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

Authors
Journal
Pharmaceutics
1999-4923
Publisher
MDPI AG
Publication Date
Volume
4
Issue
4
Identifiers
DOI: 10.3390/pharmaceutics4040531
Keywords
  • Review
Disciplines
  • Computer Science
  • Pharmacology

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.

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