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Related Experiment Videos

Predicting mini-tablet dissolution performance utilizing X-ray computed tomography.

Tohn Borjigin1, Xi Zhan1, Jiangwei Li1

  • 1Biogen, 225 Binney St., Cambridge, MA, USA.

European Journal of Pharmaceutical Sciences : Official Journal of the European Federation for Pharmaceutical Sciences
|December 9, 2022
PubMed
Summary
This summary is machine-generated.

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X-ray computed microtomography (XRCT) and deep learning accurately detect defects in mini-tablets (MTs). This non-destructive method predicts drug dissolution performance, optimizing manufacturing for better quality control.

Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Medical Imaging

Background:

  • Mini-tablets (MTs) offer advantages in pediatric dosing and drug release but can suffer manufacturing defects affecting performance.
  • X-ray computed microtomography (XRCT) is established for monolithic tablet defect analysis, but its application to MTs is less explored.

Purpose of the Study:

  • To develop and validate a workflow using XRCT and deep learning for analyzing defects in enteric-coated MTs.
  • To correlate physical defects identified by XRCT with MT dissolution performance.
  • To enable non-destructive prediction of MT dissolution using imaging data.

Main Methods:

  • Reconstructed XRCT images of enteric-coated MTs were analyzed using deep learning convolutional neural networks.
  • Key physical features, including micro-crack volume and enteric coat thickness, were extracted.
Keywords:
X-ray computed microtomographyconvolutional neural network image segmentationmini-tabletsoral solid dosagesquality by designtablet dissolution

Related Experiment Videos

  • Individual MT dissolution studies were performed and correlated with XRCT-derived physical parameters.

    • Main Results:

    • A novel workflow successfully identified and quantified internal and coating defects in MTs.
    • Significant correlations were established between physical parameters (e.g., micro-crack volume, coat thickness) and dissolution performance.
    • Non-destructive XRCT imaging enabled accurate prediction of MT dissolution behavior.

    Conclusions:

    • The developed XRCT and deep learning workflow provides mechanistic insight into MT physical variability during manufacturing.
    • This approach allows for the optimization of tableting and coating parameters to meet dissolution criteria.
    • Quality is enhanced through rational development and non-destructive quality control of the final drug product.