Predicting Powder Blend Flowability from Individual Constituent Properties Using Machine Learning.

Journal: Pharmaceutical research
PMID: 40244512

Abstract

PURPOSE: Predicting powder blend flowability is necessary for pharmaceutical manufacturing but challenging and resource-intensive. The purpose was to develop machine learning (ML) models to help predict flowability across multiple flow categories, identify key predictive features, and arrive at formulations with improved flow properties.

Authors

  • Anna Owasit
    Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, 138 Warren St, Newark, NJ, 07103, USA.
  • Siddharth Tripathi
    Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, 138 Warren St, Newark, NJ, 07103, USA.
  • Rajesh DavĂ©
    Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, 138 Warren St, Newark, NJ, 07103, USA.
  • Joshua Young
    Department of Ophthalmology, New York University School of Medicine, New York, New York.

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