In regulated pharmaceutical environments, method validation is a cornerstone of quality assurance. Yet many validated methods still fail to perform reliably in routine use. This disconnection between regulatory compliance and real-world functionality is all too common. The good news? With a more pragmatic and risk-based approach, it is entirely possible to ensure that your analytical method is not just validated but truly fit for purpose.

Understanding the Problem: Compliance vs Performance

Most professionals have encountered a method that, although fully validated, performs poorly in practice. This issue often stems from a narrow focus on ticking regulatory boxes without fully understanding or controlling method variability. Validation should never be a one-off box-ticking exercise. As emphasised in the latest ICH Q2(R2) and Q14 guidelines, method validation is part of a broader analytical lifecycle.

A validated method should meet its acceptance criteria, but those criteria must be scientifically justified and relevant to the specification limits of the product. Crucially, they must reflect the method’s actual variability, not just theoretical targets. For example, one common pitfall is relying solely on correlation coefficients to evaluate linearity, which, as discussed in the course, can be misleading. Linearity should be evaluated through more robust means such as residual plots and assessment of the y-intercept.

Step One: Evaluate Method Readiness

Before launching into formal validation, take a step back and assess whether your method is ready. Pre-validation readiness should address three essential questions:

1. Are you confident the method will pass validation?
2. Are you happy to live with the method in routine QC?
3. Have risks to method performance been properly mitigated?

A useful tool here is a method development report or pre-validation performance summary. This summary should demonstrate adequate performance for key parameters such as accuracy, precision, specificity, and robustness, all tied to the analytical target profile.

Informal studies during development can and should be used to characterise performance. These might include evaluating solution stability over several days, robustness testing across different analysts, or filter recovery trials. It’s also wise to assess instrument-specific behaviours, like autosampler precision, which are often sources of hidden variability.

Step Two: Link Validation Criteria to Product Specifications

One of the core messages from the training course is that validation acceptance criteria should be linked directly to product specifications and total measurement uncertainty. If a drug product must meet an assay specification of ±5%, then a method with an RSD of 3% might not be acceptable, even though 3% is numerically less than 5%.

Why? Because analytical variability is expressed as ±2 standard deviations, representing 95% confidence. A 3% RSD translates to ±6% at that confidence level, meaning your method cannot reliably distinguish between a product that is in or out of specification.

This is where understanding the relationship between accuracy, precision, and specification range becomes essential. Visual tools like operational characteristic (OC) curves or plots of allowable total error zones help demonstrate whether your method is suitable. For example, if your method has high precision but measurable bias, you might still meet overall uncertainty limits, but only if both are small enough to stay within the predefined specification range.

Step Three: Strengthen the Analytical Control Strategy

Validation is just one pillar of the analytical quality system. Others include instrument qualification, system suitability testing, and ongoing method monitoring. The ICH Q14 framework introduces the concept of an Analytical Control Strategy, which integrates all of these elements.

Consider implementing the following:

• Robustness testing during development, not during validation. Assess the impact of small deliberate variations in critical parameters such as mobile phase composition, pH, temperature, or detection wavelength.
• Replicate agreement checks, to assess sample preparation and sampling error. These are especially important for heterogeneous or non-solution dosage forms, where stratification or particle settling can distort results.
• Use of well-characterised reference standards, including qualified secondary standards and tracking any changes in assigned potency due to moisture content or degradation.
• Trending system suitability data over time to catch drift or instability early. This includes bracketing standard precision, tailing factor, resolution, and even replicate agreement in multi-well formats.

System suitability criteria should not be arbitrarily adopted from compendial defaults (e.g., plate count >2000). Instead, derive limits from development data using statistical tools such as mean ±3 standard deviations. This ensures your system suitability is sensitive enough to detect real performance changes.

Step Four: Prepare the Method for Real-World Use

Another key point is to consider whether the method is actually workable in a GMP environment:

• Can analysts realistically follow the SOP?
• Are the instructions clear enough to avoid misinterpretation?
• Is the sample preparation robust to small operational variations?
• Does the method require specialist skills or equipment?

Seemingly minor issues—like faded volumetric flask markings or unclear integration instructions—can introduce real risk. The training emphasised examples where method failure was traced back to simple usability oversights, such as an incorrect volumetric flask size or missing documentation about wavelength selection.

Also consider how the method will behave across different laboratories. If the method is intended for transfer, inter-lab reproducibility (a.k.a. ruggedness) becomes key. Differences in integration approach, analyst technique, or even glassware can cause method variability to exceed acceptable limits.

Step Five: Monitor and Adapt Post-Validation

Method validation is not the end of the journey. According to ICH Q14 and USP <1220>, methods must be monitored throughout their lifecycle. Unexpected results, out-of-trend behaviour, or system suitability failures can all signal the need for reassessment.

This is where a knowledge management system comes in. Trending data on replicate agreement, accuracy, and robustness can help you decide whether a method update or revalidation is required. The use of statistical process control (SPC) charts for key performance indicators, such as standard RSDs or Y-intercept shifts, is recommended.

Changes within the method operable design region (MODR) can often be implemented without revalidation, providing they remain within pre-approved parameters. However, if a change affects the analytical target profile—e.g., a change to the impurity reporting threshold or assay range—revalidation will be required.

Final Thoughts

Ensuring your analytical method is fit for purpose means going beyond regulatory compliance. It requires a structured, science-based approach that ties validation criteria to real-world use and performance. By building a comprehensive validation strategy that includes readiness evaluation, risk assessment, practical usability, and lifecycle monitoring, you create a method that serves both your lab and your patients.

In short: don’t just validate your method. Make sure it works.

Written by Educo Life Sciences Expert, Mark Powell

Dr. Mark Powell is a Fellow of the Royal Society of Chemistry (RSC) with over 30 years’ experience as an analytical chemist. In 2003, he helped to set up a UK-based contract research and manufacturing company specialising in early-stage drug development, where he ran the analytical development programme. His responsibilities included commissioning and validating laboratory data systems and training staff.

In 2013, he set up his own company which offers training and consultancy services to the pharmaceutical industry. These include guiding the CMC aspects of drug development programmes and training in areas such as chromatography, dissolution testing, data integrity, method development/validation, analytical instrument qualification, technical writing and auditing.

Mark has delivered many training courses on analytical method development, validation and lifecycle management. He has provided support to professionals and organisations so that they are equipped to validate, verify and transfer analytical methods. Mark draws on his extensive experience of method development and validation and provides multiple examples throughout the course.

This article was written from an interview and from materials taken from the course, Validation, Verification and Transfer of Analytical Methods.