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Active learning in pharmaceutical 3D printing: a multi-dataset comparison.

Moe Elbadawi1, Noorul Fathima Abdul Kafoor2, Hanxiang Li3

  • 1School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End Road, London,, E1 4DQ, UK. m.elbadawi@qmul.ac.uk.

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Summary
This summary is machine-generated.

Active learning (AL) accelerates 3D printed medicine development by enabling machine learning (ML) with small datasets. This approach achieved 100% accuracy in predicting pharmaceutical 3D printing success.

Keywords:
Active machine learningAdditive manufacturingArtificial intelligenceComputational modellingDrug developmentIn silicoSustainability

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Area of Science:

  • Pharmaceutical Manufacturing
  • Computational Chemistry
  • Materials Science

Background:

  • Machine learning (ML) offers significant potential for advancing 3D printed medicines.
  • The development of 3D printing technology in pharmaceuticals is often limited by the need for extensive datasets for ML model training.
  • Emerging pharmaceutical manufacturing technologies like 3D printing require innovative approaches to data utilization.

Purpose of the Study:

  • To investigate the efficacy of active learning (AL), a machine learning strategy, for predicting the printability of 3D printed pharmaceutical formulations.
  • To evaluate AL's performance with limited datasets, addressing a key challenge in pharmaceutical 3D printing.
  • To compare AL's predictive accuracy against traditional ML methods in the context of 3D printing medicines.

Main Methods:

  • Active learning (AL) was employed to predict the printability of formulations across three distinct 3D printing technologies: fused deposition modelling (FDM), vat polymerization, and selective laser sintering (SLS).
  • The study utilized three datasets with varying numbers of formulations (1437 FDM, 650 vat polymerization, 297 SLS).
  • Model performance was assessed based on predictive accuracy as the size of the training dataset increased.

Main Results:

  • Active learning (AL) achieved 60% predictive accuracy starting with as few as 33 formulations.
  • Increasing the training data size further enhanced the predictive performance of the AL models.
  • The study recorded a 100% predictive accuracy using AL, the highest reported to date for pharmaceutical 3D printing applications.
  • AL demonstrated superior performance compared to traditional machine learning approaches for these datasets.

Conclusions:

  • Active learning (AL) is a viable and effective strategy for accelerating the development of 3D printed medicines, particularly when dealing with limited data.
  • This research validates the potential of machine learning (ML) modeling with small datasets, broadening its applicability in pharmaceutical research and development.
  • The findings suggest that AL can significantly improve the efficiency and success rate of 3D printing pharmaceutical formulations.