Biology subjects

Snedden, C. E.

Publications and source records attributed to Snedden, C. E..

3 recordsLinked to original sources

bioRxiv2026

Data aggregation and mechanistic modeling enable dose-response analysis of SARS-CoV-1 in non-human primates

Dose-response modeling provides estimates of infectious and lethal doses, which can be used to inform control and prevention measures. Unfortunately, data from experimental challenge studies, which are needed to perform dose-response modeling, are often sparse. For example, non-human primate (NHP) challenge studies tend to have small samples sizes and little dose variation, often with only one or two dose levels per study. Thus, it is infeasible to apply traditional dose-response modeling approaches to data from single NHP studies. To address this challenge, we developed a mechanistic Bayesian model that aggregates and analyzes NHP pathogen load data across multiple studies. Our model links dose-infectivity to pathogen kinetics, which allows us to estimate the infectious dose and evaluate dose effects on within-host viral kinetics simultaneously. With this model, we obtained the first-ever ID50 estimate for SARS-CoV-1 in NHPs using data compiled from six NHP challenge studies. Our work demonstrates the value in reusing previous data from animal experiments. Our modeling framework can be applied to other pathogens, enabling robust dose-response inference when individual challenge studies are inconclusive. Author summaryDose-response models are used to estimate pathogen doses needed to cause infection in humans, so they are useful for informing outbreak control policies. Unfortunately, performing dose-response modeling can be difficult due to limitations in the available data. If the pathogen causes significant risk of severe disease or death in humans, then controlled human infections cannot be performed. Additionally, experimental challenge studies of relevant animal models, such as non-human primates (NHPs), often have small sample sizes and limited dose ranges, which make dose-response modeling unfeasible using data from single studies. We developed an approach to aggregate data across multiple challenge studies to enable dose-response modeling in the absence of dose-response experiments. We applied our approach to data from six NHP challenge studies to perform the first-ever dose-response analysis of SARS-CoV-1 in NHPs. Our approach also included a mechanistic, mathematical model of within-host pathogen kinetics, which allowed us to assess the effect of SARS-CoV-1 dosage on patterns of viral RNA shedding. The framework we developed can be readily applied to other host-pathogen systems, and the mechanistic components of our model contribute to a growing movement towards understanding dose effects beyond simple infectivity.

microbiology↗

bioRxiv2026

Exposure route drives SARS-CoV-2 infection patterns in non-human primates

Public health policy and clinical interventions against infectious diseases rely on understanding the factors that govern the initiation and progression of infections inside hosts. Animal infection experiments are essential tools to study these complex, multi-scale processes under controlled conditions, and they have shown that the dose and route of exposure can influence within-host disease dynamics. However, observed differences are difficult to quantify and to disentangle from confounding factors because small sample sizes limit the statistical power and scientific scope of individual studies. Here, by compiling and analyzing the largest published database of non-human primate challenge experiments (107 studies; 721 animals; 22,183 observations), we quantify how exposure conditions and demographic factors shape within-host SARS-CoV-2 infection kinetics in the respiratory and gastrointestinal tracts. We show that exposure route has stronger effects on kinetics than dose, age, sex, or species, with directly exposed tissues exhibiting distinct spatiotemporal kinetics from non-exposed tissues. We estimate 50% infectious doses for different tissues and show that they vary greatly (from <101.2 up to >107.4 pfu) depending on the exposure route. We find that dose effects on kinetics are also route-and tissue-specific, primarily influencing nasally-inoculated animals. Our results suggest that exposure route drives infection kinetics more strongly than dose in a critical model system for translational medicine, and they demonstrate the untapped potential for meta-analysis to extract additional insights from costly animal experiments.

microbiology↗

bioRxiv2023

PCR data accurately predict infectious virus: a characterization of SARS-CoV-2 in non-human primates

Researchers and clinicians often rely on molecular assays like PCR to identify and monitor viral infections instead of the resource-prohibitive gold standard of viral culture. However, it remains unclear when (if ever) PCR measurements of viral load are reliable indicators of replicating or infectious virus. Here, we compare total RNA, subgenomic RNA, and viral culture results from 24 studies of SARS-CoV-2 in non-human primates using bespoke statistical models. On out-of-sample data, our best models predict subgenomic RNA from total RNA with 91% accuracy, and they predict culture positivity with 85% accuracy. Total RNA and subgenomic RNA showed equivalent performance as predictors of culture positivity. Multiple cofactors, including exposure conditions and host traits, influence culture predictions for total RNA quantities spanning twelve orders of magnitude. Our model framework can be adapted to compare any assays, in any host species, and for any virus, to support laboratory analyses, medical decisions, and public health guidelines.

microbiology↗