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:
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:
During the course of method validation, common mistakes fall into three general categories:
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.
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.
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.
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:
These best practices in method validation will save time and money during production of a batch.
No matter how rigorously performed, method validation alone is not enough. Everything has to be well and fully documented. Common mistakes include:
The following is a list of best practices that will help assure robust, scientifically valid, and reproducible method validation.
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.