Bio-similars or Bio-differents? What we can learn from real world data?

The lecture by Mykola Gryshchenko addresses a practical question that arises ever more often in the daily work of an ART clinic: can gonadotropin biosimilars be regarded as a full equivalent of the originator products, or do clinically meaningful differences exist between them. The speaker frames the topic not through marketing claims but from the standpoint of what accumulated real-world data actually teach us.

The discussion rests on a conceptual distinction between two notions. A biosimilar is a biological medicine that is highly similar to its reference product in structure, quality and biological activity, with no clinically meaningful differences. The term biodifferents underscores the opposite view: gonadotropins are complex glycoproteins, and features of manufacturing, isoform profile and glycosylation can give rise to subtle differences that are not always apparent in a product's standard specifications.

For this reason the question of equivalence cannot be reduced to a formal confirmation of biosimilarity. For protein hormones, whose action depends on receptor binding and on pharmacokinetics, even small differences in isoform composition could in theory be reflected in the ovarian response. The lecture helps the practising clinician grasp why similar and identical are not synonyms, and where the boundary lies between the regulatory concept of a biosimilar and clinical reality.

The central part is devoted to comparing two types of evidence. Randomized controlled trials remain the gold standard, yet they are conducted in tightly selected populations and under strict protocols, which limits the transfer of conclusions to the heterogeneous flow of real patients. Real-world data, by contrast, reflect everyday conditions — the full spectrum of ages, ovarian reserve and concomitant factors — and reveal how a product behaves outside the idealized frame of a clinical trial.

The speaker shows that these two sources of evidence do not compete but complement each other. A randomized trial answers whether a product works under controlled conditions; real-world data answer whether that result is reproduced in an ordinary clinic. Discrepancies between them do not necessarily signal an error: they point to factors that a formal study may have failed to capture and provide grounds for closer analysis.