Applications of Machine Learning in Solid Oral Dosage Form Development.

Journal: Journal of pharmaceutical sciences
Published Date:

Abstract

This review comprehensively summarizes the application of machine learning in solid oral dosage form development over the past three decades. In both academia and industry, machine learning is increasingly applied for multiple preformulation/formulation and process development studies. Further, this review provides the authors' perspectives on how pharmaceutical scientists can use machine learning for right projects and in right ways; some key ingredients include (1) the determination of inputs, outputs, and objectives; (2) the generation of a database containing high-quality data; (3) the development of machine learning models based on dataset training and model optimization; (4) the application of trained models in making predictions for new samples. It is expected by the authors and others that machine learning will promisingly play a more important role in tomorrow's projects for solid oral dosage form development.

Authors

  • Hao Lou
    Drug Product Technologies, Process Development, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA; Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, KS 66047, USA.
  • Bo Lian
    College of Pharmacy, University of Arizona, Tucson, AZ 85721, United States.
  • Michael J Hageman
    Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, KS 66047, USA. Electronic address: mhageman@ku.edu.