Review article: clinical pharmacology of current and investigational hepatitis B virus therapies
Bibliographic record
Abstract
BACKGROUND: Treatment of hepatitis B virus (HBV) infection with current therapy suppresses HBV DNA, but loss of hepatitis B surface antigen (HBsAg; functional cure), is rare. Multiple compounds are under investigation. AIMS: To describe the pharmacology, including drug interactions, efficacy, safety and mechanisms of action of investigational compounds for HBV infection. METHODS: Descriptive review using PubMed and Google to identify literature/conference papers on investigational compounds (≥Phase 2) with data on efficacy and safety in HBV-infected patients. RESULTS: UI/mL) HBsAg in 85% of patients (12 weeks treatment). There are some safety concerns with investigational agents (e.g., increased bile acids with bulevirtide, and liver enzyme flares with REP-2139) which will require a risk benefit assessment compared with current therapies. Single and multidose pharmacokinetic data are available for bulevirtide, JNJ-56136379, ABI-H0731; no such data are available for REP-2139, ARO-HBV, inarigivir. Initial drug interaction assessments have been performed with bulevirtide and inarigivir (only in vitro). CONCLUSIONS: There are promising investigational therapies for HBV infection. Increasing the potential for HBsAg loss may result in more patients achieving functional cure. However, many knowledge gaps remain such as pharmacokinetics in those with HBV, cirrhosis and renal impairment but also the interaction potential between investigational therapies, risk-benefit profiles, and potential for drug interactions with medications used to treat comorbidities associated with aging.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".