Integrating Elastic Tensor and PC-SAFT Modeling with Systems-Based Pharma 4.0 Simulation, to Predict Process...

Area of Science:

• Pharmaceutical Sciences
• Materials Science
• Chemical Engineering

Background:

• Comminution of Biopharmaceutics Classification System (BCS) II Active Pharmaceutical Ingredients (APIs) below 1 μm enhances dissolution.
• Nanoparticle formation alters crystal habits, improving wettability via polymer adsorption.
• Current dissolution models fail to account for nanoparticle formation, stabilizer effects, and interfacial tension.

Purpose of the Study:

• To develop a predictive method for API stability and nanosuspension thermodynamic stability.
• To quantify API stability during comminution using elastic tensor analysis.
• To model solubility enhancement in ternary nanosuspension mixtures.

Main Methods:

• Elastic tensor analysis for API stability assessment.
• Novel thermodynamic model based on stabilizer-coated nanoparticle Gibbs energy minimization.
• Solubility prediction using Perturbed-Chain Statistical Association Fluid Theory (PC-SAFT) modeling.
• Integration into a Pharma 4.0 algorithm for predicting critical material attributes and process parameters.

Main Results:

• Elastic tensor analysis quantified API stability during comminution.
• The novel thermodynamic model successfully predicted system solubility.
• The combined elastic tensor and PC-SAFT approach, integrated into a Pharma 4.0 algorithm, provided a validated method.
• The algorithm accurately predicted critical material quality attributes and key process parameters.

Conclusions:

• A multi-level, validated method was developed by merging elastic tensor and PC-SAFT analysis within a Pharma 4.0 framework.
• This approach enables prediction of critical material quality attributes and key process parameters for nanosuspension development.
• The findings offer a path towards predictable and controlled nanosuspension manufacturing, addressing current limitations in understanding process-induced transformations.