Getting to HBV cure: The promising paths forward
Bibliographic record
Abstract
Chronic HBV infection is a global public health burden estimated to impact nearly 300 million persons worldwide. Despite the advent of potent antiviral agents that effectively suppress viral replication, HBV cure remains difficult to achieve because of the persistence of covalently closed circular DNA (cccDNA), HBV-DNA integration into the host genome, and impaired immune response. Indefinite treatment is necessary for most patients to maintain level of viral suppression. The success of direct-acting antivirals (DAAs) for hepatitis C treatment has rejuvenated the search for a cure for chronic hepatitis B (CHB), though an HBV cure likely requires an additional layer: immunomodulators for restoration of robust immune responses. DAAs such as entry inhibitors, capsid assembly modulators, inhibitors of subviral particle release, cccDNA silencers, and RNA interference molecules have reached clinical development. Immunomodulators, namely innate immunomodulators (Toll-like receptor agonists), therapeutic vaccines, checkpoint inhibitors, and monoclonal antibodies, are also progressing toward clinical development. The future of the HBV cure possibly lies in triple combination therapies with concerted action on replication inhibition, antigen reduction, and immune stimulation. Many obstacles remain, such as overcoming translational failures, choosing the right endpoint using the right biomarkers, and leveraging current treatments in combination regimens to enhance response rates. This review gives an overview of the current therapies for CHB, HBV biomarkers used to evaluate treatment response, and development of DAAs and immune-targeting drugs and discusses the limitations and unanswered questions on the journey to an HBV cure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".