Unleashing the power of machine learning in nanomedicine formulation development

Artificial intelligence (AI) is being integrated into nearly every aspect of modern life, and the collaboration between machine learning (ML) – a subfield of AI – and microfluidic fabrication techniques has the potential to accelerate the development of nanomedicines. Here, we present a machine learning workflow designed to optimize the microfluidic-based formulation of nanomedicines. A database of almost 200 unique nanomedicine formulations with over 550 total measurements was curated by producing liposomes, lipid nanoparticles, and poly(lactic-co- glycolic acid) (PLGA) nanoparticles, either empty or loaded with the model therapeutic agent curcumin (CURC), using a benchtop microfluidic system. Materials and flow parameters (input features), including the reagent concentrations, total flow rates, and aqueous:organic flow rate ratios were systematically varied, and the resulting particles were characterized for their hydrodynamic diameter (dH), polydispersity index (PdI), and encapsulation efficiency (EE) (output parameters). These data were used to train, test, and validate 13 different ML models, using a freely available and open-source librries, with the task of returning the most accurate prediction of the nanomedicine attributes – dH, PdI, EE. The most accurate ML models, based on random forest regression, were implemented to yield particles with user-specified attributes. Finally, the proposed ML workflow, dubbed MicrofluidicML, was compared against generative large language models – Open AI ChatGPT, Google’s Gemini, DeepSeek. MicrofluidicML provides a workflow where the researcher has complete governance and control of the input data, the computational overhead is much lower compared to the amount of data required to train a generative AI model, and the post-hoc SHAP analysis provides key information towards the critical factors affecting nanoparticle formation. The application of ML in the field of nanomedicine is inevitable, and MicrofluidicML represents a step towards implementing a computationally lightweight ML framework for accelerating nanomedicine development.