Surreal illustration symbolizing the journey to hepatitis B remission, with graphs representing treatment trajectories.

Decoding Hepatitis B: Can We Predict Treatment Success?

"New research identifies patterns in hepatitis B surface antigen levels that may help predict the effectiveness of long-term antiviral therapy, offering hope for more personalized treatment approaches."


Chronic hepatitis B (CHB) is a significant global health challenge, particularly in endemic regions. While nucleos(t)ide analogues (NAs) have revolutionized treatment, effectively suppressing the hepatitis B virus (HBV), the long-term outcomes remain variable. A key goal of therapy is the loss of hepatitis B surface antigen (HBsAg), indicating viral clearance, but this is rarely achieved with NA therapy alone.

The challenge lies in predicting which patients will respond best to long-term NA treatment. Traditional approaches often fall short, highlighting the need for more sophisticated methods to assess treatment efficacy. Researchers have been exploring the potential of monitoring HBsAg levels over time to identify patterns that correlate with treatment success.

This article delves into a recent study that used group-based trajectory models (GBTMs) to analyze HBsAg kinetics in patients receiving long-term NA therapy. By identifying distinct patterns of HBsAg decline, the study aims to provide clinicians with a more accurate tool for predicting treatment outcomes and tailoring therapy to individual patients.

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HBsAg Kinetics and Functional Cure

HBsAg loss or seroconversion is considered the ideal endpoint of antiviral therapy and the ultimate treatment goal in chronic hepatitis B. Baseline HBsAg levels are associated with the likelihood of achieving HBsAg loss, which defines a functional cure. However, optimal HBsAg cut-offs for predicting HBsAg loss have not been systematically established, and research has identified a need to analyze HBsAg trends and forecast the time to seroclearance during nucleos(t)ide analogue therapy.

From Seroconversion to Functional Cure

HBsAg seroclearance and HBeAg seroconversion are described as critical immunologic milestones in the natural history and treatment of chronic hepatitis B. HBeAg seroconversion generally reflects the initial establishment of immune-mediated control over viral replication and is considered an important prerequisite for subsequent HBsAg decline. More recent work has focused on HBsAg trajectories during long-term nucleos(t)ide analogue therapy and their association with long-term treatment outcomes, supporting the use of varying HBsAg levels as watersheds for personalized management and clinical-trial screening.

Unlocking Treatment Success: Tracking HBsAg Trajectories

Surreal illustration symbolizing the journey to hepatitis B remission, with graphs representing treatment trajectories.

The study enrolled 329 treatment-naive CHB patients, infected with either genotype B or C, who received NA therapy for at least five years. Researchers used GBTMs to identify distinct patterns in their HBsAg levels over time. The findings revealed three distinct groups of patients based on their HBsAg kinetics, both for those who were hepatitis B e antigen (HBeAg)-positive and HBeAg-negative.

The research team discovered that the rate of HBsAg decline in the first five years of treatment varied significantly among the groups. Patients in Group 1 experienced a much faster decline compared to Groups 2 and 3. Notably, HBsAg levels at baseline and at 12 months, combined with the decline in HBsAg during the first year, were strong predictors of which trajectory a patient would follow.

  • HBeAg-Positive Patients: An HBsAg decline of >30% from baseline at 12 months, combined with an HBsAg level of <100 IU/mL at 12 months, predicted trajectory pattern 1 with high accuracy.
  • HBeAg-Negative Patients: A baseline HBsAg level of <1000 IU/mL, along with an HBsAg decline of >50% from baseline at 12 months and an HBsAg level of <200 IU/mL at 12 months, effectively predicted trajectory pattern 1.
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New Predictive Biomarkers

Recent research is examining whether HBsAg glycan isomers can predict on-treatment HBsAg decline in patients with low-HBsAg chronic hepatitis B receiving nucleos(t)ide therapy. The glycan isomer may serve as a complementary qualitative marker reflecting the cccDNA-dependent, replication-linked component of antigen production in patients with high HBsAg levels. Other work has evaluated the predictive value of hepatitis B virus pregenomic RNA and HBsAg kinetics in 65 HBeAg-positive children who received at least 96 weeks of nucleos(t)ide analogue therapy.

