How drug onset rate and duration of action affect drug forgiveness

Medication nonadherence is one of the largest problems in healthcare today, particularly for patients undergoing long-term pharmacotherapy. To combat nonadherence, it is often recommended to prescribe so-called "forgiving" drugs, which maintain their effect despite lapses in patient adherence. Nevertheless, drug forgiveness is difficult to quantify and compare between different drugs. In this paper, we construct and analyze a stochastic pharmacokinetic/pharmacodynamic (PK/PD) model to quantify and understand drug forgiveness. The model parameterizes a medication merely by an effective rate of onset of effect when the medication is taken (on-rate) and an effective rate of loss of effect when a dose is missed (off-rate). Patient dosing is modeled by a stochastic process that allows for correlations in missed doses. We analyze this "on/off" model and derive explicit formulas that show how treatment efficacy depends on drug parameters and patient adherence. As a case study, we compare the effects of nonadherence on the efficacy of various antihypertensive medications. Our analysis shows how different drugs can have identical efficacies under perfect adherence, but vastly different efficacies for adherence patterns typical of actual patients. We further demonstrate that complex PK/PD models can indeed be parameterized in terms of effective on-rates and off-rates. Finally, we have created an online app to allow pharmacometricians to explore the implications of our model and analysis.

Assessment of the nlmixr R package for population pharmacokinetic modeling: A metformin case study

AIM

nlmixr offers first-order conditional estimation (FOCE), FOCE with interaction (FOCEi) and stochastic approximation estimation-maximisation (SAEM) to fit nonlinear mixed-effect models (NLMEM). We modelled metformin's pharmacokinetic data using nlmixr and investigated SAEM and FOCEi's performance with respect to bias and precision of parameter estimates, and robustness to initial estimates.

METHOD

Compartmental models were fitted. The final model was determined based on the objective function value and inspection of goodness-of-fit plots. The bias and precision of parameter estimates were compared between SAEM and FOCEi using stochastic simulations and estimations. For robustness, parameters were re-estimated as the initial estimates were perturbed 100 times and resultant changes evaluated.

RESULTS

The absorption kinetics of metformin depend significantly on food status. Under the fasted state, the first-order absorption into the central compartment was preceded by zero-order infusion into the depot compartment, whereas for the fed state, the absorption into the depot was instantaneous followed by first-order absorption from depot into the central compartment. The means of relative mean estimation error (rMEE) ( ME E SAEM ME E FOCEi ) and rRMSE ( RMS E SAEM RMS E FOCEi ) were 0.48 and 0.35, respectively. All parameter estimates given by SAEM appeared to be narrowly distributed and were close to the true value used for simulation. In contrast, the distribution of estimates from FOCEi were skewed and more biased. When initial estimates were perturbed, FOCEi estimates were more biased and imprecise.

DISCUSSION

nlmixr is reliable for NLMEM. SAEM was superior to FOCEi in terms of bias and precision, and more robust against initial estimate perturbations.

Research Progress of Population Pharmacokinetic of Metformin

Metformin is commonly used as first-line treatment for T2DM (type2 diabetes mellitus). Owing to the high pharmacokinetic (PK) variability, several population pharmacokinetic (PPK) models have been developed for metformin to explore potential covariates that affect its pharmacokinetic variation. This comprehensive review summarized the published PPK studies of metformin, aimed to summarize PPK models of metformin. Most studies described metformin pharmacokinetics as a 2-compartment (2-CMT) model with 4 study describing its pharmacokinetics as 1-compartment (1-CMT). Studies on metformin PPK have shown that obesity, creatinine clearance (CL_Cr), gene polymorphism, degree of renal function damage, and pathological conditions all have a certain impact on the PK parameters of metformin. It is particularly important to formulate individualized dosing regimens. For future PPK studies of metformin, we believe that more attention should be paid to special populations.

Optimization of trial duration to predict long-term HbA1c change with therapy: A pharmacometrics simulation-based evaluation

Glycated hemoglobin (HbA1c) is the main biomarker of diabetes drug development. However, because of its delayed turnover, trial duration is rarely shorter than 12 weeks, and being able to predict long-term HbA1c with precision using data from shorter studies would be beneficial. The feasibility of reducing study duration was therefore investigated in this study, assuming a model-based analysis. The aim was to investigate the predictive performance of 24- and 52-week extrapolations using data from up to 4, 6, 8 or 12 weeks, with six previously published pharmacometric models of HbA1c. Predictive performance was assessed through simulation-based dose-response predictions and model averaging (MA) with two hypothetical drugs. Results were consistent across the methods of assessment, with MA supporting the results derived from the model-based framework. The models using mean plasma glucose (MPG) or nonlinear fasting plasma glucose (FPG) effect, driving the HbA1c formation, showed good predictive performance despite a reduced study duration. The models, using the linear effect of FPG to drive the HbA1c formation, were sensitive to the limited amount of data in the shorter studies. The MA with bootstrap demonstrated strongly that a 4-week study duration is insufficient for precise predictions of all models. Our findings suggest that if data are analyzed with a pharmacometric model with MPG or FPG with a nonlinear effect to drive HbA1c formation, a study duration of 8 weeks is sufficient with maintained accuracy and precision of dose-response predictions.

Estimating heritability of glycaemic response to metformin using nationwide electronic health records and population-sized pedigree

Background

Variability of response to medication is a well-known phenomenon, determined by both environmental and genetic factors. Understanding the heritable component of the response to medication is of great interest but challenging due to several reasons, including small study cohorts and computational limitations.

