The main risk in modern biosimilar development is no longer failure to demonstrate clinical efficacy. It is the presence of analytical gaps introduced early and locked in later, when they are expensive or impossible to fix.
This page explains which analytical gaps most often derail biosimilar programs, why regulators consider them high risk, and how developers can close them early to reduce regulatory delay and redesign.
What questions this page answers:
Biosimilar developers, CMC and analytical development teams and regulatory affairs leads usually ask the same practical questions once development is underway. This page addresses them directly.
Which analytical gaps most commonly appear in biosimilar programs
Why regulators treat these gaps as high risk
How each gap can be fixed in a regulator-aligned way
When these issues must be addressed to avoid rework
Which analytical capabilities are required to support similarity claims
A biosimilar is a biological medicine developed to be highly similar to an already approved reference product, with no clinically meaningful differences in safety, purity, or potency. Because biologics are produced in living systems, inherent variability cannot be eliminated. A biosimilar is therefore never an exact molecular replica.
For this reason, biosimilarity must be demonstrated through analytical and functional evidence rather than inferred from clinical outcomes alone. In practice, regulators assess similarity primarily by comparing critical quality attributes and biological activity across multiple dimensions.
Biosimilarity is established by analytics and functional data, not assumed from clinical efficacy.
Regulators now treat analytical similarity as the primary evidence supporting biosimilar approval. Both the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) increasingly allow reduced comparative clinical efficacy studies when analytical and functional similarity are convincingly demonstrated.
This shift reflects a regulatory consensus that sensitive analytical methods are better at detecting product differences than late-stage clinical endpoints. Recent guidance documents and reflection papers explicitly position analytics as the foundation of the totality-of-evidence framework for biosimilars.In practical terms, weak analytics cannot be compensated for by larger or longer clinical studies. If analytical uncertainty remains unresolved, it becomes a structural regulatory risk rather than a data gap.
Here we explore the common analytical gaps that derails biosimilar drug development projects during early and late stages.
Incomplete glycosylation characterization remains one of the most common and consequential gaps in biosimilar programs. Teams often rely on limited methods or single profiles that fail to capture structural complexity.
Glycosylation directly influences pharmacokinetics, effector function, stability, and immunogenicity. When heterogeneity is superficially assessed, regulators are left with unresolved questions about clinical relevance and process control.
To close this gap, developers should apply orthogonal analytical approaches rather than a single glycan assay. Released glycan analysis should be complemented with site-specific, intact, or subunit-level methods. Just as importantly, glycosylation should be benchmarked quantitatively against multiple reference product lots collected over time.
Regulators expect clear identification of glycoforms that qualify as critical quality attributes, along with a scientific rationale linking observed differences to functional impact or demonstrating lack of impact. Overlap alone is insufficient. Variability must be shown to be controlled and comparable.
Another frequent gap arises when functional assays do not reflect the therapeutic mechanism of action. Generic binding assays are often used because they are simple to implement, yet they frequently lack the sensitivity needed to detect clinically meaningful differences.
When a biosimilar has multiple biological activities, reliance on a single assay leaves entire mechanisms untested. This creates uncertainty even if structural similarity appears strong.
Closing this gap requires designing assays that are explicitly mechanism-of-action relevant. Where multiple biological functions exist, a panel of complementary functional assays is usually necessary. Assay sensitivity should be demonstrated using stressed material, known variants, or engineered differences.
Regulators expect validated, discriminatory assays with a clear justification for why each assay was selected. Functional results should align with structural data rather than exist as isolated evidence.
Similarity margins are sometimes defined visually or based on limited datasets. While this may appear reasonable early on, it undermines regulatory confidence once submissions are reviewed in detail.
Unjustified ranges make it difficult for regulators to distinguish natural reference variability from meaningful product differences. As a result, similarity conclusions appear subjective rather than evidence-based.To address this, developers should analyze a statistically meaningful number of reference product lots collected over time. Visual overlap should be replaced with appropriate statistical methods, and acceptance criteria should be aligned with biological relevance rather than convenience.
