Connecting the Dots for Successful Bio Process Validation
The key to success in the lifecycle approach to process validation is robust process and product understanding. Scientific evidence regarding process performance, linking process parameters and raw materials (inputs) to quality attributes (outputs) is the foundation upon which the control strategy is developed (Stage 1), qualified (Stage 2b), and monitored for changes and opportunities for improvement (Stage 3). Companies that use risk management and statistical tools to continually increase process and product understanding, linking scientific evidence and knowledge across the lifecycle, will see the benefits of the lifecycle approach in robust supply and reduced regulatory risks.
In Stage 1 (process development), the deepest process understanding is obtained using Quality by Design (QbD) principles outlined in ICH Q8, enabled by design of experiments (DOE), to statistically link process inputs and outputs. Especially for bio processes, in which there is often a large number of process parameters to consider, at multiple intermediate process stages, statistical tools are key to efficiently gaining the empirical evidence necessary to establish robust manufacturing operating ranges. Newer product platforms such as cell and gene therapies and continuous manufacturing present additional complexities best addressed by strategic statistical experimental design and analysis. Although this may seem like extra work, compared to past practices, the effort and resources put forth in Stage 1 are an important investment in later stages of the lifecycle of the process.
During Process Performance Qualification (Stage 2b), the control strategy is confirmed to provide “a high degree of assurance” that the process is capable of reproducibly meeting requirements. In order to do this, knowledge gained during Stage 1 is used to identify the key indicators of process performance (process parameters, intermediate quality attributes, final product quality attributes, etc.) that can be used to provide this assurance about the process performance. The deeper the process understanding developed during Stage 1, the greater the opportunities for savings during PPQ. For example, homogeneity studies for bulk drug substance performed pre-PPQ can eliminate the need to perform such studies in the PPQ batches. Similarly, a thorough understanding of the sources and relative magnitudes of variability within a batch (e.g. lyophilization chambers (shelves within chambers, locations within shelves), filling needles, etc.) allows for a precise estimate of the number of within-batch samples required to support statistical assessments of PPQ, thereby reducing risk and waste. And a thorough understanding of the multivariate effects of process inputs on product quality reduces the risk of failure during PPQ and beyond.
The accumulated knowledge from Stages 1 and 2 provides the basis for developing an efficient and robust CPV program for a bio process. Given the large number of process parameters and quality attributes in many biological dosage forms, as well as the high costs associated with sampling and testing, it is particularly important to have developed the scientific understanding of which are the key performance indicators, and what the capability is of each, in order to design a monitoring program that will allow companies to “… guard against overreaction to individual events as well as against failure to detect unintended process variability,” as indicated in the 2011 FDA Guidance.
Over the course of 1.5 days, experts from industry and FDA will present success stories, discuss specific validation challenges of next generation products & technologies and potential solutions, and answer questions related to all stages of the lifecycle. Day 2 will feature a Collaborative Problem Solving Workshop, with participant discussions of emerging challenges facilitated by expert speakers, followed by a panel discussion. From beginning to end, the 2019 ISPE Process Validation Workshop on Challenges in Bio Process Validation: Current and Next Generation 20-21 June in Boston, MA, will connect the dots for successful lifecycle process validation. Don’t miss out!
