Catalytic Signal Transduction Theory Enabled Virtual Screening of Nanomaterials for Medical Functions

Conspectus Developing biocompatible catalytic nanomaterials (NMs) to target cancer reactive oxygen species (ROS) has provided an alternative chemotherapy strategy for cancer. Compared to the traditional chemotherapy strategy, this catalytic NM-based strategy can take advantage of the nanoscale and catalytic effects of materials and thus is promising to conquer the troubles from which traditional strategies usually suffer, e.g., high dosage, low targeting, severe toxicity and side effects, and susceptibility to drug resistance. Therefore, the corresponding research has been one of the hottest topics in the frontier interdisciplinary field combining materials, chemistry, biology, and medicine. So far, many NMs have been reported to have potential in cancer catalytic therapy. Despite the progress, the chemicobiological mechanisms and principles, which underlie ROS-targeted catalysis and subsequent cancer therapeutic functions of NMs have remained elusive. Therefore, although numerous inorganic NMs have been synthesized with their structural information deposited in publicly available databases, it is still challenging to computationally design or screen the appropriate candidates with the desired medical functions from the libraries. In this Account, catalytic signal transduction theory has been proposed to bridge the gap between the knowledge of catalytic activities and medical functions of inorganic NMs. The atomistic-level mechanisms responsible for the activities of inorganic NMs in catalyzing cellular ROS conversions, namely, H₂O₂ activation, H₂O₂ dismutation, O₂ activation, and O₂^•- dismutation, have been studied by density functional theory calculations. On the basis of the mechanisms, the kinetic equations and prediction models for some of the catalyses have been developed, making it possible to systematically and quantitatively describe the relationships between the surface structures and catalytic activities of NMs. The catalytic signal transduction theory assumes that the catalytic activities of NMs dominate their therapeutic activities in cancer catalytic therapy and that the order of catalytic activities mainly determines the order of therapeutic activities for NMs of the same series. According to this theory, the prediction models have been implemented into computer programs with the aid of machine learning to realize high-throughput computational screening of candidate NMs toward cancer catalytic therapy. The results have revealed mechanisms and rules on how inorganic NMs induce cancer cell death by catalyzing the chemical conversions of cellular ROS. They provided theoretical tools for the in silico design and screening of candidate NMs for cancer therapy. To make catalytic signal transduction a general approach of tuning life with catalytic NMs, the challenge now remains to achieve substrate selectivity for catalytic NMs and a systematic and deep understanding of their interactions with entire biosystems.