Regulators expect transparent statistical methodology, consistency across datasets, and clear evidence that observed differences fall within natural reference variability.
Assessing a critical quality attribute with only one analytical technique is another recurring weakness. Even highly sophisticated methods capture only part of the molecular picture.Single-method approaches increase the risk of missing subtle but clinically relevant differences. They also weaken the robustness of similarity conclusions.
Developers should use orthogonal and complementary techniques for each critical quality attribute wherever feasible. When cross-validation is possible, converging evidence strengthens regulatory confidence. If only one method is used, a clear scientific justification is required.
Regulators look for multiple lines of evidence rather than reliance on a single result.
Defining critical quality attributes too late in development forces analytical teams into reactive mode. At that stage, methods are built to justify decisions rather than guide them.
Early CQA definition allows analytical strategy, functional assays, and process development to evolve coherently. It also reduces the risk that late discoveries will trigger redesign.
Regulators expect a science-based, risk-driven CQA definition strategy that aligns analytical attributes with functional relevance and clinical impact.
Analytical data sometimes lacks a clear clinical narrative, even when the data itself is robust. This disconnect makes it difficult for regulators to assess biosimilarity as a totality of evidence.
To resolve this, analytical attributes should be explicitly mapped to mechanism of action, pharmacokinetics, and known clinical drivers. Functional and PK data should be used to support arguments that remaining differences are not clinically meaningful.
Regulators expect a coherent story linking molecular similarity to patient-level outcomes, even when clinical efficacy studies are reduced or waived.
In biosimilar development, analytical and functional studies carry substantially more regulatory weight than comparative clinical efficacy trials.
|
Aspect |
Analytical testing |
Clinical efficacy studies |
|
Sensitivity to differences |
High |
Low |
|
Detects subtle molecular changes |
Yes |
No |
|
Drives regulatory similarity decision |
Yes |
No |
|
Can replace other studies |
Yes |
No |
|
Role in approval |
Foundational |
Confirmatory |
Analytical gaps become structural once development is underway. Cell lines, manufacturing processes, and comparability strategies are typically fixed early, leaving little room for correction later.
When gaps are discovered late, teams often face repeated studies, revised acceptance criteria, or changes to regulatory strategy. These fixes increase cost and extend timelines without improving scientific certainty.
Early analytical rigor reduces regulatory risk by preventing these downstream consequences.
Crystal Bio Solutions builds regulatory-ready analytical packages early in development. This includes structural characterization, functional assay development, and statistically robust similarity strategies aligned with FDA and EMA expectations.
Frequently asked questions about biosimilar drug development
What is an analytical gap in a biosimilar program?
An analytical gap is missing, insufficient, or non-discriminatory evidence showing that a biosimilar matches the reference product in critical quality attributes and biological function.
Can analytics reduce or replace comparative clinical efficacy studies?
Regulators increasingly rely on sensitive analytical and functional data to establish similarity. Clinical efficacy endpoints are usually less sensitive to subtle product differences.
What are common mistakes during biosimilar development?
Frequent issues include incomplete glycosylation characterization, weak or non-mechanistic functional assays, and similarity ranges that lack statistical justification.
How many reference product lots are needed to justify variability ranges?
There is no fixed number. Regulators expect a statistically defensible set of lots collected over time that captures natural variability.
What makes a functional assay fit for biosimilarity?
The assay should be mechanism-of-action relevant, discriminatory, validated, and able to detect meaningful differences using appropriate controls.
When should critical quality attributes be defined?
As early as possible, ideally before key process decisions are locked, and refined as understanding evolves.
Why are late fixes so costly?
Once manufacturing and comparability plans are fixed, closing analytical gaps may require repeating studies or revising regulatory strategy.
How can analytical gaps be avoided from the start?
Use orthogonal methods, build mechanism-based functional assay panels, define CQAs early, and justify similarity ranges statistically using sufficient reference lot history.
Written by Crystal Bio Solutions Scientific Team, February 2026.