Rational Design of Metal-Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy.

ABSTRACT: Despite improvements in cancer survival rates, metastatic and surgery-resistant cancers, such as pancreatic cancer, remain challenging, with poor prognoses and limited treatment options. Enhancing drug bioavailability in tumors, while minimizing off-target effects, is crucial. Metal-organic frameworks (MOFs) have emerged as promising drug delivery vehicles owing to their high loading capacity, biocompatibility, and functional tunability. However, the vast chemical diversity of MOFs complicates the rational design of biocompatible materials. This study employed machine learning and molecular simulations to identify MOFs suitable for encapsulating gemcitabine, paclitaxel, and SN-38, and identified PCN-222 as an optimal candidate. Following drug loading, MOF formulations are improved for coll

SUBMITTER: Melle F 

PROVIDER: S-EPMC12747470 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature