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Related Experiment Video

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Developing a machine learning enabled integrated formulation and process design framework for a pharmaceutical

Varun Sundarkumar1, Zoltan K Nagy1, Gintaras V Reklaitis1

  • 1Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, USA.

Aiche Journal. American Institute of Chemical Engineers
|January 15, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a formulation and process design framework for pharmaceutical 3D printing using drop-on-demand technology. It enables automated prediction of printing conditions for personalized drug manufacturing.

Keywords:
3D printing, machine learningFormulation and process design for pharmaceuticalsIndustry 4.0Pharma 4.0additive manufacturingartificial neural networkspharmaceutical manufacturingpharmaceutical product process design

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Area of Science:

  • Pharmaceutical Manufacturing
  • Industrial Innovation
  • Additive Manufacturing

Background:

  • The pharmaceutical industry requires Industry 4.0 integration for advanced manufacturing.
  • Technologies like AI and 3D printing are key to automating and personalizing drug production.

Purpose of the Study:

  • To develop a formulation and process design (FPD) framework for pharmaceutical drop-on-demand (DoD) 3D printing.
  • To automate the prediction of formulation properties and printing conditions for consistent drug product quality.

Main Methods:

  • Building a machine learning model to predict DoD printing outcomes from input conditions (forward problem).
  • Solving and validating the inverse problem: predicting input conditions for desired DoD printing outcomes.

Main Results:

  • A functional FPD framework was developed.
  • The framework successfully simulated and predicted DoD printing parameters.

Conclusions:

  • The FPD framework enhances automation in pharmaceutical 3D printing.
  • This approach facilitates the personalized production of drug products with specific attributes.