The in silico workbench applications below are an ongoing work-in-progress which will be updated when more data is available. The workbench is intended for use by researchers and scientists to assist with clinical trial design and for exploratory and educational purposes. It is NOT intended for use in the diagnosis, cure, mitigation, or treatment of any disease.
Data provided are not intended to suggest that any product and/or dosing regimens are safe and effective for any use and are for information purposes only. If you would like advice or support on a clinical study design, please use the Contact Us form to send a message. Please see Terms and Conditions for additional information.
COMPOUND SCREENING DASHBOARD
Based on rapidly emerging information on COVID-19, how do we prioritize candidate therapeutics for antiviral use in a pandemic? In the Clinical Pharmacology Triage section of this Compound Screening Dashboard, you can select compounds of interest and then access a heat map that shows which ones rate high and low on proof of hope for COVID-19, based on key metrics such as Cmax/EC50 and Cminss/EC50.
PK/PD Simulators
These simulations are presented to inform researchers and provide a tool for discussing target concentrations in the relevant tissue over time. Not presented is the variability of target concentration, but between-subject variability in pharmacokinetics was simulated. The simulations have not accounted for differences in protein binding between the in vitro cell culture systems and protein binding in either plasma or lung.
Azithromycin (AZM)
Chloroquine (CQ)
Darunavir (DVR-RTV)
Hydroxychlroquine (HCQ)
Ivermectin (IVM)
Lopinavir/ritonavir (LPV/RTV)
Nelfinavir (NFV)
Viral Kinetic Model
Viral dynamics models provide valuable insight into the life cycle of infectious agents. This in silico viral kinetic model reveals the challenges of monotherapy treatment: a monotherapy treatment must be initiated very early in viral infection and be quite potent. However, combination therapy where different steps of the SARS-CoV-2 life cycle are modestly impacted may be an alternate strategy. Numerous model structures have been proposed, but the model for extremely fast and short-duration replication (e.g., influenza) derive from Baccam 2006. The model used here is from Gonçalves 2020 and expands the infected cell population.
We performed simulations with this viral kinetic model and found that combinations of therapeutics targeting specific rate constants have greater probability of efficacy, especially in the treatment of early infection phase in COVID-19 patients. By targeting multiple points central to viral replication within infected host cells or release from those cells, we may be able to reduce both viral load and host cell infection. Access the full manuscript which is available as a preprint: Michael Dodds, Rajesh Krishna, Antonio Gonclaves, Craig Rayner. Model-Informed Drug Repurposing: Viral Kinetic Modeling to Prioritize Rational Drug Combinations for COVID-19. Authorea. June 12, 2020. DOI: 10.22541/au.159200535.51965254Hi
Epidemiological Model
This epidemiology model is a type of Susceptible-Exposed-Infectious-Recovered (SEIR) model, a mathematical formalism commonly used for modeling how infectious diseases spread in populations. Such models are the most common tools that have been used to historically model the spread of malaria, HIV, seasonal influenza, and other circulating viruses. This is the main tool used by epidemiologists and statisticians to better understand the ongoing coronavirus epidemic (see Hauser et al. (2020)). SEIR models can be linked to pharmacometrics models and other types of models used in healthcare to assess new pharmaceutical interventions (see Rayner et al. (2013), Kamal et al. (2017)).
There are a number of epidemiological models for COVID-19 available, including mechanistic models such as the susceptible-exposed-infections-recovered (SEIR) framework, agent-based models, and curve-fitting models. Our is a basic SEIR model that is well-suited for educational purposes to simulate hypothetical COVID-19 pandemics and understand the impact of therapeutics. This model should not be used to make predictions about the current pandemic as it is not calibrated to any particular situation or region.