Using a limited sampling strategy to investigate the interindividual pharmacokinetic variability in metformin: A large prospective trial

Metformin-'BRAINS & AIMS' pharmacological/prescribing principles of commonly prescribed (Top 100) drugs: Education and discussion

We review pharmacological/prescribing principles relating to metformin according to our mnemonic framework: 'BRAINS & AIMS' (Benefits, Risks, Adverse Effects, Interactions, Necessary prophylaxis, Susceptibilities, Administering, Informing, Monitoring and Stopping): Benefits: Metformin's licensed uses: Type 2 diabetes mellitus (T2DM) treatment, reduction in risk or delay of onset. No clear evidence metformin influences patient-important outcomes [Cochrane Review (2020) of 18 RCTs (n = 10 680)]. Risks: Inexpensive, essential WHO list drug; use contraindicated/not tolerated in 15%: for example, contraindication: lactic acidosis in renal impairment (eGFR <30 mL/min/1.73 m² ). Adverse effects: Common gastrointestinal (GI) side effects are dose-related and include abdominal pain, decreased appetite, diarrhoea (usually transient), nausea and vomiting, altered taste; vitamin B₁₂ deficiency. Rare: acute metabolic acidosis (lactic acidosis/diabetic ketoacidosis). Interactions (pharmacokinetic) occur with drugs impairing renal function and hence metformin excretion, and drugs inhibiting organic cation transporter 1 or 2 (OCT1, OCT2), and/or multidrug and toxin extrusion protein 1 (MATE1/2-K), such as cimetidine, ranolazine, trimethoprim and verapamil, and inducers such as rifampicin. The risk of hypoglycaemia may increase when metformin is used in combination with other medications for diabetes (pharmacodynamic interaction). Necessary prophylaxis: Detect/treat vitamin B₁₂ deficiency. Susceptible groups: Elderly/renal/liver impairment (lactic acidosis); safe in pregnancy/breastfeeding. Administering: Initially 500 mg once daily (morning) with breakfast; titrate only after 1 week. Informing (relevant BRAINS & A(I)MS principles). Monitoring: Renal function beforehand, and 6-12 monthly, HbA1c 3-6 monthly until controlled. Serum vitamin B₁₂ levels if deficiency is suspected/risk factors for. Stopping: Encourage patients to continue medication, unless deteriorating renal/liver function. Reasons for deprescribing: no harms from stopping suddenly.

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.

Minimally-Invasive and Efficient Method to Accurately Fit the Bergman Minimal Model to Diabetes Type 2

Introduction

Diabetes mellitus is a global burden that is expected to grow 25 % by 2030. This will increase the need for prevention, diagnosis and treatment of diabetes. Animal and individualized in silico models will allow understanding and compensation for inter and intra-individual differences in treatment and management strategies for diabetic patients. The method presented here can advance the concept of personalized medicine.

Methods

Twenty experiments were performed with Sprague-Dawley rats with streptozotocin induced experimental diabetes in which the insulin-glucose response curve was recorded over 60-100 min using only an insulin pump and a percutaneous glucose sensor. The information was used to fit the five-parameter Bergman Minimal Model to the experimental results using a genetic algorithm with a root-mean-squared optimization rule.

Results

The Bergman Minimal Model parameters were estimated with high accuracy, low prediction bias, and low average root-mean-squared error of 15.27 mg/dl glucose.

Conclusions

This study demonstrates a simple method to accurately parameterize the Bergman Minimal Model. We used Sprague-Dawley rats since their physiology is close to that of humans. The parameters can be used to objectively characterize the physiological severity of diabetes. In this way, planned treatments can compensate for natural variations of conditions both inter and intra patients. Changes in parameters indicate the patient's diabetic condition using values of glucose effectiveness ( S G = p 1 ) and insulin sensitivity ( S I = p 3 / p 2 ). Quantifying the diabetic patient's condition is consistent with the trend toward personalized medicine. Parameter values can also be used to explain atypical research results of other studies and increase understanding of diabetes.

Oral and intravenous pharmacokinetics of metformin with and without oral codeine intake in healthy subjects: A cross-over study

The aim of the study was to investigate if there is a clinically relevant drug interaction between metformin and codeine. Volunteers were randomized to receive on four separate occasions: (A) orally administered metformin (1 g), (B) intravenously administered metformin (0.5 g), (C) five doses of tablet codeine 25 mg; the last dose was administered together with oral metformin (1 g), and (D) five doses of tablet codeine 25 mg; the last dose was administered together with metformin (0.5 g) intravenously. Blood samples were drawn for 24 h after administration of metformin, and for 6 h after administration of codeine and analyzed using liquid chromatography and tandem mass spectrometry. Healthy volunteers genotyped as CYP2D6 normal metabolizers (*1/*1) without known reduced function variants in the OCT1 gene (rs12208357, rs34130495, rs34059508, and rs72552763) were invited. The median absorption fraction of metformin was 0.31 and was not influenced by codeine intake. The median time to maximum concentration (Tmax) after oral intake of metformin was 2 h without, and 3 h with codeine (p = 0.06). The geometric mean ratios of the areas under the plasma concentration time‐curve (AUCs) for morphine and its metabolites M3G and M6G for oral intake of metformin‐to‐no metformin were 1.21, 1.31, and 1.27, respectively, and for i.v. metformin‐to‐no metformin 1.28, 1.34, and 1.30, respectively. Concomitant oral and i.v. metformin increased the plasma levels of morphine, M3G and M6G. These small pharmacokinetic changes may well contribute to an increased risk of early discontinuation of metformin. Hence, a clinically relevant drug‐drug interaction between metformin and codeine seems plausible.

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.