(S-062) Development of a Quantitative Systems Pharmacology Model for Hepatitis B Virus Infection and Hepatitis D Virus Co-Infection.

Clémence Boivin-Champeaux – University of Florida; Stephan Schmidt – University of Florida; Scott Balsitis – Gilead Sciences; Ana Ruiz-Garcia – Gilead Sciences; Francine Johansson Azeredo – University of Florida; Justin Feigelman – Gilead Sciences

Objectives: The Hepatitis B virus (HBV), identified as a hepatotropic, double-stranded DNA virus, gives rise to both acute and chronic diseases. HBV infection not only jeopardizes health outcomes but also results in a substantial socioeconomic burden. Therefore, the goal of our project is to establish and verify a quantitative systems pharmacology (QSP) model for HBV to characterize and predict the dynamic interplay between the virus and the patient’s immune response as well as changes therein over time. Once developed and verified, this model will be expanded to hepatitis delta virus (HDV) coinfections. Ultimately, we will use this QSP platform to inform future decision-making in the treatment and, ideally, cure of HBV and HDV.

Methods: The acute HBV infection QSP model was implemented in MATLAB and consists of three biological compartments—liver, plasma, and lymph—representing the spatial complexity of HBV infection and immune regulation. Model structure includes over 100 biological species and simulates key processes such as viral replication, innate and adaptive immune responses, cytokine signaling, and hepatocyte turnover. Parameter values and initial conditions were derived from previously published models [1-3], experimental literature [4-6], and quantitative databases including CYTOCON DB and fIVE DB. Manual calibration was performed using digitized clinical data from eight acute HBV studies [7-14] to accurately capture the time course of key biomarkers such as HBV DNA, HBsAg, and ALT. To evaluate model behavior and biological plausibility, we conducted perturbation analyses targeting immune components, local sensitivity analysis (LSA) to identify influential parameters, and generated a virtual population (VPOP) to reflect patient variability in biomarker trajectories.

Results: The model accurately reproduced clinical dynamics of key biomarkers (HBV DNA, HBsAg, ALT), capturing hallmark phases of acute infection: peak viremia, ALT elevation during immune clearance, and biomarker resolution. Perturbation analyses underscored critical roles for CD8⁺ T cells, myeloid dendritic cells (mDCs), and regulatory T cells (Tregs) in determining infection fate. LSA revealed dominant influence of adaptive immune parameters and viral replication kinetics across biomarkers. The generated VPOP successfully spanned clinical variability, confirming the model’s robustness and biological relevance.

Conclusions: This QSP model provides a mechanistic framework for simulating acute HBV infection and evaluating immune-pathogen interactions. It captures both central trends and variability in patient responses, offering a valuable tool for hypothesis testing, biomarker analysis, and treatment strategy design. Future extensions will include modeling of chronic HBV infection and HDV co-infection to support model-informed drug development (MIDD) and clinical decision-making.

Citations: [1] Asín-Prieto E, Parra-Guillen ZP, Gómez Mantilla JD, et al. A quantitative systems pharmacology model for acute viral hepatitis B. Computational and Structural Biotechnology Journal. 2021;19:4997-5007. doi:10.1016/j.csbj.2021.08.052
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[14] Panduro A, Roman S, Fierro NA, Rebello‐Pinho JR. Viral Kinetics of an Acute Hepatitis B Virus Subgenotype F1b Infection in a Mexican Subject. Clinical Liver Disease. 2022;19(2):41-48. doi:10.1002/cld.1178

Keywords: Hepatitis B virus, Quantitative systems pharmacology, Immune response modeling