On-Treatment Versus Off-Treatment Control

Current hepatitis B therapy is effective at achieving on-treatment viral suppression, but the cited review distinguishes this from off-treatment viral suppression. Loss of HBsAg is identified as the best surrogate marker of off-treatment viral suppression. This distinction helps explain why treatment success cannot be judged solely by suppression achieved while therapy continues.

These trajectory patterns were significantly linked to key treatment outcomes. For HBeAg-positive patients, following trajectory pattern 1 was associated with higher rates of HBeAg loss. Both HBeAg-positive and HBeAg-negative patients in Group 1 were more likely to achieve HBsAg levels below 100 IU/mL or even HBsAg loss, which is the ultimate goal of treatment.

A New Era of Personalized Hepatitis B Treatment?

This study offers a promising step toward personalized management of chronic hepatitis B. By using GBTMs to analyze HBsAg kinetics, clinicians may be able to identify patients who are most likely to benefit from long-term NA therapy and tailor their treatment strategies accordingly.

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Toward Novel Therapies

A 2025 horizon-scanning review describes chronic hepatitis B as a major global public health challenge affecting over 254 million individuals, with substantial mortality linked to the limited curative potential of current therapies. It surveys emerging trends and future directions in the development of novel anti-hepatitis B virus therapeutics. One highlighted study evaluated the nucleic acid polymer REP 2139 in treatment-naive participants who were HBeAg-negative, anti-HDAg-positive, HDV RNA-positive, had serum HBsAg concentrations exceeding 1000 IU/mL, and had a body mass index above 16 kg/m2.

The ability to predict treatment outcomes early on could help avoid unnecessary long-term treatment for patients who are unlikely to achieve HBsAg loss. This could reduce the risk of drug resistance, minimize side effects, and lower healthcare costs. It also allows for exploring alternative treatment options, such as combination therapies or immunomodulatory agents, for those predicted to have a less favorable response.

While these findings are encouraging, further research is needed to validate these results in larger, more diverse populations and to explore the long-term clinical implications of trajectory-based treatment strategies. However, this study provides a valuable framework for future research and may ultimately lead to improved outcomes for patients with chronic hepatitis B.

What the trajectories add-and do not establish

The pattern distinguishes a group with faster antigen decline from two groups with slower declines in both HBeAg categories. These are group-level patterns, so they describe differences among patients rather than proving an individual patient's future outcome.[1]

Early measurements classified patients into trajectory pattern 1 with high sensitivity and specificity in this study; that classification is not itself evidence that the pattern causes later antigen loss.[1]

Long-term HBsAg trajectories modeled in chronic hepatitis B

Sample size329[1]
Main outcomeSerum HBsAg kinetic trajectory patterns and association with therapeutic outcomes[1]
Effect sizeMedian annual HBsAg decline: HBeAg-positive, 0.78 vs 0.10 vs 0.10 log10IU/mL; HBeAg-negative, 0.71 vs 0.08 vs 0.09 log10IU/mL[1]
Times cited14[1]

How this trajectory study relates to other HBsAg work

An earlier entecavir study reported a predictive algorithm for HBsAg loss using a baseline threshold and a large early decline. Its endpoint was HBsAg loss, whereas the present study assessed trajectory-pattern prediction, so the two algorithms address related but distinct outcomes.[1] [2]

What follow-up evidence would clarify

  • Would trajectory classification perform similarly in patients with genotypes beyond those represented in the study?[1]

Terms used in the trajectory analysis

Group-based trajectory model
A method for identifying clusters with similar changes in an indicator over time.[1] [3]
HBsAg loss
An outcome predicted by serum HBsAg trajectory in chronic hepatitis B patients receiving long-term analogue therapy.[1]

About this Article -

Written with AI assistance from published research, and reviewed by the Mystum team. See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1111/liv.13564, Alternate LINK

