Siska, C. C. | Search bioRxiv

bioRxiv2023

AI-based antibody discovery platform identifies novel, diverse and pharmacologically active therapeutic antibodies against multiple SARS-CoV-2 strains

A critical aspect of a successful pandemic response is expedient antibody discovery, manufacturing and deployment of effective lifesaving treatments to patients around the world. However, typical drug discovery and development is a lengthy multi-step process that must align drug efficacy with multiple developability criteria and can take years to complete. In this context, artificial intelligence (AI), and especially machine learning (ML), have great potential to accelerate and improve the optimization of therapeutics, increasing their activity and safety as well as decreasing their development time and manufacturing costs. Here we present a novel, cost-effective and accelerated approach to therapeutic antibody discovery, that couples AI-designed human antibody libraries, biased for improved developability attributes with high throughput and sensitive screening technologies. The applicability of our platform for effective therapeutic antibody discovery is demonstrated here with the identification of a panel of human monoclonal antibodies that are novel, diverse and pharmacologically active. These first-generation antibodies, without the need for affinity maturation, bind to the SARS-CoV-2 spike protein with therapeutically-relevant specificity and affinity and display neutralization of SARS-CoV-2 viral infectivity across multiple strains. Altogether, this platform is well suited for rapid response to infectious threats, such as pandemic response. IMPORTANCEExpedient discovery and manufacturing of lifesaving therapeutics is critical for pandemic response. The recent COVID pandemic has highlighted the current inefficiencies and the need for improvements. To this end, we present our therapeutic antibody discovery platform that couples artificial intelligence (AI) and innovative high throughput technologies, and we demonstrate its applicability to rapid response. This platform enabled the isolation, characterization, and rapid identification of effective broadly neutralizing SARS-CoV-2 antibodies with good developability attributes, anticipated to fit our current process development and manufacturing platform. As such, this would benefit cost-of-goods and improve therapeutic access to patients. The AI-derived antibodies represent an advantageous therapeutic modality that can be developed and deployed fast, thus well suited for rapid response to infectious threats, such as pandemic response.

immunology↗

bioRxiv2020

Designing Feature-Controlled Humanoid Antibody Discovery Libraries Using Generative Adversarial Networks

We demonstrate the use of a Generative Adversarial Network (GAN), trained from a set of over 400,000 light and heavy chain human antibody sequences, to learn the rules of human antibody formation. The resulting model surpasses common in silico techniques by capturing residue diversity throughout the variable region, and is capable of generating extremely large, diverse libraries of novel antibodies that mimic somatically hypermutated human repertoire response. This method permits us to rationally design de novo humanoid antibody libraries with explicit control over various properties of our discovery library. Through transfer learning, we are able to bias the GAN to generate molecules with key properties of interest such as improved stability and developability, lower predicted MHC Class II binding, and specific complementarity-determining region (CDR) characteristics. These approaches also provide a mechanism to better study the complex relationships between antibody sequence and molecular behavior, both in vitro and in vivo. We validate our method by successfully expressing a proof-of-concept library of nearly 100,000 GAN-generated antibodies via phage display. We present the sequences and homology-model structures of example generated antibodies expressed in stable CHO pools and evaluated across multiple biophysical properties. The creation of discovery libraries using our in silico approach allows for the control of pharmaceutical properties such that these therapeutic antibodies can provide a more rapid and cost-effective response to biological threats.

immunology↗