The pandemic has prompted the pharma/biopharma industry and regulators to develop and approve new medicines and vaccines in record time. Additionally, the growth of cell & gene therapies means pharma manufacturers must find efficiencies while maintaining rigorous compliance protocols. As a result, there is an increasing need to predict potential quality issues before they happen.

Incidents stemming from quality violations have already impacted millions of doses of COVID-19 vaccines. How can pharma manufacturers effectively manage rapid production processes while adhering to quality requirements?

Over the past year, pharma’s go-to-market timeline has become more compressed, partly driven by the urgent need to deliver COVID-19 therapeutics and vaccines. At this point in 2Q21, as vaccine production ramps up, pharma quality adherence will be even more closely scrutinized—with significant economic and societal penalties in the balance.

Additionally, the rise of cell & gene therapies and the production of personalized, micro batches means pharma manufacturers must rethink their approach to ensuring production quality. They will need to find efficiencies while maintaining rigorous compliance protocols. Increasingly, quality organizations must predict potential quality issues before they happen, leveraging large amounts of data generated during the process. There will also be a greater need to integrate quality performance with safety and pharmacovigilance information to provide a comprehensive view throughout the complete therapy lifecycle.
Beyond these immediate challenges, pharma quality teams face several internal and external obstacles.

Within their enterprise, many of these challenges stem from a lack of data integration and interoperability between disparate MES, ERP and quality management systems—thereby impacting both the efficiency and effectiveness of the quality management processes. Externally, a lack of deep collaboration with contract manufacturers, or an over-reliance on manual processes and data exchange, also impacts quality performance and gives rise to data integrity related issues. All these obstacles lead to a lack of ability to plan and address repeat incidents. A continuously evolving regulatory landscape also requires added agility and the ability to proactively adapt to new requirements.

For traditional therapeutics, there is a significant amount of data that is already available. It can be harnessed to improve quality performance during the manufacturing process and manage product quality complaints. Data can also be used to identify systemic issues, address aging quality records, identify bottlenecks and predict future performance. For example, data can be mined using certain clustering techniques to find systemic issues, while advanced monitoring techniques can be used for early signal detection and investigate customer complaints. Past quality performance can be used to predict completion times for corrective actions, and readily available automation techniques can help significantly reduce cycle-times for batch disposition.

Often, companies fail to reap the full benefit of their data assets, due to the inherent challenges with integrating rigid systems of records and harmonizing data from across their enterprise and contract service providers. Attempts to do so often turn into large data warehouse projects with less than optimal results.  

When it comes to next generation cell and gene therapies, a significant amount of data is collected at the point of care and throughout the process. This presents a significant opportunity for proactive monitoring to avoid potential quality issues in the end-to-end life cycle. From site activation, patient enrollment and apheresis to manufacturing process, quality review and drug product release—hundreds of data points are collected that can provide proactive insights into both manufacturing and quality performance. This will require greater partnering between quality, operation and manufacturing.

Traditionally, quality teams have been more focused on the management of quality related events, supporting inspections and conducting retrospective reporting. In the future, there will be an ever-increasing need for greater collaboration between quality and broader operations. To accelerate this partnering, quality organizations should pivot to data-driven techniques and provide proactive insights to upstream processes. There should also be a shift to proactive analysis which can flag potential issues before they happen, as opposed to retrospective reporting. Similarly, manufacturing and supply chain operations will need greater visibility into quality performance for improved planning. Quality organizations will also focus on automation and find opportunities to reduce cycle-times when it comes to supporting major processes such as annual product reviews and batch disposition.    

Over the last 6+ years, EY has worked with some of the leading biopharma companies in the industry to develop a Quality Analytics and Decision Support (QUADS) platform, designed to proactively monitor quality performance, flag potential issues and improve productivity in quality and operations.