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Validation: concept, & considerations

Validation: concept, & considerations. Beni Kaufman. Will be presenting:. Review The concept Validation Components and their measurement experimental design of PCR validation Process vs. Modular validation. References:. Guidance for Industry: Bioanalytical Method Validation.

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Validation: concept, & considerations

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  1. Validation: concept, & considerations Beni Kaufman

  2. Will be presenting: • Review • The concept • Validation Components and their measurement • experimental design of PCR validation • Process vs. Modular validation

  3. References: • Guidance for Industry: Bioanalytical Method Validation. U.S. Department of Health and Human Services, Food and Drug Administration (FDA), Center for Drug Evaluation and Research (CDER), Center for Veterinary Medicine (CVM) May 2001 • PCR Validation & Performance Characteristics Analytical Environmental Immunochemical Consortium (AEIC) Biotech Consensus Paper; S. Charlton, R. Giroux, D. Hondred, C. Lipton, K. Worden • Validation of Analytical procedures: Methodology, International Conference on Harmonization of Technical Requirements for the Registration of Pharmaceuticals for Human Use, 1996

  4. Warning Politicallysensitivematerial! Politically!!! sensitive material!! Discuses components… avoid criteria!

  5. Validation Components

  6. Selectivity(Specificity) • The ability of the analytical method to differentiate (and quantify) the analyte in the presence of other components in the sample (to amplify only the Sequence of interest.) Selectivity may be affected by: • Interference: • Cross amplification of non target sequences (function of, Primer design) • Matrix effects: • Background signal (Sybr green) • Quality & quantity of DNA • Reaction conditions (master-mix, thermocycling profile)

  7. Selectivity(Cont.) Assessed by: • Fragment length analysis (right size amplicon) • Electrophoresis gel analysis • CE

  8. Selectivity(Cont.) Dissociation Curve do not use r2774, 02-08-2006, 15Hr 58Min.mxp • Assessed by: • Melting curve analysis

  9. Precision • The closeness of agreement (degree of scatter) between a series of measurements obtained from multiple sampling of the same homogeneous sample under the prescribed conditions. • Variation among rep.s within an assay • Same as Repeatability • Measured by parameters of variation, mostly %CV

  10. Precision parameters:

  11. Accuracy/Trueness • The closeness of mean test results to the true value of the analyte. Qualitative assay: Measured by error rate: % false positive = False positives/ # of negatives % false negative = False negatives/# of positives

  12. Accuracy/Trueness (cont.) • Quantitative assay: • The mean recovery at several points across the quantitative range % Recovery =100 (observed/actual) (also, the deviation of the mean from the true value)

  13. Accuracy/Trueness measured

  14. Linearity & Range • Linearity: The ability of the assay (within a given range) to obtain test results which are directly proportional to the concentration/amount of the analyte • Range: The interval between the upper & lower concentrations of an analyte for which the assay has suitable levels of precision, accuracy & linearity.

  15. Linearity & Range (cont.) • Linearity and Range can be evaluated simultaneously • Demonstrated on a dilution series (transgene genomic DNA/null genomic DNA) across a relevant range of concentrations • The Range is established by confirming acceptable degrees of linearity, accuracy, & precision, within or at the extremes of a specified range.

  16. Linearity evaluated • Linearity is evaluated by a plot of signals as a function of analyte concentration & linear regression analysis.

  17. Sensitivity Two concepts of sensitivity: • Change in response per amount of reactant -> dose-response curve In PCR the dose response is derived from the amplification efficiency - We optimize the assay for a maximal dose response (~100% amp. Efficiency) Therefore, • dose-response is reflected in the standard curve. It’s captured in the Linearity component & • is the basis for quantification • The source of our resolution power

  18. Sensitivity (cont.) • Limit of detection (LOD), The minimum amount of target analyte that can be detected with a given level of confidence • Applies to QL & QT PCR Limit of quantification (LOQ), The lowest amount of target analyte that can be quantified with acceptable levels of precision and accuracy. • Applies only to QT PCR

  19. Determining LOD & LOQ: “Spiking” series: • Decreasing amounts of transgenic seed are mixed in with conventional seed to create a series of seed pools with varying proportion of transgenes. • Seed pools are ground to flour • DNA isolated from flour and used for PCR; targeting the corresponding target sequence.

