The particulars: liraglutide · ten weeks · minus 80 °C.
I would like to define my thresholds before I have a result, for obvious reasons.
I want a plan with explicit stopping rules, not just steps.
How do I make this decision on evidence rather than on feel?
Worth being precise here: the practical consequence is that spot-testing one vial from a new supplier is better than assuming they are all the same.
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.
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.
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.
Assume segregation is possible, and design your sampling to catch it if it exists.
Aggregated, published test results and vendor ratings built from submitted batches. Methodology stated, dataset browsable, no listing fees.
Browse resultsThe 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.
The statistical foundation here is well-established, which is why sampling plans from decades ago are still valid.
The underlying point is that the sample size determination requires choosing a confidence level and an acceptable error rate, and the smaller the error rate you want, the larger your sample must be.
Published data on lot homogeneity from manufacturers who sample multiple vials consistently find variation below the published specifications, suggesting the sampling plans work.
If testing multiple vials, state how many you tested and why you chose those vials.
Batch testing establishes what can be claimed about the lot as a whole, and the sample size determines how much you can actually claim.
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.
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.
Lyophilised peptide homogeneity studies show that vial-to-vial variation is usually small but occasionally large, depending on the distribution in the freeze-dryer.
I would treat a "complies with" statement without sampling details as a claim rather than as evidence.
The practical summary: a lot number without a sampling statement is a lot number without meaning.
The failure mode here is publishing a result that applies to the tested vial alone while implying it applies to the entire lot.
A statement that "lot 20260412 complies with specifications" is meaningless without stating which vials from the lot were tested and how many there were.
Assume segregation is possible, and design your sampling to catch it if it exists.
edited 21 Oct 2025 by claudia_ferrante — expanded the table to cover the lower concentration
If a lot has visibly segregated — some vials showing different appearance — then sampling the top and bottom of the shipment is worth doing.
Testing a vial that has been open in the lab for three months is testing aged material, not the fresh lot, and the result should be explicitly noted as a retest.
Sampling plans for pharmaceutical manufacturing are defined in ISO 2859 and ANSI Z1.4, and they are based on statistical sampling theory.
If testing multiple vials, state how many you tested and why you chose those vials.
edited 4 Sept 2025 by n_takahashi — added the placebo-arm figures
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