A Fluorinated BODIPY-Based Zirconium Metal-Organic Framework for In Vivo Enhanced Photodynamic Therapy.

Photodynamic therapy (PDT), an emergent noninvasive cancer treatment, is largely dependent on the presence of efficient photosensitizers (PSs) and a sufficient oxygen supply. However, the therapeutic efficacy of PSs is greatly compromised by poor solubility, aggregation tendency, and oxygen depletion within solid tumors during PDT in hypoxic microenvironments. Despite the potential of PS-based metal-organic frameworks (MOFs), addressing hypoxia remains challenging. Boron dipyrromethene (BODIPY) chromophores, with excellent photostability, have exhibited great potential in PDT and bioimaging. However, their practical application suffers from limited chemical stability under harsh MOF synthesis conditions. Herein, we report the synthesis of the first example of a Zr-based MOF, namely, 69-L2, exclusively constructed from the BODIPY-derived ligands via a single-crystal to single-crystal post-synthetic exchange, where a direct solvothermal method is not applicable. To increase the PDT performance in hypoxia, we modify 69-L2 with fluorinated phosphate-functionalized methoxy poly(ethylene glycol). The resulting 69-L2@F is an oxygen carrier, enabling tumor oxygenation and simultaneously acting as a PS for reactive oxygen species (ROS) generation under LED irradiation. We demonstrate that 69-L2@F has an enhanced PDT effect in triple-negative breast cancer MDA-MB-231 cells under both normoxia and hypoxia. Following positive results, we evaluated the in vivo activity of 69-L2@F with a hydrogel, enabling local therapy in a triple-negative breast cancer mice model and achieving exceptional antitumor efficacy in only 2 days. We envision BODIPY-based Zr-MOFs to provide a solution for hypoxia relief and maximize efficacy during in vivo PDT, offering new insights into the design of promising MOF-based PSs for hypoxic tumors.

A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research.

Owing to their distinct physical and chemical properties, inorganic nanoparticles (NPs) have shown promising results in preclinical cancer therapy, but designing and engineering them for effective therapeutic purposes remains a challenge. Although a comprehensive database of inorganic NP research is not currently available, it is crucial for developing effective cancer therapies. In this context, machine learning (ML) has emerged as a transformative tool, but its adaptation to nanomedicine is hindered by inexistent or small datasets. Here we assembled a large database of inorganic NPs, comprising experimental datasets from 745 preclinical studies in cancer nanomedicine. Using descriptive statistics and explainable ML models we mined this database to gain knowledge of inorganic NP design patterns and inform future NP research for cancer treatment. Our analyses suggest that NP shape and therapy type are prominent features in determining in vivo efficacy, measured as a percentage of tumour reduction. Moreover, our database provides a large-scale open-access resource for discriminative ML that the broader nanotechnology community can utilize. Our work blueprints data mining for translational cancer research and offers evidence for standardizing NP reporting to accelerate and de-risk inorganic NP-based drug delivery, which may help to improve patient outcomes in clinical settings.