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Solid dosage forms such as tablets and capsules undergo rigorous manufacturing processes to ensure stability and effectiveness. Their dissolution and absorption properties are influenced significantly by the choice of excipients (inactive ingredients that serve various roles in the formulation), and the methodology applied during production. The manufacturing parameters, such as compression force and granulation techniques, significantly affect dissolution rates. Elevated compression forces...
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Process parameter optimization model for tablet compression based on random forest and proximal policy optimization

Jianqiang Du1, Ting Wang2, Weifeng Zhu3

  • 1School of Intelligent Medicine and Information Engineering, Jiangxi University of Chinese Medicine, Nanchang 330004, China; Nanchang Normal University, Nanchang 330032, China.

International Journal of Pharmaceutics
|October 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Random Forest-Proximal Policy Optimization (RF-PPO) model to optimize tablet manufacturing processes, reducing manual intervention and ensuring consistent tablet weight for improved pharmaceutical production.

Keywords:
Feature selectionLactobacillus tabletsProcess parameter optimizationProximal policy optimizationRandom forestTablet quality

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Area of Science:

  • Pharmaceutical Manufacturing
  • Process Optimization
  • Machine Learning Applications

Background:

  • Tablet manufacturing quality is often affected by process parameter fluctuations and manual adjustments.
  • Current automatic adjustment systems lack precision, necessitating human intervention.
  • Variability in tablet compression impacts overall product quality and consistency.

Purpose of the Study:

  • To develop an intelligent process parameter optimization model for tablet manufacturing.
  • To address quality fluctuations in tablet compression lines.
  • To reduce reliance on manual intervention in pharmaceutical production.

Main Methods:

  • Integrated Random Forest (RF) and Proximal Policy Optimization (PPO) algorithms into a novel RF-PPO model.
  • Utilized feature selection to identify critical process parameters (CPPs).
  • Employed offline validation and MATLAB/Simulink simulations for model evaluation.

Main Results:

  • The RF prediction model demonstrated high accuracy (R² > 0.92, low RMSE).
  • The RF-PPO model optimized parameters with millisecond-level response time (0.0030 s).
  • The model effectively minimized deviation from the nominal tablet weight (0.8 g).

Conclusions:

  • The RF-PPO model offers a promising solution for intelligent pharmaceutical manufacturing.
  • It significantly reduces manual intervention and maintains tablet weight within the premium product range.
  • The model enhances tablet quality by ensuring consistent weight and optimizing process parameters.