Literature DB >> 17452096

Investigation of an artificial intelligence technology--Model trees. Novel applications for an immediate release tablet formulation database.

Q Shao1, R C Rowe, P York.   

Abstract

This study has investigated an artificial intelligence technology - model trees - as a modelling tool applied to an immediate release tablet formulation database. The modelling performance was compared with artificial neural networks that have been well established and widely applied in the pharmaceutical product formulation fields. The predictability of generated models was validated on unseen data and judged by correlation coefficient R(2). Output from the model tree analyses produced multivariate linear equations which predicted tablet tensile strength, disintegration time, and drug dissolution profiles of similar quality to neural network models. However, additional and valuable knowledge hidden in the formulation database was extracted from these equations. It is concluded that, as a transparent technology, model trees are useful tools to formulators.

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Year:  2007        PMID: 17452096     DOI: 10.1016/j.ejps.2007.03.004

Source DB:  PubMed          Journal:  Eur J Pharm Sci        ISSN: 0928-0987            Impact factor:   4.384


  4 in total

1.  Quality by design approach: application of artificial intelligence techniques of tablets manufactured by direct compression.

Authors:  Buket Aksu; Anant Paradkar; Marcel de Matas; Ozgen Ozer; Tamer Güneri; Peter York
Journal:  AAPS PharmSciTech       Date:  2012-09-06       Impact factor: 3.246

2.  Quetiapine Fumarate Extended-release Tablet Formulation Design Using Artificial Neural Networks.

Authors:  Esher Özçelik; Burcu Mesut; Buket Aksu; Yıldız Özsoy
Journal:  Turk J Pharm Sci       Date:  2017-11-20

3.  Machine Learning Technology Reveals the Concealed Interactions of Phytohormones on Medicinal Plant In Vitro Organogenesis.

Authors:  Pascual García-Pérez; Eva Lozano-Milo; Mariana Landín; Pedro Pablo Gallego
Journal:  Biomolecules       Date:  2020-05-11

4.  Combining Medicinal Plant In Vitro Culture with Machine Learning Technologies for Maximizing the Production of Phenolic Compounds.

Authors:  Pascual García-Pérez; Eva Lozano-Milo; Mariana Landín; Pedro Pablo Gallego
Journal:  Antioxidants (Basel)       Date:  2020-03-04
  4 in total

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