A high-throughput platform for biophysical antibody developability assessment to enable AI/ML model training

Antibodies must bind their targets with high affinity and specificity to achieve useful therapeutic activity. They must also possess additional properties—collectively referred to as developability properties—that ensure favorable production, formulation, and in vivo performance. Both types of properties—comprising a dozen interacting but distinct attributes—are inherent to an antibody amino acid sequence. Identification or selection of antibodies possessing suitable binding characteristics is now routine, and de novo computational design models, trained on extensive complementarity-determining region sequence and structural data, are rapidly improving. Developability properties, by way of contrast, remain difficult to predict—largely due to insufficient training data—with empirical testing being used heavily to avoid challenges in late-stage antibody development. To fill this gap, we built a high-throughput antibody developability assay platform designed to generate the large datasets needed to train improved machine learning (ML) models. We optimized and automated known developability assays [ Jain et al., 2017 ], and developed a robust integrated data analytics pipeline. Here we report data on 246 antibodies—representing approved, clinical-stage, and preregistration molecules—across a panel of 10 developability assays, in a “tidy data” format suitable for AI/ML modeling. We used these data to propose updated developability warning thresholds based on 106 approved antibodies, and to confirm preliminarily that predictive models do improve with more training data. Our high-throughput platform PROPHET-Ab enables data generation at the scale needed to develop improved ML models to predict antibody developability.