Methods

Here, we study the heritability of variation in the glycaemic response to metformin, first-line therapeutic agent for type 2 diabetes (T2D), by leveraging 18 years of electronic health records (EHR) data from Israel’s largest healthcare service provider, consisting of over five million patients of diverse ethnicities and socio-economic background. Our cohort consists of 80,788 T2D patients treated with metformin, with an accumulated number of 1,611,591 HbA1C measurements and 4,581,097 metformin prescriptions. We estimate the explained variance of glycated hemoglobin (HbA1c%) reduction due to inheritance by constructing a six-generation population-size pedigree from national registries and linking it to medical health records.

Results

Using Linear Mixed Model-based framework, a common-practice method for heritability estimation, we calculate a heritability measure of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}^{2}=12.6 \%$$\end{document}h2=12.6% (95% CI, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$6.1 \%\! -\!19.1 \%$$\end{document}6.1%−19.1%) for absolute reduction of HbA1c% after metformin treatment in the entire cohort, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}^{2}=21.0 \%$$\end{document}h2=21.0% (95% CI, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7.8 \%\! -\!34.4 \%$$\end{document}7.8%−34.4%) for males and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}^{2}=22.9 \%$$\end{document}h2=22.9% (95% CI, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10.0 \%\! -\!35.7 \%$$\end{document}10.0%−35.7%) in females. Results remain unchanged after adjusting for pre-treatment HbA1c%, and in proportional reduction of HbA1c%.

Conclusions

To the best of our knowledge, our work is the first to estimate heritability of drug response using solely EHR data combining a pedigree-based kinship matrix. We demonstrate that while response to metformin treatment has a heritable component, most of the variation is likely due to other factors, further motivating non-genetic analyses aimed at unraveling metformin’s action mechanism.

Individuals in a population might respond differently to the same medication and this phenomenon is commonly attributed to either genes or the environment. Here, we studied the familial aspects of the response to metformin, a medication used in the treatment of type 2 diabetes. We combined information from 18 years of medical records identifying newly treated patients with type 2 diabetes with information about how the trait was inherited within their families. We calculated a metric that tells us how well differences in people’s genes account for differences in their traits, and demonstrate that although the difference in response to metformin is in part explained by the genes people with type 2 diabetes inherit, most of it is not explained by genes. This finding contributes to a better understanding of differences in metformin response and might help inform treatment in future.

Kalka and Gavrieli et al. assessed the heritability of variation in the glycaemic response to metformin by leveraging electronic health records data gathered from a large cohort of patients with diabetes and combining it with pedigree information. The authors show that although the variability in this response has a heritable component, most of it is likely non-genetic.

Does the intact nephron hypothesis provide a reasonable model for metformin dosing in chronic kidney disease?

This research explored the intact nephron hypothesis (INH) as a model for metformin dosing in patients with chronic kidney disease (CKD). The INH assumes that glomerular filtration rate (GFR) will account for all kidney drug handling even for drugs eliminated by tubular secretion like metformin. We conducted two studies: (1) a regression analysis to explore the relationship between metformin clearance and eGFR metrics, and (2) a joint population pharmacokinetic analysis to test the relationship between metformin renal clearance and gentamicin clearance. The relationship between metformin renal clearance and eGFR metrics and gentamicin clearance was found to be linear, suggesting that a proportional dose reduction based on GFR in patients with CKD is reasonable.

Medroxyprogesterone Reverses Tolerable Dose Metformin-Induced Inhibition of Invasion via Matrix Metallopeptidase-9 and Transforming Growth Factor-β1 in KLE Endometrial Cancer Cells

This study was performed to evaluate the anticancer effects of tolerable doses of metformin with or without medroxyprogesterone (MPA) in endometrial cancer cells. Cell viability, cell invasion, and levels of matrix metallopeptidase (MMP) and transforming growth factor (TGF)-β1 were analyzed using three human endometrial adenocarcinoma cell lines (Ishikawa, KLE, and uterine serous papillary cancer (USPC)) after treatment with different dose combinations of MPA and metformin. Combining metformin (0, 100, 1000 µM) and 10 µM MPA induced significantly decreased cell viability in a time- and dose-dependent manner in Ishikawa cells, but not in KLE and USPC cells. In KLE cells, metformin treatment alone significantly inhibited cell invasion in a dose-dependent manner. The inhibitory effect of metformin was reversed when 10 µM MPA was combined, which was significantly inhibited again after treatment of MMP-2/9 inhibitor and/or TGF-β inhibitor. Changes of MMP-9 and TGF-β1 according to combinations of MPA and metformin were similar to those of invasion in KLE cells. In conclusion, the anticancer effects of tolerable doses of metformin varied according to cell type and combinations with MPA. Anti-invasive effect of metformin in KLE cells was completely reversed by the addition of MPA; this might be associated with MMP-9 and TGF-β1.

Association between Metformin Use and Clinical Outcomes Following Pancreaticoduodenectomy in Patients with Type 2 Diabetes and Pancreatic Ductal Adenocarcinoma

Retrospective studies on the association between metformin and clinical outcomes have mainly been performed on patients with non-resectable pancreatic ductal adenocarcinoma and may have been affected by time-related bias. To avoid this bias, recent studies have used time-varying analysis; however, they have only considered the start date of metformin use and not the stop date. We studied 283 patients with type 2 diabetes and pancreatic ductal adenocarcinoma following pancreaticoduodenectomy, and performed analysis using a Cox model with time-varying covariates, while considering both start and stop dates of metformin use. When start and stop dates were not considered, the metformin group showed significantly better survival. Compared with previous studies, adjusted analysis based on Cox models with time-varying covariates only considering the start date of postoperative metformin use showed no significant differences in survival. However, although adjusted analysis considering both start and stop dates showed no significant difference in recurrence-free survival, the overall survival was significantly better in the metformin group (Hazard ratio (HR), 0.747; 95% confidence interval (CI), 0.562–0.993; p = 0.045). Time-varying analysis incorporating both start and stop dates thus revealed that metformin use is associated with a higher overall survival following pancreaticoduodenectomy in patients with type 2 diabetes and pancreatic ductal adenocarcinoma.

