Predicting acetalated dextran nanoparticle features: Controlled synthesis, formulation, and testing in a high-throughput process

The hydrophobic, pH-labile, and biodegradable acetalated dextran (Ac(e)Dex) is a promising material for drug delivery in nanomedicine. However, fundamental knowledge about the structure-property relationships is still missing, which hinders its application in preclinical and clinical trials. In this study, we synthesized a library of 36 Ac(e)Dex derivatives with different molar masses, types, and degrees of functionalization. A high-throughput formulation screening (> 1000 formulations) was conducted using a liquid handling robot optimizing the concentration of polymer, the solvent, and the addition of additives. Selected formulations were scaled up and evaluated for their stability. To further correlate polymer properties with stability, a machine learning (ML) model was developed, providing a predictive tool for Ac(e)Dex nanoparticle degradation based on synthesis/formulation data. The novelty of this work lies in the integrated synthesis-to-prediction pipeline combining controlled polymer synthesis, high-throughput formulation, and ML-based stability modeling, rather than introducing new chemical mechanisms. By eludicating how structural parameters (molar mass, type, and degree of functionalization) influence formulation properties (i.e., size, dispersity, repeatability) and particle stability, this work enables standardized comparisons of Ac(e)Dex between different studies and supports its future preclinical development.