Therapeutic monoclonal antibodies repurposing in oncology via IMGT/mAb-KG embeddings.

backgroundCancer remains one of the leading causes of mortality worldwide, accounting for approximately 9.7 million deaths in 2022. Faced with this significant public health challenge, therapeutic monoclonal antibodies (mAbs) have emerged as promising alternatives that may minimize the side effects associated with conventional treatments such as radiotherapy and chemotherapy. To support mAb research and development, IMGT®, the international ImMunoGeneTics information system, has established two standardized data sources namely IMGT/mAb-DB, a comprehensive database for mAbs, and, more recently, IMGT/mAb-KG, a dedicated knowledge graph for mAbs. Despite these advances, the development of therapeutic mAbs remains both time-consuming and financially burdensome—costs can reach up to $2.8 billion. To address this challenge and accelerate cancer treatment, mAb repurposing represents a promising alternative.

resultsCompared to existing general drug repurposing frameworks, in this study, we leveraged a specialized subset of an expert-curated mAb-specific data model dedicated to the oncology domain IMGT/mAbOnco-KG, to develop a scientific hypothesis generation application for mAb repurposing. We conducted an exhaustive benchmarking of 28 transductive knowledge graph embedding models, identifying BoxE as the superior architecture for capturing asymmetric relations inherent in mAb-target-disease interactions. A user-friendly web interface provides access to the tool, incorporating a novel trackback support allowing researchers to visualize the biological subgraphs supporting each prediction.

conclusionOur application demonstrates the potential of transductive knowledge graph embedding techniques in the oncology domain by enabling the repurposing of existing mAbs for new therapeutic uses. Using this tool, we have identified two novel mAbs, loncastuximab tesirine and glofitamab, both currently undergoing clinical trials for the treatment of chronic lymphocytic leukemia. This decision-support tool thus facilitates the discovery of new therapeutic opportunities by effectively repositioning existing mAbs for oncological indications, potentially accelerating the development of cancer therapies and addressing critical public health needs. To handle new clinical indication entities, our future research direction is to build a foundation model in immunogenetics. In addition, due to the probabilistic nature of the generation process, experimental validation by specialists in mAbs engineering field is an important next step. CLINICAL TRIAL NUMBER: Not applicable.