Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand

Analytical Method Validation When to use Any time the question is whether an analytical procedure is fit for its intended purpose: designing a validation study, evaluating validation data, verifying a compendial procedure, transferring a procedure to another laboratory or instrument, or defending any of these in a report. The two rules 1. Establish which framework governs before designing anything. The same assay validates differently under ICH Q2(R2), USP <1225 , ICH M10, CLSI EP, and ISO/IEC 17025. They differ in which characteristics are required, how the studies are laid out, and whether numeric acceptance criteria are supplied at all. Blending them produces a protocol that satisfies none of them. 2. State acceptance criteria before collecting data. Criteria chosen after seeing results are not acceptance criteria, and deciding them post hoc is a standing audit finding. ICH Q2(R2) deliberately supplies almost no numeric criteria — they have to come from the specification, the analytical target profile (ICH Q14 section 3), or development data. ICH M10 is the exception: it supplies explicit numbers, and they differ between chromatographic assays and ligand binding assays. Scope This skill plans studies, computes the statistics correctly, and structures the documentation. It does not decide that a procedure is validated, release a batch, accept or reject a run, close an investigation, or substitute for the analyst, the technical reviewer, the quality unit, or the regulator. Every script reports computations and supported findings; only the responsible reviewers can make the fitness for purpose decision. JSON uses null for unavailable statistics. Copyright boundary ICH guidelines are published openly and licensed for reuse with acknowledgement, so their requirements are encoded directly in this skill. USP general chapters, CLSI EP documents, and ISO standards are copyrighted and paywalled. For those, this skill supplies the designation, scope, and where to obtain an authorised copy — never the text, never invented thresholds. Do not ask an agent to retrieve, transcribe, or reconstruct their content. If a number matters and it lives in a paywalled document, read it from the authorised copy. Frameworks Key Governs Numeric criteria supplied ich q2r2 Release and stability testing of drug substances and products Almost none — you derive them ich m10 Bioanalytical concentration measurement (PK, TK, BE) Yes, and they differ by modality usp 1220 Compendial procedure lifecycle, three stages Paywalled usp 1225 / usp 1226 Validation / verification of compendial procedures Paywalled clsi Clinical laboratory measurement procedures (EP series) Paywalled iso 17025 Lab developed and modified methods under accreditation No — "to the extent necessary" Q2(R2) replaced Q2(R1) in November 2023 and restructured the characteristics. Range is now the parent characteristic (section 3.2), containing response (linearity) and validation of lower range limits (DL/QL). Accuracy and precision are section 3.3 and may be evaluated in combination against a single criterion. Robustness is treated as a development activity and cross refers to ICH Q14. Multivariate procedures are addressed explicitly (2.5 and 3.2.2.3), and Annex 2 adds worked examples for techniques Q2(R1) never covered — quantitative ¹H NMR, NIR, quantitative LC/MS, qPCR, biological assays, and particle size. A Q2(R1) shaped protocol — a flat list of linearity, range, accuracy, precision, specificity, LOD, LOQ, robustness — is out of date. Note also the error correction dated 30 November 2023 to Table 5 and Tables 6–11. Scripts Script Question answered plan validation.py Which framework, which characteristics, what study layout, what protocol? check response.py Does the calibration model actually hold across the range? check accuracy precision.py What is the recovery, and how much of the variability is between days? check detection limits.py What are DL and QL by each allowed approach, and do they serve the reporting threshold? check bioanalytical run.py Which supported ICH M10 numerical checks raise findings? compare methods.py Are two procedures equivalent, at a pre stated margin? Commands below use placeholder data filenames; supply your own controlled data. The printed numerical examples were re executed with synthetic repository fixtures under tests/analytical method validation/fixtures/ on Python 3.13.3. They are computational smoke tests, not evidence that a physical assay is validated. All scripts take format table tsv json . Provenance, guideline citations, and caveats go to stderr; data goes to stdout, so out.tsv keeps them separate. Exit code is 0 for no findings, 1 when findings were raised, 2 for bad input — so any of them can gate a workflow. Version 2.0 tightens the numerical input contract: QL confirmation needs explicit bias/CV limits; intercept based limits need independent curves; M10 runs need complete labelled calibrator/QC records; grouped accuracy uses group means; unavailable JSON statistics are null . Re run saved analyses rather than comparing their exit codes with version 1.x unchanged. Workflow 1. Fix the framework and the required characteristics Q2(R2) Table 1 decides what is required from the measured attribute , not from the technique. For an assay: specificity, response, accuracy, repeatability, intermediate precision. For a limit test: specificity and DL only. For an identity test: specificity alone. Attributes accepted include assay , impurity (quantitative), impurity limit , and identity . Reportable range comes from the specification. Q2(R2) Table 2 gives worked examples — 80–120% of declared content for an assay, 70–130% for content uniformity, reporting threshold to 120% of the specification for an impurity. 