The challenges with developing therapeutic monoclonal antibodies for pandemic application

A Review of the Currently Available Antibody Therapy for the Treatment of Coronavirus Disease 2019 (COVID-19)

Monoclonal antibodies are a promising treatment for COVID-19. However, the emergence of SARS-CoV-2 variants raised concerns about these therapies' efficacy and long-term viability. Studies reported several antibodies, that received authorization for COVID-19 treatment, are not effective against new variants or subvariants of SARS-CoV-2, hence their distribution has to be paused. Here, the authors reviewed the status of the currently available monoclonal antibodies for COVID-19 treatment, their potential as a therapeutic agent, and the challenges ahead. To address these issues, the authors presented general information on SARS-CoV-2 and how monoclonal antibodies work against SARS-CoV-2. The authors then focus on the antibodies that have been deployed for COVID-19 treatment and their current status, as well as the evidence supporting their potential as an early intervention against COVID-19. Lastly, the authors discussed some leading obstacles that hinder the development and administration of monoclonal antibodies for the treatment of COVID-19.

In silico proof of principle of machine learning-based antibody design at unconstrained scale

Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody-binding parameters. The simulation framework enables the computation of synthetic antibody-antigen 3D-structures, and it functions as an oracle for unrestricted prospective evaluation and benchmarking of antibody design parameters of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (one dimensional: 1D) data can be used to design conformational (three dimensional: 3D) epitope-specific antibodies, matching, or exceeding the training dataset in affinity and developability parameter value variety. Furthermore, we established a lower threshold of sequence diversity necessary for high-accuracy generative antibody ML and demonstrated that this lower threshold also holds on experimental real-world data. Finally, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.