Joe DiMartino, solution manager, Luminata at ACD/Labs, told us QbD helps lower patient risk, the risk of supply chain disruption, and helps avoid the unanticipated risk of product contamination.
To learn more, Outsourcing-Pharma.com talked with DiMartino – who presented at AAPS 2017 in San Diego, CA – about the evolving regulatory landscape and how companies can leverage QbD to help mitigate risk in drug development.
Why have global regulatory authorities been pushing QbD?
In the past there was an accepted culture of quality-by-testing. Organizations relied on setting up appropriate testing procedures to detect defects in product before release.
This outlook is more risky than the newer push for quality-by-design, whereby organizations delivering products for human consumption are being required to design processes in such a way that controls are implemented to ensure defects are not introduced.
What are some of the regulations in place that call for a QbD approach?
The requirement to establish a Quality Target Product Profile (QTPP) is a major impact of QbD on process development. This is accomplished through:
- Evaluation of input Material Quality Attributes (MQA)
- Evaluation of the quality impact of Critical Process Parameters (CPP)
- Consolidated evaluation of every MQA and CPP for all input materials and unit operations
MQA assessment requires the careful consideration of input materials to ensure that their physical/(bio)chemical properties or characteristics are within appropriate limits, ranges, or distributions.
Furthermore, for CPP assessment, unit operation process parameter ranges must be evaluated to determine the impact of parameter variability on product quality.
The contribution of each unit operation in any pharmaceutical or biopharmaceutical manufacturing process—whether it be synthetic steps in a chemical process (i.e., filtering, stirring, agitating, heating, chilling, etc.) or product formulation must be assessed.
How has an evolving drug development landscape necessitated these regulations?
With externalization of specific components of the supply chain there’s an increased risk that quality by testing may miss something due to the lack of direct access to data.
Internal regulatory and quality groups don’t have the intimate access to data and facilities that they had historically. As a consequence quality-by-design accounts for some of this proximity risk.
What are the benefits of QbD in drug development?
QbD comprehensively determines the dependencies of product quality on process variation.
Any potential quality risks, therefore, can be accounted for and appropriately mitigated before commercialization.
How is ACD/Labs’ platform different from similar solutions?
Luminata from ACD/Labs is a unique informatics solution for the management of process and impurity data with unparalleled functionality and benefits for development organizations. Luminata unifies and assembles the overwhelming quantity of analytical and chemical data, routinely generated in drug development, in real time.
Built on the multi-technique, vendor-agnostic ACD/Spectrus Platform; Luminata offers comprehensive data standardization—accomplished by aggregating chemical reaction information, and the associated formation and fate of impurities, with chromatographic and spectral data. It facilitates efficient organization of analytical knowledge for processes and associated impurities, at every stage.
What benefits does it provide customers?
Luminata affords unprecedented data integrity for vast and varied chemical and analytical data with line of sight from a given batch of product to all associated entities throughout its synthesis and manufacture. It provides scientists real-time access to information in support of efficient decision-making, collaboration, and reporting.
Luminata enables organizations to establish impurity control strategies in accordance with Quality-by-design (QbD) principles without hindering progress—it removes tedious transcription and reduces errors. Importantly, it provides on-demand access to all the relevant data for the review of a process and allows for easy and comprehensive batch-to-batch comparisons.