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Predicting physical stability of solid dispersions by machine learning techniques.

Run Han1, Hui Xiong2, Zhuyifan Ye1

  • 1State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China.

Journal of Controlled Release : Official Journal of the Controlled Release Society
|August 30, 2019
PubMed
Summary
This summary is machine-generated.

A new machine learning model predicts the physical stability of amorphous solid dispersions (SDs), overcoming the lengthy testing times. This AI approach accelerates the development of effective drug formulations.

Keywords:
Machine learningMolecular modelingPhysical stabilitySolid dispersion

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Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Computational Chemistry

Background:

  • Amorphous solid dispersion (SD) is a key technique for enhancing the solubility of poorly water-soluble drugs.
  • Physical instability of SDs remains a significant challenge, hindering their widespread application.
  • Conventional stability testing is time-consuming, taking months and offering unpredictable outcomes.

Purpose of the Study:

  • To develop a novel, rapid prediction model for the physical stability of solid dispersion formulations using machine learning.
  • To identify key molecular descriptors influencing SD physical stability.
  • To provide theoretical guidance for rational SD formulation design.

Main Methods:

  • Collected 646 stability data points for SD formulations.
  • Utilized over 20 molecular descriptors to characterize the data.
  • Employed an improved maximum dissimilarity algorithm (MD-FIS) for data partitioning into training, validation, and testing sets.
  • Compared eight machine learning algorithms, with Random Forest (RF) showing the best performance.
  • Validated the RF model with experimental data for 17β-estradiol (ED)-PVP SDs and investigated mechanisms via molecular modeling.

Main Results:

  • Developed a machine learning-based prediction model for SD physical stability.
  • Achieved a prediction accuracy of 82.5% using the Random Forest (RF) model.
  • Identified critical input parameters influencing SD stability through RF model analysis.
  • Experimental validation confirmed the model's predictive capability for ED-PVP SDs.

Conclusions:

  • An intelligent AI model was successfully developed to predict the physical stability of amorphous solid dispersions.
  • This predictive model significantly benefits the rational design and development of SD formulations.
  • The integrated methodology combining experimental, theoretical, modeling, and AI approaches offers a powerful tool for future drug formulation development across various dosage forms.