Bulevirtide als erster spezifischer Wirkstoff gegen Hepatitis-D-Virusinfektionen – Mechanismus und klinische Wirkung
Bibliographic record
Abstract
Blocking the cell entry of pathogens is a suitable approach to prevent new infections. However, the therapeutic use of entry inhibitors in chronically infected patients has had limited success. For the treatment of chronic hepatitis D virus (HDV) infections, a promising agent based on this mode of action, Bulevirtide (BLV), was conditionally approved in July 2020. Previously, no drugs were available for HDV, and treatment relied on off-label use of interferon alpha/peginterferon alpha (IFNα/Peg-IFNα). In this review, we provide an overview of the basic mechanism of action of BLV and summarize the clinical data available to date.HDV infection manifests as a co-infection or superinfection of hepatitis B virus (HBV) infections and affects 4.5-15% of HBV patients worldwide. HDV utilizes the envelope proteins of HBV for dissemination. BLV acts by blocking the HBV/HDV receptor sodium taurocholate co-transporting polypeptide (NTCP), preventing HBV/HDV entry into hepatocytes. BLV lowers HDV serum RNA levels and normalizes alanine aminotransferase (ALT) levels in HBV/HDV-infected individuals. It has an excellent safety profile, even when administered at high doses (10 mg daily) for 48 weeks. In combination with Peg-IFNα, BLV shows synergistic effects on lowering serum HDV RNA, but also on hepatitis B surface antigen (HBsAg) levels. This resulted in a functional cure in a subset of patients when 2 mg BLV plus Peg-IFNα was administered. The mechanism of this likely immune-mediated elimination will be investigated in follow-up studies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".