SARS-CoV-2 vaccine production has encountered delays; many countries do not get enough vaccine at the agreed time. Although it might only be delays by weeks, this has an impact on health, politics and society. We know that vaccine making is a complex, time-consuming biological process and the product needs to go through a series of filtrations, purifications and quality checks. However, we do not know the specifics of the reasons for delays at the individual manufacturers. It seems that some issues are related to reduced yields1, quality and cleanliness2 as well as difficulties in the scale-up3. This is in line with the FDA’s assessment that 62% of drug shortages are due to manufacturing and product quality problems.4
How can manufacturers be proactive and avoid delays?
Design for Manufacturing
Vaccine manufacturing processes are complex, difficult and sensitive to changes leading to fluctuations in productivity, and variability in manufacturing. This makes it important to have deep understanding of the process and its boundaries. Organizations should leverage Design of Experiment (DoE) principles to identify cause and effect relationships between input variables and the resulting outcomes and to identify the critical process parameters that can have a positive impact on quality and yield; further, they need to define new valid operating ranges for these process variables to drive yield increases while maintaining quality.
As for vaccine manufacturing the actual process is critical, manufacturers must develop detailed process unit operations in process development for scale-up and transfer to manufacturing. Critical Process Parameters (CPPs) and Critical Quality Attributes (CQAs) and their appropriate ranges are identified and defined during process development. These must then be translated for full-scale commercial operations.
Often these translations do not proceed as predicted. When this happens, additional batches must be run at full-scale to test out and adjust new process conditions and to identify and define any new process parameters that were not evident during small-scale process development. A validatable process with built-in quality – Quality by Design (QbD) and diligent process development leads to greater understanding and fewer problems at larger scales as well as faster and more predictable time-to-market.
When an issue occurs in manufacturing, it might not be detected early enough to avoid an impact on the active ingredient and/or the final product. The consequence can be that entire batches of the product cannot be used and are wasted. A new batch must be produced and an investigation of the batch failure has to take place. Both have significant impact on the costs and the delivery time. While this is the case for all pharmaceutical products, the impact on large-molecule biologics manufacturing is even greater. Such processes are more complex, costly and more sensitive to slight changes and contamination.
To address this, organizations must tightly control the manufacturing process and obtain an early warning of when a process starts to trend out of control. It is also useful to be able to remove known outliers and recalculate quickly. Identify the root cause for batch failure to eliminate recurrence.
Control Your Process
It is essential to have full visibility into and control over the manufacturing process from a regulatory perspective – Continued Process Verification – CPV, as well as to ensure process performance and product quality. To do so you need to be able to identify your Critical Process Parameters (CPPs) and the Critical Quality Attributes (CQAs). This is only possible when you can access your data from development and manufacturing, from the lab and the shop floor. Ongoing process verification of performance as designed and monitoring of variability combined with automated alerts and monitoring-by-exception can help ensure the process remains in a state of control and provides the required yield at the defined quality.
Enable Your CDMOs
You never walk alone – this is also the case in vaccine manufacturing. To ensure the production of the agreed quantities organizations are typically leveraging different manufacturing sites within their organization and/or an extended network including Contract Development and Manufacturing Organizations (CDMOs). Transferring a manufacturing process from one site to another or from one site to a CDMO requires the manufacturer to baseline the operation of the process at the current location and set operational requirements for the new site based on the significant process variables that affect process performance and product quality. This requires setting valid operating ranges for these process variables so the new site or the CDMO can replicate a validated, stable process as soon as possible to contribute to a timely vaccine delivery.
BIOVIA provides a validation-ready solution for Manufacturing Analytics that aggregates and contextualizes process and quality data automatically for analysis, reporting and decision-making. BIOVIA Discoverant helps identify CPPs and operating ranges required for sustainable production and CQAs and supports the scale-up and transfer of a validatable process for in-house or contractor operations. It allows analyzing and pinpointing of root cause issues quickly, reducing the time to identify problems from months to hours.
Discoverant supports ongoing process verification with automated alerts for review-by-exception and provides Signal Monitoring Dashboards for process performance monitoring across internal and external (i.e. CDMO) manufacturing networks.
Optimize processes, product quality, yield and collaboration in the production of vaccines.
Avoid product shortage and ensure contractual compliance of delivery with Discoverant!
More on Optimized Manufacturing
- Covid-19 vaccine manufacturing disruptions
- Pfizer’s McPherson plant filling Covid-19 vaccine dinged for repeat offenses
- Drug makers are ramping up Covid vaccine production after fixing initial manufacturing delays
- US Food & Drug Administration, Drug Shortages and Potential Solutions, 2019 (updated February 2021), 33