PPQ Batches That Do Not Surprise You

Performance Qualification should be the final confirmation that your process is

ready for commercial reality—not an adventure in firefighting. Deviations during

PPQ derail filings, force repeat batches, and shake regulator confidence.

This playbook delivers predictable PPQ runs. You will map risks, monitor critical

parameters, align sampling plans, and run disciplined data reviews so every batch

proves process robustness.

Why predictable PPQ matters

• Regulatory approval: Successful PPQ is often the last gate before launch.

Failures mean resubmissions and delays.

• Cost control: Repeat batches consume materials, capacity, and labor.

• Operational readiness: PPQ confirms the handoff from development to

commercial manufacturing.

• Organizational confidence: Smooth PPQ builds trust between CMC, quality,

and regulatory teams.

Step 1: Refresh the risk map ahead of each campaign

• Assemble cross-functional experts (process engineering, QA, QC, validation) to

review process FMEA or risk assessments.

• Update assumptions with new development data, change controls, and deviation

learnings.

• Prioritize high-impact/high-probability scenarios and assign mitigation actions

before the first PPQ batch.

• Document residual risks, contingency plans, and triggers for escalation.

Step 2: Lock down critical process parameters and CQAs

• Define CPPs and CQAs with acceptable ranges, alarm limits, and action limits.

• Ensure automation and data historians capture parameters at the required

frequency and granularity.

• Set up real-time monitoring dashboards for operators and process engineers.

• Document corrective actions for excursions, including decision trees for hold,

rework, or batch rejection.

Step 3: Align sampling and analytical readiness

• Collaborate with QC to confirm sample quantities, timepoints, storage, and

transport requirements.

• Validate analytical methods and equipment readiness; perform mock runs if

necessary.

• Establish lab capacity plans and turnaround commitments aligned with PPQ

schedules.

• Create a tracker to monitor sample status and results in real time.

Step 4: Execute with disciplined oversight

• Hold pre-batch readiness huddles to confirm materials, equipment, and people are

prepared.

• Assign roles for batch execution, data collection, deviation management, and

observer responsibilities.

• Capture data in real time; review trends at defined checkpoints rather than

waiting for batch completion.

• Use communication protocols for instant escalation when parameters drift.

Step 5: Conduct rapid, structured data reviews

• Convene data review meetings within 24-48 hours of batch completion.

• Review CPPs, CQAs, sampling results, deviations, and operator logbooks.

• Assess against predefined acceptance criteria and document conclusions.

• Decide on readiness for the next batch, required corrective actions, or

additional monitoring.

Metrics that prove PPQ stability

• Planned versus unplanned deviations per batch.

• Time from batch completion to data review sign-off.

• Percentage of critical parameters within target ranges.

• Analytical turnaround against commitment.

• Successful completion of PPQ campaign versus planned number of batches.

45-day roadmap

1. Days 1-10: Review recent PPQ runs, document delay drivers, and update risk

assessments.

2. Days 11-20: Refresh CPP/CQA lists, finalize sampling plans, and verify

analytical readiness.

3. Days 21-30: Configure real-time monitoring dashboards and conduct a mock

data review using legacy batch data.

4. Days 31-45: Execute the next PPQ batch using the enhanced process, track

metrics, and iterate before subsequent batches.

Frequently asked questions

• How many PPQ batches do we need? Apply science- and risk-based rationale.

Justify the number to regulators using historical data and process knowledge.

• What documentation should we maintain? Risk assessments, readiness reviews,

batch records, deviation reports, data review minutes, and PPQ summary report.

• How do we handle unexpected deviations? Follow predefined decision trees,

perform root cause analysis quickly, and document rationale for continuing or

repeating batches.

• Can we automate data analysis? Yes. Use statistical process control tools

to flag trends and integrate with data historians for faster reviews.

Sustain the win

Hold data-review meetings after each PPQ batch, update risk maps with real-world

learnings, and rotate PPQ leadership to build depth. Share success metrics with

regulatory and supply-chain stakeholders to maintain support. When PPQ runs lack

surprises, launch timelines stay intact.