Rapid In Vivo Screening of Monoclonal Antibody Cocktails Using Hydrodynamic Delivery of DNA-Encoded Modified Antibodies
Bibliographic record
Abstract
Background: Monoclonal antibodies (mAbs) are potent treatment options for infectious diseases. The rapid isolation and in vivo validation of therapeutic mAb candidates, including mAb cocktails, are essential to combat novel or rapidly mutating pathogens. The rapid selection and production of mAb candidates in sufficient amount and quality for preclinical studies are a major limiting step in the mAb development pipeline. Methods: Here, we developed a method to facilitate the screening of therapeutic mAbs in mouse models. Four conventional mAbs were transformed into single-chain variable fragments fused to the fragment crystallizable (Fc) region of a human IgG1 (scFv-IgG). These scFv-IgG were expressed individually or as a cocktail in vitro and in mice following transfection or hydrodynamic delivery of the corresponding plasmids. Results: This method induced high expression of all scFv-IgG and provided protection in two murine infection models. Conclusions: This study highlights the benefits of this approach for the rapid, low-cost screening of therapeutic mAb candidates.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".