Conditions: QST · an HPLC purity test.
I want the working, not the result — I need to be able to redo it with different numbers.
I care about the precision as well as the value — I want to know how many figures are real.
How many significant figures are actually justified here?
The part that matters: sampling plans exist precisely because testing everything is expensive, and they define the statistical relationship between sample size and lot-wide inference.
Under AQL sampling plans, testing two vials from a fifty-vial lot gives you an operating characteristic curve that tells you what risks you are accepting.
| Test | Answers | Does NOT answer |
|---|---|---|
| RP-HPLC, area % | What fraction of detected material is the target | How much target is present |
| Quantified content | Milligrams of peptide per vial | What the impurities are |
| ESI-MS identity | Whether the molecular weight matches | Purity, or isomeric substitution |
| Peptide mapping | Sequence, localised to a fragment | Quantity |
| Karl Fischer | Water content of the solid | Solvent content |
| LAL endotoxin | Pyrogen load in EU/mg | Sterility |
| Sterility test | Growth in defined media over 14 days | Endotoxin, or bioburden count |
The underlying point is that if the lot was manufactured in multiple batches, testing vials from each batch separately establishes whether batch-to-batch variation is acceptable.
Published data on lot homogeneity from manufacturers who sample multiple vials consistently find variation below the published specifications, suggesting the sampling plans work.
Assume segregation is possible, and design your sampling to catch it if it exists.
edited 8 May 2025 by tobias_maartens — added the method parameters
Aggregated, published test results and vendor ratings built from submitted batches. Methodology stated, dataset browsable, no listing fees.
Browse resultsWorth being precise here: the honest statement is that unless you have tested multiple vials or have segregation data, you are making an assumption about lot homogeneity that may not hold.
If the entire lot failed qualification, a retest on a different vial is sometimes done, but reporting a retest result under the same lot number is misleading.
If you have reason to suspect inhomogeneity — different appearance in different vials, or a long or warm shipment — testing more vials is the diagnostic move.
One qualification: testing more vials gives better confidence, but at some point the cost outweighs the benefit.
The practical summary: a lot number without a sampling statement is a lot number without meaning.
Stated carefully, batch testing establishes what can be claimed about the lot as a whole, and the sample size determines how much you can actually claim.
The statistical foundation here is well-established, which is why sampling plans from decades ago are still valid.
Published segregation failures show that even modern automated processes sometimes produce lots with measurable vial-to-vial variation.
Sampling plans for pharmaceutical manufacturing are defined in ISO 2859 and ANSI Z1.4, and they are based on statistical sampling theory.
Worth noting that thermal excursions during shipping affect different vials differently, so the lot may not be homogeneous even if it left the factory that way.
If testing multiple vials, state how many you tested and why you chose those vials.
Two vials tested from a lot of ten is very different from two vials tested from a lot of ten thousand, and most certificates do not state the lot size.
Stratified sampling — testing one vial from the top, one from the middle, and one from the bottom of a shipment — is cheap insurance against segregation.
Assume segregation is possible, and design your sampling to catch it if it exists.
If a lot has visibly segregated — some vials showing different appearance — then sampling the top and bottom of the shipment is worth doing.
Acceptance Sampling by Attributes defines the number of samples you need from a lot to claim a specified quality level at a specified risk — it is in ANSI standard Z1.4.
The practical summary: a lot number without a sampling statement is a lot number without meaning.
Ask PeptideStack is a static archive. Posting is closed, but the norms are worth stating: answer the question that was asked, show your working, cite the trial or the certificate, and say plainly where the evidence runs out.