Comparing antiviral strategies against COVID-19 via multiscale within-host modelling
Within-host models of COVID-19 infection dynamics enable the merits of different forms of antiviral therapy to be assessed in individual patients. A stochastic agent-based model of COVID-19 intracellular dynamics is introduced here, that incorporates essential steps of the viral life cycle targeted by treatment options. Integration of model predictions with an intercellular ODE model of within-host infection dynamics, fitted to patient data, generates a generic profile of disease progression in patients that have recovered in the absence of treatment. This is contrasted with the profiles obtained after variation of model parameters pertinent to the immune response, such as effector cell and antibody proliferation rates, mimicking disease progression in immunocompromised patients. These profiles are then compared with disease progression in the presence of antiviral and convalescent plasma therapy against COVID-19 infections. The model reveals that using both therapies in combination can be very effective in reducing the length of infection, but these synergistic effects decline with a delayed treatment start. Conversely, early treatment with either therapy alone can actually increase the duration of infection, with infectious virions still present after the decline of other markers of infection. This suggests that usage of these treatments should remain carefully controlled in a clinical environment.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
PaccMann$^{RL}$ on SARS-CoV-2: Designing antiviral candidates with conditional generative models
With the fast development of COVID-19 into a global pandemic, scientists around the globe are desperately searching for effective antiviral therapeutic agents. Bridging systems biology and drug discovery, we propose a de…
Drug DiscoveryDeepVir -- Graphical Deep Matrix Factorization for "In Silico" Antiviral Repositioning: Application to COVID-19
This work formulates antiviral repositioning as a matrix completion problem where the antiviral drugs are along the rows and the viruses along the columns. The input matrix is partially filled, with ones in positions whe…
Matrix CompletionMachine-Learning Driven Drug Repurposing for COVID-19
The integration of machine learning methods into bioinformatics provides particular benefits in identifying how therapeutics effective in one context might have utility in an unknown clinical context or against a novel p…
BIG-bench Machine LearningDCcov: Repositioning of Drugs and Drug Combinations for SARS-CoV-2 Infected Lung through Constraint-Based Modelling
The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no effective antiviral drug except treatments for symptomatic therapy. Flux balance analysis is an efficient method to analyze metabolic …
In silico Prediction of Mozenavir as potential drug for SARS-CoV-2 infection via Binding Multiple Drug Targets
Since the epidemic began in November 2019, no viable medicine against SARS-CoV-2 has been discovered. The typical medication discovery strategy requires several years of rigorous research and development as well as a sig…
BlockingMolecular Docking