Population pharmacokinetic modeling and noncompartmental analysis demonstrated bioequivalence between metformin component of metformin/vildagliptin fixed‐dose combination products and metformin immediate‐release tablet sourced from various countries

Chitnis, Shripad D.; Han, Yi; Yamaguchi, Masayuki; Mita, Sachiko; Zhao, Rongsheng; Sunkara, Gangadhar; Kulmatycki, Kenneth

doi:10.1002/cpdd.191

Cited by 9 publications

(10 citation statements)

References 36 publications

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“…The PK profile of the disposition phase was consistent with values from the literature. [5] However, in the present study, multiple peaks during the absorption phase were found, which implies a possible diversity of absorption PK profile depending on the formulation and population. Unlike conventional bioequivalence analysis, a population PK model can reflect the difference of formulation on specific parameters and reflected simulation can be performed.…”

Section: Transl Clin Pharmacolcontrasting

confidence: 43%

“…Besides the PK parameters of the absorption phase, the values of PK parameters were consistent with values from literature. [5] In the present study, multiple peaks were observed during the absorption phase of metformin, which is not consistent with previous PK analysis of metformin. However, the combined absorption …”

Section: Discussioncontrasting

confidence: 32%

“…[3,4] The peak plasma concentration of metformin occurs approximately 3 h after administration, with a large volume of distribution (63~276 L), which indicates the considerable uptake from the tissue. [5] Metformin is not bound to plasma protein and its elimination half-life is 1.7~4.5 h, primarily eliminated by renal excretion (> 99%). [4,6] Change of formulation can affect the PK profile of metformin especially during the absorption process.…”

Section: Transl Clin Pharmacolmentioning

confidence: 99%

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Population pharmacokinetic analysis of metformin administered as fixed-dose combination in Korean healthy adults

Choi

Jeon²,

Han

2018

Transl Clin Pharmacol

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Metformin, an oral antihyperglycemic agent, is widely used as the first-line pharmacotherapy for type 2 diabetes mellitus (T2DM). It has been in use for several decades as numerous different formulations. However, despite its use, population pharmacokinetic (PK) modeling of metformin is not well developed. The aim of the present study was to evaluate the effect of formulation on PK parameters by developing a population PK model of metformin in Koreans and using this model to assess bioequivalence. We used a comparative PK study of a single agent and a fixed-dose combination of metformin in 36 healthy volunteers. The population PK model of metformin was developed using NONMEM (version 7.3). Visual predictive checks and bootstrap methods were performed to determine the adequacy of the model. The plasma concentration-time profile was best described by a two-compartment, first-order elimination model with first-order absorption followed by zeroorder absorption with lag time. From the covariate analysis, formulation had significant effect (p < 0.01) on relative bioavailability (F = 0.94) and first-order absorption constant (K a = 0.83), but the difference was within the range of bioequivalence criteria. No other covariate was shown to have significant effect on PK parameters. The PK profile of the disposition phase was consistent with the published literature. However, in the present study, the multiple peaks found during the absorption phase implied the possible diversity of absorption PK profile depending on formulation or population. Unlike traditional bioequivalence analysis, the population PK model reflects formulation differences on specific parameters and reflected simulation can be performed.

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Section: Transl Clin Pharmacolcontrasting

confidence: 43%

Section: Discussioncontrasting

confidence: 32%

Section: Transl Clin Pharmacolmentioning

confidence: 99%

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Population pharmacokinetic analysis of metformin administered as fixed-dose combination in Korean healthy adults

Choi

Jeon²,

Han

2018

Transl Clin Pharmacol

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“…Population pharmacokinetics demonstrated that body weight had a significant effect on metformin and vildagliptin on CL/F. 15 The average weight of subjects of White and Black subjects (70.4 ± 11.0 kg) in a previous study 14 was much higher than that in the present study (58.4 ± 5.99 kg), which may be one of the reasons for the relatively increased exposure in Chinese subjects.…”

Section: Discussioncontrasting

confidence: 54%

Pharmacokinetics and Bioequivalence of a Generic Fixed‐Dose Combination Tablet of Metformin Hydrochloride/Vildagliptin Versus a Branded Product in Healthy Chinese Subjects Under Fed and Fasting Conditions

