By Dustin Lafferty, Singota Solutions

Significant confusion persists across the pharmaceutical/biopharmaceutical industry relevant to early-phase analytical development and validation. While several guidance documents serve as a de facto standard, distinguishing method validation requirements from suggestions, when and how to apply those guidelines often is not a straightforward task.

It is critical to establish a product/project’s analytical life cycle early and to revisit findings throughout a project. Biopharma sponsors and their CDMO partners can reduce cost and risk, as well as accelerate a drug’s development journey, by executing method qualification before and during Phase 1. The data generated eventually feeds into method validation, facilitating speedier regulatory approval and commercialization.

Method Qualification Vs. Validation

Qualification comprises a reduced set of testing requirements to demonstrate the method is suitable for its intended purpose, but it typically is not as in-depth or robust as method validation. A qualification still aims to achieve accuracy and precision, linearity and specificity, but the method testing’s robustness is limited: the drug product’s critical parameters have not been determined, so the full analytical scope is unknown. For most projects, Phase 1 simply is too early in the product life cycle to have identified and fully characterized impurities or degradant compounds (including more potent impurities or degradants that must be controlled to a greater degree).

As a project reaches method validation (i.e., late in Phase 2 or early in Phase 3) and submission to a regulatory agency, data gleaned during qualification can be applied to developing validation analytics. Testing must ensure proper detection limits and a full enough linearity have been established that the method is accurate to detect and characterize specific impurities, related substances, or degradants.

Method qualification and validation both are guided by a series of publications, including ICH Q14 Analytical procedure development,[i] ICH Q2(R2) Validation of analytical procedures,[ii] 1225 Validation of Compendial Procedures (USP),[iii] and Analytical Procedures and Methods Validation for Drugs and Biologics (FDA).[iv] Common language is identifiable across all those documents, but the ICH guidances are informally considered the current industry standard. Q14 and Q2(R2) currently are in the draft stage; the former is a new document, while the latter will supersede Q2(R1), which has been the accepted standard for nearly 20 years.

In addition to when they are performed and burden of evidence required, the key difference between method qualification and validation is the amount of data researchers have available to begin their work. Looking at a potential assay (particularly an assay in related substances analysis), researchers can make some determinations despite having relatively little information about the compound.

Researchers can begin with benchtop forced degradation studies of the API (e.g., thermal degradation, performance in an acidic environment, propensity to oxidize readily, etc.). After a sample has been degraded, a high-performance liquid chromatography (HPLC) method can be used not only to examine some of those degradants’ general characteristics, but also to confirm the main compound’s peak purity. This test also helps to confirm coelution (i.e., wherein the degradant elutes in the HPLC chromatogram at the same time as the target analyte, so it may not be observable, perhaps obscured by the much larger analyte peak) is not taking place.

Importantly, these types of studies can be completed quickly and cost-efficiently in the development lab during the qualification stage since — when working with an unfamiliar or new-to-market molecule, compound, or protein — the limited information actually makes for faster turnaround. Despite this incomplete information about the molecule, researchers still can get a “ballpark idea” of potential degradants and impurities.

Consider the following: when a client brings Singota a new product, we aim to have at least some purified material to use as a reference standard so we can be confident the main analyte we observe in our analysis is their compound. Often, the researchers who initially developed the drug will pass along information that helps in this respect. A rudimentary version of the product is given to us, or at least a formulation that we can make in the lab with in-house materials.

We can perform a broad range of analyses, wherein (returning to the HPLC example from above) we might run a basic mobile phase combination and a slow gradient to see exactly where the major analyte peak appears on the chromatogram, and then run the same gradient with the reference standard. If they exhibit similar chromatograms, we can see where any related compound peaks might elute, as well. Applying a reverse-phase HPLC method, as well, can reveal whether the organic solvent is strong enough to drive good elution of the main peak, or whether a more polar/non-polar compound is necessary in the organic solvents.

So, we can quickly learn about the molecule’s chemistry from those first few analyses. We are able to narrow the gradient range, observe whether the mobile phase is strong enough, and examine peak quality (and thus determine whether a modifier to the mobile phase will be necessary to improve peak quality). From there, we can optimize the organic mobile phase system (e.g., by adjusting salt, pH, or temperature to improve chromatogram quality). Even without API, if a reference standard is available, early development can collect that information because we are working with pure material.

