O224: What is in the pipeline for treatment of Hepatitis B? (IFN‐lambda, TLR‐agonists, entry inhibitors etc.)
Bibliographic record
Abstract
Traditionally chronic hepatitis B has been treated with antiviral agents (nucleos(t)ide analogues) and immune modifying treatment (Peg interferon). The latest antiviral agents are very potent, carry a low rate of resistance and appear to be safe in the mid-term. These potent therapies, such as Entecavir and Tenofovir, give an outstanding viral control but no true immune control over the virus. Therefore, the likelihood that we have to give these agents for decades if not lifelong is rather high. Peg interferon can be given for shorter duration of about 1 year but is associated with side effects and should be administered subcutaneously. These drawbacks mean that we have to look for better treatments to reach immune control and if possible even eradication of chronic hepatitis B. One way is to combine our strongest available antivirals with Peg interferon in different regimens. The CCC DNA of HBV which harbors in the nucleus of the hepatocyte is the template of viral replication and could be the target for several new compounds. Approaches from the molecular side are entry inhibitors (Myrcludex B). Myrcludex B is currently studied in chronic HBV patients. Other new treatments target the immune response. Peg interferon lambda is a new immune modifying drug with less side effects than Peg interferon α because it targets the liver in a more specific way. Studies are currently ongoing comparing Peg interferon lambda to Peg interferon α. Different approaches to stimulate the innate or adaptive immune response are the use of toll like receptor agonists and therapeutic vaccination.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.044 |
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".