Time-dependent behavior and modeling of drug particle depots in nonionic surfactant systems

• Nonionic surfactants modulate Valsartan solubility and permeability. • Micellar encapsulation lowers free drug flux above CMC. • Molecular dynamics confirm Valsartan encapsulation in the micelles. • Solubilization capacity inversely correlates with surfactant CMC. • Predictive model supports rational drug formulation design. The solubilization and transport of poorly water-soluble drugs present significant engineering challenges due to the interplay of physicochemical, colloidal, and interfacial processes in biological environments. In particular, the gastrointestinal tract represents a dynamic system where dissolution, bile salt–mediated micellization of poorly soluble drugs, particle precipitation and aggregation, and membrane permeation occur concurrently and govern overall drug bioavailability. In this study, we investigate the behavior of Valsartan, a poorly soluble model drug, in the presence of four nonionic surfactants to understand its colloidal states and solubilization mechanisms. Valsartan was found to exist in multiple colloidal states: as freely dissolved molecules, micelle-solubilized drug, and precipitated particles. A combination of molecular dynamics (MD) simulations and experimental techniques was used to investigate Valsartan–surfactant interactions. Quantitative analysis revealed that micelle encapsulation capacities ranged from 2 to 7 molecules per micelle, leading to solubilization enhancement factors of up to 57-fold compared with the aqueous solubility of Valsartan, with encapsulation efficiency correlating strongly with the surfactant’s critical micelle concentration (CMC). While measurements of Valsartan flux across a model membrane confirmed that higher surfactant concentrations reduce free drug transport, this observation serves as contextual support for the focus of the study. Building on these results, we developed and validated a chemical engineering model that integrates dissolution, particle formation, micellization, and membrane permeation kinetics, accurately predicting free drug flux within ± 5 % of experimental values. This integrated approach provides both molecular-level insight and a practical predictive tool for the engineering design and optimization of drug delivery systems.