Humanized anti-CD11d monoclonal antibodies suitable for basic research and therapeutic applications
Bibliographic record
Abstract
Abstract Background Immunomodulatory agents targeting the CD11d/CD18 integrin are in development for the treatment of several pathophysiologies including neurotrauma, sepsis, and atherosclerosis. Murine anti-human CD11d therapeutic antibodies have successfully improved neurological and behavioral recovery in rodent neurotrauma models. Here, we present the progression of CD11d-targeted agents with the development of humanized anti-CD11d monoclonal antibodies. Methods Primary human leukocytes and the THP-1 monocytic cell line were used to determine the binding of the CD11d antibodies, determine binding affinities, and assess outside-in signaling induced by CD11d antibody binding. In addition, a rat model of spinal cord injury was employed to demonstrate that the humanized monoclonal antibodies retained their therapeutic function in vivo. These determinations were made using a combination of flow cytometry, western blotting, immunohistochemistry, biochemical assays, and a locomotor behavioral assessment. Results Flow cytometric analysis demonstrated that the humanized anti-CD11d clones bind both human monocytes and neutrophils. Using a THP-1 model, the humanized anti-CD11d-2 clone was then determined to bind both the active and inactive CD11d/CD18 conformations without inducing inflammatory cell signaling. Finally, an investigation using anti-CD11d-2 as a detection tool uncovered a mismatch between total and surface-level CD11d and CD18 expression that was not altered by CK2 inhibition. Conclusions By developing humanized anti-CD11d monoclonal antibodies, new tools are now available to study CD11d biology and potentially treat inflammation arising from acute neurotrauma via CD11d targeting.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".