Machine learning-optimized charge transfer nanocrystals for synergistic NIR photothermal therapy, ferroptosis, and electrochemical detection of breast cancer

Machine learning optimized nanocrystals with narrow band in the NIR-I region were synthesized. • DBT-F 4 TCNQ derive nanocrystals offer excellent photothermal and sensing performance. • Designed theranostic platform integrates cancer therapy with precise electrochemical sensing. • DBT-F 4 TCNQ derive nanocrystals demonstrated effective glutathione depletion. Despite significant advances in near-infrared (NIR) photothermal agents, most current systems face challenges including large band gaps and poorly defined crystalline morphology, compromised responsiveness in the tumor microenvironment due to inefficient and time-consuming optimization, and dependence on separate, cancer type-specific diagnostic platforms for effective treatment. To overcome these challenges, we rationally synthesized a novel class of charge transfer complex-derived nanocrystals (CTNs) with well-defined morphology and a significantly reduced band gap in the NIR-I region, achieved for the first time through the unique combination of DBT as the electron donor with F 4 TCNQ, TCNB, and DTTCNQ as acceptors. Additionally, we employed machine learning (ML) to systematically optimize donor-to-acceptor ratios in CTNs, ultimately identifying DBT-F 4 TCNQ as the most effective formulation, with strong NIR absorption, excellent photothermal conversion, and superior electrocatalytic efficiency. Interestingly, ML-optimized DBT-F 4 TCNQ derived from CTNs demonstrated effective cysteine-triggered intracellular depletion of glutathione (GSH), thereby promoting reactive oxygen species (ROS) accumulation and inducing ferroptosis. Additionally, the in vivo antitumor efficacy was validated through NIR-triggered photothermal therapy combined with ROS-mediated ferroptosis, while the diagnostic potential of the designed CTNs was demonstrated via in-direct electrochemical monitoring of choline released from 4T1 cancer cells. Thus, the designed ML-optimized CTNs derived from the novel DBT-F 4 TCNQ combination hold great promise as a next-generation theranostic platform by integrating therapeutic efficacy with diagnostic precision.