Title: Trajectories Of Serum Hepatitis B Surface Antigen Kinetics In Patients With Chronic Hepatitis B Receiving Long-Term Nucleos(T)Ide Analogue Therapy

Subject: Hepatology

Journal: Liver International

Publisher: Wiley

Authors: Wei-Fan Hsu, Chuen-Fei Chen, Hsueh-Chou Lai, Wen-Pang Su, Chia-Hsin Lin, Po-Heng Chuang, Sheng-Hung Chen, Ching-Hsiang Chen, Hung-Wei Wang, Guan-Tarn Huang, Cheng-Yuan Peng

Published: 2017-09-15

Everything You Need To Know

1

What is hepatitis B surface antigen (HBsAg), and why is it important in understanding hepatitis B treatment?

The hepatitis B surface antigen (HBsAg) is a protein found on the surface of the hepatitis B virus (HBV). Its presence in the blood indicates an active HBV infection. Monitoring HBsAg levels is crucial because they correlate with the viral load and the overall disease activity in chronic hepatitis B (CHB). Declining HBsAg levels often signal a positive response to antiviral therapy, making it a key marker for assessing treatment success and predicting long-term outcomes.

2

What are group-based trajectory models (GBTMs), and how were they used in the study?

Group-based trajectory models (GBTMs) are statistical methods used to identify distinct patterns of change over time within a group of individuals. In the context of chronic hepatitis B (CHB), researchers used GBTMs to analyze how hepatitis B surface antigen (HBsAg) levels changed over time in patients undergoing nucleos(t)ide analogues (NAs) therapy. By identifying different trajectories of HBsAg decline, the study aimed to predict which patients would respond best to the treatment. This approach provides a more sophisticated way to assess treatment efficacy compared to traditional methods.

3

What is the role of nucleos(t)ide analogues (NAs) in treating chronic hepatitis B (CHB), and what challenges remain?

Nucleos(t)ide analogues (NAs) are antiviral medications commonly used to treat chronic hepatitis B (CHB). They work by suppressing the hepatitis B virus (HBV), preventing it from replicating and causing further liver damage. The study highlights that while NAs are effective in suppressing HBV, achieving hepatitis B surface antigen (HBsAg) loss (viral clearance) remains a challenge. Monitoring HBsAg levels and using group-based trajectory models (GBTMs) can help clinicians understand which patients are most likely to benefit from long-term NA therapy and to personalize treatment.

4

How did the study identify predictors of treatment success for chronic hepatitis B?

Predicting treatment success involves identifying which patients will respond favorably to antiviral therapy for chronic hepatitis B (CHB). The study found that the decline in hepatitis B surface antigen (HBsAg) levels, particularly in the first 12 months of nucleos(t)ide analogues (NAs) therapy, is a strong predictor of long-term outcomes. For example, in hepatitis B e antigen (HBeAg)-positive patients, an HBsAg decline of >30% from baseline at 12 months, combined with an HBsAg level of <100 IU/mL at 12 months, accurately predicted a favorable trajectory (Group 1). In HBeAg-negative patients, a baseline HBsAg level of <1000 IU/mL, along with an HBsAg decline of >50% from baseline at 12 months and an HBsAg level of <200 IU/mL at 12 months effectively predicted a favorable trajectory (Group 1).

5

What does personalized treatment mean for patients with chronic hepatitis B, and how does this research contribute to it?

Personalized treatment in the context of chronic hepatitis B (CHB) means tailoring the treatment approach to individual patient characteristics to optimize outcomes. The study suggests that by using group-based trajectory models (GBTMs) to analyze hepatitis B surface antigen (HBsAg) kinetics, clinicians can better predict which patients will benefit from nucleos(t)ide analogues (NAs) therapy. This allows for more informed decisions about treatment strategies, potentially including the duration of therapy and the use of additional interventions to enhance viral clearance. The goal is to improve the chances of achieving HBsAg loss and, ultimately, better long-term outcomes for patients.

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