Mathematical artificial intelligence design of mutation-proof COVID-19 monoclonal antibodies

TL;DR

This paper introduces a novel AI and algebraic topology-based approach to design mutation-proof monoclonal antibodies for COVID-19, aiming to overcome challenges posed by viral mutations and improve treatment efficacy.

Contribution

It presents a deep mutational scanning framework combined with topological AI to develop mutation-resistant mAbs, validated by extensive data and experimental results.

Findings

Topological AI-designed mAbs are effective against WHO-designated variants.

The methodology is validated with tens of thousands of mutational data points.

Predictions align with experimental and population-level genomic data.

Abstract

Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have compromised existing vaccines and posed a grand challenge to coronavirus disease 2019 (COVID-19) prevention, control, and global economic recovery. For COVID-19 patients, one of the most effective COVID-19 medications is monoclonal antibody (mAb) therapies. The United States Food and Drug Administration (U.S. FDA) has given the emergency use authorization (EUA) to a few mAbs, including those from Regeneron, Eli Elly, etc. However, they are also undermined by SARS-CoV-2 mutations. It is imperative to develop effective mutation-proof mAbs for treating COVID-19 patients infected by all emerging variants and/or the original SARS-CoV-2. We carry out a deep mutational scanning to present the blueprint of such mAbs using algebraic topology and artificial intelligence (AI). To reduce the risk of clinical trial-related failure, we select five mAbs either with FDA EUA or in clinical trials as our starting point. We demonstrate that topological AI-designed mAbs are effective to variants of concerns and variants of interest designated by the World Health Organization (WHO), as well as the original SARS-CoV-2. Our topological AI methodologies have been validated by tens of thousands of deep mutational data and their predictions have been confirmed by results from tens of experimental laboratories and population-level statistics of genome isolates from hundreds of thousands of patients.