Abstract
Heparan sulfate (HS) is a cell surface polysaccharide recently identified as a coreceptor with the ACE2 protein for the S1 spike protein on SARS-CoV-2 virus, providing a tractable new therapeutic target. Clinically used heparins demonstrate an inhibitory activity but have an anticoagulant activity and are supply-limited, necessitating alternative solutions. Here, we show that synthetic HS mimetic pixatimod (PG545), a cancer drug candidate, binds and destabilizes the SARS-CoV-2 spike protein receptor binding domain and directly inhibits its binding to ACE2, consistent with molecular modeling identification of multiple molecular contacts and overlapping pixatimod and ACE2 binding sites. Assays with multiple clinical isolates of SARS-CoV-2 virus show that pixatimod potently inhibits the infection of monkey Vero E6 cells and physiologically relevant human bronchial epithelial cells at safe therapeutic concentrations. Pixatimod also retained broad potency against variants of concern (VOC) including B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), and B.1.1.529 (Omicron). Furthermore, in a K18-hACE2 mouse model, pixatimod significantly reduced SARS-CoV-2 viral titers in the upper respiratory tract and virus-induced weight loss. This demonstration of potent anti-SARS-CoV-2 activity tolerant to emerging mutations establishes proof-of-concept for targeting the HS-Spike protein-ACE2 axis with synthetic HS mimetics and provides a strong rationale for clinical investigation of pixatimod as a potential multimodal therapeutic for COVID-19.
| Original language | English |
|---|---|
| Pages (from-to) | 527-545 |
| Number of pages | 19 |
| Journal | ACS Central Science |
| Volume | 8 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 25 May 2022 |
Bibliographical note
Funding Information:The authors would like to thank Zucero Therapeutics for provision of pixatimod and Queensland Health Forensic & Scientific Services, Queensland Department of Health, for the provision of QLD02 and QLD935 SARS-CoV-2 isolates. V. Ferro acknowledges support from the Australian Research Council (DP170104431). T. Bergström acknowledges support of the Swedish Research Council. A. A. Khromykh, and A. Suhrbier acknowledge funding support from the Australian Infectious Diseases Research Centre. Computational (and/or data visualization) resources and services used in this work were provided by the eResearch Office, Queensland University of Technology, Brisbane, Australia, and with the assistance of resources from the National Computational Infrastructure (NCI Australia), an NCRIS enabled capability supported by the Australian Government. N. S. Gandhi is supported through the Advance Queensland Industry Research Fellowship. A. Suhrbier is supported by an Investigator grant from the National Health and Medical Research Council of Australia and acknowledges philanthropic support from inter alia Clive Berghofer and Lyn Brazil, as well as contract R&D funding from Zucero. M. A. Skidmore, S. E. Guimond, C. J. Mycroft-West, and J. E. Turnbull acknowledge the support of the University of Keele. J. E. Turnbull and E. A. Yates acknowledge the support of the University of Liverpool and contract R&D funding from Zucero. Z. Yang acknowledges the Danish National Research Foundation (DNRF107) and the Lundbeck Foundation. Y.-H. Chen acknowledges the Innovation Fund Denmark. R. Karlsson acknowledges the European Commission (GlycoImaging H2020-MSCA-ITN-721297).
Publisher Copyright:
© 2022 The Authors. Published by American Chemical Society.