Pleiotropic effects of metformin: Shaping the microbiome to manage type 2 diabetes and postpone ageing

Metformin is the first-choice therapy to lower glycaemia and manage type 2 diabetes. Continuously emerging epidemiological data and experimental models are showing additional protective effects of metformin against a number of age-related diseases (ARDs), e.g., cardiovascular diseases and cancer. This evidence has prompted the design of a specific trial, i.e., the Targeting Aging with Metformin (TAME) trial, to test metformin as an anti-ageing molecule. However, a unifying or prevailing mechanism of action of metformin is still debated. Here, we summarize the epidemiological data linking metformin to ARD prevention. Then, we dissect the deeply studied mechanisms of action explaining its antihyperglycemic effect and the putative mechanisms supporting its anti-ageing properties, focusing on studies using clinically pertinent doses. We hypothesize that the molecular observations obtained in different models with metformin could be indirectly mediated by its effect on gut flora. Novel evidence suggests that metformin reshapes the human microbiota, promoting the growth of beneficial bacterial species and counteracting the expansion of detrimental bacterial species. In turn, this action would influence the balance between pro- and anti-inflammatory circulating factors, thereby promoting glycaemic control and healthy ageing. This framework may reconcile diverse observations, providing information for designing further studies to elucidate the complex interplay between metformin and the metabiome harboured in mammalian body compartments, thereby paving the way for innovative, bacterial-based therapeutics to manage type 2 diabetes and foster a longer healthspan.

A non-linear pharmacokinetic-pharmacodynamic relationship of metformin in healthy volunteers: An open-label, parallel group, randomized clinical study

Background

The aim of this study was to explore the pharmacokinetic-pharmacodynamic (PK-PD) relationship of metformin on glucose levels after the administration of 250 mg and 1000 mg of metformin in healthy volunteers.

Methods

A total of 20 healthy male volunteers were randomized to receive two doses of either a low dose (375 mg followed by 250 mg) or a high dose (1000 mg followed by 1000 mg) of metformin at 12-h intervals. The pharmacodynamics of metformin was assessed using oral glucose tolerance tests before and after metformin administration. The PK parameters after the second dose were evaluated through noncompartmental analyses. Four single nucleotide polymorphisms in MATE1, MATE2-K, and OCT2 were genotyped, and their effects on PK characteristics were additionally evaluated.

Results

The plasma exposure of metformin increased as the metformin dose increased. The mean values for the area under the concentration-time curve from dosing to 12 hours post-dose (AUC_0-12h) were 3160.4 and 8808.2 h·μg/L for the low- and high-dose groups, respectively. Non-linear relationships were found between the glucose-lowering effect and PK parameters with a significant inverse trend at high metformin exposure. The PK parameters were comparable among subjects with the genetic polymorphisms.

Conclusions

This study showed a non-linear PK-PD relationship on plasma glucose levels after the administration of metformin. The inverse relationship between systemic exposure and the glucose-lowering effect at a high exposure indicates a possible role for the intestines as an action site for metformin.

Trial registration

ClinicalTrials.gov NCT02712619

A Longitudinal HbA1c Model Elucidates Genes Linked to Disease Progression on Metformin

One-third of type-2 diabetic patients respond poorly to metformin. Despite extensive research, the impact of genetic and nongenetic factors on long-term outcome is unknown. In this study we combine nonlinear mixed effect modeling with computational genetic methodologies to identify predictors of long-term response. In all, 1,056 patients contributed their genetic, demographic, and long-term HbA1c data. The top nine variants (of 12,000 variants in 267 candidate genes) accounted for approximately one-third of the variability in the disease progression parameter. Average serum creatinine level, age, and weight were determinants of symptomatic response; however, explaining negligible variability. Two single nucleotide polymorphisms (SNPs) in CSMD1 gene (rs2617102, rs2954625) and one SNP in a pharmacologically relevant SLC22A2 gene (rs316009) influenced disease progression, with minor alleles leading to less and more favorable outcomes, respectively. Overall, our study highlights the influence of genetic factors on long-term HbA1c response and provides a computational model, which when validated, may be used to individualize treatment.

Effects of SLC22A1 Polymorphisms on Metformin-Induced Reductions in Adiposity and Metformin Pharmacokinetics in Obese Children With Insulin Resistance

Steady-state population pharmacokinetics of a noncommercial immediate-release metformin (hydrochloride) drug product were characterized in 28 severely obese children with insulin resistance. The concentration-time profiles with double peaks were well described by a 1-compartment model with 2 absorption sites. Mean population apparent clearance (CL/F) was 68.1 L/h, and mean apparent volume of distribution (V/F) was 28.8 L. Body weight was a covariate of CL/F and V/F. Estimated glomerular filtration rate was a significant covariate of CL/F (P < .001). SLC22A1 genotype did not significantly affect metformin pharmacokinetics. The response to 6 months of metformin treatment (HbA_1c , homeostasis model assessment for insulin resistance, fasting insulin, and glucose changes) did not differ between SLC22A1 wild-type subjects and carriers of presumably low-activity SLC22A1 alleles. However, SLC22A1 variant carriers had smaller reductions in percentage of total trunk fat after metformin therapy, although the percentage reduction in trunk fat was small. The median % change in trunk fat was -2.20% (-9.00% to 0.900%) and -1.20% (-2.40% to 7.30%) for the SLC22A1 wild-type subjects and variant carriers, respectively. Future study is needed to evaluate the effects of SLC22A1 polymorphisms on metformin-mediated weight reduction in obese children.

