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Publication Detail
Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development
  • Publication Type:
    Journal article
  • Authors:
    O'Reilly CS, Elbadawi M, Desai N, Gaisford S, Basit AW, Orlu M
  • Publication date:
    12/2021
  • Journal:
    Pharmaceutics
  • Volume:
    13
  • Issue:
    12
  • Article number:
    2187
  • Status:
    Published
  • Country:
    Switzerland
  • PII:
    pharmaceutics13122187
  • Language:
    English
  • Keywords:
    Artificial intelligence, industry 4.0, additive manufacturing, thin film manufacture, personalized pharmaceuticals, semi-solid extrusion (SSE), computer vision, drug-loaded systems, digital pharmaceutics & digital medicine, mobile 3D printing drug products
  • Notes:
    This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Abstract
Orodispersible films (ODFs) are an attractive delivery system for a myriad of clinical applications and possess both large economical and clinical rewards. However, the manufacturing of ODFs does not adhere to contemporary paradigms of personalised, on-demand medicine, nor sustainable manufacturing. To address these shortcomings, both three-dimensional (3D) printing and machine learning (ML) were employed to provide on-demand manufacturing and quality control checks of ODFs. Direct ink writing (DIW) was able to fabricate complex ODF shapes, with thicknesses of less than 100 ┬Ám. ML algorithms were explored to classify the ODFs according to their active ingredient, by using their near-infrared (NIR) spectrums. A supervised model of linear discriminant analysis was found to provide 100% accuracy in classifying ODFs. A subsequent partial least square algorithm was applied to verify the dose, where a coefficient of determination of 0.96, 0.99 and 0.98 was obtained for ODFs of paracetamol, caffeine, and theophylline, respectively. Therefore, it was concluded that the combination of 3D printing, NIR and ML can result in a rapid production and verification of ODFs. Additionally, a machine vision tool was used to automate the in vitro testing. These collective digital technologies demonstrate the potential to automate the ODF workflow.
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