(533f) 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.