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Machine Learning-Informed Nano Co-Assembly Inhibits Fibroblast Activation Protein and Improves Drug Delivery in Fibrotic Tissue

Title: Machine Learning-Informed Nano Co-Assembly Inhibits Fibroblast Activation Protein and Improves Drug Delivery in Fibrotic Tissue
Authors: Liu, Zehua; Long, Qiang; Liu, Yihao; Sun, Xiuqiao; Zhang, Baoding; Liao, Binxin; Wu, Weibin; Hai, Wangxi; Zhang, Pei; Lian, Wenhua; Zhu, Yuewen; Wang, Zheng; Wu, Caisheng; Deng, Xianming; Santos, Helder A.; Ye, Xiaofeng
Contributors: Faculty of Pharmacy; Division of Pharmaceutical Chemistry and Technology; Nanomedicines and Biomedical Engineering; Divisions of Faculty of Pharmacy
Publisher Information: Wiley Blackwell
Publication Year: 2026
Collection: Helsingfors Universitet: HELDA – Helsingin yliopiston digitaalinen arkisto
Subject Terms: Fibroblast activation protein; Myocardial reperfusion injury; Pancreatic tumor; Quantitative structure-property analysis; Responsive drug delivery; Self-assembly nanoparticles; Pharmacy
Description: Nanoparticle-based drug delivery faces persistent challenges, including complex fabrication processes and limited lesional accumulation. Here we introduce SP-13786 (SP), a precise small-molecule inhibitor of fibroblast activation protein (FAP), as a universal and effective excipient enabling facile co-precipitation into stable nanoparticles (SCAN) with diverse hydrophobic drugs. Screening of 861 compounds revealed a broadly enhanced colloidal stability and drug loading by SP. Corresponding simulations and explainable machine learning (XML) showed SCAN assembly hinges on balanced aromaticity, rigidity, and nitrogen-mediated interaction, offering interpretable framework for co-assembly nanomedicine. Biological assessment demonstrate that SCAN enhances drug delivery and therapeutic efficacy in FAP-positive cells, therefore attentuate the fibrosis-induced drug penetration barriers, increasing drug accumulation within the fibrotic tissue. The improved bioavailability correlate with superior therapeutic outcomes in multiple disease models with progressive fibrosis. Overall, we establish SP as a versatile nanotherapeutic platform combining simplicity in preparation, mechanistic insights provided by XML, and broad applicability for diseases characterized by pathological fibrosis and impaired drug delivery. ; Peer reviewed
Document Type: article in journal/newspaper
File Description: application/pdf
Language: English
Relation: National Science Foundation for Distinguished Young Scholars of China (No. 82125019, 8212500418), National Key R&D Program (No. 2022YFA1105100), Academy of Finland (No. 340129, 370362), Finnish Foundation for Cardiovascular Research, National Natural Science Foundation of China (No. 8212500418, 22025702, 81671832, 81571826, 21574019, 22494692, 22537003, 92253303, and 82502529), Shanghai Sailing Program (No. 24YF2726200), China Postdoctoral Science Foundation (No. 2025M772913), UMCG Research Funds, New Cornerstone Science Foundation through the XPLORER PRIZE, Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China (No. JYB2025XDXM506).; https://hdl.handle.net/10138/629518; 001702823900001
Availability: https://hdl.handle.net/10138/629518
Rights: cc_by ; info:eu-repo/semantics/openAccess ; openAccess
Accession Number: edsbas.AA7229C2
Database: BASE