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Pharmaceutics 2012, 4(4), 531-550; doi:10.3390/pharmaceutics4040531

Artificial Neural Networks in Evaluation and Optimization of Modified Release Solid Dosage Forms

Department of Pharmaceutical Technology, Faculty of Pharmacy, Belgrade University, Vojvode Stepe 450, Belgrade 11221, Serbia
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Received: 23 July 2012 / Revised: 24 September 2012 / Accepted: 5 October 2012 / Published: 18 October 2012
(This article belongs to the Special Issue Solid Dosage Forms)
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Abstract

Implementation of the Quality by Design (QbD) approach in pharmaceutical development has compelled researchers in the pharmaceutical industry to employ Design of Experiments (DoE) as a statistical tool, in product development. Among all DoE techniques, response surface methodology (RSM) is the one most frequently used. Progress of computer science has had an impact on pharmaceutical development as well. Simultaneous with the implementation of statistical methods, machine learning tools took an important place in drug formulation. Twenty years ago, the first papers describing application of artificial neural networks in optimization of modified release products appeared. Since then, a lot of work has been done towards implementation of new techniques, especially Artificial Neural Networks (ANN) in modeling of production, drug release and drug stability of modified release solid dosage forms. The aim of this paper is to review artificial neural networks in evaluation and optimization of modified release solid dosage forms.
Keywords: artificial neural networks; modified release; pharmaceutical development artificial neural networks; modified release; pharmaceutical development
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Ibrić, S.; Djuriš, J.; Parojčić, J.; Djurić, Z. Artificial Neural Networks in Evaluation and Optimization of Modified Release Solid Dosage Forms. Pharmaceutics 2012, 4, 531-550.

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