Validating Analytical Methods for Biopharmaceuticals, Part 1: Development and Optimization - Validation of analytical methods can be more easily accomplished by breaking the task down into a series

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Validating Analytical Methods for Biopharmaceuticals, Part 1: Development and Optimization
Validation of analytical methods can be more easily accomplished by breaking the task down into a series of planned steps.


BioPharm International


ANALYTICAL METHOD DEVELOPMENT It is the responsibility of the analytical method development (AMD) scientist to include the test method's details in the standard operating procedure (SOP), including optimization of assay elements (such as mixing volumes and statistical data reduction). If practical, all AMD data should ideally be generated in a GMP environment. In other words, we should generate all development data with qualified equipment by qualified personnel, and properly document and summarize the data in an AMD report approved by quality assurance (QA).

Oftentimes, methods are not developed from the ground up but rather only optimized for a particular product and product matrix. In any case, always follow a thorough optimization process, which includes incorporating the best-fit data reduction function (for example, five-parameter, logistic parallel-line statistics for an ELISA assay).12,13

A well-planned and controlled experimental design that emphasizes QC release-testing suitability will prevent multiple, unsuccessful trial-and-error efforts. Scientific and regulatory concerns must be balanced with potential economical restrictions. There are excellent tools published by the American Society for Standards and Testing (ASTM) to establish efficient design of experiment (DOE) templates and to help establish appropriate system suitability criteria.14-18

QA approval is required at many points in method development and validation (as indicated in red in Figure 1). Ideally, the process does not continue until it has been approved. Data generated using a final, optimized method may be used to set acceptance criteria for the AMV protocol. All instruments and equipment should be qualified and all relevant software should be validated, ensuring that all AMD data and results (summarized later in the AMV protocol) are valid from a compliance perspective. The main tasks of analytical method development and optimization (indicated in yellow in Figure 1) are discussed below.

ICH Q2A/B Validation. The ICH Q2A and Q2B validation requirements should be evaluated twice: once (at least partially) during or after method optimization and once during the AMV studies. We need to know before writing the AMV protocol whether the method is suitable for the target specifications — as well as in comparison to the current method's in-process and product specifications. Table 1 lists all validation characteristics that typically must be evaluated for each analytical procedure. The corresponding anticipated in-process and product specifications for the new method form a checklist. An alternate, graphical presentation of the product specifications can be found in Figure 2. This is for a typical example, not a universal table.

Results within and outside the product specifications must be reliable. If the boundaries are fuzzy it is not possible to clearly differentiate between acceptable and unacceptable (out-of-specification) results, and material may be improperly accepted or rejected. The analytical method and instrumentation must be capable of bracketing the assay ranges required by ICH Q2B (illustrated with red arrows in Figure 2) to ensure that these requirements can be met during AMD, AMV, and routine testing. This is why it is critical that instrument and method requirements (design qualifications) are thoroughly considered during selection of the new method. Details of the ICH validation characteristics will be covered in Part II of this article.

Assay Bias and Analyte Response Factors. All analytical procedures are associated with a degree of bias, particularly biological assays that test for the purity, potency, and molecular interactions of biopharmaceuticals.5 Also, appropriate reference standards may not be readily available, as the product may be one of a kind. The evaluation of the assay's accuracy and bias can be the most difficult part of the development and validation process. Comparing the results of the new method to those of the old method is often meaningful only when accounting for assay bias. As long as we can compensate for the bias by modifying release specifications, we should be able to properly assess the quality of the production process and the product and remain in compliance.


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