Contributors
Natacha Go, Novadiscovery SA, Lyon, France. Title: Application of a mechanistic model to design RTI prophylaxis trials.
Joshua T. Schiffer, Fred Hutchinson Cancer Research Center, University of Washington, Seattle. Title: Modeling SARS-CoV-2 shedding, therapy and transmission.
Joshua T. Schiffer, Fred Hutchinson Cancer Research Center, University of Washington, Seattle. Title: Modeling SARS-CoV-2 shedding, therapy and transmission.
Institution/ Affiliation
Natacha Go, Novadiscovery SA, Lyon, France.
Joshua T. Schiffer, Fred Hutchinson Cancer Research Center, University of Washington, Seattle.
Joshua T. Schiffer, Fred Hutchinson Cancer Research Center, University of Washington, Seattle.
Presentation Details (date, conference, etc.)
May 6, 2021, IMAG/MSM WG on Multiscale Modeling and Viral Pandemics
- Natacha Go Slides, Video, Novadiscovery SA, Lyon, France. Title: Application of a mechanistic model to design RTI prophylaxis trials. Abstract: There is a good chance to soon control the COVID-19 pandemic but other respiratory tract infections (RTIs) will continue to impact public health. Viral lower RTIs in children are associated with hospitalization, wheezing and asthma inception, while upper RTIs are less severe but have a high prevalence. Viral RTIs are also triggers for exacerbations of chronic pulmonary diseases. Vaccination against prevalent viruses like RSV and RV are currently neither available nor in the near future and therefore non-specific immunomodulation for RTI prophylaxis is promising to fill this gap. For example the oral bacterial lysate Broncho-Vaxom (OM-85) has demonstrated efficacy efficacy in prevention of recurrent RTIs, specifically in at-risk pediatric population (in et al., 2018; DOI:10.1016/j.intimp.2017.10.032). For targeting other populations and RTI-indications, robust efficacy data need to be generated, but clinical trials are strongly impacted by the pandemic. Globally, all trials other than dedicated to COVID-19 are experiencing delays or even halts, e.g. due to patient recruitment issues. At the same time, RTI burden changes - through lockdown or social distancing - with an uncertain trajectory. If at all and how RTI prophylaxis trials are feasible to conduct in the near future is a completely open question.
For a better design of RTI prophylaxis trials in the currently moving frame, we have developed a dedicated multiscale in silico approach. A mechanistic pharmacokinetics /pharmacodynamics and within-host viral infection disease model is interfaced with a population-scale (between-host) SIRS disease burden model - thereby accounting for seasonality and extrinsic factors through time-dependent transmission. On the back of this model and a Virtual Population we conduct in silico clinical trials with variations in observational periods, eligibility criteria (defining the included at-risk population) and follow-up giving us efficacy metrics and sample size estimates as outputs. We demonstrate how the model can be used to address and rationalize efficacy heterogeneity in clinical data through links between population, follow-up, regimen and clinical efficacy. The integrated SIRS was then used to mimic lockdown as COVID-19 containment in line with RCGP 2019-2020 data, allowing us to adapt the instantaneous control group prevalence and efficacy dependent on this modulation. We also show how and why different containment scenarios vary in their impact of demonstrated efficacy, recruitment needs and difficulty through analyses of the predicted outcome distributions. On a longer term, we wish to highlight the capability of computational systems biology for RTI prophylaxis trial design under rapidly changing conditions and that such a modeling approach can supporting go-no/go decisions in clinical development for a wide range of RTI-prophylaxis oriented indications. - Joshua T. Schiffer Slides, Video, Fred Hutchinson Cancer Research Center, University of Washington, Seattle. Title: Modeling SARS-CoV-2 shedding, therapy and transmission. Abstract: I will describe our group’s multi-scale modeling of SARS-CoV-2. Our models include an intra-host model which captures viral load trajectories and immune responses and can be used as a tool to simulate clinical trials, a model intended to capture the impact of viral load on transmission and super-spreader events as well as the impacts of masking and vaccination on transmission, and an epidemiologic model of SARS-CoV-2 transmission in King County Washington that captures the impact of vaccination, non-pharmaceutical interventions and novel SARS-CoV-2 variants of concern. The Fred Hutchinson Cancer Center web page on viral modeling in King County Washington is at https://covidmodeling.fredhutch.org/.