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Machine learning recovers corrupted pharmaceutical 3D printing formulation data.

Olima Uddin1, Yusuf Ali Mohammed1, Simon Gaisford2

  • 1School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End Road, London E1 4DQ, UK.

International Journal of Pharmaceutics
|November 18, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning, specifically denoising autoencoders (DAEs), can effectively reconstruct corrupted pharmaceutical formulation data, safeguarding personalized medicine production against cyberattacks and ensuring patient safety.

Keywords:
Artificial IntelligenceDigital ResilienceDrug DevelopmentFused Deposition ModellingQuality Control

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Area of Science:

  • Pharmaceutical manufacturing
  • Digital health
  • Machine learning applications

Background:

  • 3D printing of pharmaceuticals enables personalized medicine but introduces cybersecurity risks.
  • Cyberattacks can corrupt critical drug formulation data, jeopardizing patient safety.
  • Developing robust data protection methods is crucial for digital pharmaceutical manufacturing.

Purpose of the Study:

  • To investigate the efficacy of denoising autoencoders (DAEs) in reconstructing corrupted pharmaceutical formulation data.
  • To simulate and address potential cyberattack scenarios affecting digital drug manufacturing data.
  • To enhance the digital resilience of pharmaceutical data integrity.

Main Methods:

  • Utilized a dataset of 1,623 pharmaceutical formulations (336 ingredients, >545,000 data points).
  • Simulated cyberattacks by introducing data deletion (1%-50%) and noise injection.
  • Evaluated multiple DAE configurations for data recovery performance.

Main Results:

  • DAEs achieved high R² scores (0.989 at 1% corruption, 0.924 at 50% corruption).
  • DAEs accurately reconstructed active pharmaceutical ingredient and excipient values, indicating meaningful pattern recognition.
  • Traditional machine learning methods failed to recover corrupted data, unlike DAEs.

Conclusions:

  • Denoising autoencoders effectively safeguard pharmaceutical formulation data against corruption.
  • DAEs offer a promising solution for enhancing digital resilience in pharmaceutical manufacturing.
  • Machine learning plays a vital role in maintaining data quality and patient safety in the digital pharmaceutical sector.