Rapid characterization of structural and functional similarity for a candidate bevacizumab (Avastin) biosimilar using a multipronged mass‐spectrometry‐based approach
Bibliographic record
Abstract
The ongoing shift from small molecule drugs to protein therapeutics in the pharmaceuticals industry presents a considerable challenge to generic drug developers who are increasingly required to demonstrate biosimilarity for biological macromolecules, a task that is decidedly more complex than doing the same for small molecule drugs. In this work, we demonstrate a multipronged mass-spectrometry-based workflow that allows rapid and facile molecular characterization of antibody-based protein therapeutics, applied to biosimilars development. Specifically, we use a combination of native mass spectrometry (MS), ion mobility spectrometry (IMS), and global time-resolved hydrogen deuterium exchange (HDX) to provide an unambiguous assessment of the structural, dynamic, and chemical similarity between Avastin (bevacizumab) and a biosimilar in the late stages of pre-clinical development. Minor structural and dynamic differences between the biosimilar and Avastin, and between lots of the biosimilar, were tested for functional relevance using Surface Plasmon Resonance-derived kinetic and equilibrium binding parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".