
BioPharma By The Numbers: Batch Failures in Biopharma Manufacturing
Key Takeaways
- Batch failures result from unmet quality standards due to process deviations, contamination, or equipment malfunctions, necessitating a structured CAPA process for resolution.
- Operator errors are a significant cause of batch failures, emphasizing the importance of skilled staff and robust training programs in biopharmaceutical manufacturing.
The third installment of the monthly BioPharma By the Numbers infographic series details batch failure trends in biopharma.
Welcome to the third installment of the monthly BioPharma By the Numbers infographic series, where we take a data-driven look at key sectors, trends, and topics in the biopharma industry. All data are sourced from the BioPlan Associates 22nd Annual Report (1). This month, we dive into the topic of batch failures.
A batch failure occurs when a production run does not meet critical quality or regulatory standards, rendering the product unusable or unsafe. These events can stem from a wide range of causes, including:
■ process deviations
■ contamination
■ equipment malfunction.
To address batch failures, facilities initiate a structured investigation through a corrective and preventive action, or CAPA, process, as such:
1. Root Cause Analysis: Technical teams investigate the failure source: Was it a media issue? A filtration step? An equipment calibration error?
2. Impact Assessment: Other batches are assessed to determine whether they were affected.
3. Documentation & Reporting: Regulatory reporting may be required, especially if product had already moved into clinical or commercial channels.
4. Process Improvements: Based on findings, the facility may revise standard operating procedures, re-train staff, change suppliers, or modify validation protocols.
Most facilities report their last failure occurring in the relatively distant past. Facilities seem to be operating at different levels of process maturity and stability. In some cases, sites are experiencing nearly four times as many batch failures as others
Why is it Important to assess batch failures?
As biomanufacturing scales, automation drives quality and consistency, while reducing operational errors, which is vital for cost-effective, reliable production. Operator errors are the most persistent cause of batch failures. Having skilled staff is crucial.
Batch failures can occur at any stage of the manufacturing process and can frequently be attributed to:
- Lack of experience within manufacturing staff
- Working conditions
- Facility development
- Planning
- Operations.
For instance, operator errors were associated with batch failures by 3.8% of respondents in clinical scale manufacturing and 3.5% in commercial manufacturing (1). This has been a persistent cause of batch failures. In 2022, operator errors were responsible for 3.8% of failures for both commercial manufacturing and clinical scale manufacturing. Having skilled experience staff is a significant challenge for the current industry; thus, the industry is called to always consider additional human factors. It is becoming increasingly important to hire experienced staff to avoid hurdles in manufacturing.
Of note, clinical-scale facilities also report higher batch loss due to equipment failures. On the other hand, commercial-scale facilities report contamination related failures (e.g., bacterial) among the top causes of batch failures.
Let’s take a detailed look at the numbers!
How can the industry address batch failures?
To sustain and accelerate progress, the industry must continue to invest in a mix of short- and long-term strategies that are targeted, scalable, and rooted in a holistic understanding of the manufacturing environment. In the short term, reducing failure rates means addressing the most immediate and controllable factors:
- Enhancing operator training
- Routine equipment maintenance and calibration
- Implementing standardized failure investigation protocols
Longer-term solutions, however, will demand structural shifts:
- Automation continues to be one of the most promising levers
- Advanced data analytics and predictive maintenance platforms can help identify failure prone equipment
- Designing processes with robustness and resilience in mind
Reference
1. BioPlan Associates.
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