Fine-Tuned Machine Learning Models for the Discovery of Dye Nanoparticles with Enhanced Lung Delivery

Drug delivery using self-assembling drug-excipient nanoparticles offers a scalable strategy to improve the solubility, bioavailability, and biodistribution of poorly soluble therapeutics. However, progress in the development and translation of these materials has been limited by the lack of safe excipients capable of stabilizing drug-rich nanoparticles that allow for extrahepatic delivery. Here, we present an adaptive machine learning-guided workflow for excipient discovery that prospectively prioritizes candidate dyes and identifies clinically relevant drugs compatible with dye-based nanoparticle formulations. Guided by model predictions, we deployed semiautomated synthesis together with in vitro and in vivo experimental validation to evaluate and characterize the resulting nanoparticles. We identified two FDA-approved dye excipients and rediscovered a third dye as favorable excipients. Fine-tuning our machine learning models enabled us to identify 35 previously unreported nanoparticle formulations utilizing these dyes. Importantly, in proof-of-concept in vivo biodistribution studies, these dye excipients enabled the encapsulation of the anticancer drug sorafenib with distinct extrahepatic biodistribution profiles, including preferential accumulation in the lung. Taken together, this work establishes an adaptive machine learning-driven framework for excipient discovery and demonstrates that small-molecule dye excipients can serve as modular regulators of nanoparticle biodistribution. These findings expand the molecular and computational toolboxes for drug-excipient nanoparticle design and prototype the excipient selection process as a potentially powerful strategy to tune in vivo drug delivery.