Once-daily delayed-release metformin lowers plasma glucose and enhances fasting and postprandial GLP-1 and PYY: results from two randomised trials

AIMS/HYPOTHESIS

Delayed-release metformin (Metformin DR) was developed to maximise gut-based mechanisms of metformin action by targeting the drug to the ileum. Metformin DR was evaluated in two studies. Study 1 compared the bioavailability and effects on circulating glucose and gut hormones (glucagon-like peptide-1, peptide YY) of Metformin DR dosed twice-daily to twice-daily immediate-release metformin (Metformin IR). Study 2 compared the bioavailability and glycaemic effects of Metformin DR dosages of 1,000 mg once-daily in the morning, 1,000 mg once-daily in the evening, and 500 mg twice-daily.

METHODS

Study 1 was a blinded, randomised, crossover study (three × 5 day treatment periods) of twice-daily 500 mg or 1,000 mg Metformin DR vs twice-daily 1,000 mg Metformin IR in 24 participants with type 2 diabetes conducted at two study sites (Celerion Inc.; Tempe, AZ, and Lincoln, NE, USA). Plasma glucose and gut hormones were assessed over 10.25 h at the start and end of each treatment period; plasma metformin was measured over 11 h at the end of each treatment period. Study 2 was a non-blinded, randomised, crossover study (three × 7 day treatment periods) of 1,000 mg Metformin DR once-daily in the morning, 1,000 mg Metformin DR once-daily in the evening, or 500 mg Metformin DR twice-daily in 26 participants with type 2 diabetes performed at a single study site (Celerion, Tempe, AZ). Plasma glucose was assessed over 24 h at the start and end of each treatment period, and plasma metformin was measured over 30 h at the end of each treatment period. Both studies implemented centrally generated computer-based randomisation using a 1:1:1 allocation ratio.

RESULTS

A total of 24 randomised participants were included in study 1; of these, 19 completed the study and were included in the evaluable population. In the evaluable population, all treatments produced similar significant reductions in fasting glucose (median reduction range, -0.67 to -0.81 mmol/l across treatments) and postprandial glucose (Day 5 to baseline AUC0-t ratio = 0.9 for all three treatments) and increases in gut hormones (Day 5 to baseline AUC0-t ratio range: 1.6-1.9 for GLP-1 and 1.4-1.5 for PYY) despite an almost 60% reduction in systemic metformin exposure for 500 mg Metformin DR compared with Metformin IR. A total of 26 randomised participants were included in study 2: 24 had at least one dose of study medication and at least one post-dose pharmacokinetic/pharmacodynamic assessment and were included in the pharmacokinetic/pharmacodynamic intent-to-treat analysis; and 12 completed all treatment periods and were included in the evaluable population. In the evaluable population, Metformin DR administered once-daily in the morning had 28% (90% CI -16%, -39%) lower bioavailability (least squares mean ratio of metformin AUC0-24) compared with either once-daily in the evening or twice-daily, although the glucose-lowering effects were maintained. In both studies, adverse events were primarily gastrointestinal in nature, and indicated similar or improved tolerability for Metformin DR vs Metformin IR; there were no clinically meaningful differences in vital signs, physical examinations or laboratory values.

CONCLUSIONS/INTERPRETATION

Dissociation of gut hormone release and glucose lowering from plasma metformin exposure provides strong supportive evidence for a distal small intestine-mediated mechanism of action. Directly targeting the ileum with Metformin DR once-daily in the morning may provide maximal metformin efficacy with lower doses and substantially reduce plasma exposure. Metformin DR may minimise the risk of lactic acidosis in those at increased risk from metformin therapy, such as individuals with renal impairment.

TRIAL REGISTRATION

Clinicaltrials.gov NCT01677299, NCT01804842 FUNDING: : This study was funded by Elcelyx Therapeutics Inc.

Metformin-associated lactic acidosis: Current perspectives on causes and risk

Although metformin has become a drug of choice for the treatment of type 2 diabetes mellitus, some patients may not receive it owing to the risk of lactic acidosis. Metformin, along with other drugs in the biguanide class, increases plasma lactate levels in a plasma concentration-dependent manner by inhibiting mitochondrial respiration predominantly in the liver. Elevated plasma metformin concentrations (as occur in individuals with renal impairment) and a secondary event or condition that further disrupts lactate production or clearance (e.g., cirrhosis, sepsis, or hypoperfusion), are typically necessary to cause metformin-associated lactic acidosis (MALA). As these secondary events may be unpredictable and the mortality rate for MALA approaches 50%, metformin has been contraindicated in moderate and severe renal impairment since its FDA approval in patients with normal renal function or mild renal insufficiency to minimize the potential for toxic metformin levels and MALA. However, the reported incidence of lactic acidosis in clinical practice has proved to be very low (<10 cases per 100,000 patient-years). Several groups have suggested that current renal function cutoffs for metformin are too conservative, thus depriving a substantial number of type 2 diabetes patients from the potential benefit of metformin therapy. On the other hand, the success of metformin as the first-line diabetes therapy may be a direct consequence of conservative labeling, the absence of which could have led to excess patient risk and eventual withdrawal from the market, as happened with earlier biguanide therapies. An investigational delayed-release metformin currently under development could potentially provide a treatment option for patients with renal impairment pending the results of future studies. This literature-based review provides an update on the impact of renal function and other conditions on metformin plasma levels and the risk of MALA in patients with type 2 diabetes.

