Optimizing critical quality attributes of fast disintegrating tablets using artificial neural networks: a scientific benchmark study.

Journal: Drug development and industrial pharmacy
PMID: 39648277

Abstract

OBJECTIVE: The objective of this study is to create predictive models utilizing machine learning algorithms, including Artificial Neural Networks (ANN), k-nearest neighbor (kNN), support vector machines (SVM), and linear regression, to predict critical quality attributes (CQAs) such as hardness, friability, and disintegration time of fast disintegrating tablets (FDTs).

Authors

  • Jagruti Desai
    Department of Pharmaceutical Technology, Ramanbhai Patel College of Pharmacy, Charotar University of Science and Technology (CHARUSAT), CHARUSAT Campus, Changa, Gujarat, India.
  • Prince Dhameliya
    Department of Pharmaceutical Technology, Ramanbhai Patel College of Pharmacy, Charotar University of Science and Technology (CHARUSAT), CHARUSAT Campus, Changa, Gujarat, India.
  • Swayamprakash Patel
    Department of Pharmaceutical Technology, Ramanbhai Patel College of Pharmacy, Charotar University of Science and Technology (CHARUSAT), CHARUSAT Campus, Changa, Gujarat, India.

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