Chemical treatment of anti‐D results in improved efficacy for the inhibition of Fcγ receptor–mediated phagocytosis
Bibliographic record
Abstract
BACKGROUND: This study investigated whether treatment of immunoglobulins anti-D or intravenous immune globulin (IVIG) with chemicals previously shown to inhibit phagocytosis could result in an enhancement of Fcgamma receptor (FcgammaR) blockade in vitro. If successful, this approach may provide the possibility of targeting these chemicals to monocyte-macrophages for increased efficacy of immunoglobulin-based therapies in vivo. STUDY DESIGN AND METHODS: For proof-of-concept, the chemical thimerosal, a prototype FcgammaR inhibitor, was combined with RhIG or IVIG. Residual chemical was removed by extensive dialysis. With a monocyte monolayer assay (MMA) and a concentration of immunoglobulin alone that results in 50 percent inhibition of MMA phagocytosis of antibody-coated red blood cells, the effect of thimerosal treatment on the ability of the immunoglobulin to show a significant enhancement of efficacy was determined. RESULTS: It is shown that combining thimerosal with anti-D, either slide and rapid tube or commercially available (WinRho SDF, Cangene), results in a highly significant increase in efficacy over anti-D alone to inhibit phagocytosis in vitro. This effect was not due to residual unbound compound or to cellular toxicity of the chemically treated immunoglobulins. Treatment of IVIG with thimerosal had no significant effect on its ability to inhibit in vitro phagocytosis. CONCLUSION: Our results indicate that it is possible to modify an immunoglobulin by chemical treatment such that the treated immunoglobulin demonstrates significantly enhanced ability to inhibit FcgammaR-mediated phagocytosis. It is also demonstrated that IVIG and anti-D appear to respond differently after chemical treatment. Further examination of this strategy is warranted and has the potential to reduce the dose, cost, and possibly, adverse effects of immunoglobulin-based therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".