  20. Sensitivity (cont.) The LOD will be lowest spike detected with an acceptable confidence level. The LOQ will be the lowest spike that can be differentiated from zerowith an acceptable confidence level

  21. Ruggedness • The effectiveness of an analytical process in face of small environmental/operating conditions, such as: • Different analysts • Different equipment • Different labs • Effectiveness is measured as changes in the precision or accuracy.

  22. Ruggedness (cont.) Effectiveness is measured as changes in the precision or accuracy: • For qualitative PCR evaluated by the changes in error rate and LOD • For quantitative PCR evaluated by HORRAT Where the Relative Standard Deviation of Reproducibility (RSDr) is given as: RSDr = 2(1-0.5lnC) ~ 2C-0.1505 (C= concentration or quantity) And HORRAT = RSDr(observed)/RSDr(expected) HORRAT is expected to be close to 1 • Horwitz, W. (1995) Protocol for the design, conduct and interpretation of method performance studies, Pure and Appl. Chem, 67:331-343

  23. Ruggedness measured

  24. Robustness • Describes the reliability of an analysis with respect to variations in method parameters. • Measured by experimentally defining the critical range of: • Template concentration • Primer concentration • Mg2 Concentration • Thermocycling temperature range Usually part of the assay optimization, prior to the validation process.

  25. Cartoon Break

  26. Seems to be a tedious process! It Is !!!

  27. But, the right experimental design Can take away some of the edge… For example:

  28. QT PCR Validation design: Experiment: • Series of conventional seed pools fortified with transgenic seed at a decreasing ratio. (For example: from 2% to 0.01% at -0.5X increments). • Highest level serves as positive control • Negative control • Five reps per level • Isolate, quantify, normalize, PCR (IQNP) • All in all: 8 spike levels x 5 replicates = 40 amplifications • Repeat 3 times, 3 different instruments, different analysts, (3 different dates (?) Astrological effect)

  29. QT PCR Validation design: Analyze • Selectivity: all amplifications yielded the right size amplicon (on gel, or by Tm) • Precision: Calculate %CV among reps within plates • Accuracy: Calculate mean % recovery within plates • Linearity: use samples as standards – create standard curve- test linearity • Range: based on results of Precision, Accuracy, & Linearity; define range. • LOD: Identify the lowest detected spike with an accepted confidence limit • LOQ: Identify lowest spike that its confidence interval does not overlap zero. • Ruggedness: HORRAT, or alternatively, ANOVA between plates, runs, annalists.

  30. QL PCR Validation design Experiment • Series of conventional seed pools fortified with transgenic seed at a decreasing ratio. (… from 2% to 0.01% at -0.5X increments). • Highest level serves as positive control • Negative control • Five reps per level • IQNP Analyze: • Selectivity: all amplifications yielded the right size amplicon (On gel or by Tm) • LOD: The lowest spike level to yield amplification = tentative LOD

  31. QL PCR Validation design Experiment: • Two plates, each plate, half null, and half spiked at tentative LOD. • Isolate, quantify, normalize, • PCR the two plates on different instruments, different analysts, etc Analyze: • Accuracy: Calculate positive and negative error rate. • Confirm LOD: if %false negative < defined criteria (5?) • Ruggedness: compare error rates between plates/instruments/analysts

  32. That wasn’t that bad wasn’t it?

  33. Not only PCR! The testing process is made of a number of consecutive steps, all can be validated, some have to be validated • Sampling • Sub-sampling • DNA Isolation • DNA Quantification • DNA Normalization • PCR • Post-PCR • Data Analysis

  34. Modular Validation The recognition that many of the applications – steps, in the testing process require independent validation of their function & For better efficiency Brought about the idea of Modular Validation A. Holst-Jensen, J-AOAC, 1995

  35. Modular Validation Validate each step (module). Once, validated, different modules can be combined in to a process that no longer require validation DNA is DNA!? IT IS NOT.

  36. Whole Process Validation • Particle size • DNA isolation efficiency • Instrument error • Matrix effect • Standards All affect the out come of the testing process, therefore, the validation is of the whole process and only in the context of the given matrix, instrumentation, & standards…

  37. You can’t “mix & match” Any deviation…will require VALIDATION.

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