Literature DB >> 17689226

A decision-support tool for the formulation of orally active, poorly soluble compounds.

Sébastien Branchu1, Philippe G Rogueda, A Philip Plumb, Walter G Cook.   

Abstract

Physicochemical data for a set of potentially poorly soluble compounds was analysed in relation to suitable formulations for these compounds. Physical chemistry was found to be a key determinant of formulation class expressed in terms of conventional, solid dispersion, lipidic/surfactant, and crystalline nanoparticle systems. This relationship was used to build a decision-support tool aimed to guide formulation selection for poorly soluble compounds during product development. Tool components included a user interface, a database of compound cases together with known formulations, and predictive modules based on statistics, decision trees, and case-based reasoning. The tool was tested and exhibited significant and consistent predictive ability across testing conditions. This type of tool has the potential to improve the efficiency and predictability of the formulation development process.

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Year:  2007        PMID: 17689226     DOI: 10.1016/j.ejps.2007.06.005

Source DB:  PubMed          Journal:  Eur J Pharm Sci        ISSN: 0928-0987            Impact factor:   4.384


  4 in total

1.  Use of preclinical dog studies and absorption modeling to facilitate late stage formulation bridging for a BCS II drug candidate.

Authors:  Filippos Kesisoglou
Journal:  AAPS PharmSciTech       Date:  2014-02       Impact factor: 3.246

2.  Computational prediction of drug solubility in lipid based formulation excipients.

Authors:  Linda C Persson; Christopher J H Porter; William N Charman; Christel A S Bergström
Journal:  Pharm Res       Date:  2013-06-15       Impact factor: 4.200

3.  Naproxen-Loaded Poly(2-hydroxyalkyl methacrylates): Preparation and Drug Release Dynamics.

Authors:  Abeer Aljubailah; Saad M S Alqahtani; Tahani Saad Al-Garni; Waseem Sharaf Saeed; Abdelhabib Semlali; Taieb Aouak
Journal:  Polymers (Basel)       Date:  2022-01-23       Impact factor: 4.329

Review 4.  Models for Predicting Drug Absorption From Oral Lipid-Based Formulations.

Authors:  Linda C Alskär; Christel A S Bergström
Journal:  Curr Mol Biol Rep       Date:  2015-10-07
  4 in total

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