Effective Method Qualification Speeds Timelines And Bolsters Product Quality

Despite working with limited information, method qualification researchers often can gather a significant amount of data on the molecule early in development, thus establishing a method that is specific, accurate, and has good linearity. Moreover, as the project progresses (usually over several years), that library of data will remain as a reference for validation. This way, drug developers can access past analyses to understand and exhibit how well the method has held up during the drug’s life cycle.

With time and experience (i.e., working with the molecule), it is possible to identify pinch points that require optimization (particularly those with significant timeline impacts). This experience also reinforces the method’s robustness because, by full validation, researchers will have prepped solutions multiple times. For example, does a given column produce hundreds of injections, or can it only produce only 50-100 injections before the column quality or peak quality drop off?

By full validation, researchers will have established detection limits all the way down to certain concentrations of sample, smoothing the path to formalizing a method during validation. Consider that a precise method may justify tightening or loosening a certain criterion. Such changes can be justified because of the data provided by a well-developed method at the qualification stage. Qualification also can indicate how stable the molecule is in its matrix (e.g., solution, tablet, etc.) throughout the course of that study.

Additionally, the CDMO development team should be partnering with manufacturing colleagues. For example, developers might let the manufacturing team know a solubility issue has the potential to arise: this is what we’re seeing in the lab with these samples. They are not at the expected potency at a certain step of manufacturing because the sample was not as soluble as we had expected it would be.

By working closely together, the development and manufacturing teams can unearth such information during early manufacturing runs, as well as give the latter team practice working with the product and working bugs out of the manufacturing process. In short, a good analytics package, assembled early in product development, gives organizations a stronger position and more reliable information to share.

An experienced analyst is best-equipped to divine detailed information from qualification activities. There is no substitute for the experience of working with different methods and technologies in the lab, applying lessons learned from previous projects or products. Studying provides a solid foundation for method work, but one’s personal toolbox is built through hands-on experience. Moreover, an enthusiastic trainee and a devoted instructor ensure that knowledge becomes institutionalized.

An analyst must be able to adapt to new challenges in method development as new information is discovered about an API or product; that is, as a method is optimized, a new challenge may be revealed that causes a sharp change in direction in the analytical control strategy.

Final Thoughts

CDMOs increasingly encounter clients who have a therapeutic application in mind for their molecule or their protein. The client knows things have to be validated, but they don’t know what that entails. In the last 10 to 15 years, a shift has occurred toward biologics in medicine (while not necessarily moving away from smaller molecules that can be produced in a lab using chemistry).

As a result, CDMOs see more biologics-oriented clients and drugs coming through the pipeline, produced by small biotechs, virtual companies, or university spinoffs with small teams: maybe the scientist and a few investors and somebody with a little manufacturing know-how. In any case, these entities do not have a whole lab and a fleet of scientists constantly making molecules. They identify or discover a substance that may have an interesting therapeutic use, patent it, seek a partner, and attempt to commercialize — contracting out almost all major tasks.

Singota shortens this early development phase by guiding the client along, providing context for what information will be critical, and when, as well as the best time(s) to gather it — even in the absence of clinical trial data. As that data starts to arrive, we apply it to make our methods stronger and, in time, fully validated. To learn more, visit us at and follow us on LinkedIn.

About The Author

Dustin Lafferty is an experienced chemist with over 15 years in the pharmaceutical industry, working in quality control as well as analytical development and validation. He earned a master’s degree in chemistry from the University of Kentucky. Contact him at

About Singota Solutions

Singota Solutions is a contract development and manufacturing organization (CDMO) focused on helping clients in the pharmaceutical, animal health, and biotechnology industries move their products through the drug development pipeline faster by being agile, accountable, and transparent.


[i] “ICH Q14 Analytical procedure development – Scientific guideline,” European Medicines Agency.

[ii] “ICH Q2(R2) Validation of analytical procedures – Scientific guideline,” European Medicines Agency.

[iii] “〈1225〉 Validation of Compendial Procedures,” United States Pharmacopeia.

[iv] “Analytical Procedures and Methods Validation for Drugs and Biologics,” United States Food & Drug Administration. 2015.