Literature DB >> 11536222

In vitro-in vivo correlation (IVIVC) models for metformin after administration of modified-release (MR) oral dosage forms to healthy human volunteers.

G Balan1, P Timmins, D S Greene, P H Marathe.   

Abstract

The objective of the current study was to develop and evaluate the internal predictability for level C and A in vitro-in vivo correlation (IVIVC) models for prototype modified-release (MR) dosage forms of metformin. In vitro dissolution data for metformin were collected for 22 h using a USP II (paddle) method. In vivo plasma concentration data were obtained from 8 healthy volunteers after administration of immediate-release (IR) and MR dosage forms of metformin. Linear level C IVIVC models were developed using dissolution data at 2.0 and 4.0 h and in vitro mean dissolution time (MDT). A deconvolution-based level A model was attempted through a correlation of percent in vivo input obtained through deconvolution and percent in vitro dissolution obtained experimentally. Further, basic and extended convolution level A IVIVC models were attempted for metformin. Internal predictability for the IVIVC models was assessed by comparing observed and predicted values for C(max) and AUC(INF). The results suggest that highly predictive level C models with prediction errors (%PE) of <5% could be developed. Mean percent in vivo input for metformin was incomplete from all formulations and did not exceed 35% of dose. The deconvolution-based level A models for all MR formulations were curvilinear. However, a unique IVIVC model applicable to all MR formulations could not be developed using the deconvolution approach. The basic convolution level A model, which used in vitro dissolution as the in vivo input, had %PE values as high as 103%. Using an extended convolution approach, which modeled the absorption of metformin using a Hill function, a level A IVIVC model with %PE as low as 11% was developed. In conclusion, the current work indicates that level C and A IVIVC models with good internal predictability may be developed for a permeability- and absorption window-limited drug such as metformin. Copyright 2001 Wiley-Liss, Inc.

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Year:  2001        PMID: 11536222     DOI: 10.1002/jps.1071

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  11 in total

1.  Improving of the accuracy of in vitro-in vivo linear correlation using kinetic models for ultra sustained release theophylline tablets.

Authors:  E Karasulu; S Aktogu; H Y Karasulu; A Aydogdu; I Tuglular; G Ertan
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2003 Oct-Dec       Impact factor: 2.441

2.  Comparison of Deconvolution-Based and Absorption Modeling IVIVC for Extended Release Formulations of a BCS III Drug Development Candidate.

Authors:  Filippos Kesisoglou; Binfeng Xia; Nancy G B Agrawal
Journal:  AAPS J       Date:  2015-08-20       Impact factor: 4.009

Review 3.  The science of USP 1 and 2 dissolution: present challenges and future relevance.

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Journal:  Pharm Res       Date:  2009-01-23       Impact factor: 4.200

4.  Statistical comparison of dissolution profiles to predict the bioequivalence of extended release formulations.

Authors:  J D Gomez-Mantilla; U F Schaefer; V G Casabo; T Lehr; C M Lehr
Journal:  AAPS J       Date:  2014-05-23       Impact factor: 4.009

5.  Prevention of tumor growth driven by PIK3CA and HPV oncogenes by targeting mTOR signaling with metformin in oral squamous carcinomas expressing OCT3.

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Journal:  Cancer Prev Res (Phila)       Date:  2015-02-13

Review 6.  In vitro-in vivo correlation: perspectives on model development.

Authors:  Ying Lu; Sungwon Kim; Kinam Park
Journal:  Int J Pharm       Date:  2011-01-13       Impact factor: 5.875

7.  Preparation and in vitro/in vivo evaluation of microparticle formulations containing meloxicam.

Authors:  Hakan Eroglu; Nihan Burul-Bozkurt; Serdar Uma; Levent Oner
Journal:  AAPS PharmSciTech       Date:  2011-11-19       Impact factor: 3.246

8.  Screening of Bioequivalent Extended-Release Formulations for Metformin by Principal Component Analysis and Convolution-Based IVIVC Approach.

Authors:  Yufeng Zhang; Hua Liu; Minghui Johnson Tang; Nicolas James Ho; Tsun Lam Shek; Zhijun Yang; Zhong Zuo
Journal:  AAPS J       Date:  2021-03-04       Impact factor: 4.009

9.  Prediction of metabolism-induced hepatotoxicity on three-dimensional hepatic cell culture and enzyme microarrays.

Authors:  Kyeong-Nam Yu; Sashi Nadanaciva; Payal Rana; Dong Woo Lee; Bosung Ku; Alexander D Roth; Jonathan S Dordick; Yvonne Will; Moo-Yeal Lee
Journal:  Arch Toxicol       Date:  2017-11-22       Impact factor: 5.153

10.  Metformin represses self-renewal of the human breast carcinoma stem cells via inhibition of estrogen receptor-mediated OCT4 expression.

Authors:  Ji-Won Jung; Sang-Bum Park; Soo-Jin Lee; Min-Soo Seo; James E Trosko; Kyung-Sun Kang
Journal:  PLoS One       Date:  2011-11-23       Impact factor: 3.240

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