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Publication Detail
Machine learning using Multi-Modal Data Predicts the Production of Selective Laser Sintered 3D Printed Drug Products
  • Publication Type:
    Journal article
  • Authors:
    Abdalla Y, Elbadawi M, Ji M, Alkahtani M, Awad A, Orlu M, Gaisford S, Basit AW
  • Publisher:
    Elsevier BV
  • Publication date:
  • Journal:
    International Journal of Pharmaceutics
  • Volume:
  • Article number:
  • Status:
  • Country:
  • Print ISSN:
  • PII:
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  • Keywords:
    additive manufacturing of medicines, computational modelling, data fusion, digital manufacturing and internet of things, drug delivery and formulations, powder bed fusion
  • Notes:
    © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Three-dimensional (3D) printing is drastically redefining medicine production, offering digital precision and personalized design opportunities. One emerging 3D printing technology is selective laser sintering (SLS), which is garnering attention for its high precision, and compatibility with a wide range of pharmaceutical materials, including low-solubility compounds. However, the full potential of SLS for medicines is yet to be realized, requiring expertise and considerable time-consuming and resource-intensive trial-and-error research. Machine learning (ML), a subset of artificial intelligence, is an in silico tool that is accomplishing remarkable breakthroughs in several sectors for its ability to make highly accurate predictions. Therefore, the present study harnessed ML to predict the printability of SLS formulations. Using a dataset of 170 formulations from 78 materials, ML models were developed from inputs that included the formulation composition and characterization data retrieved from Fourier-transformed infrared spectroscopy (FT-IR), X-ray powder diffraction (XRPD) and differential scanning calorimetry (DSC). Multiple ML models were explored, including supervised and unsupervised approaches. The results revealed that ML can achieve high accuracies, by using the formulation composition leading to a maximum F1 score of 81.9%. Using the FT-IR, XRPD and DSC data as inputs resulted in an F1 score of 84.2%, 81.3%, and 80.1%, respectively. A subsequent ML pipeline was built to combine the predictions from FT-IR, XRPD and DSC into one consensus model, where the F1 score was found to further increase to 88.9%. Therefore, it was determined for the first time that ML predictions of 3D printability benefit from multi-modal data, combining numeric, spectral, thermogram and diffraction data. The study lays the groundwork for leveraging existing characterization data for developing high-performing computational models to accelerate developments.
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