Analytical Methods Without Proper Validation

Using analytical methods without proper validation is one of the most critical weaknesses within a laboratory. Even if a method appears technically sound in theory, without documented experimental evidence there is no proof that it produces reliable, accurate, and reproducible results under real-world conditions of use. A common example: a method adapted from a pharmacopeia for a matrix different from the original may seem to work well in day-to-day operation, but without documented validation there is no way to prove that it is accurate and precise for that specific matrix.

In industrial, pharmaceutical, cosmetic, and chemical environments, decisions about batch release, regulatory compliance, and product safety depend directly on the quality of analytical data. Therefore, applying a non-validated method means taking on technical, financial, and regulatory risk.

What ISO/IEC 17025 Requires for Method Validation

The ISO/IEC 17025 standard requires that non-standardized methods, or standardized methods used outside their original scope, be validated before routine use, with documented evidence of accuracy, precision, selectivity, and working range. In the pharmaceutical sector, Brazil’s health regulatory agency, ANVISA, requires method validation as a prerequisite for product registration and release.

What Is Analytical Method Validation?

Analytical method validation is the systematic process of demonstrating, through experimental studies and statistical data treatment, that a method is fit for its intended purpose. In other words, it is not enough for the method to work — it must be proven to work with consistent performance.

This process involves evaluating parameters such as specificity, linearity, working range, precision, accuracy, and robustness. Each of these parameters provides evidence about the method’s behavior under different conditions.

Validation establishes acceptable performance limits. Thus, when the method is applied routinely, there is a technical basis for trusting that the result actually reflects the concentration or characteristic being assessed.

Without validation, the laboratory operates on assumption, not evidence.

Consequences of Inadequate Validation

When a method is implemented without formal validation, significant technical uncertainties arise. First, there is no guarantee that the method is specific to the analyte of interest. Matrix interferences can produce falsely elevated or reduced results.

Furthermore, the absence of precision studies makes it impossible to assess the method’s intrinsic variability. As a consequence, differences between analyses can be misinterpreted.

Another critical point is linearity. Without statistical proof of the relationship between concentration and analytical response, the result calculation may be based on invalid assumptions.

From a regulatory standpoint, non-validated methods compromise audits and inspections. The lack of documented evidence weakens the technical defense of reported results. In more critical situations, it can lead to data rejection and the need to repeat studies.

Difference Between a Developed Method and a Validated Method

Developing a method means establishing experimental conditions that allow a given analyte to be detected or quantified. Validating a method means demonstrating that these conditions produce reliable results within defined criteria.

A method may show a good instrumental response and still not be suitable for its intended application. For example, it may not be robust against small operational variations, or it may fail to maintain acceptable precision across different days or analysts.

Therefore, development is a creative, experimental stage. Validation is an evidentiary, statistical stage. Both are complementary, but not equivalent.

How to Structure a Proper Analytical Method Validation

Proper validation requires prior technical planning. Before experimental execution, it is necessary to define the method’s objective, working range, acceptance criteria, and statistical strategy.

During execution, tests must be conducted under controlled conditions, with complete documentation and full data traceability. Appropriate statistical treatment is then applied to each evaluated parameter.

Furthermore, results must be critically interpreted. It is not just a matter of calculating values, but of assessing whether performance meets the requirements established for the intended application.

Validation should not be seen as a regulatory formality. It is a scientific tool for reducing uncertainty and increasing reliability.

LIMS as Support for Validation and Data Integrity

In this context, the Laboratory Information Management System (LIMS) can play a strategic role in conducting and maintaining validations. The system makes it possible to organize protocols, record raw data, control method versions, and ensure full traceability of the analyses performed.

In addition, LIMS reduces the risk of transcription errors and facilitates the consolidation of statistical data. As a result, the validation process becomes more structured and auditable.

The system also supports management of the method’s lifecycle, including periodic reviews and revalidations whenever significant changes occur. In this way, it helps maintain technical consistency over time.

However, it is important to emphasize that LIMS does not replace statistical planning or the critical interpretation of results. It organizes and protects data, but technical evaluation remains the responsibility of the specialized team.

Conclusion

Using analytical methods without proper validation undermines the laboratory’s scientific foundation. Without structured experimental evidence, results are no longer technically defensible and instead represent an operational and regulatory risk.

Validating a method means reducing uncertainty, controlling variables, and objectively demonstrating that the procedure is fit for its purpose. It also strengthens the laboratory’s credibility and supports strategic decisions based on reliable data.

When validation is carried out with technical rigor and supported by tools such as a LIMS, the laboratory builds an environment of traceability, data integrity, and scientific maturity.

In today’s landscape, where regulatory requirements and competitiveness go hand in hand, validated analytical methods are not merely a regulatory requirement. They are the foundation of applied science conducted responsibly.