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  1. Preparedness Research

Strengthening coronavirus models with systems biology and machine learning

Research to help inform development and FDA review of COVID-19 medical countermeasures

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Image representing AI (artificial intelligence) and SARS-CoV-2 virus particles (the virus that causes COVID-19)

Background | Project description | Project outcomes | Additional reading | Publications | Presentations

Performer: Commonwealth Scientific and Industrial Research Organisation (CSIROExternal Link Disclaimer), Australia
Project leader: Professor Seshadri S. Vasan, DPhil (Oxon)External Link Disclaimer
Contract value: $2,053,902 
Project dates: September 2021 – September 2026  

View the June 2022 FDA Grand Rounds lecture by Dr. Vasan about this research: Some Perspectives on Data Science and Coronaviruses - webcastExternal Link Disclaimer (59 minutes)  

Background

Nonclinical (in vitro and animal) models play an important role in helping to select promising medical countermeasure (MCM) candidates for evaluation in clinical trials. The successful development of nonclinical models for a new emerging infectious disease requires consideration of a range of complex host-pathogen factors. 

The scientific community currently uses several methods to evaluate nonclinical models in support of COVID-19 MCM development, including virus replication, histopathology, immunology data read-outs, and observation of clinical signs. The research performed during this project will help refine the methods to assess existing nonclinical models for SARS-CoV-2 and develop new models using novel approaches and technologies, which can ultimately support development and evaluation of MCMs against COVID-19 as well pathogens that may emerge with pandemic potential (e.g., “Disease-X”).

Project description

In this Medical Countermeasures Initiative (MCMi) regulatory science project, Australia’s national science agency CSIRO (Commonwealth Scientific and Industrial Research Organisation) and its global partners will use systems biology and machine learning approaches to enhance the understanding of nonclinical model responses to SARS-CoV-2. 

They will perform genomic and bioinformatic analysis of the SARS-CoV-2 virus and its key variants (including delta and omicron), as well as multi-omics analyses (including transcriptomics, metabolomics, lipidomics, and proteomics) on different types of samples collected from a range of nonclinical in vitro (organoid/tissue) models and in vivo models, as well as from clinical studies on COVID-19 and long COVID-19. The project will use samples from ongoing and planned studies, as well as biobanked samples from previous research experiments (see informational references listed under additional reading).

CSIRO will be collaborating with the following academic, industry, and public sector partners in Australia, India, the UK, and the US, to achieve the project’s outcomes:

Project outcomes

During this project, CSIRO and national and international partners listed above, will identify differentially modulated biomarkers of COVID-19 immunity, disease progression and severity, and MCM efficacy. Since the multi-omic response variables will result in a multi-dimensional big data set, appropriate machine learning approaches will be used to reduce the dimensionality of data (without losing important information) and find correlations.

This project will also enable the comparison of host-pathogen responses across different in vitro microphysiological systems (MPS), such as 3D tissues and organoids, with pertinent COVID-19 nonclinical models and human studies, enabling enhanced and host-directed MCM screening methods, and laying the groundwork for extending this approach to emerging pathogens. 

Additionally, CSIRO’s approaches to quantifying coronavirus evolution, virulence and virus-host interactions will be refined in this project through an advanced analysis of SARS-CoV-2 mutations of structural, epidemiological, and functional consequence. 

Major objectives of this project include: 

  • Characterizing SARS-CoV-2 virus and emerging variants of concern (such as omicron) through genomic and bioinformatic analysis,
  • Evaluating in vitro (e.g., MPS, 3D tissues, and organoids) models of coronavirus infection to support MCM evaluation,
  • Identifying virology and systems biology biomarkers of host responses using samples from COVID-19 nonclinical and human studies, and
  • Iteratively subjecting multi-omics data from in vitro and in vivo experiments to integrated analysis using artificial intelligence (AI) and machine learning.

This will enable comparison between in vitro and in vivo models and clinical data, and help refine these models of human disease. Better understanding correlations in this data can support public health emergency preparedness and response with additional regulatory science models to enable rapid response to future emerging pathogens. 
 
This project is funded through the MCMi Regulatory Science Extramural Research program.

Additional reading

Bauer, D.C., Tay, A.P., …, Tachedjian, M., Drew, T.W. and Vasan, S.S. (2020). Supporting pandemic response using genomics and bioinformatics: A case study on the emergent SARS-CoV-2 outbreak. Transboundary & Emerging Diseases, 67(4):1453-1462. DOI: 10.1111/tbed.13588External Link Disclaimer.

Beale, D.J., Shah, R., Karpe, A.V., Hillyer, K.E., McAuley, A.J., Au, G.G., Marsh, G.A. and Vasan, S.S. (2021). Metabolic profiling from an asymptomatic ferret model of SARS-CoV-2 infection. Metabolites, 11(5):327. DOI: 10.3390/metabo11050327External Link Disclaimer.

Callaway, E. (2020). Labs rush to study coronavirus in transgenic animals - some are in short supply. Nature, 579:183. DOI: 10.1038/d41586-020-00698-xExternal Link Disclaimer.

