Mastering Complexity in Cancer Nanomedicine: From Text Mining to Meta-Synergy and Automated Discovery

The field of drug delivery, particularly the area of cancer nanomedicine, is undergoing a significant transformation driven by the convergence of artificial intelligence (AI) and wet lab automation. In my talk, I will present transformative approaches my laboratory has developed to master the complexities in the field and solve hard biomedical problems. First, I will describe how we leverage text mining and computational approaches to construct dynamic knowledge bases that enable hypothesis generation for cell-specific drug delivery. By systematically mining biomedical literature, we can uncover hidden relationships between drugs, targets, and disease mechanisms that would otherwise remain obscured in the vast scientific literature. Second, I will describe our concept of meta-synergy—a framework we introduced to optimize and classify multi-drug combinations across multiple dimensions including chemical loading, pharmacodynamics, and pharmacokinetics. This approach moves beyond simple two-drug synergy to explore the "synergy of synergies," combining chemical, pharmacological, and biological cooperation to achieve therapeutic effects that are greater than the sum of their parts. Third, I will describe how we utilize automation and advanced screening methodologies, particularly aggregation-induced emission (AIE), to accelerate nanoparticle discovery and characterization. These high-throughput approaches enable us to navigate the vast design space of nanomedicine formulations more efficiently. I will argue that rather than avoiding or simplifying complexity, we should use AI to embrace it as an opportunity. By developing sophisticated tools and frameworks to work within this complexity, we can manifest new therapeutic possibilities and pave the way for more effective, precise, and adaptive cancer treatment strategies.