Predicting the viability of pharmaceutical formulations for continuous direct compression using machine learning
Laura Pereira Diaz1, Stéphanie Marchal2, Paul Kroll2
1CMAC, University of Strathclyde, Technology and Innovation Centre, 99 George Street, Glasgow G1 1RD, UK; Strathclyde Institute of Pharmacy & Biomedical Sciences, 161 Cathedral St, Glasgow G4 0RE, UK.
International Journal of Pharmaceutics
|May 1, 2026
Summary
Artificial intelligence and Machine Learning (ML) models predict pharmaceutical formulation viability for continuous direct compression (cDC). This data-driven approach accelerates development and improves manufacturing decisions.
Area of Science:
- Pharmaceutical Sciences
- Chemical Engineering
- Computational Science
Background:
- Pharmaceutical formulation involves combining active pharmaceutical ingredients (API) and excipients, where changes can affect bulk properties like powder flowability.
- These properties significantly influence manufacturing processes, highlighting the need for predictive techniques.
- Subtle API physical property variations (e.g., particle size, shape) also impact drug product manufacturability.
Purpose of the Study:
- To present a novel approach using Artificial Intelligence (AI) and Machine Learning (ML) to predict pharmaceutical formulation viability.
- To support the optimization of the transition from formulation development to manufacturing.
- To enable early assessment of formulation suitability for continuous direct compression (cDC).
Main Methods:
- Development and integration of three complementary ML models.
- Application of data-driven models for predictive screening of formulation properties.
- Utilizing digital design principles to assess manufacturability.
Main Results:
- The combined ML modeling approach provides a practical framework for early viability assessment.
- Enables predictive screening of pharmaceutical formulations for cDC.
- Supports informed decision-making in formulation development.
Conclusions:
- AI and ML offer powerful tools to accelerate pharmaceutical development.
- The presented modeling approach enhances the prediction of formulation manufacturability.
- Facilitates a more efficient and data-driven process for developing viable pharmaceutical formulations for cDC.
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