Analytical Method 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 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, number of replicates, 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).

Often, methods are not developed from the ground up, but are 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).13,14

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.15–19

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 Q2(R1) Validation. The ICH Q2(R1) validation requirements should be evaluated twice: once (at least partially) during or after method optimization and once formally during the AMV studies. We need to know before writing the AMV protocol whether the method is suitable to support a desired process capability in relevant in-process or product (target) 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 a typical example, not a universal table.

Figure 2. All routine release specifications are shown that will generate numerical results. Pass-fail (or present-absent) is not shown, as it cannot be represented graphically. The green (bold) arrows represent examples of product specifications. The valid assay range of the new method must be capable of bracketing the product specifications. This is reflected in the ICH Q2(R1) requirements (yellow arrows) for assay range.2 The instrument (and method) design requirements must bracket the ICH Q2(R1) requirements. The red (dotted) arrows represent examples of the instrument (and method) design requirements for assay range. A, B, C, and D correlate to ICH Q2(R1) categories 2, 3, 4, and 5 as presented in Table 1.
Results in 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 Q2(R1) (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.

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, because 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.

Accuracy can be estimated by measuring the recovery of various spiked levels of particular analytes. Many critical assays of product purification efficiency and product quality determine product purity and impurities simultaneously (for example, protein composition by capillary zone electrophoresis [CZE] or high performance size exclusion chromatography [HP-SEC]). Whenever relative percentages of various analytes are estimated using a single assay, response factors must be established and integrated (normalized) in the calculations in order to accurately report purity and impurity levels. Using different detectors to measure analyte signals (for example, HP-SEC with ultraviolet detection to measure protein aggregation versus laser-light scattering or refractive-index detection) affects these relative percentages and should be thoroughly evaluated during AMD. A simple way to directly compare response factors from various detectors during AMD is to connect all detectors in-parallel (or inline).10

Stability. Samples, standards (secondary, in-house, or working), controls, and critical reagents should be evaluated for degradation during storage and potential freeze-thaw cycles. For final container testing, the analyte and matrix of samples, in-house standards, and controls may be similar since they all could come from the same production process. In any case, the negative effects of time on the bench during actual testing (room temperature), repetitive freeze-thaw cycles, and long-term storage of all materials used to generate test results should be evaluated and expiration times should be established. Reviewing and integrating historical data from the previous development and validation of the current method or the stability program may save time. Reagent expiration times should be evaluated carefully—any degradation could negatively impact test result quality. In general, vendor certificates of analysis can be used as evidence of reagent stability unless reagents are diluted or otherwise changed before storage and use.

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