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Related Concept Videos

Biopharmaceutical Factors Influencing Drug Product Design: Overview01:22

Biopharmaceutical Factors Influencing Drug Product Design: Overview

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Rational drug product design integrates knowledge of the drug’s physicochemical properties, formulation components, manufacturing techniques, and intended route of administration. Each factor influences the drug’s performance, including how it is released, absorbed, and eliminated in the body.The physicochemical properties of a drug—such as solubility, stability, and particle size—affect its compatibility with excipients and the choice of dosage form. Excipients, though...
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  11. Optimization Of Print Parameters For Batch And Continuous Manufacturing Of Three-dimensional (3d) Printed Dosage Forms Using Artificial Intelligence And Machine Learning.
  1. Home
  3. Research Domains
  5. Engineering
  7. Manufacturing Engineering
  9. Precision Engineering
  11. Optimization Of Print Parameters For Batch And Continuous Manufacturing Of Three-dimensional (3d) Printed Dosage Forms Using Artificial Intelligence And Machine Learning.

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Optimization of print parameters for batch and continuous manufacturing of three-dimensional (3D) printed dosage forms using artificial intelligence and machine learning.

Kshitij Chitnis1, Yizhou Lu2, Benjamin Rhoads2

  • 1Pharmaceutical Engineering and 3D Printing (PharmE3D) Lab, School of Pharmacy, University of Mississippi, University, MS, 38677, USA.

Drug Delivery and Translational Research
|November 4, 2025

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
3D printingAnd optimizationArtificial intelligenceFused deposition modeling

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Machine learning optimizes fused deposition modeling parameters for pharmaceutical 3D printing. This approach successfully created zero-defect oral dosage forms (printlets), improving manufacturing efficiency and quality.

Area of Science:

  • Pharmaceutical Technology
  • Materials Science
  • Computational Science

Background:

  • Three-dimensional printing (3D printing) offers personalized pharmaceutical oral dosage forms (printlets) with enhanced features like versatile drug release and improved patient compliance.
  • Optimizing 3D printing parameters is critical for ensuring the quality of these printlets.
  • Machine learning (ML) can significantly reduce development time and costs by optimizing these parameters.

Purpose of the Study:

  • To optimize fused deposition modeling (FDM) printing parameters for both batch and continuous manufacturing of pharmaceutical printlets.
  • To integrate ML algorithms for predicting and selecting optimal processing parameters to achieve defect-free printlets.
  • To validate the ML model's effectiveness across various materials and printing conditions.
Machine learning
Polylactic acid
Polyvinyl alcohol
Thermoplastic urethane

Main Methods:

  • A three-level full factorial design was used to generate data for training ML algorithms.
  • Image segmentation was employed to analyze printlets for defects.
  • Gaussian Process Regressor (GPR) and Efficient Global Optimization (EGO) were utilized for parameter prediction and selection.
  • The developed algorithm was tested and validated by printing and characterizing printlets.

Main Results:

  • The ML algorithm successfully predicted parameter sets for both batch (R²=0.8783) and continuous (R²=0.9364) printing, achieving zero-defect printlets.
  • The algorithm demonstrated adaptability to various materials within a 190-220 ℃ temperature range.
  • Flow rate was identified as a significant factor influencing printlet quality, more so than print speed, temperature, or infill density.

Conclusions:

  • Adaptive ML, specifically GPR and EGO, effectively optimizes FDM printing parameters for high-quality pharmaceutical printlets.
  • This ML-driven approach enhances the efficiency and reliability of 3D printing for pharmaceutical dosage manufacturing.
  • The study validates the potential of ML in creating defect-free, personalized medicines via 3D printing.