Literature DB >> 9863943

Experimental design for a pharmaceutical formulation: optimisation and robustness.

B Campisi1, D Chicco, D Vojnovic, R Phan-Tan-Luu.   

Abstract

In pharmaceutical industries, the formulator is usually faced with the optimisation of the excipient mixture composition aimed to prepare a product with the required characteristics. Experimental research methodology represents an efficient approach for solving such optimisation problems. Planning mixture experiments using specific designs allows to analyse the blending properties of each mixture component and estimate an empirical model approximating the response of interest as a function of excipient proportions. In this study the evolution of theophylline solubility in a four-component system with constraints was analysed using two mixture design approaches: a classical mixture component proportion approach and a mathematically independent variable approach. An optimal region characterised by high solubility values was found and further explored in order to verify the insensitivity of theophylline solubility to slight variations of the excipient mixture composition.

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Year:  1998        PMID: 9863943     DOI: 10.1016/s0731-7085(98)00175-7

Source DB:  PubMed          Journal:  J Pharm Biomed Anal        ISSN: 0731-7085            Impact factor:   3.935


  3 in total

1.  Towards more optimal medical diagnosing with evolutionary algorithms.

Authors:  V Podgorelec; P Kokol
Journal:  J Med Syst       Date:  2001-06       Impact factor: 4.460

2.  Rapid development and optimization of tablet manufacturing using statistical tools.

Authors:  Eutimio Gustavo Fernández; Silvia Cordero; Malvina Benítez; Iraelio Perdomo; Yohandro Morón; Ada Esther Morales; Milagros Gaudencia Arce; Ernesto Cuesta; Juan Lugones; Maritza Fernández; Arturo Gil; Rodolfo Valdés; Mirna Fernández
Journal:  AAPS PharmSciTech       Date:  2008-05-06       Impact factor: 3.246

3.  Predicting complexation performance between cyclodextrins and guest molecules by integrated machine learning and molecular modeling techniques.

Authors:  Qianqian Zhao; Zhuyifan Ye; Yan Su; Defang Ouyang
Journal:  Acta Pharm Sin B       Date:  2019-05-08       Impact factor: 11.413

  3 in total

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