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Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development
Colm S O'Reilly1, Moe Elbadawi1, Neel Desai1
1Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK.
Pharmaceutics
|December 28, 2021
Summary
3D printing and machine learning (ML) enable on-demand manufacturing and quality control of orodispersible films (ODFs). This digital approach automates ODF production and verification, paving the way for personalized medicine.
Area of Science:
- Pharmaceutical Technology
- Digital Manufacturing
- Analytical Chemistry
Background:
- Orodispersible films (ODFs) offer significant clinical and economic benefits but lack personalized, on-demand, and sustainable manufacturing methods.
- Current ODF production paradigms do not align with modern personalized medicine or sustainable manufacturing principles.
Purpose of the Study:
- To integrate 3D printing and machine learning (ML) for on-demand ODF manufacturing and quality control.
- To develop automated methods for ODF fabrication, ingredient classification, and dosage verification.
Main Methods:
- Direct ink writing (DIW) 3D printing was used to fabricate ODFs with precise thickness control (<100 µm).
- Machine learning algorithms, including linear discriminant analysis (LDA) and partial least squares (PLS), were applied for ODF classification and dosage verification using near-infrared (NIR) spectroscopy.
- Machine vision tools were employed to automate in vitro testing of ODFs.
Main Results:
- DIW successfully fabricated complex ODF structures with sub-100 µm thicknesses.
- LDA achieved 100% accuracy in classifying ODFs based on active ingredients via NIR spectra.
- PLS algorithms demonstrated high accuracy in dose verification (R² values of 0.96-0.99) for paracetamol, caffeine, and theophylline.
- Automated in vitro testing was successfully implemented using machine vision.
Conclusions:
- The synergistic combination of 3D printing, NIR spectroscopy, and ML enables rapid production and verification of ODFs.
- These digital technologies collectively offer a pathway to automate the entire ODF workflow, supporting personalized and on-demand pharmaceutical manufacturing.
