Identifying developability risks for clinical progression of antibodies using high-throughput in vitro and in silico approaches

With the growing significance of antibodies as a therapeutic class, identifying developability risks early during development is of paramount importance. Several high-throughput in vitro assays and in silico approaches have been proposed to de-risk antibodies during early stages of the discovery process. In this review, we have compiled and collectively analyzed published experimental assessments and computational metrics for clinical antibodies. We show that flags assigned based on in vitro measurements of polyspecificity and hydrophobicity are more predictive of clinical progression than their in silico counterparts. Additionally, we assessed the performance of published models for developability predictions on molecules not used during model training. We find that generalization to data outside of those used for training remains a challenge for models. Finally, we highlight the challenges of reproducibility in computed metrics arising from differences in homology modeling, in vitro assessments relying on complex reagents, as well as curation of experimental data often used to assess the utility of high-throughput approaches. We end with a recommendation to enable assay reproducibility by inclusion of controls with disclosed sequences, as well as sharing of structural models to enable the critical assessment and improvement of in silico predictions.

Automated summary

With the increasing importance of antibodies as therapeutic agents, it is crucial to identify developability risks early in the development process. Various high-throughput in vitro assays and in silico approaches have been proposed to mitigate these risks. This review analyzes published experimental assessments and computational metrics for clinical antibodies and finds that in vitro measurements of polyspecificity and hydrophobicity are more predictive of clinical progression than in silico counterparts. It also highlights the challenges of model generalization and the reproducibility of computed metrics.