A 3D Tumor‐Mimicking In Vitro Drug Release Model of Locoregional Chemoembolization Using Deep Learning‐Based Quantitative Analyses
Xiaoya Liu, Xueying Wang, Yucheng Luo, Meijuan Wang, Zijian Chen, Xiaoyu Han, Sijia Zhou, Jiahao Wang, Jian Kong, Hanry Yu, Xiaobo Wang, Xiaoying Tang, Qiongyu Guo
Abstract
Primary liver cancer, with the predominant form as hepatocellular carcinoma (HCC), remains a worldwide health problem due to its aggressive and lethal nature. Transarterial chemoembolization, the first-line treatment option of unresectable HCC that employs drug-loaded embolic agents to occlude tumor-feeding arteries and concomitantly delivers chemotherapeutic drugs into the tumor, is still under fierce debate in terms of the treatment parameters. The models that can produce in-depth knowledge of the overall intratumoral drug release behavior are lacking. This study engineers a 3D tumor-mimicking drug release model, which successfully overcomes the substantial limitations of conventional in vitro models through utilizing decellularized liver organ as a drug-testing platform that uniquely incorporates three key features, i.e., complex vasculature systems, drug-diffusible electronegative extracellular matrix, and controlled drug depletion. This drug release model combining with deep learning-based computational analyses for the first time permits quantitative evaluation of all important parameters associated with locoregional drug release, including endovascular embolization distribution, intravascular drug retention, and extravascular drug diffusion, and establishes long-term in vitro-in vivo correlations with in-human results up to 80 d. This model offers a versatile platform incorporating both tumor-specific drug diffusion and elimination settings for quantitative evaluation of spatiotemporal drug release kinetics within solid tumors.