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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Application of AI in Tablet Development: An Integrated Machine Learning Framework for Pre-Formulation Property

Masugu Hamaguchi^1,2, Tomoki Adachi³, Noriyoshi Arai¹

¹Department of Mechanical Engineering, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Kanagawa, Japan.

|May 4, 2026

Summary

This summary is machine-generated.

This study introduces an AI framework to optimize tablet development by integrating formulation, process, and material data. The AI framework improves tablet hardness and disintegration time predictions, aiding pre-formulation decisions.

Keywords:

QSPR machine learning particle size distribution pharmaceutics pre-formulation prediction tablet formulation

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Area of Science:

Pharmaceutical Sciences
Computational Chemistry
Materials Science

Background:

Tablet development involves optimizing multiple quality attributes under budget constraints.
Formulation-property relationships in mixture systems are highly nonlinear.
Pre-formulation decision-making requires advanced predictive models.

Purpose of the Study:

To propose an AI framework for organizing tablet formulation, process, and raw material data.
To enrich conventional features with physically motivated mixture descriptors.
To support pre-formulation decision-making by predicting tablet quality attributes.

Main Methods:

Developed an AI framework integrating formulation, process, and material property data.
Constructed mixture-level scalar descriptors and incorporated particle size distribution (PSD) summaries.
Compared three feature sets (MP, MPD, MPDD) using six machine learning models and cross-validation strategies.

Main Results:

Mixture-descriptor augmentation improved predictions for tablet hardness and disintegration time in interpolation settings.
Smaller gains were observed for flow function, with mixed effects for cohesion and thickness.
Extrapolation-oriented evaluation showed potential improvements for hardness but degradation for disintegration time prediction.

Conclusions:

The proposed AI framework and feature enrichment strategies can aid pre-formulation decision-making.
Careful selection and dimensionality control of descriptors are crucial for robust extrapolation.
The study highlights the need for robust AI models in complex pharmaceutical mixture development.