IMMU-03 Contributed Talks

Jonah Hall

UBC

"Optimization of Pertussis Immunization Using Mathematical Models"

Pertussis (whooping cough), caused by Bordetella pertussis, is most severe in infants, with most deaths occurring in unvaccinated infants under three months of age. Vaccination with the DTaP (priming) and TdaP (booster) immunizations is effective, with TdaP given during pregnancy and DTaP in infancy. However, immunomodulation can dampen the IgG response in infants born to vaccinated mothers. We hypothesize that adjusting the vaccination schedule could reduce immunomodulation and enhance vaccine efficacy. Since empirically testing multiple schedules is impractical, we propose using mathematical modeling alongside two experimental mouse models to determine an optimal schedule. Pregnant and infant mice will be immunized following a murine analog of standard vaccination. These data will inform our model, allowing us to estimate key immune parameters. Once parametrized, our model will propose schedules that maximize infant antibody response. A second mouse experiment will test these schedules, comparing immune responses to assess their efficacy. This approach will help evaluate immunomodulation mechanisms and refine vaccination strategies. The mechanistic evaluation of immunomodulation is of significance due to its lack of effective investigations to date.

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Peter Rashkov

Institute of Mathematics and Informatics, Bulgarian Academy of Science, Sofia, Bulgaria

"Towards a mathematical model of the methotrexate effect on immunogenicity to adalimumab in axial spondyloarthritis"

Axial spondyloarthritis (SpA) is a chronic inflammatory disease impacting the joints in the axial skeleton (e.g. chest, spine, pelvis). Tumor necrosis factor inhibitors (TNFi), such as the monoclonal antibody adalimumab, are used to treat severe cases, but therapy is often discontinued due to loss of efficacy, not least resulting from immunogeniocity and development of anti-drug antibodies (ADA). The disease-modifying drug methotrexate (MTX) has shown potential in reducing the formation of ADA to various TNFi in rheumatoid arthritis (Krickaert et al, 2012), but little is known about its mode of action. We adapt a mechanistic mathematical model for immunogencity towards adalimumab based on Chen et al. 2014 to describe the impact of MTX in reducing immunogenicity, and parametrise it based on patient data from a multicentric randomised trial (Ducoureau et al, 2020). ODEs describe the pharmacokinetics and pharmacodynamics of the therapeutic compounds (adalimumab only or adalimumab and MTX, depending on patient cohort), the dynamics of T and B lymphocytes, antigen presenting cells, and some relevant cytokines for the disease. Due to the large size of this model, we employ several reduced models to estimate some of the parameter values. The model is used to simulate several scenarios in order to elucidate the most likely modes of action of MTX to reduce immunogenicity by comparing the simulated and measured ADA titres along 5 hospital visits. This is joint work with Sara Sottile (Bologna, Italy) and Denis Mulleman (Tours, France). This work is based upon work from COST Action ENOTTA (CA21147), supported by COST (European Cooperation in Science and Technology), and Contract KP-06-DKOST-13 of the Bulgarian Fund for Scientific Research.

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Adnan Khan

Lahore University of Management Sciences

"Antibiotic Resistance and Dosing in Bacterial Biofilms"

In this talk, we will present effective antibiotic regimens in the presence of drug-resistant bacteria in biofilms. We begin by discussing models of in-vivo antimicrobial resistance transfer within bacterial biofilms, focusing on various one-dimensional biofilm models. Our approach includes modeling resistance acquisition through horizontal gene transfer between resistant and susceptible strains while also accounting for the role of persistor cells. We examine the effects of periodic antibiotic dosing at a constant level, showing that it may not always lead to bacterial eradication. To address this challenge, we utilize a numerical optimization algorithm to determine the optimal antibiotic dosing strategy. Additionally, we analyze how changes in different model parameters impact the qualitative behavior of the optimal dosing regimen.

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