Selective Chemokine Receptor-Targeted Depletion of Pathological Cells as A Therapeutic Strategy for Inflammatory, Allergic and Autoimmune Diseases
Bibliographic record
Abstract
Targeting cell surface antigens or receptors with lytic monoclonal antibodies and specific ligand-directed fusion proteins in order to eliminate cancer cells has been in development for at least forty years. More recently, leukocyte populations known to drive a host of allergic, autoimmune and inflammatory diseases have been targeted. For fusion protein constructs, a number of different classes of cellular toxins have been fused to a variety of ligands such as monoclonal antibodies, growth factors and cytokines. Although there has been great clinical success using these biologics, there are some limitations. The target antigens are often expressed on normal cells leading to side effects. More recently, several groups have explored the use of chemokine receptor ligands and antibodies to target leukocytes and cancer cells. There are a number of inducible chemokine receptors that are only up-regulated in inflammation and their expression is relatively restricted to pathological cells. This confers another degree of specificity on biologics that are composed of chemokine receptor targeting agents. This review discusses articles, recent patents and patent applications that explore the selective depletion of pathological cells by targeting chemokine receptors with chemokine ligands, monoclonal antibodies and different bispecific constructs as a therapeutic strategy for allergic, autoimmune and inflammatory diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".