Biosimilarity? No one said it was going to be easy.
A smart and timely cross-post from RAPS ...
Sandoz Raises Questions With FDA Draft Guidance on Statistical Approaches for Biosimilars
Martin Schiestl, chief science officer at Novartis' Sandoz, on Tuesday explained how the US Food and Drug Administration's (FDA) draft guidance on statistical approaches to evaluate analytical similarity poses risks that could end up causing true biosimilars to be rejected randomly.
Schiestl told attendees of DIA's biosimilars conference in Bethesda, MD, that the problem is related to equivalence testing, which FDA says in the draft, "is typically recommended for quality attributes with the highest risk ranking and should generally include assay(s) that evaluate clinically relevant mechanism(s) of action of the product for each indication for which approval is sought."
The draft, released about a month ago, also notes: "Determining an appropriate margin is a critical but challenging step for equivalence testing in any setting. Ideally, it would be possible to establish and pre-specify a biologically or clinically meaningful equivalence margin based on scientific knowledge or past experience. Often, however, such a margin is not readily available for every quality attribute deemed important enough for Tier 1 testing in a biosimilar development program. With this limitation, FDA currently recommends use of an equivalence margin that is a function of the reference product's variability for the attribute being tested."
But Schiestl noted that monitoring the mean is useful in process development and post approval process monitoring.
However, for lot release decisions, "Compliance with a preset mean is an impossible criteria."
He added, "Strict adherence to equivalence testing for Tier 1 attributes makes biosimilar development a gamble. Justifications which may overrule a failed equivalence test should be added in the guidance."
Such justifications may include a scientific understanding of a variation and an "inconsistent mean of the reference product which might be caused by inherent process fluctuations within acceptable ranges, manufacturing changes or movements within a design space," Schiestl added.
A smart and timely cross-post from RAPS ...
Sandoz Raises Questions With FDA Draft Guidance on Statistical Approaches for Biosimilars
Martin Schiestl, chief science officer at Novartis' Sandoz, on Tuesday explained how the US Food and Drug Administration's (FDA) draft guidance on statistical approaches to evaluate analytical similarity poses risks that could end up causing true biosimilars to be rejected randomly.
Schiestl told attendees of DIA's biosimilars conference in Bethesda, MD, that the problem is related to equivalence testing, which FDA says in the draft, "is typically recommended for quality attributes with the highest risk ranking and should generally include assay(s) that evaluate clinically relevant mechanism(s) of action of the product for each indication for which approval is sought."
The draft, released about a month ago, also notes: "Determining an appropriate margin is a critical but challenging step for equivalence testing in any setting. Ideally, it would be possible to establish and pre-specify a biologically or clinically meaningful equivalence margin based on scientific knowledge or past experience. Often, however, such a margin is not readily available for every quality attribute deemed important enough for Tier 1 testing in a biosimilar development program. With this limitation, FDA currently recommends use of an equivalence margin that is a function of the reference product's variability for the attribute being tested."
But Schiestl noted that monitoring the mean is useful in process development and post approval process monitoring.
However, for lot release decisions, "Compliance with a preset mean is an impossible criteria."
He added, "Strict adherence to equivalence testing for Tier 1 attributes makes biosimilar development a gamble. Justifications which may overrule a failed equivalence test should be added in the guidance."
Such justifications may include a scientific understanding of a variation and an "inconsistent mean of the reference product which might be caused by inherent process fluctuations within acceptable ranges, manufacturing changes or movements within a design space," Schiestl added.