Alina Volkova (1,2)*, Victor Sokolov (1,2)*, Florencia Tettamanti (3), Meghna Verma (4), Yaroslav Ugolkov (2,5)*, Kirill Peskov (1,2,5)*, Weifeng Tang (4), Holly Kimko (4)
(1) Modeling and Simulation Decisions FZ - LLC, Dubai, UAE, (2) Marchuk Institute of Numerical Mathematics, Moscow, Russia, (3) Clinical Pharmacology and Quantitative Pharmacology, CPSS, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK, (4) Clinical Pharmacology and Quantitative Pharmacology, CPSS, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, USA, (5) Research Center of Model-Informed Drug Development, Sechenov First Moscow State Medical University, Moscow, Russia *A.V., Y.U., K.P., V.S. were supported by the Russian Science Foundation (Grant Number 23-71-10051).
Objectives: Systemic lupus erythematosus (SLE) is an autoimmune disease with complex heterogeneous etiology and pathophysiology, effectively treated with compounds targeting type 1 interferon (IFN1) signaling, such as anifrolumab (the only marketed antagonist of IFN1 receptors for treatment of moderate to severe SLE), sifalimumab (anti-IFNa monoclonal antibody), and daxdilimab (antibody, depleting plasmacytoid dendritic cells) (1–3). However, patients’ response to different Type I IFN targeting therapies varies widely, and increased understanding of underlying mechanisms is needed (4,5). The objectives of this work are to develop a quantitative systems pharmacology (QSP) model of IFN1-induced inflammation in SLE and apply it to compare the treatment effects of anifrolumab, sifalimumab and daxdilimab in various subpopulations of SLE patients.
Methods: A QSP model was developed comprised of a system of 17 nonlinear ordinary differential equations with 52 parameters, 29 of which were fixed on physiological values taken from published sources and the others were estimated from individual data of 254 patients from anifrolumab phase 2b clinical trial (6). The model was parametrized using nonlinear mixed effects modeling methodology implemented in Monolix (version 2020R1). Data handling and post-processing of the Monolix outputs was performed in R (version 4.0.2).
Results: The developed QSP model incorporates key mechanisms of type 1 IFN-inducible inflammation in SLE, including IFN1 production by immune cells, binding of IFN1 subtypes to IFNAR1 leading to complex formation and subsequent expression of interferon signature genes (IFNGS). In addition, the model describes pharmacokinetics of anifrolumab, sifalimumab and daxdilimab. The model captured the relationship between plasma IFNa concentration and IFNGS along with IFNGS response in patients with different IFNGS status, and was successfully validated using independent (not used in the parameter estimation procedure) data from 10 clinical studies of all three drugs, including 5 anifrolumab clinical studies, 4 sifalimumab clinical studies and 1 daxdilimab clinical study. Following a global sensitivity analysis, the baseline level of IFNa and IFNAR1 were found to contribute the most to the anifrolumab- and daxdilimab-mediated IFNGS response, whereas IFNGS change following sifalimumab treatment is primarily dependent on the proportion of IFNa in IFN1. Additionally, analysis of the normalized concentration-response relationship showed a 25% higher pharmacodynamic effect of anifrolumab compared to the other drugs considered.
Conclusions: A first-in-class QSP model of IFN1-mediated inflammation in SLE was developed and validated using heterogeneous clinical data from three therapies with different mechanisms of action. The model analysis has shown that multiple individual patient characteristics may be used to maximize SLE patient benefit, and that anifrolumab possesses the most prominent pharmacological effect on IFNGS compared to the other therapies considered in the model.
References:
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- Khamashta M, Merrill JT, Werth VP, Furie R, Kalunian K, Illei GG, Drappa J, Wang L, Greth W, CD1067 study investigators. Sifalimumab, an anti-interferon-α monoclonal antibody, in moderate to severe systemic lupus erythematosus: a randomised, double-blind, placebo-controlled study. Ann Rheum Dis (2016) 75:1909–1916. doi: 10.1136/annrheumdis-2015-208562
- Karnell JL, Wu Y, Mittereder N, Smith MA, Gunsior M, Yan L, Casey KA, Henault J, Riggs JM, Nicholson SM, et al. Depleting plasmacytoid dendritic cells reduces local type I interferon responses and disease activity in patients with cutaneous lupus. Sci Transl Med (2021) 13:eabf8442. doi: 10.1126/scitranslmed.abf8442
- Jung SM, Kim W-U. Targeted Immunotherapy for Autoimmune Disease. Immune Netw (2022) 22:e9. doi: 10.4110/in.2022.22.e9
- Vital EM, Merrill JT, Morand EF, Furie RA, Bruce IN, Tanaka Y, Manzi S, Kalunian KC, Kalyani RN, Streicher K, et al. Anifrolumab efficacy and safety by type I interferon gene signature and clinical subgroups in patients with SLE: post hoc analysis of pooled data from two phase III trials. Ann Rheum Dis (2022) 81:951–961. doi: 10.1136/annrheumdis-2021-221425
- Furie R, Khamashta M, Merrill JT, Werth VP, Kalunian K, Brohawn P, Illei GG, Drappa J, Wang L, Yoo S, et al. Anifrolumab, an Anti-Interferon-α Receptor Monoclonal Antibody, in Moderate-to-Severe Systemic Lupus Erythematosus. Arthritis Rheumatol Hoboken NJ (2017) 69:376–386. doi: 10.1002/art.39962
Reference: PAGE 32 (2024) Abstr 11249 [www.page-meeting.org/?abstract=11249]
Poster: Clinical Applications