Expanding a quantitative systems pharmacology (QSP) modeling framework for acute Hepatitis B to characterize chronic infection
Natalia Riva (1,2), Iñaki F. Troconiz (1,2,3) Joris Vandenbossche (4), Xavier Woot de Trixhe (4), Thomas Kakuda (5), Oliver Ackaert (4), Juan José Perez-Ruixo (4), Huybrecht T’jollyn (4*), Zinnia P Parra-Guillen (1,2,*)
(1) Pharmacometrics & Systems Pharmacology Research Unit, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Spain; (2) IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; (3) Institute of Data Science and Artificial Intelligence, DATAI, University of Navarra, Pamplona, Spain; (4) Janssen R&D, Beerse, Belgium; (5) Janssen R&D, Brisbane, CA, USA * These authors share senior authorship.
Objectives: Chronic hepatitis B virus (HBV) infection is associated with high morbidity and mortality and represents an unmet medical need with functional cure rates remaining very low (1% per year) (1). Mathematical models have been largely developed to gain insight into HBV dynamics and treatment effects, although greater efforts are still needed to understand the virological-immunological-pharmacological interaction that may be needed to eradicate the virus (2). In this regard, a quantitative systems pharmacology (QSP) model for HBV under acute scenario was recently developed, providing a comprehensive and quantitative overview of the interplay between the different components of the immune system and the viral response (3). The aim of the current work is to extend the existing QSP model to chronic HBV disease accounting for the HBV lifecycle and its interactions with the immune system as an initial step before assessing pharmacological effects.
Methods: To build the ordinary differential equations (ODEs)-based structure, model parameters were either (i) obtained directly from the literature (3,4), (ii) estimated using in vivo or in vitro data digitized when needed, or (iii) calibrated to achieve the expected behavior of the system (i.e. time course and levels of different biomarkers). Simulations with the final model were compared with the existing acute QSP model and patient viral marker profiles obtained from the literature. Finally, a sensitivity analysis and parameter scan were performed to explore the impact of all processes on clinically relevant viral markers, including standard viral load (HBV-DNA), liver function test (ALT), and hepatitis B surface antigen (HBsAg), as well as time to cure (HBV-DNA < 2000 IU/mL) among others. Matlab/Simbiology R2022b was used during the analyses.
Results: First, the viral lifecycle of the acute HBV model was extended to better reflect relevant processes for chronic HBV by adding: (i) explicit proliferation of healthy and infected hepatocytes, (ii) integration of viral DNA within infected hepatocytes (iHep1) with only cccDNA to form a second type of infected hepatocytes (iHep2) containing both, cccDNA and integrated DNA, (iii) transcription of integrated and cccDNA to different mRNA (coreRNA, pregenomic RNA, and sRNA accounting for Pre-S1 and S2 mRNA), (iv) their subsequent translation to HBeAg and HBsAg as well as RNA and DNA containing virion formation, and finally (v) release to plasma. These additions enabled to account for all relevant viral biomarkers: HBeAg, HBV-RNA virions, HBcrAg, HBsAg, and HBV-DNA virions. Second, in the viral kinetic model, the roles of the innate (inhibition of viral transcription by endogenous interferon), cellular (lytic activity of cytotoxic T lymphocytes and Natural Killer cells on iHep1 and 2), and humoral (neutralization of RNA and DNA virions and HBsAg) immune responses were adjusted to account for the granularity increase and expanded to account for the appearance of new biomarkers (e.g., antibodies against HBeAg). To increase model flexibility, scalar parameters allowed to adjust relative transcription activity for cccDNA and integrated DNA or alter immune responses against iHep2. Importantly, the refined structure was able to capture the dynamics of the response to acute HBV infection and reproduce the rate of chronic HBV infection.
Conclusions: An existing QSP model for acute HBV has been successfully expanded to account for the HBV life cycle and additional mechanisms, such as DNA integration into the hepatocyte, characteristic of chronic hepatitis B. This new framework extends the quantitative characterization of the complex host-virus interactions and provides avenues to increase the mechanistic understanding of the disease by incorporating the PKPD properties of the standard of care therapies (e.g. nucleos(t)ide analogs) and anti-HBV agents.
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