Nanoemulsion-based Cancer Therapy: Innovations Driven by Artificial Intelligence

Abstract

Cancer is a significant global health concern, marked by unregulated cell proliferation, tissue damage, and elevated mortality rates. Chemotherapy and radiation are traditional methods that do not target specific cells, which leads to serious side effects and low effectiveness because of drug resistance. Nanoemulsions are an interesting and growing approach to treat cancer because they can deliver drugs that are solubilized and become bioavailable. Conventional chemotherapy is often limited by poor specificity and associated systemic side effects. Nanoemulsion-based drug delivery systems help overcome these limitations through their nanoscale droplet size (20–200 nm), high surface area, and excellent biocompatibility, thereby enabling more efficient and targeted drug delivery. Nonetheless, optimizing stability, controlling release kinetics, and achieving predictable therapeutic outcomes across diverse patient populations remain significant challenges. The use of Artificial Intelligence (AI) in nanoemulsion research is changing how formulations are made and used in clinical settings. AI-driven computational models enable rapid testing of formulation parameters, accurate predictions of physicochemical properties, and identification of the best drug-excipient combinations with few experimental trials. AI-based models achieved predictive accuracy up to R² = 0.94 for key nanoemulsion properties. This reduced formulation screening by 60–70% and supported the development of cancer nanoemulsions based on quality-by-design principles. The integration of nanoemulsion technology with artificial intelligence enables precise formulation optimization, reduces early-stage formulation failures by as much as 30%, and facilitates the development of personalized, patient-focused cancer therapies. This combined method makes drugs more bioavailable, reduces side effects, and improves delivery to tumors. Going forward, new AI-based methods such as lipid screening and predictive modeling are expected to further accelerate the development of nanoemulsions.