Tuesday, June 15 at 02:15pm (PDT)Tuesday, June 15 at 10:15pm (BST)Wednesday, June 16 06:15am (KST)
SMB2021 FollowTuesday (Wednesday) during the "CT04" time block.
Pennsylvania State University
"Modeling PrEP on Demand for Prevention of HIV"
In order to prevent the spread of HIV, antiretroviral therapy (ART) for HIV drugs can be administered to high-risk individuals in advance of exposure, as pre-exposure prophylaxis (PrEP). PrEP with the ART combination drug Truvada taken daily has been demonstrated to effectively reduce the risk of HIV infection. However daily dosing can be onerous, and studies suggest that short-term use of ARTs around the time of exposure may be just as effective at reducing HIV risk. Here we investigate such “on-demand” PrEP. We build a mathematical framework in which we integrate a pharmacokinetic/pharmacodynamic (PK/PD) model developed by measuring mucosal tissue concentrations of tenofovir and emtricitabine (Truvada) (Cottrell et al. 2016) into an in-host stochastic model of early HIV infection with PrEP treatment based on virus dynamics. Armed with this model, we predict risk of infection under different on-demand PrEP regimens with regards to time of dosing and dosage relative to time of exposure. Thus we predict practical on-demand PrEP regimens in terms of dosage and timing required to obtain most effective protection for lower female genital tract (FGT).
Esteban Abelardo Hernandez Vargas
"Topological Data Analysis in Infectious Diseases"
Pathogens have important implications in many aspects of health, epidemiology, and evolution. Topological Data Analysis (TDA) is used here to help in identifying the behaviour of a biological system from a global perspective. Using data sets of the immune response during influenza-pneumococcal co-infection in mice, we employ here topological data analysis to simplify and visualise high dimensional data sets. Persistent shapes of the simplicial complexes of the data in the three infection scenarios were found: single viral infection, single bacterial infection, and co-infection. The immune response was found to be distinct for each of the infection scenarios and it was uncovered that the immune response during the co-infection has three phases and two transition points.
Merck & Co., Inc
"BUILDING A MECHANISTIC MODELING PLATFORM FOR HIV CURE DRUG DEVELOPMENT"
Current antiretroviral therapy (ART) effectively controls HIV in most patients but does not cure it. To develop drugs towards a HIV cure, novel approaches – such as reactivation of latent provirus (“shock”) and immunotherapies – are being explored. Mechanistic mathematical models that describe both within-host viral load dynamics and immunologic control of HIV infection are essential to integrate clinical data, assess therapeutic response, and generate hypotheses in support of HIV cure drug development. We built the Immune Viral Dynamics Modeling (IVDM) platform, based on recently developed mathematical models which integrate potential mechanisms that may lead to a cure. To inform the IVDM parameters, we created a dataset of “artificial” subjects by concatenating post-ATI (analytical treatment interruption) viral load profiles from eight ACTG clinical studies with on-ART viral load from a clinical study of raltegravir. This way, we created a dataset that describes the course of infection from initiation of treatment to ATI. Key parameters that govern latent reservoir seeding and immunological control were estimated using a nonlinear mixed effects approach (Monolix). From the estimated parameter distributions, we sampled a virtual population and ran clinical trial simulations (CTS) to assess potential curative interventions.