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By PRIYA NEGI 1,455 views

An Expert Guide to Assay Development and Validation Best Practices

Assay development is an intricate and meticulous process that poses a number of challenges. Assay validation is essential to provide crucial information about the safety and efficacy of biologic products and drugs.

Yet, assay development and validation is often an expensive process, with additional problems involved in execution, maintaining, and transferring. It is also a fast-changing field with new approaches evolving on a regular basis. Against this backdrop, industry experts and regulatory authorities have collectively identified some best practices in assay development and validation.

A recent e-book by BioPharm International mentions that the pharmaceutical analytical community has been slow in adapting these identified best practices. That makes it particularly critical to have a clear idea about these practices.

Best Practice # 1: Quality by Design (QbD)

The traditional assay development approach tests method variables serially, following a one-at-a-time formula. QbD stresses the application of a systematic methodology that makes it possible to generate a comprehensive understanding of different sources of method variation. It simultaneously facilitates controlling the method.

Below is a diagrammatic representation of assay development and validation by QbD (inspired by):

Best Practice # 2: Performing Risk Assessment

Risk assessment is an integral part of the QbD approach. Once an appropriate analytical method has been identified, a risk assessment needs to be implemented to identify areas or steps in the assay that may influence its accuracy, linearity, precision, robustness, signal to noise, specificity, and stability.

The application of the traditional FMEA (failure mode and effects analysis) risk assessment approach is necessary, but not sufficient for QbD. Supplementing FMEA with impact on CQA and uncertainty is critical for ranking the risks. Such a comprehensive risk assessment process needs to be applied to every step of the assay.

Risk assessment is performed during assay development and prior to assay validation. Every risk weighed between medium and high needs a mitigation plan. Risk mitigation plans involve both experimental and non-experimental elements.

Experimental aspects of risk mitigation involve method elements such as flow rate, gradient, column temperature, etc. Non-experimental elements typically relate to fixing the materials and operators, etc.

It is also necessary to conduct additional risk assessment during assay transition from the drug development stage to the commercial stage. The focus of reassessment during this phase is on equipment availability, laboratory environment and practices, reagents sources, and testing cycles times.

Best Practice # 3: Design of Experiment (DoE)

DoE is the statistical component of the QbD approach. DoE aims at achieving optimization, robustness, and screening. It consists of executing experimental design using appropriate variables in combination with a statistical assessment of responses obtained.

The exploration of the design space involves the use of graphical or mathematical procedures. The statistical assessment enables building up the quality of finished drug products. It thereby facilitates meeting the enhanced quality standards that regulatory authorities demand.

Best Practice # 4: Method Qualification

After completing all the stages described above, a method qualification needs to be performed during the first phase of drug development. If the process has reached stage two or three, then a method validation is necessary.

The minimum requirements to ensure good practice in assay validation are:

  • Comprehensive training of all analysts.
  • Calibration or qualification of all the systems used in the validation.
  • Clear definition of all the requirements of method validation.
  • Use of representative materials during method validation.
  • Following defined protocols during all the tests for method validation.
  • Meeting all prospective criteria of method validation.
Priya Negi