(M-013) Development of a quantitative systems pharmacology model for Hepatitis B Virus infection and Hepatitis D virus co-infection.

Justin Feigelman, Master of science in computational biology & bioinformatics – Sr Clinical Pharmacologist II, Gilead Sciences; Valvanera Vozmediano Esteban, Ph. D. in Pharmacology – Senior Director MID, CTI; Nieves Velez de Mendizabal, PhD – Sr Director, Clinical Pharmacology, Gilead Sciences; Aksana Jones, Master degree in pharmacometrics – Principal Scientist I, Clinical Pharmacology, Gilead Sciences; Ana Ruiz-Garcia, PhD pharmacokinetics – Exec Director, Clinical Pharmacology, Gilead Sciences; Stephan Schmidt, PhD – Professor & Director, University of Florida; Francine Johansson Azeredo, PhD – Research Assistant Professor, University of Florida

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.

Methods: A systematic literature search on HBV, focusing on its pathogenesis, clinical aspects, surrogate endpoints, and available disease progression models was conducted. A database of biomarkers like HBV DNA, hepatitis B surface antigen, hepatitis B e antigen, and alanine aminotransferase, and existing models was established to develop a disease modeling framework for acute and chronic HBV infections. A QSP model for acute HBV infection was then constructed using MATLAB to capture the dynamic interplay between the virus life cycle and the host immune response. Parametrization of our model was determined based on literature, experimental data, and the CYTOCON database. Simulations were conducted to explore various scenarios of acute HBV infection. We will incorporate clinically relevant endpoints such as the previously mentioned biomarkers and immune components like cytokines and immune cell concentrations (natural killer, CD8+ T cells) from publicly available data to appropriately assess disease progression.

Results: We examined a total of 71 publications and found 24 containing relevant information on quantifying HBV infections over time. These publications included information on key processes relevant to acute and chronic HBV infections, viral dynamics, respective biomarkers, and parameters related to the host immune system (both innate and adaptive responses). Based on this information, we developed a QSP-based disease modeling framework for acute HBV QSP, which includes 44 species and 110 parameters distributed across three compartments (liver, plasma, and lymph).

Conclusions: We laid the foundation for a QSP-based disease modeling framework for characterizing and predicting the dynamic interplay between HBV and its human host. The model will be expanded to a QSP-based disease-drug-trial model going forward by including different drugs with different mechanisms of action under different treatment conditions to select optimal treatment regimens. Once established and verified for HB, this platform will be expanded to HDV.

Citations: [1] 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