Farr, R.J., Rootes, C.L., Rowntree, L.C., Nguyen, T.H.O., Hensen, L., Kedzierski, L., Cheng, A.C., Kedzierska, K., Au, G.G., Marsh, G.A., Vasan, S.S., Foo, C.H., Cowled, C. and Stewart, C.R. (2021). Altered microRNA expression in COVID-19 patients enables identification of SARS-CoV-2 infection. PLoS Pathogens, 17(7): e1009759. DOI: 10.1371/journal.ppat.1009759External Link Disclaimer.

Jayaraman, K.S. (2020). Ferret chosen as animal model to test coronavirus vaccines. Nature Asia. DOI: 10.1038/nindia.2020.60External Link Disclaimer.

Malladi, S.K., Patel, U.R., …, Vasan, S.S., Ringe, R.P. and Varadarajan, R. (2021). Immunogenicity and protective efficacy of a highly thermotolerant, trimeric SARS-CoV-2 receptor binding domain derivative. ACS Infectious Diseases, 7(8):2546-2564. DOI: 10.1021/acsinfecdis.1c00276External Link Disclaimer.

Marsh, G.A., McAuley, A.J., …, Gilbert, S.C., Lambe, T. and Vasan, S.S. (2021). ChAdOx1 nCoV-19 (AZD1222) vaccine candidate significantly reduces SARS-CoV-2 shedding in ferrets. npj Vaccines, 6(1):67. DOI: 10.1038/s41541-021-00315-6External Link Disclaimer. 

McAuley, A.J., Kuiper, M.J., …, Smith, T.R.F., Broderick, K.E. and Vasan, S.S. (2021). Experimental and in silico evidence suggests vaccines are unlikely to be affected by D614G mutation in SARS-CoV-2 spike protein. npj Vaccines, 5:96. DOI: 10.1038/s41541-020-00246-8External Link Disclaimer.

Muñoz-Fontela, C., Dowling, W.E., …, Vasan, S.S., Henao-Restrepo, A.M. and Barouch, D.H. (2020). Animal models for COVID-19. Nature, 586:509-515. DOI: 10.1038/s41586-020-2787-6External Link Disclaimer.

Riddell, S., Goldie, S., …, Smith, T.R.F., Broderick, K.E. and Vasan, S.S. (2021). Live virus neutralisation of the 501Y.V1 and 501Y.V2 SARS-CoV-2 variants following INO-4800 vaccination of ferrets. Frontiers in Immunology, 12:694857. DOI: 10.3389/fimmu.2021.694857External Link Disclaimer.

Li, C.-X., Gao, J., Zhang, Z., Chen, L., Li, X., Zhou, M., & Wheelock, Å. M. (2021). Multiomics integration-based molecular characterizations of COVID-19. In Briefings in Bioinformatics (Vol. 23, Issue 1). Oxford University Press (OUP). https://doi.org/10.1093/bib/bbab485External Link Disclaimer

Singanallur, N. B., van Vuren, P. J., McAuley, A. J., et al. (2022). At Least Three Doses of Leading Vaccines Essential for Neutralisation of SARS-CoV-2 Omicron Variant. In Frontiers in Immunology (Vol. 13). Frontiers Media SA. https://doi.org/10.3389/fimmu.2022.883612External Link Disclaimer

Publications

Presentations

McAuley, Alexander. Use of Human Tissue Models for Screening of Drugs Against SARS-CoV-2 Infection. Presented at CSIRO-FDA-UK-ICN Cutting Edge Virtual Symposium on Coronaviruses with “Disease X” Potential [Online]. 28-30 September 2022.

Beale, David; Shah, Rohan; Karpe, Avinash; Hillyer, Katie; Vasan, Vasan. A critical appraisal of the ferret model of SARS-CoV-2 infection from a metabolomics perspective. In: Prof. Markus R Wenk, editor/s. 9th International Singapore Lipid Symposium; 1st to 5th March 2021; National University of Singapore. The International Lipidomics Society; 2021. 1. http://hdl.handle.net/102.100.100/392548?index=1External Link Disclaimer Record Identifier: CSIRO EP21948

Cooper, Darcie. The long-term psychological consequences of COVID-19 infection in an observational cohort study. Presented at CSIRO-FDA-UK-ICN Cutting Edge Virtual Symposium on Coronaviruses with “Disease X” Potential [Online]. 28-30 September 2022.

Au, Gough. Characterisation of SARS-CoV-2 delta variant infection in a human ACE2 knock-in mouse model. Presented at CSIRO-FDA-UK-ICN Cutting Edge Virtual Symposium on Coronaviruses with “Disease X” Potential [Online]. 28-30 September 2022.

CSIRO FDA UK-ICN: Cutting Edge Symposium on ‘Coronaviruses with Disease-X Potential’External Link Disclaimer scheduled on September 28-30, 2022. Peer-reviewed papers from this symposium will be published open-access in the Journal of General Virology. Please check this section for updates.

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