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Related Experiment Video

Updated: Jan 13, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

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Published on: August 29, 2025

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A multitask modelling framework for tablet manufacturability and quality attributes in direct compression using

Manuel Borja1, Jens Dhondt2, Johny Bertels2

  • 1Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure links 653, Gent, 9000, Belgium.

International Journal of Pharmaceutics
|October 29, 2025
PubMed
Summary

This study introduces a novel neural network framework to predict drug product manufacturability and quality attributes simultaneously during direct compression. This integrated approach accelerates formulation development by linking raw material properties to final product success.

Keywords:
Direct compressionFormulation developmentHybrid modellingMultitask modellingNeural networks

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Area of Science:

  • Pharmaceutical Sciences
  • Chemical Engineering
  • Computational Modeling

Background:

  • Drug product development requires assessing manufacturing feasibility and critical quality attributes (CQAs).
  • Traditional modeling approaches often address manufacturability or CQAs separately, overlooking their intrinsic link.
  • Direct compression is a common manufacturing process for solid dosage forms.

Purpose of the Study:

  • To develop a joint modeling framework using neural networks for direct compression.
  • To estimate both the manufacturability of a formulation and its quality attributes concurrently.
  • To accelerate formulation and process development for drug products.

Main Methods:

  • Utilized a joint neural network modeling framework.
  • Input features included raw material properties, blending ratios, and direct compression processing conditions.
  • Assessed interaction modeling techniques like attention mechanisms and embedded expert knowledge via monotonicity rules.

Main Results:

  • Demonstrated the feasibility of a joint modeling framework for manufacturability and quality attribute prediction.
  • The integrated approach effectively leverages the inherent relationship between formulation properties, processability, and final product quality.
  • Attention mechanisms and monotonicity rules enhanced model performance in capturing complex material interactions.

Conclusions:

  • A joint modeling framework offers a powerful approach to simultaneously predict manufacturability and quality attributes in direct compression.
  • This methodology can significantly aid formulation scientists in optimizing drug product development.
  • The proposed framework streamlines the assessment of formulation and process parameters, leading to faster development timelines.