Computational Studies on Antibody Drug Conjugates (ADCs) for Precision Oncology

Abstract

AbstractAfter decades of technological research, the basic understanding of Antibody‐drug conjugates (ADCs) has resulted in the development of therapeutic agents for cancer patients. In this work, we have studied the mechanism of only nine FDA‐approved ADCs (Nat Rev Clin Oncol. 2021;18(6):327‐344) by computational methods, while many more ADCs are in preclinical and clinical development. The biological and Absorption, distribution, metabolism, excretion, and toxicity (ADMET) risk prediction for cytotoxic payloads is estimated to predict their bioavailability as drugs for the treatment of cancer patients. Other potential targets for the cytotoxic payloads are accessed by SwissTargetPrediction. Docking for the optimized structures of drugs and linkers are carried out by AutoDock tools. CABS‐flex 2.0 web server is used for Molecular Dynamics (MD) simulations of antigens and antibodies (IgG1, IgG4) and potential binding pockets for antibodies are searched by the PrankWeb server. HDOCK web server is used to find the docking of (Antigens‐ Antibodies‐ (linker‐payloads)) complexes. Protein‐ligand interaction profiler (PLIP) web server is used to find the noncovalent interactions in ADCs. Results indicated higher toxicity for the studied payloads, yet drug likeliness is observed for all studied cytotoxic payloads. The predicted targets for the payloads are mostly phosphodiesterase and protease electrochemical transporter. Strong Hydrogen bond Interactions have been observed for the ADCs. The cytotoxic payloads showed a specific binding location for the target antigens. Hopefully, these studies will help to improve the design patterns and facilitate the optimal allocation of ADCs for precision oncology in the future.