Run acceptance criteria

A brief description of the criteria used to assess run acceptance (Section 10.3.3d) should be included in the validation report. For method validation all of the data generated from the QC samples and used for statistical analysis are generally reported, and run acceptance is based primarily on the calibration standard run acceptance criteria. This is distinct from the procedure for sample analysis where QCs are also used to establish run acceptance or failure based on the criteria established during validation. 

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Run acceptance criteria N/A Runs accepted based on standard curve acceptance criteria Standard curve and QC acceptance criteria. Apply 4-6-30 rule or other appropriate statistical criteria that align with the prestudy acceptance criteria... 

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

Run acceptance criteria that have been embraced for both chromatographic and LBAs require at least two-thirds of all QC results for a run to be within a specific percentage (e.g., 15%, 20%, 25%, or 30%) of the corresponding nominal reference values, with at least 50% of the results within the specified limit for each QC concentration. Assays of conventional small-molecule drugs have adopted a 4-6-15 rule [7,10,18]. In contrast, a 4-6-30 rule was proposed for LBAs of... 

During sample analysis, only the LQC, MQC, and FIQC defined during validation need to be run in duplicate with every assay. The precision and accuracy should conform to that established during validation. Typically, run acceptance criteria also require that at least one QC at each concentration level is acceptable, and that four out of the six QCs assayed must be acceptable. It is recommended that method acceptance criteria that are consistent with the validation be used during sample analysis [11,12]. 

Because validation QCs are used to assess the overall accuracy and precision of the method during the validation phase, no runs initiated for validation may be eliminated due to QC failure. Therefore, the acceptance criteria for the standard curves are used to accept each assay. At the conclusion of the validation, run acceptance criteria for QCs to be used in-study are summarized and run acceptance criteria are established based on this data. 

As described in this chapter, prestudy validation runs are accepted based on the standard-curve acceptance criteria. No run acceptance criteria are applicable for prestudy validation sample assessments i.e., no run can be rejected due to poor validation sample performance during accuracy and precision evaluation, and all data from the prestudy validation runs are reported without exceptions. In some cases, there may be assignable cause (e.g., technical issues) for removal of a validation sample data point before the calculation of the cumulative mean. Exclusion is applied at the end of the validation study period and must be documented as described in the documentation section. 

For each in-study run, the standard curve must satisfy criteria described in the standard-curve section however, run acceptance is based primarily on the performance of the QC samples. When using total error for ligand binding assays of macromolecules, the run acceptance criteria recommended in the precision and accuracy section requires that at least four of six (67%) QC results must be within 30% of their nominal values, with at least 50% of the values for each QC level satisfying the 30% limit. The recommended 4-6-30 rule imposes limits simultaneously on the allowable random error (imprecision) and systematic error (mean bias). If the application of an assay requires a QC target acceptance limit different than the 30% deviation from the nominal value, then prestudy acceptance criteria for precision and accuracy should be adjusted so that the limit for the sum of the interbatch imprecision and absolute mean RE is equal to the revised QC acceptance limit. 

A validation plan should be written before the initiation of the prestudy validation experiments. Alternatively, reference to an appropriate SOP can be made to ensure that a documented outline exists for the experiments required for prestudy validation. This plan can be a stand-alone document or can be contained in a laboratory notebook or some comparable format. The documentation should include a description of the intended use of the method under consideration and a summary of the performance parameters to be validated that should include, but may not be limited to, standard curve, precision and accuracy, range of quantification, specificity and selectivity, stability, dilutional linearity, robustness, batch size, and run acceptance criteria. The plan should include a summary of the proposed experiments and the target acceptance criteria for each performance parameter studied. 

Run Acceptance Criteria. Run acceptance criteria are used to accept or reject a run because of its performance. As a consequence, no dehned run acceptance criteria are applicable during method development. Prestudy validation runs are accepted based on the standard curve acceptance criteria. No run can be rejected because of poor performance of a sample during precision and accuracy evaluation all data from prestudy validation runs are reported unless there has been a clearly recognizable error during sample preparation or measurement. Despite the fact that the standard curve must satisfy the criteria described for standard curves above, in-study runs are accepted based primarily on the performance of the QC samples. As stated above in the paragraph on precision and accuracy, the 4-6-30 rule is recommended (i.e., at least four of six QC samples must be within 30% of their theoretical values and at least 50% of the values for each level must satisfy the 30% limit. 

From the analyst s viewpoint, the more demanding the acceptance criteria, the higher the risk of run failure the severity of these problems increases with the number of compounds to be analyzed by a given method, since failure to meet the established run acceptance criteria for... 

All of the hardware, software, clever methodologies and validation go for nothing if analysts are unahle to assure the end user of the data that the method was in control throughout sample analysis and that the results can he trusted within the stated uncertainty limits. In addition to within-run acceptance criteria based on analysis of QCs and calibrators within a single batch run, control charts are also often used as a means for assessing whether or not the method is in control. Use of only the former as quality control indicators can often lead to missing problems that affect even the most fully validated methods. 

Method and Run Acceptance Criteria A description of the method. Run acceptance criteria (Section 9.8.5 and 10.3.3d). 

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