Pre-study analytical method validation: comparison of four alternative approaches based on quality level estimation and tolerance intervals

Abstract

In industry and in laboratories, it is crucial to continuously control the validity of the analytical methods used to follow the products’ quality characteristics. Validity must be assessed at two levels. The “pre-study” validation aims at demonstrating before use that the method will be able to achieve its objectives. The “in-study” validation is intended to verify, by inserting QC samples in routine runs, that the method remains valid over time. At these two levels, the analytical method will be claimed valid if it is possible to prove that a sufficient proportion of analytical results is expected to lie within given acceptance limits [−λ,λ] around the nominal value. This paper presents and compares four approaches to checking the validity of a measurement method at the pre-study level. They can be classified into two categories. In the first, a lower confidence bound for the estimated probability π of a result lying within the acceptance limits is computed and compared to a given acceptance level. Maximum likelihood and delta methods are used to estimate the quality level π and the corresponding estimator variance. Two approaches are then proposed to derive the confidence bound: the asymptotic maximum-likelihood approach and a method proposed by Mee [1]. The second category of approaches checks whether a tolerance interval for hypothetical future measurements lies within the predefined acceptance limits [−λ,λ]. β-expectation and β-content tolerance intervals are investigated and compared in this context. These four approaches are illustrated on a bioanalytical HPLC-UV analytical process and compared through simulations