Prestudy validation assays

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. 

Chapter 5 discusses in depth the statistical considerations related to LB A development and validation. In addition to the most appropriate algorithms for describing the nonlinear calibration curves typically found in LBAs, the authors also provide further insight into the performance characteristics to be evaluated during assay validation, including the concepts of total error in prestudy validation and the use of the 4-6-X rule. The decision rules at the prestudy validation and routine assay implementation stages are also discussed in some detail in Chapter 5. 

During prestudy validation, the performance of the assay with respect to specificity and selectivity is confirmed with the most relevant compounds and matrices. Selectivity is expressed as acceptable recovery, using the same criteria that are applied during the assessment of accuracy. The recommended target acceptance criterion for selectivity is that acceptable recovery (e.g., 80 120% relative to buffer control) is obtained in at least 80% of the matrices evaluated. 

During the prestudy validation phase, samples should be spiked at the anticipated LLOQ and the ULOQ of the assay. These validation samples should be assayed as part... 

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. 

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. 

During sample analysis before assessing the QC samples for acceptance, the standard curve must be deemed appropriate by predetermined criteria. Only after the curve is accepted may the assessment of QC samples continue. QC sample results determine whether the assay run is valid. Acceptance criteria can be based on 4-6-20 rule or on Total Error and should be predicated on the criteria used in both the development and the prestudy validation phase. Overall, the immunoassay is a highly sensitive assay that can be used to quantify protein and peptide drugs in a biological matrix, often routinely in the pg/mL range. 

Spiked samples are analyzed over multiple runs with replicate determinations during method development and prestudy validation. QC samples are used during in-study validation to monitor the performance of the assay. Limits for minimum... 

Parallelism. Parallelism is a characteristic that is typically assessed during in-study validation. It is conceptually similar to dilutional linearity with the difference that it is determined by dilutions of actual study samples (incurred samples). In some cases, samples from a preclinical pilot study may be available during prestudy validation so that assessment of parallelism may be performed at that time. Moreover, when an assay is validated with the aim to replace another assay, incurred samples from a previous study may be available for evaluation of this performance characteristic. 

The stages of validation of biomarker assays include establishment of the biomarker (development), so-called prevalidation, prestudy validation, and in-study validation [13-15]. The following short discussion will focus on the GLP-like definitive and relative quantitative assays. As the development and validation of an assay for novel biomarkers is quite diverse, the application of strict validation procedures appears problematic. Therefore, upon establishment of the prototype assay in the development phase, a formalized validation plan should be developed that... 

To have an assay to validate, a robust assay must be developed. Chapter 3 discusses, in a systematic and pragmatic way, the approaches to the development of LBAs. This discussion focuses on the practical aspects of LBA development for use in the GLP-compliant environment. A structured strategy for the development of a validatable LBA that would withstand the rigor of prestudy and in-study method validation... 

The acceptability of in-study batches/runs is based on the performance of standard calibrators and quality control samples run in an assay. As mentioned previously, it is desirable to have prestudy method acceptance criteria consistent with the in-study batch acceptance criteria. If not, a higher percentage of assay failures can be expected. This rationale was the genesis for the 4-6-30 rule as recommended by DeSilva et al. [3 5]. The standard curve acceptance criteria for macromolecule LB As are that at least 75% of the standard points should be within 20% of the nominal concentration (%RE of the back-calculated values), except at the LLOQ and ULOQ where the value should be within 25%. This requirement does not apply to anchor concentrations, which are typically outside the validation range of the assay and are used to facilitate and improve the nonlinear curve fitting. 

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