2. Generate the protocol and fill in the criteria Every bracketed field is a decision to make and record before data collection. The protocol skeleton deliberately refuses to pre fill acceptance criteria for Q2(R2) work, because there is no defensible default. 3. Evaluate the response Input is level,response , one row per injection; repeated rows at the same level are replicates, and supplying them is what makes the linearity test possible. Real output from a curve that a coefficient of determination would wave through: r² = 0.983 and the model is unusable: −9.0% back calculated error at the bottom of the range, lack of fit p = 1.5 × 10⁻⁶, non random residual signs. r² is not evidence of linearity — it rises with range and is nearly insensitive to curvature. The lack of fit F test against pure error and the residual pattern provide diagnostics, which is why Q2(R2) 3.2.2.1 asks for an analysis of the deviation of points from the line rather than a correlation coefficient alone. Use weight 1/x2 only when supported by the variance model for a wide range curve. The script flags heteroscedasticity when the residual variance in the top third of the range exceeds the bottom third by more than 10×, as a heuristic warning. Unequal variance does not itself bias OLS coefficients; choose weights from a justified error model, then assess low end performance. Weighted lack of fit assumes the weights represent inverse variances. A significant test is not a practical acceptance criterion. 4. Evaluate accuracy and precision Input is level,measured,group , where group is the intermediate precision factor — day, analyst, or instrument. Record independent sample preparation IDs separately from repeat injections: reinjecting one preparation estimates injection repeatability, not the whole procedure. The bundled one way model estimates one between group component. If day, analyst, and instrument change together, it cannot identify their separate contributions; use a planned crossed or nested study and a matching model when those components matter. Grouped accuracy intervals use independent group means with equal group weighting; individual injections are not counted as independent evidence across days. To check the six at 100% design alternative, state test concentration 100 when nominal values are percentages (or the actual concentration). Repeatability of 0.07% RSD looks superb; intermediate precision is 1.65%, twenty three times larger, because the variability lives entirely between days. Reporting the within day figure as the procedure's precision would understate routine performance by more than an order of magnitude. This is why the script fits a one way random effects model rather than pooling. Two traps the script handles for you: Precision is estimated within each level, never pooled across levels. Pooling 80/100/120% results into one standard deviation turns the range itself into apparent imprecision. The script reports per level; even pooling percent recoveries can confound level specific bias with imprecision. require ci within limit enforces that the whole confidence interval sits inside the limit, not just the mean. Q2(R2) 3.3.1.4 asks for the interval to be compatible with the criterion; a mean that scrapes inside on six replicates has not demonstrated much. 5. Establish DL and QL, and confirm them The two estimates differ by about 1.9× from the choice of σ. The example limits of 10% bias/CV are illustrative protocol choices, not Q2 defaults. Supply intercepts curves.csv (column intercept ) only for independent low range calibration curves: their intercept SD is not the standard error of one fitted intercept. The helper uses unweighted low range fits; it does not automate visual detection or establish a validated limit. Q2(R2) 3.2.3.5 therefore requires the limit and the approach used to determine it to be reported, and an estimated limit to be confirmed with samples at or near it. For an impurity procedure the QL must be at or below the reporting threshold. Reaching for 3.3σ/slope reflexively, reporting one number with no named approach, and never confirming it are three separate findings. 6. Bioanalytical runs under ICH M10 modality is mandatory and has no default, because the criteria genuinely differ: Chromatographic Ligand binding assay Calibration tolerance ±15%, ±20% at LLOQ ±20%, ±25% at LLOQ and ULOQ Accuracy / precision ±15% / ≤15% CV (±20% / ≤20% at LLOQ) ±20% / ≤20% CV (±25% / ≤25% at LLOQ and ULOQ) A&P design 4 QC levels, 5 replicates/run, ≥3 runs over ≥2 days 5 QC levels, 3 replicates/run, ≥6 runs over ≥2 days Total error no such criterion ≤30%, ≤40% at LLOQ and ULOQ ISR agreement ±20% for ≥2/3 of repeats ±30% for ≥2/3 of repeats Applying the ±15% chromatographic numbers to a ligand binding assay, or importing the LBA total error criterion into a chromatographic method, are both common and both wrong. The run check requires calibrators and QCs, at least six passing calibration levels, explicit LLOQ/ULOQ labels and duplicate QCs at three levels. It treats each row as one reportable sample, not one LBA well. It flags failed calibrators for documented exclusion/refitting and checks: at least 2/3 of all QCs and at least 50% at each level. A run can pass the overall fraction while a single level fails completely. Check blanks/zero samples, QC bracketing, the study size dependent 5% QC count, plate/batch rules, and revised ranges separately; the script does not establish full M10 compliance. 7. Transfer and method comparison Two errors this replaces: "p 0.05, no significant difference, therefore the methods are equivalent." Failing to detect a difference is not evidence of equivalence, and on a small transfer dataset that outcome is close to guaranteed. TOST tests the hypothesis that matte

analytical-method-validation