--- name: analytical-method-validation description: 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 binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works. license: MIT compatibility: Requires Python 3.11+. Scripts use only the standard library - no numpy, scipy, or network access. Statistical distributions are computed from first principles so results are reproducible in any conforming interpreter. allowed-tools: Read Write Edit Bash metadata: version: "1.0" skill-author: K-Dense Inc. last-reviewed: "2026-07-27" ---

# 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

```bash 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

```bash 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

```bash 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

```bash 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

```bash 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

```bash 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

```bash 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

```bash 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 mand