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

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

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:

• Barwon Health
• Birla Institute of Technology and Science
• Inovio Pharmaceuticals (self-funded)
• James Cook University
• Murdoch Children’s Research Institute
• National Center for Advancing Translational Sciences (self-funded)
• Swinburne University of Technology
• University of Melbourne
• University of New South Wales
• University of Oxford
• University of Texas Medical Branch
• University of York

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.13588.
• 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/metabo11050327.
• 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-x.
• 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.1009759.
• Jayaraman, K.S. (2020). Ferret chosen as animal model to test coronavirus vaccines. Nature Asia. DOI: 10.1038/nindia.2020.60.
• 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.1c00276.
• 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/s41541-021-00315-6. 
• 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/s41541-020-00246-8.
• 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/s41586-020-2787-6.
• 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.2021.694857.

Related links

• Expert commentary: Omicron coronavirus variant (CSIRO media statement, December 1, 2021)
• New studentship at York to support FDA regulatory science (University of York news, October 12, 2021)
• New funding to fast track COVID-19 treatments (CSIRO press release, July 1, 2021)