The Primary Glucose-Lowering Effect of Metformin Resides in the Gut, Not the Circulation: Results From Short-term Pharmacokinetic and 12-Week Dose-Ranging Studies

OBJECTIVE

Delayed-release metformin (Met DR) is formulated to deliver the drug to the lower bowel to leverage the gut-based mechanisms of metformin action with lower plasma exposure. Met DR was assessed in two studies. Study 1 compared the bioavailability of single daily doses of Met DR to currently available immediate-release metformin (Met IR) and extended-release metformin (Met XR) in otherwise healthy volunteers. Study 2 assessed glycemic control in subjects with type 2 diabetes (T2DM) over 12 weeks.

RESEARCH DESIGN AND METHODS

Study 1 was a phase 1, randomized, four-period crossover study in 20 subjects. Study 2 was a 12-week, phase 2, multicenter, placebo-controlled, dose-ranging study in 240 subjects with T2DM randomized to receive Met DR 600, 800, or 1,000 mg administered once daily; blinded placebo; or unblinded Met XR 1,000 or 2,000 mg (reference).

RESULTS

The bioavailability of 1,000 mg Met DR b.i.d. was ∼50% that of Met IR and Met XR (study 1). In study 2, 600, 800, and 1,000 mg Met DR q.d. produced statistically significant, clinically relevant, and sustained reductions in fasting plasma glucose (FPG) levels over 12 weeks compared with placebo, with an ∼40% increase in potency compared with Met XR. The placebo-subtracted changes from baseline in HbA1c level at 12 weeks were consistent with changes in FPG levels. All treatments were generally well tolerated, and adverse events were consistent with Glucophage/Glucophage XR prescribing information.

CONCLUSIONS

Dissociation of the glycemic effect from plasma exposure with gut-restricted Met DR provides strong evidence for a predominantly lower bowel-mediated mechanism of metformin action.

Let Visuals Tell the Story: Medication Adherence in Patients with Type II Diabetes Captured by a Novel Ingestion Sensor Platform

BACKGROUND

Chronic diseases such as diabetes require high levels of medication adherence and patient self-management for optimal health outcomes. A novel sensing platform, Digital Health Feedback System (Proteus Digital Health, Redwood City, CA), can for the first time detect medication ingestion events and physiological measures simultaneously, using an edible sensor, personal monitor patch, and paired mobile device. The Digital Health Feedback System (DHFS) generates a large amount of data. Visual analytics of this rich dataset may provide insights into longitudinal patterns of medication adherence in the natural setting and potential relationships between medication adherence and physiological measures that were previously unknown.

OBJECTIVE

Our aim was to use modern methods of visual analytics to represent continuous and discrete data from the DHFS, plotting multiple different data types simultaneously to evaluate the potential of the DHFS to capture longitudinal patterns of medication-taking behavior and self-management in individual patients with type II diabetes.

METHODS

Visualizations were generated using time domain methods of oral metformin medication adherence and physiological data obtained by the DHFS use in 5 patients with type II diabetes over 37-42 days. The DHFS captured at-home metformin adherence, heart rate, activity, and sleep/rest. A mobile glucose monitor captured glucose testing and level (mg/dl). Algorithms were developed to analyze data over varying time periods: across the entire study, daily, and weekly. Following visualization analysis, correlations between sleep/rest and medication ingestion were calculated across all subjects.

RESULTS

A total of 197 subject days, encompassing 141,840 data events were analyzed. Individual continuous patch use varied between 87-98%. On average, the cohort took 78% (SD 12) of prescribed medication and took 77% (SD 26) within the prescribed ±2-hour time window. Average activity levels per subjects ranged from 4000-12,000 steps per day. The combination of activity level and heart rate indicated different levels of cardiovascular fitness between subjects. Visualizations over the entire study captured the longitudinal pattern of missed doses (the majority of which took place in the evening), the timing of ingestions in individual subjects, and the range of medication ingestion timing, which varied from 1.5-2.4 hours (Subject 3) to 11 hours (Subject 2). Individual morning self-management patterns over the study period were obtained by combining the times of waking, metformin ingestion, and glucose measurement. Visualizations combining multiple data streams over a 24-hour period captured patterns of broad daily events: when subjects rose in the morning, tested their blood glucose, took their medications, went to bed, hours of sleep/rest, and level of activity during the day. Visualizations identified highly consistent daily patterns in Subject 3, the most adherent participant. Erratic daily patterns including sleep/rest were demonstrated in Subject 2, the least adherent subject. Correlation between sleep /rest and medication ingestion in each individual subject was evaluated. Subjects 2 and 4 showed correlation between amount of sleep/rest over a 24-hour period and medication-taking the following day (Subject 2: r=.47, P<.02; Subject 4: r=.35, P<.05). With Subject 2, sleep/rest disruptions during the night were highly correlated (r=.47, P<.009) with missing doses the following day.

CONCLUSIONS

Visualizations integrating medication ingestion and physiological data from the DHFS over varying time intervals captured detailed individual longitudinal patterns of medication adherence and self-management in the natural setting. Visualizing multiple data streams simultaneously, providing a data-rich representation, revealed information that would not have been shown by plotting data streams individually. Such analyses provided data far beyond traditional adherence summary statistics and may form the foundation of future personalized predictive interventions to drive longitudinal adherence and support optimal self-management in chronic diseases such as diabetes.

