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Novel methodology to assess and predict segregation risk in a continuous direct compression via mini-batch blending
Paul Kroll1, Tong Deng2, Lucas Massaro Sousa3
1F. Hoffmann-La Roche AG, Synthetic Molecules Pharmaceutical Development, Grenzacherstrasse 124, 4070 Basel, Switzerland; MUVON Therapeutics AG, Weinbergstrasse 35, c/o ETH Zürich WEH G-Stock, 8092 Zürich, Switzerland.
International Journal of Pharmaceutics
|April 18, 2026
Summary
This study developed a method to predict powder segregation in continuous direct compression. The findings link powder flowability and bulk density to segregation risk, aiding early-stage process assessment.
Area of Science:
- Pharmaceutical Sciences
- Chemical Engineering
- Materials Science
Background:
- Powder segregation is a critical issue in pharmaceutical manufacturing, impacting drug product uniformity and efficacy.
- Continuous manufacturing processes like continuous direct compression (cDC) require robust methods to predict and mitigate segregation.
- Mini-batch blending (MBB) integrated into cDC offers opportunities for real-time assessment but needs validated segregation prediction models.
Purpose of the Study:
- To develop and validate a methodology for assessing and predicting powder segregation in a continuous direct compression via mini-batch blending (cDC via MBB) process.
- To establish correlations between powder properties and segregation behavior.
- To create practical models for early-stage risk assessment of segregation in cDC.
Main Methods:
- Utilized a representative test rig to study 44 formulations with varying active pharmaceutical ingredients (APIs) and excipients (5-40% drug load).
- Quantified segregation using particle size distributions and chemical assay data (change in standard deviation of API concentration, Δstd).
- Employed multivariate analysis to identify correlations between segregation intensity (SID50), flow function coefficient (ffc), and bulk density.
Main Results:
- A strong correlation was found between segregation intensity (SID50) and the flow function coefficient (ffc), leading to an empirical linear relationship for segregation tendency.
- A secondary model successfully related segregation behavior (Δstd) to measurable bulk properties: flow function coefficient and bulk density.
- The developed models provide a practical framework for early-stage segregation risk assessment in cDC processes with limited material.
Conclusions:
- The proposed methodology offers a practical approach for predicting powder segregation in cDC via MBB processes.
- Flowability (ffc) and bulk density are key predictors of segregation risk.
- The methodology, while specific to the studied system, shows potential for broader application in powder handling processes after validation.
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