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

Pharmaceutics

Svetlana Ibrić¹  Jelena Djuriš²  Jelena Parojčić² 

[1] Department of Pharmaceutical Technology, Faculty of Pharmacy, Belgrade University, Vojvode Stepe 450, Belgrade 11221, Serbia;

关键词: artificial neural networks;    modified release;    pharmaceutical development;   

DOI  :  10.3390/pharmaceutics4040531

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.