Metformin-associated lactic acidosis

: Metformin is the most commonly prescribed oral antidiabetic agent. Despite a good safety profile in most patients with diabetes, the risk of metformin-associated lactic acidosis is real if safety guidelines are ignored. Experience with 3 cases of metformin-associated lactic acidosis is reported. Two cases were caused by inappropriate use of metformin in the presence of renal, cardiac and hepatic failure and 1 case followed an intentional overdose. The literature was reviewed on the clinical presentation, prevalence, pathogenesis, prognosis and management of metformin-associated lactic acidosis. This report highlights the importance of proper patient selection, clinical and laboratory monitoring and recommendation on when to stop the drug in ambulatory and hospitalized patients to prevent this unusual but potentially lethal complication.

Population pharmacokinetics of metformin in healthy subjects and patients with type 2 diabetes mellitus: simulation of doses according to renal function

BACKGROUND AND OBJECTIVE

Metformin is contraindicated in patients with renal impairment; however, there is poor adherence to current dosing guidelines. In addition, the pharmacokinetics of metformin in patients with significant renal impairment are not well described. The aims of this study were to investigate factors influencing the pharmacokinetic variability, including variant transporters, between healthy subjects and patients with type 2 diabetes mellitus (T2DM) and to simulate doses of metformin at varying stages of renal function.

METHODS

Plasma concentrations of metformin were pooled from three studies: patients with T2DM (study A; n = 120), healthy Caucasian subjects (study B; n = 16) and healthy Malaysian subjects (study C; n = 169). A population pharmacokinetic model of metformin was developed using NONMEM(®) version VI for both the immediate-release (IR) formulation and the extended-release (XR) formulation of metformin. Total body weight (TBW), lean body weight (LBW), creatinine clearance (CLCR; estimated using TBW and LBW) and 57 single-nucleotide polymorphisms (SNPs) of metformin transporters (OCT1, OCT2, OCT3, MATE1 and PMAT) were investigated as potential covariates. A nonparametric bootstrap (n = 1,000) was used to evaluate the final model. This model was used to simulate 1,000 concentration-time profiles for doses of metformin at each stage of renal impairment to ensure metformin concentrations do not exceed 5 mg/l, the proposed upper limit.

RESULTS

Creatinine clearance and TBW were clinically and statistically significant covariates with the apparent clearance and volume of distribution of metformin, respectively. None of the 57 SNPs in transporters of metformin were significant covariates. In contrast to previous studies, there was no effect on the pharmacokinetics of metformin in patients carrying the reduced function OCT1 allele (R61C, G401S, 420del or G465R). Dosing simulations revealed that the maximum daily doses in relation to creatinine clearance to prescribe to patients are 500 mg (15 ml/min), 1,000 mg (30 ml/min), 2,000 mg (60 ml/min) and 3,000 mg (120 ml/min), for both the IR and XR formulations.

CONCLUSION

The population model enabled doses of metformin to be simulated for each stage of renal function, to ensure the concentrations of metformin do not exceed 5 mg/l. However, the plasma concentrations of metformin at these dosage levels are still quite variable and monitoring metformin concentrations may be of value in individualising dosage. This study provides confirmatory data that metformin can be used, with appropriate dosage adjustment, in patients with renal impairment.

Population PK/PD analysis of metformin using the signal transduction model

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

Metformin, a biguanide glucose lowering agent, is commonly used to manage type 2 diabetes. The molecular mechanisms of metformin have not been fully identified, but turnover of biomarkers such as glucose and signalling pathways or translocation of glucose transporters are closely related to the glucose-lowering effects of metformin. The PK/PD of metformin have been investigated in healthy humans and patients with type 2 diabetes mellitus and modelling has been performed using an indirect response model.

WHAT THIS STUDY ADDS

The purpose of this investigation was to develop a population PK/PD model for metformin using a signal transduction model in healthy humans and predict the PK/PD profile in patients with type 2 diabetes. The aim was to compare a previous model (a biophase model) with the signal transduction model, and use a more appropriate model to follow the actions of metformin. Additionally, our developed model was appropriate to predict the time course of plasma metformin and fasting plasma glucose (FPG) concentrations in patients with type 2 diabetes. To our knowledge, this is the first published population PK/PD analysis using the signal transduction model for metformin. AIMS To develop a population pharmacokinetic (PK) and pharmacodynamic (PD) model for metformin (500 mg) using the signal transduction model in healthy humans and to predict the PK/PD profile in patients with type 2 diabetes.

METHODS

Following the oral administration of 500 mg metformin to healthy humans, plasma concentrations of metformin were measured using LC-MS/MS. A sequential modelling approach using NONMEM VI was used to facilitate data analysis. Monte Carlo simulation was performed to predict the antihyperglycaemic effect in patients with type 2 diabetes.

RESULTS

Forty-two healthy humans were included in the study. Population mean estimates (relative standard error, RSE) of apparent clearance, apparent volume of distribution and the absorption rate constant were 52.6 l h(-1) (4.18%), 113 l (56.6%) and 0.41 h(-1) , respectively. Covariate analyses revealed that creatinine clearance (CL(CR) ) significantly influenced metformin: CL/F= 52.6 × (CL(cr) /106.5)(0.782) . The signal transduction model was applied to describe the antihyperglycaemic effect of metformin. The population means for efficacy, potency, transit time and the Hill coefficient were estimated to be 19.8 (3.17%), 3.68 µg ml(-1) (3.89%), 0.5 h (2.89%) and 0.547 (9.05%), respectively. The developed model was used to predict the antihyperglycaemic effect in patients with type 2 diabetes. The predicted plasma glucose concentration value was similar to previous values.

