A risk-based analysis of the AAPS conference report on quantitative bioanalytical methods validation and implementation.

The 3rd American Association of Pharmaceutical Scientists (AAPS)/Food and Drug Administration (FDA) Bioanalytical workshop in 2006 concluded with several new recommendations regarding the validation of bioanalytical methods in a report published in 2007. It was aimed to conciliate or adapt validation principles for small and large molecules and an opportunity to revisit some of the major decision rules related to acceptance criteria given the experience accumulated since 1990. The purpose here is to provide a "risk-based" reading of the recommendations of 3rd AAPS/FDA Bioanalytical Workshop. Five decision rules were compared using simulations: the proposed pre-study FDA and Total Error Rules, the rules based on the beta-Expectation Tolerance and beta-gamma-Content Tolerance Interval and, finally, the 4-6-20 rule for in-study acceptance of runs. The simulation results demonstrated that the beta-Expectation Tolerance Rule controls appropriately the risk. The beta-gamma-Content Tolerance Interval was found to be too conservative, depending on the objective, and to lead to a high rate of rejection of procedures that could be considered as acceptable. On the other side, the FDA and the AAPS/FDA workshop Total Error Rule, combined or not, did not achieve their intended objective. With these rules, the risk is high to deliver results in study that would not meet the targeted acceptance criteria. This can be explained because, first, there is confusion between the quality of a procedure and the fitness of purpose of the results it could produce and, second, between the initial performances of a procedure, for example evaluated during pre-study validation and the quality of the future results.

Using tolerance intervals in pre-study validation of analytical methods to predict in-study results. The fit-for-future-purpose concept.

It is recognized that the purpose of validation of analytical methods is to demonstrate that the method is suited for its intended purpose. Validation is not only required by regulatory authorities, but is also a decisive phase before the routine use of the method. For a quantitative analytical method the objective is to quantify the target analytes with a known and suitable accuracy. For that purpose, first, a decision about the validity of the method based on prediction is proposed: a method is declared proper for routine application if it is considered that most of the future results generated will be accurate enough. This can be achieved by using the "beta-expectation tolerance interval" (accuracy profile) as the decision tool to assess the validity of the analytical method. Moreover, the concept of "fit-for-purpose" is also proposed here to select the most relevant response function as calibration curve, i.e. choosing a response function based solely on the predicted results this model will allow to obtain. This paper reports four case studies where the results obtained with quality control samples in routine were compared to predictions made in the validation phase. Predictions made using the "beta-expectation tolerance interval" are shown to be accurate and trustful for decision making. It is therefore suggested that an adequate way to conciliate both the objectives of the analytical method in routine analysis and those of the validation step consists in taking the decision about the validity of the analytical method based on prediction of the future results using the most appropriate response function curve, i.e. the fit-for-future-purpose concept.