analytical-method-validation – skill Khác cho agent AI

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

cd skills/analytical-method-validation/scripts
python3 plan_validation.py --list-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

cd skills/analytical-method-validation/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

python3 plan_validation.py --framework ich-q2r2 --attribute assay --technique hplc --range-use assay

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

python3 plan_validation.py --framework ich-q2r2 --attribute impurity --protocol > protocol.md

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

python3 check_response.py -i calibration.csv --max-back-calc-error 2

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:

statistic                           value
distinct levels                     5
slope                               166.6000
intercept                           2495.0000
intercept CI includes 0             no
coefficient of determination (r2)   0.9830
lack-of-fit F                       469.5294
lack-of-fit p                       1.5139e-06
runs test p                         0.0492

level     n  mean_response  mean_back_calculated  relative_error_pct
50.0000   2  10075.0000     45.4982               -9.0036
75.0000   2  15150.0000     75.9604               1.2805
100.0000  2  20050.0000     105.3721              5.3721
125.0000  2  24050.0000     129.3818              3.5054
150.0000  2  26450.0000     143.7875              -4.1417

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

python3 check_accuracy_precision.py -i ap.csv --accuracy-limit 2 --rsd-limit 1.0 --design-check assay

Input is level,measured,group, where group is the inter

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