CONCLUSIONS

The population signal transduction model was developed and evaluated for metformin use in healthy volunteers. Model evaluation by non-parametric bootstrap analysis suggested that the proposed model was robust and parameter values were estimated with good precision.

Influences of organic cation transporter polymorphisms on the population pharmacokinetics of metformin in healthy subjects

This study investigated the effects of genetic polymorphisms in organic cation transporter (OCT) genes, such as OCT1-3, OCTN1, MATE1, and MATE2-K, on metformin pharmacokinetics. Of particular interest was the influence of genetic polymorphisms as covariates on the variability in the population pharmacokinetics (PPK) of metformin using nonlinear mixed effects modeling (NONMEM). In a retrospective data analysis, data on subjects from five independent metformin bioequivalence studies that used the same protocol were assembled and compared with 96 healthy control subjects who were administered a single oral 500 mg dose of metformin. Genetic polymorphisms of OCT2-808 G>T and OCTN1-917C>T had a significant (P<0.05) effect on metformin pharmacokinetics, yielding a higher peak concentration with a larger area under the serum time-concentration curve. The values obtained were 102±34.5 L/h for apparent oral clearance (CL/F), 447±214 L for volume of distribution (V d/F), and 3.1±0.9 h for terminal half-life (mean±SD) by non-compartmental analysis. The NONMEM method gives similar results. The metformin serum levels were obtained by setting the one-compartment model to a first-order absorption and lag time. In the PPK model, the effects of OCT2-808 G>T and OCTN1-917C>T variants on the CL/F were significant (P<0.001 and P<0.05, respectively). Thus, genetic variants of OCTN1-917C>T, along with OCT2-808 G>T genetic polymorphisms, could be useful in titrating the optimal metformin dose.

Clinical pharmacokinetics of metformin

Metformin is widely used for the treatment of type 2 diabetes mellitus. It is a biguanide developed from galegine, a guanidine derivative found in Galega officinalis (French lilac). Chemically, it is a hydrophilic base which exists at physiological pH as the cationic species (>99.9%). Consequently, its passive diffusion through cell membranes should be very limited. The mean ± SD fractional oral bioavailability (F) of metformin is 55 ± 16%. It is absorbed predominately from the small intestine. Metformin is excreted unchanged in urine. The elimination half-life (t(&frac12;)) of metformin during multiple dosages in patients with good renal function is approximately 5 hours. From published data on the pharmacokinetics of metformin, the population mean of its clearances were calculated. The population mean renal clearance (CL(R)) and apparent total clearance after oral administration (CL/F) of metformin were estimated to be 510 ± 130 mL/min and 1140 ± 330 mL/min, respectively, in healthy subjects and diabetic patients with good renal function. Over a range of renal function, the population mean values of CL(R) and CL/F of metformin are 4.3 ± 1.5 and 10.7 ± 3.5 times as great, respectively, as the clearance of creatinine (CL(CR)). As the CL(R) and CL/F decrease approximately in proportion to CL(CR), the dosage of metformin should be reduced in patients with renal impairment in proportion to the reduced CL(CR). The oral absorption, hepatic uptake and renal excretion of metformin are mediated very largely by organic cation transporters (OCTs). An intron variant of OCT1 (single nucleotide polymorphism [SNP] rs622342) has been associated with a decreased effect on blood glucose in heterozygotes and a lack of effect of metformin on plasma glucose in homozygotes. An intron variant of multidrug and toxin extrusion transporter [MATE1] (G>A, SNP rs2289669) has also been associated with a small increase in antihyperglycaemic effect of metformin. Overall, the effect of structural variants of OCTs and other cation transporters on the pharmacokinetics of metformin appears small and the subsequent effects on clinical response are also limited. However, intersubject differences in the levels of expression of OCT1 and OCT3 in the liver are very large and may contribute more to the variations in the hepatic uptake and clinical effect of metformin. Lactic acidosis is the feared adverse effect of the biguanide drugs but its incidence is very low in patients treated with metformin. We suggest that the mean plasma concentrations of metformin over a dosage interval be maintained below 2.5 mg/L in order to minimize the development of this adverse effect.

AMP-activated protein kinase is activated in adipose tissue of individuals with type 2 diabetes treated with metformin: a randomised glycaemia-controlled crossover study

AIMS/HYPOTHESIS

The hypoglycaemic actions of metformin have been proposed to be mediated by hepatic AMP-activated protein kinase (AMPK). As the effects of metformin and the role of AMPK in adipose tissue remain poorly characterised, we examined the effect of metformin on AMPK activity in adipose tissue of individuals with type 2 diabetes in a randomised glycaemia-controlled crossover study.

METHODS

Twenty men with type 2 diabetes (aged 50-70 years) treated with diet, metformin or sulfonylurea alone were recruited from North Glasgow University National Health Service Trusts' diabetes clinics and randomised to either metformin or gliclazide for 10 weeks. Randomisation codes, generated by computer, were put into sealed envelopes and stored by the hospital pharmacist. Medication bottles were numbered, and allocation was done in sequence. The participants and investigators were blinded to group assignment. At the end of each phase of therapy adipose biopsy, AMPK activity (primary endpoint) and levels of lipid metabolism and signalling proteins were assessed. In parallel, the effect of metformin on AMPK and insulin-signalling pathways was investigated in 3T3-L1 adipocytes.

