Abstract
Quality by Design (QbD), a current trend employed to develop and optimise various critical pharmaceutical processes, is a systematic approach based on the ethos that quality should be designed into the product itself, not just end tested after manufacture. The present work details a step-wise application of QbD principles to optimise process parameters for production of particles with modified functionalities, using dry particle coating technology. Initial risk assessment identified speed, air pressure, processing time and batch size (independent factors) as having high-to-medium impact on the dry coating process. A design of experiments (DOE) using MODDE software employed a D-optimal design to determine the effect of variations in these factors on identified responses (content uniformity, dissolution rate, particle size and intensity of Fourier transform infrared (FTIR) C = O spectrum). Results showed that batch size had the most significant effect on dissolution rate, particle size and FTIR; with an increase in batch size enhancing dissolution rate, decreasing particle size (depicting absence of coated particles) and increasing the FTIR intensity. While content uniformity was affected by various interaction terms, with speed and batch size having the highest negative effect. Optimal design space for producing functionalised particles with optimal properties required maximum air pressure (40psi), low batch size (6g), speed between 850 to 1500 rpm and processing times between 15 to 60 minutes. The validity and predictive ability of the revised model demonstrated reliability for all experiments. Overall, QbD was demonstrated to provide an expedient and cost effective tool for developing and optimising processes in the pharmaceutical industry.
Highlights
Quality in the pharmaceutical industry has traditionally been assured via quality by testing (QbT), where failure results in whole batches being discarded at significant cost
This paper aims to optimise the process parameters of a dry particle coating technology via applying a step-wise quality by design (QbD) approach
The study discusses the risk assessment carried out for critical process parameters (CPPs) and critical quality attributes (CQAs) followed by design of experiments (DOE) model verification, analysis of variance (ANOVA) and main/interaction effects on CQAs
Summary
Quality in the pharmaceutical industry has traditionally been assured via quality by testing (QbT), where failure results in whole batches being discarded at significant cost. In this approach, a lack of understanding of the critical process parameters (CPPs) renders manufacturing issues difficult to diagnose and can lead to substantial losses [1,2]. Quality by design (QbD) provides a unique opportunity for the pharmaceutical industry. The funders played no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
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