Construction of PANoptosis‐Inhibiting Carbonized Polymer Dots via Machine Learning Potential for Mitigating Chemodrug‐Induced Nephrotoxicity
Xinchen Liu, Jiaxin Zhang, Xiangyu Yan, Nuo Li, Yuchao Dong, Zihao Wang, Daowei Li, Yong Du, Huan Wang
Abstract
Chemotherapy-induced nephrotoxicity, particularly acute kidney injury (AKI), is a severe complication that is driven by multiple regulated cell death (RCD) pathways. However, current nephroprotective strategies predominantly focus on apoptosis inhibition by relieving oxidative stress. PANoptosis is a newly discovered form of RCD pathway incorporating key features of pyroptosis, apoptosis, and necroptosis, providing a potential target for synergistic multi-mechanistic nephroprotection. Herein, the enhanced sampling method is introduced for molecular dynamics based on trained machine learning force fields to guide the construction of bioactive carbonized polymer dots (Lu-CDs) with specific pharmacophoric moieties of the flavonoid compound. Unlike conventional carbonized polymer dots with solely antioxidative activity, Lu-CDs demonstrate both radical scavenging and PANoptosis-inhibiting activities. In chemodrug-induced AKI mice, a very low dose of Lu-CDs can elicit prolonged renal accumulation and superior nephroprotective efficacy compared to the small-molecule flavonoid compound and the antioxidative carbonized polymer dots. The findings of this study highlight a design strategy for bioactive nanodrugs based on the machine learning force fields for molecular dynamics and propose PANoptosis inhibition as a promising approach to mitigate chemodrug-induced nephrotoxicity.