For reference: GL Biochem · an endotoxin test.
I would like the arithmetic checked rather than the conclusion asserted.
I have deliberately not used an online calculator because I want to be able to check the result.
What is the general form of this calculation?
Concretely, batch testing establishes what can be claimed about the lot as a whole, and the sample size determines how much you can actually claim.
Published segregation failures show that even modern automated processes sometimes produce lots with measurable vial-to-vial variation.
The part that matters: 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.
Sampling plans for pharmaceutical manufacturing are defined in ISO 2859 and ANSI Z1.4, and they are based on statistical sampling theory.
The caveat is that sampling is a trade-off between cost and confidence, and neither test nor assumption is cost-free.
The practical summary: a lot number without a sampling statement is a lot number without meaning.
Aggregated, published test results and vendor ratings built from submitted batches. Methodology stated, dataset browsable, no listing fees.
Browse resultsThe relevant detail is that a certificate that reports one test result on one vial extrapolates to claim that all two hundred vials in the lot are identical, which is an assumption worth questioning.
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.
The underlying point is that a statement that "lot 20260412 complies with specifications" is meaningless without stating which vials from the lot were tested and how many there were.
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.
If testing multiple vials, state how many you tested and why you chose those vials.
Sampling plans exist precisely because testing everything is expensive, and they define the statistical relationship between sample size and lot-wide inference.
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.
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.
One qualification: testing more vials gives better confidence, but at some point the cost outweighs the benefit.
Assume segregation is possible, and design your sampling to catch it if it exists.
Stated carefully, most suppliers test one vial per lot and report the result as lot homogeneity, which is sampling one item from one lot and extrapolating wildly.
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.
Published data on lot homogeneity from manufacturers who sample multiple vials consistently find variation below the published specifications, suggesting the sampling plans work.
The practical summary: a lot number without a sampling statement is a lot number without meaning.
Concretely, the practical consequence is that spot-testing one vial from a new supplier is better than assuming they are all the same.
If the lot was manufactured in multiple batches, testing vials from each batch separately establishes whether batch-to-batch variation is acceptable.
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 29 Jul 2025 by k_szabo — expanded the table to cover the lower concentration
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.