Top Analytical Method Validation Mistakes and How to Avoid Them – Part 2

Getting method validation right is a perennial challenge. It continues to be a persistent and costly problem in pharmaceutical development and manufacturing.  As we detailed in the first article in this series, Top Early-Stage Method Validation Mistakes, poor or inadequate method validation can delay product approval and result in regulatory problems during commercialization. By that stage, it is far more costly to fix these problems than it would have been to address them early on, during drug development and well before manufacturing.

To review, the top four early-stage validation mistakes that are often flagged in audits as ”inadequate method validation” or ”use of non-validated methods for critical decision making,” include the following:

1. Improper design of validation study.
2. Not asking the right 10 questions prior to developing reliable methods for the product’s intended use.
3. Not preparing diligently enough for further methods testing.
4. Insufficient method optimization.

The six drug substance attributes that must be supported in method validation

This article will focus on mistakes made during and after method validation. When providing the FDA and EMA with required ICH-compliant method validation data, the following key attributes of drug substances and drug products must be supported and documented:

1. Identity
2. Strength
3. Quality
4. Purity
5. Potency
6. Reproducibility

Frequent mistakes made during method validation

During the course of method validation, common mistakes fall into three general categories:

1. Non-compliant handling of protocol deviations.
2. Inadequate oversight of validation.
3. Failing to understand the objective of validation.

Mistake #1: Inadequate robustness of studies

Robustness studies must be designed with a scientifically sound method that is based on sample specification. To be certain the sample meets specs, the method must be challenged against various parameters. A generic approach won’t work. For example, if the column manufacturer makes a slight change in the column chemistry, particle size or pore size, only a solid robustness study will document any change in the sample specification.

Mistake #2: Inadequate acceptance criteria

We all want to finish studies as quickly as possible so that project timelines can stay on track. To do so, some CDMOs set overly wide acceptance criteria in the protocol, compromising method accuracy.  A cautionary note: wide or inaccurate sample acceptance criteria will likely result in obtaining inaccurate sample results.

Mistake #3: Lack of filter study for standard or sample solutions

Regardless of whether the solution is clear and free from any particulate matter, a filter study should be conducted during method validation studies. This will come in handy if the sample needs to be filtered in the future, as frequently happens.

Mistake #4: Not challenging the column enough during validation studies

Method validation typically begins with a new column, and a seasoned scientist will assess column performance and system suitability criteria. However, it is very important to determine column robustness in real life. Some questions that should be answered include these:

1. Does the column still meet system suitability criteria after 500 injections?
2. How does the column perform in a long run, if say one operates it under upper or lower limits of pH or buffer mobile phase conditions?
3. Will the above information be captured in the method?

These best practices in method validation will save time and money during production of a batch.

Mistakes made after method validation work is completed

No matter how rigorously performed, method validation alone is not enough. Everything has to be well and fully documented. Common mistakes include:

1. Inadequate validation report that fails to capture of ALL observations and deviations.
2. Failure to capture critical findings from forced degradation and validation studies in the method.
3. Failure to include limit of detection (LOD) and limit of quantitation (LOQ) values in the purity method.
4. Providing inadequate instruction/procedure for the method.
5. Failure to conduct method transfer.
6. Failure to provide column cleaning/system cleaning procedures.

9 best practices that help avoid method validation mistakes

The following is a list of best practices that will help assure robust, scientifically valid, and reproducible method validation.

1. Capture all activities in the report, along with any deviation, justification and amendment.
2. Method validation should be peer reviewed and approved by QA.
3. Clearly note all critical findings from the forced degradation and validation studies.
4. It is crucial that the limit of LOD and LOQ be captured in the method for accurate sample results reporting.
5. Engage the QC staff and/or receiving department in the method procedural and instructions. Capture their feedback and comments in the method report.
6. For method transfer, obtain feedback from the testing lab and incorporate these comments and observations in the method.
7. At a minimum, conduct an annual review of the method, data trending, LIR related to the method and analyst feedback.
8. Provide clear instruction for column cleaning and storage.
9. Assure that each column is assigned to a specific project.

Summary

Developing and validating analytical methods that are appropriate for a drug candidate’s intended use, testing the methods carefully and documenting each step and observation along the way while avoiding mistakes is the fastest, least expensive route to successful manufacturing and regulatory approval.