Building a Computational and Experimental Rapid Response Pipeline to Counter the Coronavirus Disease 2019 Outbreak and Emerging Biothreats

Kathryn Arrildt | 20-ERD-064

Project Overview

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs, especially where protective candidates can be rapidly identified or developed for emerging biothreats and escape variants. Current cutting-edge technologies for this purpose still rely on pathogen-exposed convalescent volunteers and a large screening effort to find a proverbial needle in a haystack. Computational design of protective antibodies based on pre-existing templates skips those requirements and allows for greater control over the breadth and target epitope, while also co-optimizing for potency and developability or other biophysical characteristics.

We approached this problem by building and expanding an in vitro experimental rapid antibody production and characterization pipeline to support development of an autonomous, closed-loop, active learning software system based on structural simulation and ground truth experimental data to design and evaluate antibody antigen interactions. Starting from early in the pandemic, we targeted SARS-CoV-2, the causative agent of COVID-19, by re-purposing neutralizing antibodies against SARS-CoV-1 that had been identified in the wake of that outbreak in the early 2000's. We successfully re-targeted three different anti-SARS-CoV-1 antibodies to neutralize SARS-CoV-2 in vitro, where the antibodies were generated externally and tested through conventional binding and neutralization assays internally or with collaborators.

As antibodies were identified from the blood of humans infected with SARS-CoV-2, we shifted to collaborate with academic partners to develop improved versions of their human-derived antibodies. This work reached its most important stage in rapid response to the emergence of the Omicron variant of concern (VOC) in late 2021. In a matter of weeks, enabled by on-demand innovation to our screening pipeline, we computationally designed and experimentally characterized derivative antibodies of COV2-2130, one of two antibodies from Vanderbilt that form the basis of the AstraZeneca Evusheld prophylactic drug product. This drug product suffers a serious loss of efficacy against Omicron BA.1 and BA.1.1, the first Omicron strains. Due to tight integration of computational design and experimental evaluation, we were able to identify a pair of designs with potent neutralization of the main targets Omicron BA.1 and BA.1.1; but also the earlier Delta variant, and subsequent Omicron strains including BA.2, BA.4, BA.5, and BA.2.75, demonstrating that our multi-target design process can, by its nature, produce robust antibody designs that strictly improve over the parental antibody. These results, recognized by a 2022 Director's Science and Technology award, have enabled the follow-on GUIDE program, to commence in FY23.

While earlier design campaigns were substantially outsourced, we have engineered better and faster processes internally to better compliment, calibrate, and speed computational designs. As part of the follow-on GUIDE program, we will stand up a rapid and high-throughput antibody production and characterization facility staffed with the expertise and capabilities to foster our current collaboration across PLS and ENG as well as other partnerships toward computational design of biologics. 

Mission Impact

This project provided a foundational collaboration in computational biology that has building science and technology tools and capabilities to meet future national security challenges, continuing under the GUIDE program. We were able to rapidly respond to a biological emergency in real time, but also lay the groundwork for expansion in funding, expertise, and new staff to enhance our capabilities to response to emerging security challenges associated with natural and technological surprise in bioscience and bioengineering. This, in turn, is providing new opportunities, capabilities, and research directions in computational biology science and technology to create new ways of responding to national biosecurity challenges.

Publications, Presentations, and Patents

Arrildt, Kathryn, Chelsy Chesterman, Jeanette Sierra, Mark Mednikov, Lynn Chen, Aaron Ruby, Thomas Desautels, Adam Zemla, Edmond Lau, Daniel Faissol, Jason Laliberte, Kate Luisi, Corey Mallett, Enrico Malito, Dong Yu, Sylvie Bertholet, Matthew Bottomley, Robert van den Berg,"Using antibody-antigen interactions to design better vaccine antigens" (Presentation, Keystone Symposia - Antibodies as Drugs (B1), Santa Fe, NM, Feb. 2- 6, 2020).

Zhu, Fangqiang, Feliza A. Bourguet, William F. D. Bennett, et al. 2022. "Large-scale application of free energy perturbation calculations for antibody design." Sci Rep 12, 12489.

Kimbrel, Jeffrey, Joseph Moon, Aram Avila-Herrera, Jose Manuel Martí, James Thissen, Nisha Mulakken, Sarah H. Sandholtz, Tyshawn Ferrell, Chris Daum, Sara Hall, Brent Segelke, Kathryn T. Arrildt, Sharon Messenger, Debra A. Wadford, Crystal Jaing, Jonathan E Allen, Monica K. Borucki. 2022. "Multiple Mutations Associated with Emergent Variants Can Be Detected as Low-Frequency Mutations in Early SARS-CoV-2 Pandemic Clinical Samples." Viruses 14, 2775; doi: