Nanomedicine & LNPs Asia 2026 Agenda

Welcome and Introduction by Conference Co-Chairperson
Nan Zhang, Associate Professor, University College Dublin, Ireland

Welcome and Introduction by Conference Co-Chairperson
Liang Zhao, Associate Professor, Beijing University of Technology, China

Presentation Details to be Confirmed.
Chwee Teck Lim, NUS Society Chair Professor, Department of Biomedical Engineering, Institute for Health Innovation & Technology (iHealthtech), Mechanobiology Institute, National University of Singapore, Singapore

Engineering Liquid Biopsy: From Automated Exosome Analysis to Hand-Powered Point-of-Care Diagnostics
Yoon-Kyoung Cho, Professor, Biomedical Engineering and Dean, UNIST (Ulsan National Institute of Science & Technology), Korea South

Liquid biopsy is transforming precision medicine by enabling minimally invasive access to circulating biomarkers for disease detection and monitoring. Among these biomarkers, extracellular vesicles (EVs) offer exceptional promise due to their molecular richness and stability. However, widespread clinical and global deployment of liquid biopsy technologies remains limited by challenges in sensitivity, automation, cost, and accessibility.

In this talk, I will present our engineering approach based on centrifugal microfluidics to address these challenges across diverse clinical settings, spanning fully automated laboratory systems to hand-powered point-of-care diagnostics. First, I will describe our lab-on-a-disc platforms for high-performance EV analysis, which enable rapid and automated isolation of nanoscale EVs from biological fluids. Building on this foundation, I will introduce a digital EV profiling strategy that allows direct, amplification-free detection of tumor-derived EV RNAs from unprocessed plasma, substantially improving sensitivity for clinically relevant mutations and treatment monitoring. I will then expand beyond centralized laboratories to introduce hand-powered centrifugal diagnostic tools designed for extreme point-of-care environments. These platforms harness manual rotation to perform bacterial enrichment and nanoplasmonic sensing, enabling rapid, electricity-free identification of infectious pathogens with high precision. Together, these technologies illustrate how microfluidic engineering can unify advanced molecular diagnostics with scalable, low-cost solutions, accelerating the translation of liquid biopsy and precision diagnostics from the laboratory to real-world clinical and global health applications.

High-Throughput Reproducible Organ-on-a-Chip Platform: A New Strategy for Drug Validation
Noo Li Jeon, Professor, Seoul National University, Korea South

Accurate prediction of human drug responses during the preclinical stage is essential for successful drug development, yet conventional 2D cultures and animal models often fail to mimic complex human physiology. This seminar introduces an advanced Organ-on-a-Chip (OoC) platform designed to provide a robust validation stage for the drug discovery pipeline.

Our system moves beyond laboratory-scale research by establishing mass-production processes and high experimental reproducibility suitable for the biotech industry. By co-culturing patient-derived 3D tumor spheroids with cancer-associated fibroblasts (CAFs), we have precisely recapitulated the dense stroma and vascular microenvironments of solid tumors.

This platform allows for the evaluation of how small-molecule therapies and large biologics, as well as such as Antibody-Drug Conjugates (ADCs), penetrate the tumor microenvironment. We will present data on patient-specific efficacy and vascular toxicity resulting from endothelial barrier disruption. By combining highly reproducible chip design with sophisticated tumor modeling, this translational research platform significantly enhances clinical predictability and overcomes the limitations of traditional preclinical models.

Presentation Details to be Confirmed.
Lorena Diéguez, Leader of the Medical Devices Research Group, INL- International Iberian Nanotechnology Laboratory, Portugal