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Predicting pharmaceutical inkjet printing outcomes using machine learning.

Paola Carou-Senra1, Jun Jie Ong2, Brais Muñiz Castro3

  • 1Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, Instituto de Materiales (iMATUS) and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, 15782, Spain.

International Journal of Pharmaceutics: X
|May 5, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning models predict inkjet printing success for personalized medicines. These models accurately forecast formulation printability and print quality, saving time and resources in pharmaceutical development.

Keywords:
2D and 3D printed drug productsAdditive manufacturing and personalized medicationsArtificial intelligence and digital healthDesign and fabrication of medicinal productsDesktop ink jet printing of pharmaceuticals and drug delivery systemsRational formulation development

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Area of Science:

  • Pharmaceutical Science
  • Materials Science
  • Computational Science

Background:

  • Inkjet printing offers a versatile, low-cost method for producing personalized medicines, including orodispersible films and polydrug implants.
  • Optimizing inkjet printing formulations and parameters is complex and time-consuming due to its multi-factorial nature.
  • Existing public data on pharmaceutical inkjet printing suggests potential for predictive modeling.

Purpose of the Study:

  • To develop machine learning (ML) models for predicting inkjet printing outcomes in pharmaceutical applications.
  • To assess the feasibility of using ML to forecast formulation printability and drug dose accuracy.
  • To reduce the empirical and time-consuming nature of optimizing inkjet printing processes.

Main Methods:

  • Consolidated a dataset of 687 formulations from in-house and literature sources.
  • Developed and optimized ML models, including random forest, multilayer perceptron, and support vector machine.
  • Utilized the dataset to train models for predicting printability and drug dose.

Main Results:

  • Optimized ML models achieved 97.22% accuracy in predicting formulation printability.
  • The models demonstrated 97.14% accuracy in predicting the quality of inkjet-printed pharmaceutical formulations.
  • The developed models provide predictive insights before formulation preparation.

Conclusions:

  • Machine learning models can accurately predict inkjet printing outcomes for pharmaceutical applications.
  • These predictive capabilities offer significant resource and time savings in drug formulation development.
  • ML-driven insights can accelerate the realization of personalized medicines via inkjet printing technology.