Linked experimental and modelling approaches for tablet property predictions.

Area of Science:

• Pharmaceutical Sciences
• Materials Science
• Chemical Engineering

Background:

• Quality-by-Design (QbD) principles are increasingly adopted for pharmaceutical manufacturing to ensure quality and efficiency.
• Direct Compression is a key tablet manufacturing method, but traditional development approaches are time- and resource-intensive.
• Developing robust models for multicomponent systems is challenging due to complex interactions.

Purpose of the Study:

• To evaluate a combined modeling and experimental approach for predicting multicomponent tablet properties.
• To assess the feasibility of extrapolating pure component model parameters from binary tablet data.
• To identify suitable mixing rules and volume fraction estimation methods for accurate predictions.

Main Methods:

• Utilized extrapolation from binary tablet data to determine pure component model parameters for difficult-to-compact materials.
• Evaluated various mixing rules (e.g., linear averaging, power law) for predicting compression and compaction properties.
• Assessed different methods for estimating component volume fractions, including theoretical relative compression rates.

Main Results:

• Extrapolation from binary tablet data proved feasible for obtaining model parameters of challenging materials.
• Linear averaging using pre-compression volume fractions was most suitable for compression parameters.
• Averaging using a power law equation form showed best agreement for compaction parameters.
• Estimations based on theoretical relative compression rates slightly outperformed constant volume fractions.

Conclusions:

• The developed framework enables prediction of multicomponent tablet properties without extensive experimental fitting.
• This approach accelerates the evaluation of the tablet's knowledge space and identifies key regions for targeted experimentation.
• It offers a pathway to establish design and control spaces, potentially bypassing laborious initial Design-of-Experiments.