Combining High-Throughput Screening and Machine Learning to Predict the Formation of Both Binary and Ternary Amorphous Solid Dispersion Formulations for Early Drug Discovery and Development.

Journal: Pharmaceutical research
PMID: 40180767

Abstract

OBJECTIVE: Amorphous solid dispersion (ASD) is widely utilized to enhance the solubility and bioavailability of water-insoluble drugs. However, conventional experimental approaches for ASD development are often resource-intensive and time-consuming. Machine learning (ML) algorithms have great potential to predict ASD formulations but face the challenge of extensive data to construct reliable models. Current study aims to predict the formation of both binary and ternary ASD by combined high-throughput screening (HTS) and ML approaches.

Authors

  • Tianshu Lu
    Institute of Applied Physics and Materials Engineering, University of Macau, Macau, 999078, China.
  • Yiyang Wu
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, China.
  • Ping Xiong
    State Grid Hubei Electric Power Research Institute, Wuhan 430077, China.
  • Hao Zhong
    Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
  • Yang Ding
    Department of Pediatrics, Sainte-Justine University Hospital and University of Montreal, Montreal, Quebec, Canada.
  • Haifeng Li
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
  • Defang Ouyang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.

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