Multivalent CXCR4-targeting nanobody formats differently affect affinity, receptor clustering, and antagonism
Bibliographic record
Abstract
The chemokine receptor CXCR4 is involved in the development and migration of stem and immune cells but is also implicated in tumor progression and metastasis for a variety of cancers. Antagonizing ligand (CXCL12)-induced CXCR4 signaling is, therefore, of therapeutic interest. Currently, there are two small-molecule CXCR4 antagonists on the market for the mobilization of hematopoietic stem cells. Other molecules with improved potencies and safety profiles are being developed for different indications, including cancer. Moreover, multiple antagonistic nanobodies targeting CXCR4 displayed similar or better potencies as compared to the CXCR4-targeting molecule AMD3100 (Plerixafor), which was further enhanced through avid binding of bivalent derivatives. In this study, we aimed to compare the affinities of various multivalent nanobody formats which might be differently impacted by avidity. By fusion to a flexible GS-linker, Fc-region of human IgG1, different C4bp/CLR multimerization domains, or via site-directed conjugation to a trivalent linker scaffold, we generated different types of multivalent nanobodies with varying valencies ranging from bivalent to decavalent. Of these, C-terminal fusion, especially to human Fc, was most advantageous with a 2-log-fold and 3-log-fold increased potency in inhibiting CXCL12-mediated Gαi- or β-arrestin recruitment, respectively. Overall, we describe strategies for generating multivalent and high-potency CXCR4 antagonistic nanobodies able to induce receptor clustering and conclude that fusion to an Fc-tail results in the highest avidity effect irrespective of the hinge linker.
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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) | 0.001 | 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".