MESO-Rx Sponsor Primal Pharma - US Domestic

Quick clarification on “same batch” and variance. A batch is a single, homogenized production run mixed, filtered, and filled under the same conditions from the same raw source. Within that, small result swings are normal and typically come from sampling technique, vial position during fill, agitation before aliquoting, lab method differences, instrument calibration and stated uncertainty, and sample prep losses. Two results that fall within the method’s combined uncertainty are statistically consistent with the same batch.

The clean way to verify is to pull a well-mixed composite, split it, run the same method at the same lab in parallel, and document chain of custody. If the spread exceeds the method’s uncertainty, we investigate; if it does not, it reflects normal analytical scatter rather than a different or inconsistent batch. If any of this sounds unfamiliar, it may just be a misunderstanding of how batches and analytical uncertainty work…
 
My thoughts.


So the breakdown seems to be…

1. 001, the first, was 262.
2. The “unknown” has to be batch p10 (238).
3. P11, the current, 241.

You’re going to tell me the alternative (001 & p10 claimed to be the same) would be a 10% variance between same batch testing lol. Likely not

And it was already confirmed that P11 testing is not P10 labeled, because if you bought in late September, you were part of the most recent batch (p11) and “you’re fine”.
 
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Quick clarification on “same batch” and variance. A batch is a single, homogenized production run mixed, filtered, and filled under the same conditions from the same raw source. Within that, small result swings are normal and typically come from sampling technique, vial position during fill, agitation before aliquoting, lab method differences, instrument calibration and stated uncertainty, and sample prep losses. Two results that fall within the method’s combined uncertainty are statistically consistent with the same batch.

The clean way to verify is to pull a well-mixed composite, split it, run the same method at the same lab in parallel, and document chain of custody. If the spread exceeds the method’s uncertainty, we investigate; if it does not, it reflects normal analytical scatter rather than a different or inconsistent batch. If any of this sounds unfamiliar, it may just be a misunderstanding of how batches and analytical uncertainty work…

Deflecting as usual.

We are seeing multiple products with ~+/-10% variances between 3rd party and vendor testing. So much for homogeneity. This isn't the first.

What good are your testing results if none of them are anywhere close to what people are receiving?

 
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