Quality by design in formulation and process development for a freeze-dried, small molecule parenteral product: a case study.

A case study has been developed to illustrate one way of incorporating a Quality by Design approach into formulation and process development for a small molecule, freeze-dried parenteral product. Sodium ethacrynate was chosen as the model compound. Principal degradation products of sodium ethacrynate result from hydrolysis of the unsaturated ketone in aqueous solution, and dimer formation from a Diels-Alder condensation in the freeze-dried solid state. When the drug crystallizes in a frozen solution, the eutectic melting temperature is above -5°C. Crystallization in the frozen system is affected by pH in the range of pH 6-8 and buffer concentration in the range of 5-50 mM, where higher pH and lower buffer concentration favor crystallization. Physical state of the drug is critical to solid state stability, given the relative instability of amorphous drug. Stability was shown to vary considerably over the ranges of pH and buffer concentration examined, and vial-to-vial variability in degree of crystallinity is a potential concern. The formulation design space was constructed in terms of pH and drug concentration, and assuming a constant 5 mM concentration of buffer. The process design space is constructed to take into account limitations on the process imposed by the product and by equipment capability.

A QbD case study: Bayesian prediction of lyophilization cycle parameters.

As stipulated by ICH Q8 R2 (1), prediction of critical process parameters based on process modeling is a part of enhanced, quality by design approach to product development. In this work, we discuss a Bayesian model for the prediction of primary drying phase duration. The model is based on the premise that resistance to dry layer mass transfer is product specific, and is a function of nucleation temperature. The predicted duration of primary drying was experimentally verified on the lab scale lyophilizer. It is suggested that the model be used during scale-up activities in order to minimize trial and error and reduce costs associated with expensive large scale experiments. The proposed approach extends the work of Searles et al. (2) by adding a Bayesian treatment to primary drying modeling.