In-study Validation

Dewe, W., Govaerts, B., Boulanger, B., Rozet, E., Chiap, P., Hubert, P. Risk management for analytical methods Conciliating the objectives of the pre-study and in-study validation phases. Chemometr. Intell. Lab. System, 85, 2007, 262-268. 

Fig. 6.1 The processes of method development and validation. Activities from pre-validation planning, method development, pre-study validation to implementation (in-study validation) of bioanalytical methods.

While a substitute matrix can be used to prepare standard cahbrators for a drug compound that exists endogenously, VS/QCs should be prepared in the authentic matrix, regardless. VS data are used during method validation to characterize the intra- and inter-mn accuracy/precision and stability. QC data are used for assay performance monitoring and to accept or reject a run during in-study validation. 

In order to use commercial reagents in a drug development program, it was important to negotiate and plan with the kit supplier to assure consistency of the Ab reagents, and that sufficient quantities would be reserved. Method robustness included the pre-study validation tests with a second lot of the capture Ab, three analysts, and three batches of radioiodinated detector Ab. Method robustness was further demonstrated by in-study validation, with four additional analysts performing sample analysis using 12 batches of radioiodinated detector Ab over a time span of approximately three years. 

Validation of bioanalytical assays in general and LBAs in particular has been the subject of intensive debate for the past 18 years or more. Chapter 4 focuses on the key agreements on a phased approach to the validation of LB As, including evaluation of all critical validation parameters prior to implementation of the method to the analysis of any study samples (prestudy validation) as well as in-study validation to assure high performance of the assay during analysis of actual study samples. Also covered in this chapter are the topics of when and how to conduct full validations, partial validations, and cross-validation. 

Table 4.4 lists the analytical performance characteristics that need to be confirmed during the prestudy validation phase [4 5,9]. In addition, this table highlights the activities that are needed to progress from method development to help ensure the method will meet the prestudy a priori criteria for method acceptance and for successful acceptance of batch runs (in-study validation). 

Performance Characteristic Method Development Prestudy Validation (Validation Phase) In-Study Validation (Implementation Phase, Sample Analysis Phase)... 

Assessment Topic Method Development Prestudy Validation In-Study Validation... 

IN-STUDY VALIDATION PHASE 4.5.1 Batch/Run Acceptance Criteria... 

Performance characteristics of ligand-binding assays are estimated using calculated concentrations from the spiked validation samples during prestudy validation and/or from the quality control samples during in-study validation. 

In-study validation entails the routine monitoring of the quality control samples to determine whether the analytical method is performing consistently over time and whether data from a particular plate or run are acceptable. In addition, especially for biomarker assays, evaluation of parallelism using incurred samples is carried out to confirm the validity and suitability of the reference standard. 

The basic aim when applying prestudy and in-study validation procedures to a measurement method is to reconcile the objectives of the two validation phases. When the tolerance interval approach is used for prestudy validation and the 4 6 2 rule is used during in-study validation, the common objective is to control the proportion n of measurement results (X — jiy) that fall within the acceptance limits [—2,+ 2]. 

The alignment of risk between the prestudy and in-study validation phases can be envisaged in two ways, as shown by Boulanger et al. [29]. On the one hand, if the number of QC samples, n, to be used and the minimum, 5, of QC samples within the acceptance limits in the s n X rule are fixed (e.g., 4 6 15), the value of 7imin should be chosen so as to ensure that if the method remains valid, the s n X rule is accepted in most cases (e.g., with a minimum probability ymin). On the other hand, for a given prestudy validation scheme and X fixed), the value of 5 QC samples within the acceptance limits (for a given n) should guarantee that most of the runs will be accepted if the method remains valid. 

The acceptance criteria used during prestudy validation and in-study validation should be consistent with each other. Lack of consistency can result in a validated method failing more often than expected during the in-study phase or vice versa. To ensure this consistency, either the commonly used in-study acceptance criteria such as the 4 6 15 rule can be modified by increasing the number of QC samples (e.g., 8 12 15 rule) or the prestudy validation criteria based on tolerance intervals can be altered to ensure that a higher proportion of the measured results fall within the acceptance limits. 

FIGURE 6.14 Workflow diagram of in study validation and sample assay of biomarker. 

The acceptance criteria for a novel biomarker can initially be determined by assay performance in prestudy method validation. Data obtained from the in-study validation using subject samples can then be used to refine the initial acceptance criteria set during the prestudy validation. For example, an assay with 50% total error may still be acceptable for detecting a twofold treatment effect observed in a clinical trial. Setting acceptance criteria a priori may not be appropriate (or even possible) in an exploratory application of novel biomarkers, since the values seen in the incurred samples may not be what is expected or predicted. 

Long-term storage stability in the biological matrix can be updated during in-study validation. 

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