Process validation is a critical requirement in the pharmaceutical industry to ensure that manufacturing processes consistently produce products meeting predefined quality standards. One of the most commonly asked questions in pharmaceutical validation is:
Why do we use three batches for process validation?
This blog explains the scientific, statistical, and regulatory reasoning behind using three batches—and why this practice remains widely accepted across the industry.
What Is Process Validation?
Process validation is documented evidence that a manufacturing process, when operated within established parameters, can consistently produce a product that meets its quality attributes.
The primary goal of validation is to:
- Demonstrate process consistency
- Reduce manufacturing risks
- Ensure product quality and patient safety
- Comply with regulatory expectations (FDA, EMA, WHO)
The Traditional Three-Batch Validation Concept
In pharmaceutical manufacturing, it is a long-standing practice to validate processes using three consecutive batches. If all three batches meet predefined acceptance criteria, the process is considered validated and suitable for commercial production.
However, it is important to note that:
📌 FDA’s “Process Validation: General Principles and Practices” guideline does not mandate three batches.
📌 The guideline does not specify a fixed number of batches.
Instead, the number of validation batches should be based on risk, process understanding, and statistical justification.
Why Are Three Batches Used for Validation?
The rationale behind using three batches is both practical and scientific.
1️⃣ First Batch – Accidental Success
The first batch may meet quality requirements by chance due to favorable conditions or variability.
2️⃣ Second Batch – Coincidental Performance
The second batch may also pass, but two data points alone are not enough to prove consistency.
3️⃣ Third Batch – Consistent Performance
If the third batch also meets all quality parameters, it demonstrates that the process is:
- Stable
- Reproducible
- Capable of delivering consistent results
This provides confidence that the process performance is not a one-time event.
Why Not Use Fewer Than Three Batches?
Using only two batches for process validation is statistically weak.
- Two data points always form a straight line
- No meaningful trend or variability can be assessed
- Insufficient data to prove process reliability
📊 At least three data points are required to evaluate variation, detect trends, and demonstrate consistency.
Why Not Use More Than Three Batches?
While it is technically acceptable to validate with more than three batches, most pharmaceutical companies choose three because:
- Additional batches increase time and cost
- Minimal added scientific value for low-to-medium risk processes
- Three batches provide sufficient confidence for most scenarios
For high-risk or complex processes, regulators may expect additional data or enhanced monitoring instead of simply more batches.
Regulatory Perspective on Validation Batches
Modern regulatory thinking emphasizes:
- Risk-based approach
- Process understanding
- Lifecycle validation
- Continued Process Verification (CPV)
Rather than focusing only on the number of batches, regulators now expect manufacturers to demonstrate:
- Robust process design
- Ongoing monitoring of critical parameters
- Continuous improvement using trend data
Continued Process Verification (CPV): The Future of Validation
With CPV, the question shifts from:
❌ “How many batches are validated?”
✅ “How well is the process understood and controlled over time?”
CPV ensures that process performance is continuously monitored throughout the product lifecycle, making validation an ongoing activity rather than a one-time event.
Conclusion
Three batches are traditionally used for pharmaceutical process validation because they:
- Provide sufficient statistical confidence
- Demonstrate process consistency
- Balance scientific rigor with operational efficiency
However, validation is no longer about a fixed number—it is about risk management, data integrity, and continuous control.