Bivalency of natalizumab promotes inhibition of dynamic VLA-4 adhesion beyond affinity gain
Bibliographic record
Abstract
Natalizumab, a monoclonal IgG4 antibody used in the treatment of multiple sclerosis (MS), inhibits α4β1 (very late antigen-4, VLA-4)-binding to vascular cell adhesion molecule-1 (VCAM-1), thereby reducing leukocyte recruitment to inflamed tissues. The intricate mechanisms underlying these effects remain unclear, particularly concerning the heavy-chain shuffling of IgG4. We conducted an in vitro study to quantify the impact of bivalent IgG and monovalent Fab forms of natalizumab on the capture and migration of human primary memory T lymphocytes under shear stress, using VCAM-1 and CXCL12-coated surfaces. IgG natalizumab at concentrations near its cell surface EC50 (half-maximal effective concentration) showed significant of capture and resistance of adherent cells to shear stress, whereas significantly higher doses of Fab natalizumab, up to 100-fold greater than its cell surface EC50, were needed to achieve similar effects. These findings highlight that receptor occupancy alone may not adequately predict the functional outcomes of inhibitor antibodies. For optimal therapeutic effect, inhibition of cell-surface adhesion may require specific kinetic and geometric binding properties of antibodies. Insight Box This study shows how natalizumab functionally inhibits α4β1 (very late antigen-4, VLA-4)-mediated T-cell adhesion by integrating quantitative affinity measurements with dynamic assays performed under shear stress. We show that the antibody's bivalency provides a major functional advantage, enabling strong inhibition of cell capture and adhesion at concentrations near its EC50 (half-maximal effective concentration), whereas monovalent Fab fragments require 10- to 100-fold higher doses. These findings offer biological insight into why receptor occupancy alone cannot predict adhesion inhibition. By combining cell-based affinity quantification with real-time adhesion and migration assays, our work reveals how kinetic and geometric properties of antibody-integrin interactions shape immune cell dynamics, informing the rational design of next-generation integrin-targeting therapeutics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".