Quality by Design (QbD) Case Studies: Integrated Use of Design of Experiments (DOE) and Multivariate Data Analysis (MVDA) In Drug Product and Process Optimization

Quality by Design (QbD) Case Studies: Integrated Use of Design of Experiments (DOE) and Multivariate Data Analysis (MVDA) in Drug Product and Process Optimization

Author/s: Goldi Kaul (Ph.D.)

Abstract:

Herein exemplified by two case studies is the combinatorial use of experimental design, optimization and multivariate techniques integrated into product and process development of a drug product with the aim of improving formulation and process understanding, as well as offering opportunities for developing control strategies to ensure product quality.

In one study, the role of poloxamer 188, water and binder addition rate, on retarding dissolution in immediate-release tablets of a model BCS class II drug was investigated by these techniques. The unexpected effect of poloxamer in tablets was accompanied by an increase in tablet-disintegration-time-mediated slowdown of tablet dissolution and by a surrogate binding effect of poloxamer at higher concentrations. It was additionally realized through MVDA that poloxamer in tablets either acts as a binder by itself or promotes binding action of the binder povidone resulting in increased intragranular cohesion. Substituting polysorbate 80 as an alternate surfactant in place of poloxamer in the formulation was found to stabilize tablet dissolution. In the second case study, a process DOE utilizing three design factors (water amount, wet massing time and lubrication time) was used to evaluate effects of the design factors on manufacturability and final product critical quality attributes (CQAs), and establish design space to ensure desired CQAs. While the DOE analysis yielded important clues into the cause-and-effect relationship between the responses and design factors, multivariate data analysis of the 70+ variables in the study provided additional information on blend flow, compressibility and tablet dissolution.

These multivariate approaches exemplify application of QbD principles and tools to drug product and process development and emphasize on the importance of an integrated approach towards data analysis.