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Related Experiment Video

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Fabrication of Compressed Hosiery and Measurement of its Pressure Characteristic Exerted on the Lower Limbs

Fabrication of Compressed Hosiery and Measurement of its Pressure Characteristic Exerted on the Lower Limbs

Published on: May 27, 2020

Continuous direct compression: Development of an empirical predictive model and challenges regarding PAT

B Bekaert¹, B Van Snick², K Pandelaere¹

¹Laboratory of Pharmaceutical Technology, Department of Pharmaceutics, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium.

International Journal of Pharmaceutics: X

|January 13, 2022

Summary

This study developed a predictive model for pharmaceutical manufacturing, optimizing blend properties and process parameters to improve tablet quality and reduce development time. The model enhances processability and aids in implementing Process Analytical Technology (PAT) tools.

Keywords:

#BP, Number of blade passes #RMB1, Number of radial mixing blades of the main blender API, Active pharmaceutical ingredient API_sd, Spray dried API BRT, Bulk residence time BU, Blend uniformity CDC, Continuous direct compression CDC-50 CU, Content uniformity C_P, Caffeine anhydrous powder Continuous direct compression Continuous manufacturing DCP, Dicalcium phosphate / Emcompress AN FD, Fill depth HM1/HM2, Hold-up mass main blender/Hold-up mass lubricant blender Imp1, Impeller speed main blender LC, Percentage label claim MCF, Main compression force MCH, Main compression height MPT_μ, Metoprolol micronized MgSt, Magnesium stearate/Ligamed MF-2-V Multivariate data-analysis NIR, Near infrared PAT PAT, Process Analytical Technology PC, Principle component PCA, Principle component analysis PCD, Pre-compression displacement PCF, Pre-compression force PCH, Pre-compression height PH101, Microcrystalline cellulose / Avicel PH-101 PH200, Microcrystalline cellulose / Avicel PH-200 PLS, Partial least squares P_DP, Paracetamol dense powder P_P, Paracetamol powder P_μ, Paracetamol micronized Predictive modeling Q2, Goodness of prediction R2Y, Goodness of fit RMSEcv, Root mean squared error of cross validation RSDTW, Relative standard deviation of tablet weight SD100, Mannitol / Pearlitol 100 SD T80, Lactose / Tablettose 80 T_P, Theophylline anhydrous powder rpm, Revolutions per minute σForce, Main compression force variability σPCD, Variability in pre-compression displacement

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Area of Science:

Pharmaceutical Sciences
Chemical Engineering
Materials Science

Background:

Optimizing pharmaceutical manufacturing processes is crucial for ensuring drug product quality and reducing development costs.
Understanding the interplay between blend properties, critical quality attributes (CQA), and critical process parameters (CPP) is essential for robust process design.
Challenges in blend processability and Process Analytical Technology (PAT) implementation can hinder efficient manufacturing.

Purpose of the Study:

To develop an empirical predictive model correlating blend properties with CQAs and CPPs for blending and tableting.
To investigate the impact of blend properties and process parameters on blending performance and tablet quality.
To identify challenges in PAT implementation and continuous direct compression (CDC) platform performance.

Main Methods:

Quantitative analysis of relationships between blend properties, CQAs, and CPPs.
Evaluation of thirty diverse ternary blends on a continuous direct compression line (ConsiGma™ CDC-50).
Assessment of impeller configuration, impeller speed, and blend composition effects on blending and compression.

Main Results:

Impeller configuration and speed significantly influenced blending performance, while blend properties had a limited impact.
Blend properties significantly affected tablet quality during compression.
An empirical predictive model was successfully developed to guide process configuration selection.

Conclusions:

The developed predictive model can reduce trial runs, development time, and costs for new drug products.
Blend properties present significant challenges for PAT implementation and CDC platform performance.
Further process development is necessary to address remaining challenges in blend properties and PAT integration.