Artificial Neural Networks to Optimize Formulation Components of a Fixed- Dose Combination of Rifampicin, Isoniazid and Pyrazinamide in a Microemulsion

Authors: Glass, B. D.; Agatonovic-Kustrin, S.; Wisch, M. H.

Source: Current Drug Discovery Technologies, Volume 2, Number 3, September 2005 , pp. 195-201(7)

Publisher: Bentham Science Publishers

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Abstract:

The aim of this study to design a stable microemulsion formulation to deliver a combination of rifampicin, isoniazid and pyrazinamide in quantities suitable for administration to a paediatric population. The chemical stability of rifampicin, isoniazid and pyrazinamide alone and in various combinations was investigated in different solvents, solubilizing agents and surfactants. An artificial neural network was used to model data from the stability studies and a sensitivity analysis was applied to optimize the selection of the formulation components. Imwitor 308 and Crillet 3, exhibiting the highest overall positive sensitivity were selected to formulate the stable microemulsion. Due to drug dose specifications and solubility limitations, the final formulation contained only rifampicin and isoniazid, since the solubility of pyrazinamide in the lipid and aqueous components of the microemulsion did not achieve the required dose. The stability and solubility of rifampicin were improved in the formulation. Solubilization of the rifampicin in the lipid droplets of the internal phase and lipophilic chains of the surfactants increased the quantity of rifampicin that can be incorporated, while protecting it from oxidative degradation and also limited its contact with isoniazid, which has been shown to affect its stability. The results of this study indicate that the Artificial Neural Network can be successfully used to optimize the choice of solvents, solubilizing agents and surfactants prior to formulation of the microemulsion, limiting the amount of experiments, thus reducing the costs during the preformulation study.

Keywords: fixed-dose combination; paediatric liquid dosage form; microemulsion; artificial neural network (ann); rifampicin; isoniazid; pyrazinamide

Document Type: Review article

DOI: http://dx.doi.org/10.2174/1570163054866864

Affiliations: 1: School of Biomedical, Bimolecular and Chemical Sciences, Pharmacy M315, University of Western Australia, Crawley, WA 6009, Australia.

Publication date: 2005-09-01

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  • Due to the plethora of new approaches being used in modern drug discovery by the pharmaceutical industry, Current Drug Discovery Technologies has been established to provide comprehensive overviews of all the major modern techniques and technologies used in drug design and discovery. The journal is the forum for publishing both original research papers and reviews describing novel approaches and cutting edge technologies used in all stages of drug discovery. The journal addresses the multidimensional challenges of drug discovery science including integration issues of the drug discovery process.

    Current Drug Discovery Technologies is an essential journal for all scientists and research managers involved in drug discovery who wish to keep abreast of all the modern techniques and technologies used in drug discovery.
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