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

Chae, Jung-Woo; Baek, In‐hwan; Lee, Byung‐yo; Cho, Seong-kwon; Kwon, Kwang‐il

doi:10.1111/j.1365-2125.2012.04260.x

Cited by 14 publications

(35 citation statements)

References 34 publications

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“…Finding covariates to explain PK variability for a better metformin dosing has been a focus of metformin PK research for long, and clearance has previously been reported to be highly variable . However, in our study, the variability was only 16%.…”

Section: Discussioncontrasting

confidence: 59%

“…Several covariates have previously been reported to affect metformin PK. Considering that metformin is eliminated primarily through the kidneys, it is unsurprising that kidney function is predictive of metformin clearance in most metformin PK models . Due to this, we included kidney function in the structural model.…”

Section: Discussionmentioning

confidence: 99%

“…Population modelling efficiently makes use of data with few subjects and maintained statistical power . Thus, several population metformin PK models have been developed with the aim to identify covariates, for example, weight, kidney function and genetic polymorphisms . Body‐weight and estimated glomerular filtration rate (eGFR; a kidney function measurement) are covariates of oral volume of distribution and oral clearance, respectively, in approximately 2/3 of the models.…”

Section: Introductionmentioning

confidence: 99%

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Using a semi‐mechanistic model to identify the main sources of variability of metformin pharmacokinetics

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Wellhagen

Christensen

et al. 2018

Basic Clin Pharma Tox

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Metformin pharmacokinetics (PK) is highly variable, and researchers have for years tried to shed light on determinants of inter‐individual (IIV) and inter‐occasion variability (IOV) of metformin PK. We set out to identify the main sources of PK variability using a semi‐mechanistic model. We assessed the influence of subject characteristics, including seven genetic variants. Data from three studies of healthy individuals with PK measurements of plasma and urine after single dose or at steady‐state were used in this study. In total, 87 subjects were included (16 crossover subjects). Single nucleotide polymorphisms in ATM, OCT1, OCT2, MATE1 and MATE2‐K were investigated as dominant, recessive or additive. A three‐compartment model with transit absorption and renal elimination with a proportional error was fitted to the data using NONMEM 7.3. Oral parameters were separated from disposition parameters as dose‐dependent absolute bioavailability was determined with support from urine data. Clearance was expressed as net renal secretion and filtration, assuming full fraction unbound and fraction excreted. Mean transit time and peripheral volume of distribution were identified as the main sources of variability according to estimates, with 94% IOV and 95% IIV, respectively. Clearance contributed only with 16% IIV. Glomerular filtration rate and body‐weight were the only covariates found to affect metformin net secretion, reducing IIV to 14%. None of the genetic variants were found to affect metformin PK. Based on our analysis, finding covariates explaining absorption of metformin is much more valuable in understanding variability and avoiding toxicity than elimination.

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Section: Discussioncontrasting

confidence: 59%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using a semi‐mechanistic model to identify the main sources of variability of metformin pharmacokinetics

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Wellhagen

Christensen

et al. 2018

Basic Clin Pharma Tox

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“…A steady-state with respect to the composition of red blood cell MTX-PGs is only reached after several months, 34 often paralleling clinical improvement 35 . Although on a different time scale (hours and days), the distribution volume for metformin increases from about 1.6 L/kg after a single dose 36 to about 4 L/kg 25 under continuous dosing. This indicates a large tissue reservoir, mainly but not only dominated by expression and activity of the various transporters.…”

Section: Resultsmentioning

confidence: 98%

A marriage of two “Methusalem” drugs for the treatment of psoriasis?

Glossmann

Reider

2013

Dermato-Endocrinology

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In this article we present arguments that the “antidiabetic” drug metformin could be useful as an add-on therapy to methotrexate for the treatment of psoriasis and, perhaps, for rheumatoid arthritis as well. Biochemical data suggest that both drugs may share a common cellular target, the AMP-activated protein kinase (AMPK). This enzyme is a master regulator of metabolism and controls a number of downstream targets, e.g., important for cellular growth or function in many tissues including T-lymphocytes. Clinical observations as well as experimental results argue for anti-inflammatory, antineoplastic and antiproliferative activities of metformin and a case-control study suggests that the drug reduces the risk for psoriasis. Patients with psoriasis have higher risk of metabolic syndrome, type 2 diabetes and cardiovascular mortality. Metformin has proven efficacy in the treatment of prediabetes and leads to a pronounced and sustained weight loss in overweight individuals. We expect that addition of metformin to methotrexate can lead to positive effects with respect to the PASI score, reduction of the weekly methotrexate dose and of elevated cardiovascular risk factors in patients with metabolic syndrome and psoriasis. For reasons explained later we suggest that only male, overweight patients are to be included in a pilot trial. On the other side of the coin are concerns that the gastrointestinal side effects of metformin are intolerable for patients under low dose, intermittent methotrexate therapy. Metformin has another side effect, namely interference with vitamin B12 and folate metabolism, leading to elevated homocysteine serum levels. As patients must receive folate supplementation and will be controlled with respect to their B12 status increased hematological toxicity is unlikely to result.

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“…Several factors contribute to the individual variations in PK, including age, renal function, and genetic polymorphisms of metformin transporters . The variability in PK likely affects metformin pharmacological effects; a population PK/PD model has been established between metformin concentration and the glucose‐lowering effects of metformin . Recently, a single‐nucleotide polymorphism (SNP) near the ataxia telangiectasia mutated ( ATM ) gene was identified as accounting for a small fraction (2.5%) of the variance in metformin response…”

mentioning

confidence: 99%

Effects ofSLC22A1Polymorphisms on Metformin-Induced Reductions in Adiposity and Metformin Pharmacokinetics in Obese Children With Insulin Resistance

Sam

Roza

Hon

et al. 2016

The Journal of Clinical Pharmacology

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Steady state population pharmacokinetics of a non-commercial 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 one-compartment model with two absorption sites. Mean population apparent clearance (CL/F) was 68.1 L/hr 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<0.001). SLC22A1genotype did not significantly affect metformin pharmacokinetics. The response to 6 months of metformin treatment (HbA1c, HOMA IR, fasting insulin, and glucose changes) was not different 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 % – 0.900 %) and −1.20 % (−2.40 % – 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.

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

Cited by 14 publications

References 34 publications

Using a semi‐mechanistic model to identify the main sources of variability of metformin pharmacokinetics

Using a semi‐mechanistic model to identify the main sources of variability of metformin pharmacokinetics

A marriage of two “Methusalem” drugs for the treatment of psoriasis?

Effects ofSLC22A1Polymorphisms on Metformin-Induced Reductions in Adiposity and Metformin Pharmacokinetics in Obese Children With Insulin Resistance

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