Antagonism of the Platelet-Activating Factor Pathway Mitigates Inflammatory Adverse Events Driven by Anti-erythrocyte Antibody Therapy in Mice
Bibliographic record
Abstract
Immune thrombocytopenia (ITP) is an autoimmune disease characterized by low platelet counts primarily due to antiplatelet autoantibodies. Anti-D is a donor-derived polyclonal Ab against the rhesus D Ag on erythrocytes used to treat ITP. Unfortunately, adverse inflammatory/hypersensitivity reactions and a Food and Drug Administration-issued black box warning have limited its clinical use. This underscores the imperative to understand the inflammatory pathway associated with anti-erythrocyte Ab-based therapies. TER119 is an erythrocyte-specific Ab with anti-D-like therapeutic activity in murine ITP, while also exhibiting a distinct inflammatory signature involving production of CCL2, CCL5, and CXCL9 but not IFN-γ. Therefore, TER119 has been used to elucidate the potential mechanism underlying the adverse inflammatory activity associated with anti-erythrocyte Ab therapy in murine ITP. Prior work has demonstrated that TER119 administration is associated with a dramatic decrease in body temperature and inflammatory cytokine/chemokine production. The work presented in the current study demonstrates that inhibiting the highly inflammatory platelet-activating factor (PAF) pathway with PAF receptor antagonists prevents TER119-driven changes in body temperature and inhibits the production of the CCL2, CCL5, and CXCL9 inflammatory cytokines in CD-1 mice. Phagocytic cells and a functional TER119 Fc region were found to be necessary for TER119-induced body temperature changes and increases in CXCL9 and CCL2. Taken together, this work reveals the novel requirement of the PAF pathway in causing adverse inflammatory activity associated with anti-erythrocyte Ab therapy in a murine model and provides a strategy of mitigating these potential reactions without altering therapeutic activity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".