Li-jun

Ling

et al. 2021

Clinical Pharm in Drug Dev

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The purpose of this study was to compare the bioequivalence and safety of test and reference preparations of fixed-dose combination tablets of metformin hydrochloride/vildagliptin 850 mg/50 mg in healthy volunteers under fasting and fed conditions for marketing authorization in China. A single-dose, randomized, open-label, 2-way crossover study was conducted. Blood samples were collected up to 36 hours after dosing in each period with a 7-day washout. Pharmacokinetic parameters, including maximum plasma concentration (C max ), time to reach C max , area under the plasma concentrationtime curve (AUC) from time 0 to the last time point of the measurable concentration, AUC from time 0 to infinity, terminal elimination half-life, and apparent clearance, were calculated using noncompartmental methods and compared between the 2 formulations. Safety profiles were assessed, including significant findings of vital signs, electrocardiogram, laboratory tests, physical examination, and adverse events. A total of 30 healthy subjects (19 men, 11 women) were randomized, and 29 subjects were treated under fasting conditions. Likewise, 30 subjects (24 men, 6 women) were randomized and treated under fed conditions. The geometric mean ratio and corresponding 90% confidence intervals of C max , AUC from time 0 to the last time point of the measurable concentration , and AUC from time 0 to infinity for both metformin hydrochloride and vildagliptin between the 2 fixed-dose combination formulations were within the bioequivalence acceptance range of 80% to 125% under fasting or fed conditions. Therefore, the generic and branded formulations were bioequivalent and well tolerated in healthy Chinese subjects.

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“…The results of C max ratios were more sensitive to the change in the in vivo dissolution rate constant than did the AUC ratios. Metformin (class III drug) was also examined for model fitting in NONMEM ® and was found to be best described by two-compartment as it provides an adequate agreement between predicted and observed concentration values [34]. Their study simulation study results were compared retrospectively to clinical data available internally in their company Novartis to show that country drug source or race has a negligible effect on the PK parameters.…”

Section: Nonmem ®mentioning

confidence: 99%

In Vitro Dissolution and in Silico Modeling Shortcuts in Bioequivalence Testing

Al-Tabakha

Alomar

2020

Pharmaceutics

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Purpose: To review in vitro testing and simulation platforms that are in current use to predict in vivo performances of generic products as well as other situations to provide evidence for biowaiver and support drug formulations development. Methods: Pubmed and Google Scholar databases were used to review published literature over the past 10 years. The terms used were “simulation AND bioequivalence” and “modeling AND bioequivalence” in the title field of databases, followed by screening, and then reviewing. Results: A total of 22 research papers were reviewed. Computer simulation using software such as GastroPlus™, PK-Sim® and SimCyp® find applications in drug modeling. Considering the wide use of optimization for in silico predictions to fit observed data, a careful review of publications is required to validate the reliability of these platforms. For immediate release (IR) drug products belonging to the Biopharmaceutics Classification System (BCS) classes I and III, difference factor (ƒ1) and similarity factor (ƒ2) are calculated from the in vitro dissolution data of drug formulations to support biowaiver; however, this method can be more discriminatory and may not be useful for all dissolution profiles. Conclusions: Computer simulation platforms need to improve their mechanistic physiologically based pharmacokinetic (PBPK) modeling, and if prospectively validated within a small percentage of error from the observed clinical data, they can be valuable tools in bioequivalence (BE) testing and formulation development.

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Population pharmacokinetic modeling and noncompartmental analysis demonstrated bioequivalence between metformin component of metformin/vildagliptin fixed‐dose combination products and metformin immediate‐release tablet sourced from various countries

Cited by 9 publications

References 36 publications

Population pharmacokinetic analysis of metformin administered as fixed-dose combination in Korean healthy adults

Population pharmacokinetic analysis of metformin administered as fixed-dose combination in Korean healthy adults

Pharmacokinetics and Bioequivalence of a Generic Fixed‐Dose Combination Tablet of Metformin Hydrochloride/Vildagliptin Versus a Branded Product in Healthy Chinese Subjects Under Fed and Fasting Conditions

In Vitro Dissolution and in Silico Modeling Shortcuts in Bioequivalence Testing

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