SynthesisCPT: pharmacometrics & systems pharmacology2026
A Systematic Review and Model-Based Meta-Analysis of Pegylated-Interferon-α-Induced HBsAg Loss in Chronic Hepatitis B Virus Infection.
Synthesis in CPT: pharmacometrics & systems pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review and Model-Based Meta-Analysis of Pegylated-Interferon-α-Induced HBsAg Loss in Chronic Hepatitis B Virus Infection.CPT: pharmacometrics & systems pharmacology · 2026Pooled it
- Current State-of-the-Art and Future Advancements of Model-Based Meta-Analysis.CPT: pharmacometrics & systems pharmacology · 2026Article
- Beyond viral suppression: combining PEG-interferon with novel immunotherapies for functional cure of chronic hepatitis B.Frontiers in cellular and infection microbiology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Pegylated-interferon-α (Peg-IFNα) is a treatment option for chronic hepatitis B virus (HBV) infection. To quantify treatment response variability, we conducted a model-based meta-analysis (MBMA) of hepatitis B surface antigen (HBsAg) loss, defined as a binary outcome based on HBsAg levels falling below the limit of detection, with Peg-IFNα-based regimens at end-of-treatment (EOT) and 24 weeks post-treatment. A systematic review of HBsAg loss in chronic HBV infection was performed, searching Embase, MEDLINE, and Cochrane (January 2000-July 2022). Studies reporting only per-protocol results were excluded; intent-to-treat (ITT) or modified ITT results were prioritized. Models described the proportion achieving HBsAg loss with respect to treatment regimens, exploring baseline clinical and demographic covariates. For the EOT model, 83 study-strata-arms (11,493 participants) were included; for the 24-week model, 58 study-strata-arms (4267 participants) were included. In both models, Peg-IFNα duration and baseline HBsAg significantly predicted HBsAg loss (p < 0.001); baseline hepatitis B e-antigen (HBeAg) was an additional predictor at EOT (p = 0.007). These covariates reduced between-trial variance by 58.1% (EOT) and 77.6% (24-week), highlighting their role in explaining heterogeneity. This MBMA supports clinical trial design by simulating outcomes with Peg-IFNα across diverse populations, optimizing trial parameters, estimating sample sizes, and informing enrichment strategies. Notably, these findings have been applied to calibrate and validate in silico trials, demonstrating utility in advancing computational approaches for HBV drug development. This approach enhances precision in predicting treatment outcomes and sets a precedent for leveraging MBMA in chronic hepatitis B research, paving the way for more effective strategies.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.