Selma El Messaoudi (1), Antonio Gonçalves (1), Annabelle Lemenuel-Diot (2), Jérémie Guedj (1)
(1) IAME, UMR 1137, INSERM, Université de Paris, Sorbonne Paris Cité Paris, France (2) Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel
Objectives: Chronic hepatitis B remains a major public health threat due to its high infectiousness and its progression to cirrhosis, hepatocellular carcinoma, and death. Current treatments allow to control the viral load at low concentrations, but do not cure the infection[1]. Most of the patients need to take a lifelong treatment to avoid relapse. Standard HBV models fail to predict the long term dynamic of Hepatitis B virus DNA (HBV DNA)[2], as they do not take into account the natural history of the disease, the various types of patients (those with Hepatitis B e antigen (HBeAg), still detectable, called HBeAg positive, and those no longer detectable, called HBeAg negative patients), and thus the dynamic of other viral products such as Hepatitis B surface antigen (HBsAg) which have a fundamental role in the process of chronic HBV infection[3,4] and in the probability of reaching functional cure.
This work aimed to :
– characterize the long-term dynamic of HBV DNA, HBsAg, and HBeAg, in HBeAg positive and negative patients treated with Lamivudine and PEG-IFNα for 12 months and followed 6 months after treatment cessation
– estimate the effect of antiviral treatments on the productions of viral transcripts by different cell subpopulations
– estimate a potential synergism of action in individuals receiving the combination of Lamivudine and PEG-IFNα
Methods: A total of 1330 patients, HBeAg-positive and HBeAg-negative patients, from 2 randomized, multi-arms and multicentric clinical trials[5,6] were included in the analysis. Patients received either PEG-IFNα monotherapy, Lamivudine monotherapy or PEG-IFNα plus Lamivudine for 48 weeks, and were followed 24 weeks after treatment cessation. HBV DNA, HBsAg, Alanine transaminase (ALT) and, for positive patients, HBeAg levels were sampled during the whole analysis. Specific viral dynamics models (one for each HBeAg status) were developed to fit HBV DNA and HBsAg levels, as well as HBeAg levels for HBeAg positive patients. The parameters were estimated using SAEM algorithm in Monolix software (v2018R2). Inter-patients variability was estimated with
stochastic approximation and a covariate analysis was performed to assess the effect of gender, BMI, race, and ALT baseline on parameters.
Results: The model included 2 subpopulations of infected cells, both producing HBV DNA, HBeAg and HBsAg at different production rates. First, non-infected cells became infected I1 subpopulation cells, responsible for the main production of virions and HBeAg. Then, I1 cells transferred into I2 subpopulation cells, containing integrated DNA into the genome host, and responsible for the non-healing process by producing huge amount of HBsAg. HBeAg loss being often the consequence of mutations in the pre-core and core promoter region, was not correlated to HBV DNA levels for HBeAg-negative patients. ALT baselines were included and allowed to characterize the dynamic not only as a consequence of a drug/effect relationship but also to take into account the natural history of the disease.
At baseline, we predicted a higher proportion of I1 in HBeAg-positive patients compared to HBeAg-negative patients (p<0.0001), and a majority of I2 for both HBeAg status. The model predicted a rapid loss of I1 cells under treatment, whereas I2 cells remained stable.
The efficacy of PEG-IFNα was estimated as a nonlinear dose/effect relationship, with respect to dose adjustment, (D50=10 for HBeAg-negative patients, and D50=0.9 for HBeAg-negative patients), and a delay for the efficacy was estimated. The efficacy of Lamivudine monotherapy was estimated as being constant over time (ε=0.999 for HBeAg-positive and 0.995 for HBeAg-negative patients). The model was able to explain the synergy between the 2 drugs, where Lamivudine exhibits the efficacy of PEG-IFNα and allows to reach undetectable viral loads faster.
Conclusions: This mechanistic model provides a new framework to understand the complex long-term dynamic of chronic HBV infection under standard of care therapy. It allowed to understand the relationship between the different HBV markers and the effect of the standard of care on these markers. This model will help to optimize combination therapy with new anti-HBV agents in future analyses.
References:
[1] Lok, A. S. et al. Hepatitis B cure: From discovery to regulatory approval. Hepatol. Baltim. Md 66, 1296–1313 (2017).
[2] Gonçalves, A. et al. What drives the dynamics of HBV RNA during treatment? J. Viral Hepat. 28, 383–392 (2021).
[3] Martinot-Peignoux, M. et al HBsAg quantification: useful for monitoring natural history and treatment outcome. Liver Int. Off. J. Int. Assoc. Study Liver 34 Suppl 1, 97–107 (2014).
[4] Chen, M. T. et al. A function of the hepatitis B virus precore protein is to regulate the immune response to the core antigen. Proc. Natl. Acad. Sci. U. S. A. 101, 14913–14918 (2004).
[5] Lau, G. K. K. et al. Peginterferon Alfa-2a, lamivudine, and the combination for HBeAg-positive chronic hepatitis B. N. Engl. J. Med. 352, 2682–2695 (2005).
[6] Marcellin, P. et al. Peginterferon alfa-2a alone, lamivudine alone, and the two in combination in patients with HBeAg-negative chronic hepatitis B. N. Engl. J. Med. 351, 1206–1217 (2004).
Reference: PAGE 29 (2021) Abstr 9760 [www.page-meeting.org/?abstract=9760]
Poster: Drug/Disease Modelling - Infection