A public-private partnership for the express development of antiviral leads: a perspective view

ABSTRACT

Introduction

The COVID-19 pandemic raises the question of strategic readiness for emergent pathogens. The current case illustrates that the cost of inaction can be higher in the future. The perspective article proposes a dedicated, government-sponsored agency developing anti-viral leads against all potentially dangerous pathogen species.

Areas covered

The author explores the methods of computational drug screening and in-silico synthesis and proposes a specialized government-sponsored agency focusing on leads and functioning in collaboration with a network of labs, pharma, biotech firms, and academia, in order to test each lead against multiple viral species. The agency will employ artificial intelligence and machine learning tools to cut the costs further. The algorithms are expected to receive continuous feedback from the network of partners conducting the tests.

Expert opinion

The author proposes a bionic principle, emulating antibody response by producing a combinatorial diversity of high q uality generic antiviral leads, suitable for multiple potentially emerging species. The availability of multiple pre-tested agents and an even greater number of combinations would reduce the impact of the next outbreak. The methodologies developed in this effort are likely to find utility in the design of chronic disease therapeutics.

KEYWORDS:

• COVID-19
• anti-virals
• emergent pathogens
• docking
• artificial intelligence

Article highlights

• Viral targets are relatively conserved between evolutionally distant species.

• The target conservation allows testing of the same leads in multiple genera and species at the same time.

• >2000 of highly potent sub-nanomolar antiviral actives are immediately available for cross-testing and analysis.

• The AI-based computational tools facilitate production of enriched libraries and ligand optimization.

• Computational tools perform stronger as integrated arrays.

• The algorithms are continuously updated by the experimental feedback.

• Improved algorithm arrays make ligand screening and optimization economical.

• Separating lead generation from the more expensive stages of drug design allows focusing on this critical step with high productivity and efficiency.

• The leads selected from a greater number of initial hits by more potent computational filters are qualitatively superior.

This box summarizes key points contained in the article.

Declaration of interest

The author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This manuscript has not been funded.