In silico predictions of tablet density using a quantitative structure-property relationship model

Area of Science:

• Pharmaceutical Sciences
• Computational Chemistry
• Materials Science

Background:

• Tablet density is a critical quality attribute influencing drug bioavailability and manufacturing.
• Predictive modeling can optimize tablet formulation and manufacturing processes.
• Quantitative Structure-Property Relationship (QSPR) models offer a computational approach to predict material properties.

Purpose of the Study:

• To develop and validate a QSPR model for predicting tablet density.
• To identify key molecular descriptors influencing tablet density.
• To explore the utility of QSPR for in silico tablet design.

Main Methods:

• Calculation of 3381 molecular descriptors for 81 active pharmaceutical ingredients (APIs).
• Preparation of 81 tablet types using direct compression with varying compression pressures (120, 160, 200 MPa).
• Application of a boosted-tree machine learning approach to build the QSPR model.

Main Results:

• The developed QSPR model demonstrated statistically significant predictive performance.
• Molecular descriptors related to average molecular weight and electronegativity of APIs were identified as crucial factors.
• A positive linear relationship was observed between these molecular descriptors and tablet density.
• The influence of powder properties on tablet density was found to be less significant compared to molecular descriptors.

Conclusions:

• QSPR modeling is a viable approach for the in silico prediction of tablet density, particularly for formulations compressed above a certain threshold.
• The study provides a deeper understanding of the molecular factors governing tablet density.
• This predictive capability can aid in the rational design and optimization of tablets.