Yaroslav Ugolkov1, Alina Volkova2,3, Kirill Peskov2,3,4, Victor Sokolov2,3
1Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 2Marchuk Institute of Numerical Mathematics, 3M&S Decisions LLC, 4Research Center of Model-Informed Drug Development, I.M. Sechenov First Moscow State Medical University
Objectives: B-cell immunity plays a crucial role in adaptive immune response. Dysregulation of autoreactive B-cell selection, excessive activation, and B lymphocyte proliferation can lead to the development of autoimmune diseases [1]. Despite recent advances in understanding autoimmunity, clinical studies indicate that up to 50% of patients do not exhibit significant improvement with existing therapies [3,4], highlighting the urgent need to identify novel therapeutic targets for autoimmune disease treatment. The aim of this work is a development of a quantitative systems pharmacology (QSP) model of B-cell immune response based on the preclinical data available in published sources. Methods: The developed QSP model consists of 13 nonlinear ordinary differential equations with 17 parameters, 12 of which were fixed based on physiological values taken from published sources, identified through systematic literature review, and 5 were estimated based on aggregated data from 6 experimental studies in mice. Model calibration was performed using maximum likelihood approach and Nelder-Mead algorithm. Model performance was assessed using numerical criteria, parameter identifiability and model goodness-of-fit diagnostics analysis. Practical identifiability of model parameters and convergence to a global minimum were assessed through the calculation on variance-covariance matrix, performing likelihood profiling and multi-start calibration procedures [5]. The model was validated using the data on IgG response to immunization from 5 experimental studies. Local and global sensitivity analyses were performed via one-at-a-time and eFAST methods, respectively. Data processing, model development and evaluation were performed in R statistics (version 4.2.2). Results: The developed model describes the activation and proliferation of antibody-secreting cells (ASC) in secondary lymphoid organs, their migration into the bone marrow, as well as their redistribution to peripheral tissues. Relative standard error of all calibrated parameters did not exceed 45% with adequate description of all data used for parameter calibration. The variability in point estimates of parameters across ten runs of the multi-start calibration procedure did not exceed 10% of the reference values. Furthermore, the model was successfully validated against IgG accumulation dynamics data. The peak and steady-state concentrations of ASC in secondary lymphoid organs were sensitive to ASC synthesis rate, demonstrating over a 2-fold change from the reference with ±20% variations in the parameter value, whereas ASC concentration in bone marrow primarily dependent on the transition rate from blood to bone marrow which reflects the availability of survival niches for ASC. By varying the parameter representing the hypothetical size of the survival niche by ~2-fold, we were able to capture between-study heterogeneity for the majority of the observed data. Conclusions: We have developed and validated a QSP model of B-cell immune response following the administration of an exogenous antigen in mice. Model evaluation revealed ASC synthesis rate and the availability of survival niches as key parameters potentially explaining heterogeneity across the studies. The proposed model can be used as a generalized submodel for the development more complex QSP modeling platforms studying the mechanisms of long-lived plasma cell maintenance autoreactive ASC cells caused effects in autoimmune diseases. This research was funded by the Russian Science Foundation (Grant Number 23-71-10051).
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Reference: PAGE 33 (2025) Abstr 11712 [www.page-meeting.org/?abstract=11712]
Poster: Methodology - New Modelling Approaches