Antibody-Mediated Depletion of Autoreactive T Lymphocytes through PD-1 Improves Disease Outcomes and Visualizes T Cell Activation in Experimental Autoimmune Encephalomyelitis
Bibliographic record
Abstract
Long-term therapeutic outcomes of multiple sclerosis (MS) remain hindered by the chronic nature of immune cell stimulation toward self-antigens. Development of novel methods to target and deplete autoreactive T lymphocytes remains an attractive target for therapeutics for MS. We developed a programmed cell death 1 (PD-1)-targeted radiolabeled mAb and assessed its ability to deplete activated PD-1+ T lymphocytes in vitro and its ability to reduce disease burden of the myelin oligodendrocyte glycoprotein 35-55 experimental autoimmune encephalomyelitis (EAE) model in C57BL/6 mice. We also investigated the upregulation of PD-1 on infiltrating lymphocytes in an animal model of MS. Finally, we demonstrate the (to our knowledge) first reported positron-emission tomography/computed tomography imaging of activated PD-1+ cells in the EAE animal model of MS. We found that the 177Lu radioisotope-labeled anti-PD-1 mAb demonstrated significant in vitro cytotoxicity toward activated CD4+PD-1+ T lymphocytes and led to significant reduction in overall disease progression in the EAE animal model. Our results show high expression of PD-1 on infiltrating lymphocytes in the spinal cords of EAE diseased animals. Positron-emission tomography/computed tomography imaging of the anti-PD-1 mAb demonstrated significant uptake in the cervical draining lymph nodes highlighting accumulation of activated lymphocytes. Targeted depletion of T lymphocytes using T cell activation markers such as PD-1 may present a novel method to reduce autoimmune attack and inflammation in autoimmune diseases such as MS. Development of multimodal nuclear theranostic agents may present the opportunity to monitor T cell activation via imaging radioisotopes and simultaneously treat MS using therapeutic radioisotopes.
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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) | 0.000 | 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 | 0.001 |
| 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".