Cell-based passive immunization for protection against SARS-CoV-2 infection
Bibliographic record
Abstract
BACKGROUND: Immunologically impaired individuals respond poorly to vaccines, highlighting the need for additional strategies to protect these vulnerable populations from COVID-19. While monoclonal antibodies (mAbs) have emerged as promising tools to manage infectious diseases, the transient lifespan of neutralizing mAbs in patients limits their ability to confer lasting, passive prophylaxis from SARS-CoV-2. Here, we attempted to solve this problem by combining cell and mAb engineering in a way that provides durable immune protection against viral infection using safe and universal cell therapy. METHODS: Mouse embryonic stem cells equipped with our FailSafe™ and induced allogeneic cell tolerance technologies were engineered to express factors that potently neutralize SARS-CoV-2, which we call 'neutralizing biologics' (nBios). We subcutaneously transplanted the transgenic cells into mice and longitudinally assessed the ability of the cells to deliver nBios into circulation. To do so, we quantified plasma nBio concentrations and SARS-CoV-2 neutralizing activity over time in transplant recipients. Finally, using similar cell engineering strategies, we genetically modified FailSafe™ human-induced pluripotent stem cells to express SARS-CoV-2 nBios. RESULTS: Transgenic mouse embryonic stem cells engineered for safety and allogeneic-acceptance can secrete functional and potent SARS-CoV-2 nBios. As a dormant, subcutaneous tissue, the transgenic cells and their differentiated derivatives long-term deliver a supply of protective nBio titers in vivo. Moving toward clinical relevance, we also show that human-induced pluripotent stem cells, similarly engineered for safety, can secrete highly potent nBios. CONCLUSIONS: Together, these findings show the promise and potential of using 'off-the-shelf' cell products that secrete neutralizing antibodies for sustained protective immunity against current and future viral pathogens of public health significance.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".