Current and future use of antibody-based passive immunity to prevent or control HBV/HDV infections
Bibliographic record
Abstract
With the increasing momentum and success of monoclonal antibody therapy in conventional medical practices, there is a revived emphasis on the development of monoclonal antibodies targeting the hepatitis B surface antigen (anti-HBs) for the treatment of chronic hepatitis B (HBV) and hepatitis D (HDV). Combination therapies of anti-HBs monoclonal antibodies, and novel anti-HBV compounds and immunomodulatory drugs presenting a promising avenue to enhanced therapeutic outcomes in HBV/HDV cure regimens. In this review, we will cover the role of antibodies in the protection and clearance of HBV infection, the association of anti-HBV surface antigen antibodies (anti-HBs) in protection against HBV and how antibody effector functions, beyond neutralization, are likely necessary. Lastly, we will review clinical data from previous and ongoing clinical trials of passive antibody therapy to provide a state-of-the-are perspective on passive antibody therapies in combinations with additional novel agents. • Antibodies play a major role in resolution of HBV infection. • Neutralization alone is likely not sufficient to clear the viral reservoir. • Fc effector functions, including vaccinal effects could boost cellular immunity. • Rapid clearance of HBV antigens with monoclonal antibodies provide opportunities for combination therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".