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; none of them concludes.
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 | Does this run meet ICH M10 for its modality? |
compare_methods.py | Are two procedures equivalent, at a pre-stated margin? |
All 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.
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 are the evidence, 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.
Add --weight 1/x2 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×, because an
unweighted fit then biases exactly the low end where a reporting threshold lives.
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 intermediate-precision factor — day, analyst,
or instrument.
level component sd rsd_pct df ci90_low_sd ci90_high_sd
100 repeatability (within group) 0.0707 0.0707 3 0.0438 0.2065
100 between-group 1.6515 1.6515 2 n/a n/a
100 intermediate precision (total) 1.6530 1.6530 2.0037 0.9554 7.2821
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, plus a level-independent view as percent of nominal.
--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
python3 check_detection_limits.py --calibration lowcal.csv --blanks blanks.csv \
--confirm-ql 0.05 --confirm-data ql_check.csv --reporting-threshold 0.05
approach sigma slope DL QL
sd-and-slope (sigma = residual SD of regression) 7.2816 5033.3490 0.0048 0.0145
sd-and-slope (sigma = SD of y-intercept) 4.3303 5033.3490 0.0028 0.0086
sd-and-slope (sigma = SD of 8 blanks) 3.7702 5033.3490 0.0025 0.0075
The same data give QL estimates spanning 1.9×, purely from the choice of σ. 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
python3 check_bioanalytical_run.py --modality chromatographic --run run1.csv
python3 check_bioanalytical_run.py --modality lba --isr isr.csv
python3 check_bioanalytical_run.py --modality lba --criteria
--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 enforces the per-level rule that gets missed: 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.
finding: QC level high: 0/2 within tolerance (0%); M10 requires at least 50% at each level
7. Transfer and method comparison
python3 compare_methods.py -i paired.csv --margin 2 --relative --slope-tolerance 0.05
mean difference (%) 1.4646
TOST margin 2.0000
TOST p-value 1.0528e-13
90% CI (TOST) 1.44127 to 1.48797
equivalent at stated margin yes
--- for contrast only ---
paired t-test p (NOT equivalence) 0.0000
OLS slope (biased here) 1.0396
Deming slope 1.0398
Passing-Bablok slope 1.0351
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 matters — that the true difference lies
inside a pre-stated margin. Here the t test says the difference is highly significant and TOST
says the methods are equivalent at ±2%; both are true, and only one answers the question.
- Ordinary least squares for method comparison. OLS assumes the reference values carry no
error, which is false when comparing two procedures, and biases the slope toward zero. Deming
(with a stated error-variance ratio) and Passing–Bablok (non-parametric, outlier-resistant) are
the appropriate regressions and are reported side by side with OLS for contrast.
The script also flags proportional bias — when the difference trends with concentration, a single
mean bias and its limits of agreement are misleading regardless of how tight they look.
What this skill exists to prevent
- Validating against ICH Q2(R1)'s structure three years after Q2(R2) replaced it.
- Acceptance criteria written after the data were seen.
- r² presented as evidence of linearity.
- Repeatability reported as the procedure's precision, with the between-day component invisible.
- One DL/QL number with no named approach and no confirmation.
- Chromatographic M10 criteria applied to a ligand binding assay, or the reverse.
- A t test's non-significance presented as equivalence at a method transfer.
References
references/framework-selection.md — which framework governs, and the questions that decide itreferences/ich-q2r2.md — structure, Table 1 and Table 2, per-characteristic recommended datareferences/ich-m10-bioanalytical.md — the full chromatographic and LBA criteria side by sidereferences/compendial-and-clsi.md — USP, CLSI and ISO designations, scope, and how to cite themreferences/statistics.md — the statistical methods, why each one, and the common errorsreferences/source-ledger.md — provenance and research dates for every claim in this skill
Assets
assets/validation-protocol-template.md — protocol structure with criteria stated up frontassets/validation-report-template.md — report structure with raw-data traceability
