A Machine Learning Platform for Interconnecting Antibody-Drug Conjugate Cytotoxic Design with Tumor Cell Biology

Antibody-drug conjugates (ADCs) represent a significant advancement in therapeutic oncology, as they precisely deliver cytotoxic drugs to target tumor cells. However, ADC development is complex due to the entangled interplay between chemical design and tumor cell biology. Therefore, a platform was developed consisting of an ADC-tumor cell interconnected multimodal framework for machine learning applications. It contains ADC records from the past two decades that details linkers, payloads, drug-antibody ratios, and cytotoxicity IC50 values. Biological interconnection was achieved through integrating omics data from ~1,400 human tumor cell lines. Moreover, a protein intensity prediction tool was developed that further enriched the multifaceted framework by concentrating on cell surface antigens. A deep learning model was trained on the framework and accurately predicted ADC in vitro activity across tumor cell lines at relevant nanomolar thresholds. This work exposes the complexities at the ADC-tumor cell interface and can significantly influence current empirical ADC design decisions.