Literature DB >> 25409918

Simulation of in vitro dissolution behavior using DDDPlus™.

May Almukainzi1, Arthur Okumu, Hai Wei, Raimar Löbenberg.   

Abstract

Dissolution testing is a performance test for many dosage forms including tablets and capsules. The objective of this study was to evaluate if computer simulations can predict the in vitro dissolution of two model drugs for which different dissolution data were available. Published montelukast sodium and glyburide dissolution data was used for the simulations. Different pharmacopeial and biorelevant buffers, volumes, and rotations speeds were evaluated. Additionally, a pH change protocol was evaluated using these buffers. DDDPlus™ 3, Beta version (Simulation Plus, Inc.), was used to simulate the in vitro dissolution data. The simulated data were compared with the in vitro data. A regression coefficient between predicted and observed data was used to assess the simulations. The statistical analysis of Montelukast sodium showed that there was a significant correlation between the in vitro release data and the predicted data for all cases except for one buffer. For glyburide, there was also a significant correlation between the experimental data and the predicted data using single pH conditions. Using the dynamic pH protocol, a correlation was significant for one biorelevant media. The simulations showed that both in vitro drug releases were sensitive to solubility effects which confirmed their BCS class II category. Computer simulations of the in vitro release using DDDPlus™ have the potential to estimate the in vivo dissolution at an early stage in the drug development process. This might be used to choose the most appropriate dissolution condition to establish IVIVC and to develop biorelevant in vitro performance tests to capture critical product attributes for quality control procedures in quality by design environments.

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Year:  2014        PMID: 25409918      PMCID: PMC4309826          DOI: 10.1208/s12249-014-0241-5

Source DB:  PubMed          Journal:  AAPS PharmSciTech        ISSN: 1530-9932            Impact factor:   3.246


  13 in total

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Journal:  Int J Pharm       Date:  2006-10-06       Impact factor: 5.875

3.  Commentary on AAPS Workshop: dissolution testing for the twenty-first century: linking critical quality attributes and critical process parameters to clinically relevant dissolution.

Authors:  Cheng Tong; Susan S D'Souza; Jan E Parker; Tahseen Mirza
Journal:  Pharm Res       Date:  2007-03-24       Impact factor: 4.200

4.  Pharmaceutical quality by design: product and process development, understanding, and control.

Authors:  Lawrence X Yu
Journal:  Pharm Res       Date:  2008-01-10       Impact factor: 4.200

Review 5.  Clinical relevance of dissolution testing in quality by design.

Authors:  Paul A Dickinson; Wang Wang Lee; Paul W Stott; Andy I Townsend; John P Smart; Parviz Ghahramani; Tracey Hammett; Linda Billett; Sheena Behn; Ryan C Gibb; Bertil Abrahamsson
Journal:  AAPS J       Date:  2008-08-07       Impact factor: 4.009

Review 6.  The science of USP 1 and 2 dissolution: present challenges and future relevance.

Authors:  Vivian Gray; Gregg Kelly; Min Xia; Chris Butler; Saji Thomas; Stephen Mayock
Journal:  Pharm Res       Date:  2009-01-23       Impact factor: 4.200

7.  Physicochemical characterization of five glyburide powders: a BCS based approach to predict oral absorption.

Authors:  Hai Wei; Chad Dalton; Marie Di Maso; Isadore Kanfer; Raimar Löbenberg
Journal:  Eur J Pharm Biopharm       Date:  2008-01-31       Impact factor: 5.571

8.  Optimizing the performance of in silico ADMET general models according to local requirements: MARS approach. solubility estimations as case study.

Authors:  Julen Oyarzabal; Joaquin Pastor; Trevor J Howe
Journal:  J Chem Inf Model       Date:  2009-12       Impact factor: 4.956

9.  In silico prediction of aqueous solubility: the solubility challenge.

Authors:  M Hewitt; M T D Cronin; S J Enoch; J C Madden; D W Roberts; J C Dearden
Journal:  J Chem Inf Model       Date:  2009-11       Impact factor: 4.956

10.  Biorelevant dissolution media as a predictive tool for glyburide a class II drug.

Authors:  Hai Wei; Raimar Löbenberg
Journal:  Eur J Pharm Sci       Date:  2006-05-20       Impact factor: 4.384

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Review 2.  Simulation Models for Prediction of Bioavailability of Medicinal Drugs-the Interface Between Experiment and Computation.

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3.  Development of Extended-Release Mini-Tablets Containing Metoprolol Supported by Design of Experiments and Physiologically Based Biopharmaceutics Modeling.

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4.  Justification of disintegration testing beyond current FDA criteria using in vitro and in silico models.

Authors:  Lukas Uebbing; Lukas Klumpp; Gregory K Webster; Raimar Löbenberg
Journal:  Drug Des Devel Ther       Date:  2017-04-11       Impact factor: 4.162

5.  An Algorithm to Identify Compounded Non-Sterile Products that Can Be Formulated on a Commercial Scale or Imported to Promote Safer Medication Use in Children.

Authors:  Varsha Bhatt-Mehta; Robert B MacArthur; Raimar Löbenberg; Jeffrey J Cies; Ibolja Cernak; Richard H Parrish Ii
Journal:  Pharmacy (Basel)       Date:  2015-11-11
  5 in total

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