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Using a Model-based Material Sparing Approach for Formulation and Process Development of a Roller Compacted Drug
Kalyan V Vasudevan1, Yu Elaine Pu2, Hossein Amini3
1Drug Product Development, Pharmaceutical Science & Technology, Bristol Myers Squibb, Summit, NJ, USA. kalyan.vasudevan@bms.com.
Pharmaceutical Research
|February 26, 2022
Summary
This study presents a material-sparing method for developing roller-compacted drug products. It uses material profiling and a mathematical model to efficiently guide formulation and process development, saving valuable drug substance.
Area of Science:
- Pharmaceutical Sciences
- Chemical Engineering
- Materials Science
Background:
- Developing roller-compacted drug products often requires significant material quantities and extensive batch testing.
- Early-stage drug development faces challenges with limited drug substance availability and aggressive timelines.
Purpose of the Study:
- To introduce a material-sparing approach for roller compaction (RC) formulation and process development.
- To demonstrate the efficacy of combining material profiling with a predictive RC mathematical model.
- To reduce material consumption and batch numbers in drug product development.
Main Methods:
- Material profiling (true density, compression, friction) and a predictive RC mathematical model were employed.
- Pre-blend powder properties and RC granule characteristics guided formulation and process parameters.
- Less than 10g of model drug compound was used for initial material profiling.
Main Results:
- Successful development of roller-compacted capsule formulations at 1.4% and 14.4% drug loadings.
- Scale-up batches were manufactured using parameters derived from the predictive model.
- Measured ribbon solid fractions closely matched the model's target solid fraction.
Conclusions:
- The material-sparing approach significantly reduces material usage and batch requirements for RC drug product development.
- This method is particularly advantageous for early-stage development with limited drug substance.
- Predictive modeling integrated with material profiling streamlines formulation and process optimization.

