MS05 - IMMU-1
Senate Chamber (#2145) in The Ohio Union

Immunobiology and Infection Subgroup Minisymposium 2023

Wednesday, July 19 at 10:30am

SMB2023 Follow Wednesday during the "MS05" time block.
Room assignment: Senate Chamber (#2145) in The Ohio Union.

Morgan Craig, Daniel Reeves

Description:

The Immunobiology and Infection Subgroup focuses on modelling applied to the various aspects of immunity and immune responses, including host-pathogen interactions. Our subgroup’s aim is to unite mathematical biologists, clinicians, and wet lab researchers working on within-host infectious disease dynamics, host immune responses, causes and effects of inflammation, disease progression and outcome, integration of experimental and clinical data into models, and model-driven experimental design to advance our understanding of the immune system. In our 2023 minisymposium, we are highlighting some of the excellent research in our subgroup focused on HIV dynamics and treatment, the generation and clinical use of CAR-T cell therapy, and the immune response to bacterial infections.

E. Fabian Cardozo-Ojeda

Fred Hutchinson Cancer Center (Vaccine and Infectious Disease Division)

"HIV-1 reservoir dynamics during hematopoietic stem cell transplantation"

The five known cases of antiretroviral therapy (ART)-free HIV long-term remission have resulted in allogeneic hematopoietic stem cell transplantation (allo-HSCT). In these cases, allo-HSCT may have reduced HIV DNA and HIV RNA levels via conditioning or graft-vs-reservoir (GvR) effects. The international consortium IciStem investigates the potential for HIV cure via allo-HSCT. In this talk, I will present our assessment of the impact of conditioning and GvR effect in the control of HIV in IciSTEM participants using ordinarily differential equation models with a nonlinear mixed-effects approach. We explore two mechanistic assumptions on the impact of allo-HSCT on reservoir reduction: infected cells are depleted (1) by conditioning only or (2) by conditioning and by GvR directly proportional to the donor T-cell chimerism levels. We fit models to longitudinal CD4+ T cell concentrations, multiple viral levels, and anti-HIV antibody levels in blood using interpolated T-cell chimerism levels from 22 IciStem participants. Using model selection theory, we found that a model with conditioning and depletion of cells proportional to the observed T-cell chimerism best explains the timing and magnitude of HIV reduction dynamics. Our model predicts that T cell proliferation allows HIV reservoir levels to recover in response to cell loss during conditioning; therefore, the GvR effect is a primary driver of reservoir reduction dynamics after allo-HSCT.
Additional authors: Salisu Garba, Pfizer; Elizabeth R Duke, Fred Hutchinson Cancer Center; Maria Salgado, IrsiCaixa Institute, Spain; Javier Martinez-Picado, IrsiCaixa Institute, Spain; Joshua Schiffer, Fred Hutchinson Cancer Center

Jessica M. Conway

Penn State (Mathematics)

"Modeling on-demand PrEP regimen to prevent HIV transmission"

In 2010, analysis of the iPrEx study results demonstrated that daily dosing with antiretroviral therapy (ART) in advance of exposure to HIV, termed pre-exposure prophylaxis (PrEP), can significantly reduce the risk of HIV transmission and population spread. However, daily adherence to a drug regimen can be difficult to maintain and may come with side-effects. In contrast, the IPERGAY study published in 2015 suggested that short-term use around the time of exposure may be just as effective at reducing HIV risk as daily use. Here we investigate short-term use, termed 'on-demand' or 'event-based' PrEP. We aim to make model-based predictions of effective on-demand drug regimen. Focusing on transmission through sexual exposure, we incorporate a deterministic model of tissue-level pharmacokinetics and pharmacodynamics (PK/PD) of Truvada into a branching-process model of early HIV infection. Thus, we predict the risk of HIV transmission and risk reduction associated with dose size and timing relative to exposure. To evaluate effectiveness of dosing strategies, we simulate strategies by sampling a virtual population and performing extensive sensitivity analyses. Hence, we aim to identify practical dosing strategies that most effectively reduce risk of HIV transmission through sexual exposure.
Additional authors: Gulsah Yeni

Chapin S. Korosec

York University, 4700 Keele St, Toronto, M3J 1P3, ON, Canada. (Modelling Infection and Immunity Lab, Mathematics and Statistics)

"Within-host evolution of SARS-CoV-2: how often are de novo mutations transmitted?"

As of March 10th, 2023, the total number of reported SARS-CoV-2 infections reached over 676 million worldwide. Despite a relatively low mutation rate, the large number of infections has allowed for substantial genetic change in SARS-CoV-2, leading to a multitude of “variants of concern”. Utilizing recently determined mutations rates (per site replication), as well as within-host parameter estimates for hospitalized SARS-CoV-2 infections, we applied a stochastic transmission bottleneck model to describe the survival probability of rare de novo SARS-CoV-2 mutations. In the first part of this talk I will briefly discuss the significance and relevance of our within-host parameters published in ref.[1]. I will then discuss our un-published work on SARS-CoV-2 within-host evolution where we compute the survival probability of neutral muta-tions (no phenotypic effect), and various mutations affecting viral life history. We examine transmission bottlenecks of varying sizes, estimating which mutations are most likely to occur de novo and be transmitted during a single infection. This work offers a null model for SARS-CoV-2 substitution rates and predicts which aspects of viral life history are most likely to suc-cessfully evolve, despite low mutation rates and repeated transmission bottlenecks. [1]. C.S. Korosec et al., JTB, vol. 564, 2023.
Additional authors: Lindi M. Wahl. Affiliation: Mathematics, Western University, 1151 Richmond St, London, N6A 5B7, ON, Canada Jane M. Heffernan. Affiliation: Centre for Disease Modelling, Mathematics and Statistics, York University, 4700 Keele St, To-ronto, M3J 1P3, ON, Canada

Adnan Khan

Lahore University of Management Sciences (Mathematics)

"Modeling Antibiotic Resistance and Effective Dosing Regimens"

In this talk we will present models for in-vivo transfer of antimicrobial resistance and determine efficient antibiotic regimens in the presence of drug resistant bacteria. We consider resistance acquisition via horizontal gene transfer (HGT) which has been identified as primary mechanism for in-vivo drug resistance. It is known that three different mechanisms are responsible for HGT, these include conjugation, transformation, and transduction. We propose deterministic ODE based models for the three processes incorporating the unique pathways involved in each one. We will look at different antibiotic dosing protocols and show that periodic dosing at a constant level may not be successful in eradicating the bacteria. We set up an optimal control problem for successful antibiotic administration and then use a numerical optimization algorithm to determine the ’best’ antibiotic dosing strategy. We study the effects of varying different model parameters on the qualitative behavior of the optimal dosing. We compare our results to those in the literature.
Additional authors: Mudassar Imran; Asgher Ali

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