RESULTS

Ten participants were initially randomised to metformin and subsequently crossed over to gliclazide, while ten participants were initially randomised to gliclazide and subsequently crossed over to metformin. No participants discontinued the intervention and the adipose tissue AMPK activity was analysed in all 20 participants. There were no adverse events or side effects in the study group. Adipose AMPK activity was increased following metformin compared with gliclazide therapy (0.057 ± 0.007 vs 0.030 ± 0.005 [mean ± SEM] nmol min(-1) [mg lysate](-1); p < 0.005), independent of AMPK level, glycaemia or plasma adiponectin concentrations. The increase was associated with reduced levels of acetyl-CoA carboxylase (ACC) protein and increased ACC Ser80 phosphorylation. In 3T3-L1 adipocytes, metformin reduced levels of ACC protein and stimulated phosphorylation of AMPK Thr172 and hormone-sensitive lipase Ser565.

CONCLUSIONS

These results provide the first evidence that metformin activates AMPK and reduces ACC protein levels in human adipose tissue in vivo. Future studies are required to assess the role of adipose AMPK activation in the pharmacological effects of metformin.

TRIAL REGISTRATION

ISRCTN51336867.

Risk of fatal and nonfatal lactic acidosis with metformin use in type 2 diabetes mellitus

BACKGROUND

Metformin is an oral anti-hyperglycemic agent that has been shown to reduce total mortality compared to other anti-hyperglycemic agents, in the treatment of type 2 diabetes mellitus. Metformin, however, is thought to increase the risk of lactic acidosis, and has been considered to be contraindicated in many chronic hypoxemic conditions that may be associated with lactic acidosis, such as cardiovascular, renal, hepatic and pulmonary disease, and advancing age.

OBJECTIVES

To assess the incidence of fatal and nonfatal lactic acidosis, and to evaluate blood lactate levels, for those on metformin treatment compared to placebo or non-metformin therapies.

SEARCH STRATEGY

A comprehensive search was performed of electronic databases to identify studies of metformin treatment. The search was augmented by scanning references of identified articles, and by contacting principal investigators.

SELECTION CRITERIA

Prospective trials and observational cohort studies in patients with type 2 diabetes of least one month duration were included if they evaluated metformin, alone or in combination with other treatments, compared to placebo or any other glucose-lowering therapy.

DATA COLLECTION AND ANALYSIS

The incidence of fatal and nonfatal lactic acidosis was recorded as cases per patient-years, for metformin treatment and for non-metformin treatments. The upper limit for the true incidence of cases was calculated using Poisson statistics. In a second analysis lactate levels were measured as a net change from baseline or as mean treatment values (basal and stimulated by food or exercise) for treatment and comparison groups. The pooled results were recorded as a weighted mean difference (WMD) in mmol/L, using the fixed-effect model for continuous data.

MAIN RESULTS

Pooled data from 347 comparative trials and cohort studies revealed no cases of fatal or nonfatal lactic acidosis in 70,490 patient-years of metformin use or in 55,451 patients-years in the non-metformin group. Using Poisson statistics the upper limit for the true incidence of lactic acidosis per 100,000 patient-years was 4.3 cases in the metformin group and 5.4 cases in the non-metformin group. There was no difference in lactate levels, either as mean treatment levels or as a net change from baseline, for metformin compared to non-metformin therapies.

AUTHORS' CONCLUSIONS

There is no evidence from prospective comparative trials or from observational cohort studies that metformin is associated with an increased risk of lactic acidosis, or with increased levels of lactate, compared to other anti-hyperglycemic treatments.

Risk of fatal and nonfatal lactic acidosis with metformin use in type 2 diabetes mellitus

BACKGROUND

Metformin is an oral anti-hyperglycemic agent that has been shown to reduce total mortality compared to other anti-hyperglycemic agents, in the treatment of type 2 diabetes mellitus. Metformin, however, is thought to increase the risk of lactic acidosis, and has been considered to be contraindicated in many chronic hypoxemic conditions that may be associated with lactic acidosis, such as cardiovascular, renal, hepatic and pulmonary disease, and advancing age.

OBJECTIVES

To assess the incidence of fatal and nonfatal lactic acidosis, and to evaluate blood lactate levels, for those on metformin treatment compared to placebo or non-metformin therapies.

SEARCH STRATEGY

A comprehensive search was performed of electronic databases to identify studies of metformin treatment. The search was augmented by scanning references of identified articles, and by contacting principal investigators.

SELECTION CRITERIA

Prospective trials and observational cohort studies in patients with type 2 diabetes of least one month duration were included if they evaluated metformin, alone or in combination with other treatments, compared to placebo or any other glucose-lowering therapy.

DATA COLLECTION AND ANALYSIS

The incidence of fatal and nonfatal lactic acidosis was recorded as cases per patient-years, for metformin treatment and for non-metformin treatments. The upper limit for the true incidence of cases was calculated using Poisson statistics. In a second analysis lactate levels were measured as a net change from baseline or as mean treatment values (basal and stimulated by food or exercise) for treatment and comparison groups. The pooled results were recorded as a weighted mean difference (WMD) in mmol/L, using the fixed-effect model for continuous data.

MAIN RESULTS

Pooled data from 347 comparative trials and cohort studies revealed no cases of fatal or nonfatal lactic acidosis in 70,490 patient-years of metformin use or in 55,451 patients-years in the non-metformin group. Using Poisson statistics the upper limit for the true incidence of lactic acidosis per 100,000 patient-years was 4.3 cases in the metformin group and 5.4 cases in the non-metformin group. There was no difference in lactate levels, either as mean treatment levels or as a net change from baseline, for metformin compared to non-metformin therapies.

AUTHORS' CONCLUSIONS

There is no evidence from prospective comparative trials or from observational cohort studies that metformin is associated with an increased risk of lactic acidosis, or with increased levels of lactate, compared to other anti-hyperglycemic treatments.