Design space development for the extraction process of Danhong injection using a Monte Carlo simulation method.

Area of Science:

• Pharmaceutical Process Optimization
• Chemical Engineering
• Quality by Design (QbD)

Background:

• Optimizing extraction processes is crucial for pharmaceutical manufacturing.
• Identifying critical process parameters (CPPs) and critical quality attributes (CQAs) is essential for process control.
• Traditional methods may not fully capture the complex relationships within a process design space.

Purpose of the Study:

• To develop and validate a probability-based design space for Danhong injection extraction.
• To optimize extraction critical process parameters (CPPs) for critical quality attributes (CQAs).
• To establish optimized calculation conditions for design space development in pharmaceutical processes.

Main Methods:

• Applied a design space approach using quadratic models to link CPPs and CQAs.
• Utilized Monte-Carlo simulation with stepwise regression for probability-based design space calculation.
• Optimized simulation parameters including simulation number, step length, significance level, and data distribution.

Main Results:

• Developed models with determination coefficients > 0.94, showing increased yields with extraction number.
• Identified an optimized calculation condition (10,000 simulations, 0.01 step length, 0.35 significance level, normal distribution).
• Successfully calculated and verified a design space with > 0.95 probability, recommending W/M ratio (8.2-10 g/g), extraction time (1.25-1.63 h), and 2 extractions.

Conclusions:

• The design space approach effectively optimizes Danhong injection extraction.
• The established optimized calculation conditions are transferable to other pharmaceutical processes.
• Recommended operating ranges ensure consistent achievement of critical quality attributes.