences.