CAPA Example

CAPA Example for Pharma Batch Failure

Pharma CAPAs break down when investigations stop at 'batch failed release testing' instead of showing how the process, controls, and quality system allowed the failure to develop.

Below is a structured CAPA example for a batch failure involving dissolution performance in a solid oral dosage product.

It demonstrates how root cause analysis, corrective actions, and effectiveness checks can be written in a way that supports audit-readiness and defensible process improvement.

Nonconformance Description

During release testing of Batch B24031 for a solid oral dosage product, dissolution results failed the approved specification at the 30-minute timepoint for 4 of 12 units.

The batch was immediately placed on hold. A review of retained in-process data showed lower-end but still passing results in recent lots, suggesting a trend that had not yet been escalated.

Potential impact included delayed release, product shortage risk, and reduced confidence that the validated process remained under adequate control.

Problem Statement

The manufacturing process produced a batch that failed dissolution requirements due to unknown causes, representing a major quality-system event with potential product availability and patient-impact implications.

Root Cause Analysis (Summary)

A structured Ishikawa and 5-Why analysis identified contributing factors across process variability, parameter control, and trend escalation.

Key contributing areas:

  • Process variability - granulation moisture and drying endpoints showed wider variation than expected
  • Compression controls - force and dwell settings drifted within a range that was technically allowed but operationally unstable
  • Specification strategy - in-process thresholds were not aligned tightly enough to final dissolution performance
  • Trending weakness - prior low-end results were reviewed but not escalated as a recurring signal

Leading hypothesis:

The most probable root cause is an inadequately controlled interaction between granulation moisture variation and compression settings, combined with process limits that were not narrow enough to protect dissolution performance at the low end of the operating range.

Alternative contributing causes remain plausible, including delayed escalation of recurring borderline results.

Corrective Actions (Overview)

Corrective actions were structured across four layers:

1. Containment

  • The failed batch and related hold-time materials were placed on QA hold
  • Release activities were stopped while batch history and equipment status were reviewed

2. Immediate Correction

  • Expanded review of granulation, drying, and compression records for the affected batch
  • Targeted sampling to compare blend and tablet performance across retained samples

3. Systemic Corrective Actions

  • Narrowed acceptable moisture and compression-control ranges tied to dissolution performance
  • Revalidated the process window with emphasis on the most sensitive operating ranges
  • Updated batch record instructions and escalation criteria for borderline dissolution trends

4. Preventive Actions

  • Portfolio review of similar products using the same process architecture
  • Trending dashboard updated to flag repeated low-end dissolution signals sooner
  • Cross-functional training completed for manufacturing, QA, and process development teams

Effectiveness Checks

Effectiveness was defined using measurable and auditable criteria:

  • Three consecutive commercial or validation batches meeting dissolution criteria without exceptions
  • Documented adherence to revised moisture, drying, and compression limits
  • Thirty-day trend review showing no repeat low-end dissolution signal cluster
  • QA audit confirming escalation is triggered for borderline recurring results

These checks ensure the batch failure is not treated as a one-time event, but as a signal to strengthen ongoing process control.

Why This Matters

One of the most common CAPA failures in pharma is labeling the issue as a batch anomaly without proving why the process window and trend review did not prevent it.

In this case, the deeper issue was systemic:

  • process windows that were too wide for true product sensitivity
  • weak escalation of repeated borderline signals
  • insufficient linkage between in-process controls and final quality performance

That is what regulators and auditors expect to see - not only the failed result, but the quality-system conditions that allowed the batch to reach that point.

See How This Was Generated

This CAPA example was generated using CAPA Engine, a structured investigation tool designed for regulated industries including medical devices, pharma, aerospace, and manufacturing.

It helps quality teams move from unstructured nonconformance descriptions to complete CAPA investigations with:

  • root cause analysis
  • corrective action plans
  • effectiveness checks
  • investigation reasoning and confidence

Frequently Asked Questions

What is a CAPA example?

A CAPA example shows how a nonconformance is investigated, including root cause analysis, corrective actions, and effectiveness checks.

What should be included in a CAPA?

A CAPA should include a clear problem statement, root cause analysis, corrective actions, and measurable effectiveness checks.

Related CAPA Examples

Curious how this compares to using ChatGPT for CAPA? Read our breakdown of where generic AI helps, where it falls short, and what a more structured CAPA approach looks like.

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