Estimating Preferred Alkane Carbon Numbers of Nonionic Surfactants in Normalized Hydrophilic-Lipophilic Deviation Theory from Dissipative Particle Dynamics Modeling

Quantitative investigation of surfactant monolayer bending tendency at an oil-polar solvent interface using DPD modeling and artificial neural networks

The bending tendency of a surfactant monolayer at an interface is critical in determining the type of emulsion formed and the proximity of the emulsion system to its equilibrium state. Despite its importance, the influence of interaction and surfactant structure on the bending tendency has not been quantitatively investigated. In this study, we develop and validate an artificial neural network (ANN) model based on the torque densities from dissipative particle dynamics (DPD) simulations to address this gap. With the validated ANN model, the relationship between surfactant monolayer bending tendency and all the interaction parameters, oil size, and surfactant structure (size and tail branching) was derived, from which the significance of each factor was ranked. With this ANN model, both the relationship and factor analysis can be instantly investigated without further DPD modeling. Furthermore, we expand the study to surfactant-oil-polar solvent (SOP) systems by varying the interaction parameters between polar solvents (PP). Our finding indicates that the interaction between polar solvents plays an important role in determining the bending tendency of surfactant monolayers; weaker intermolecular attraction between polar solvents makes surfactants tend to bend toward the oil phase (tend to form oil in polar solvent emulsion). Factor analysis reveals that increasing the repulsion between head-head (HH) or head-oil (HO) makes the model surfactants more polar-solvophilic, while increasing the repulsion between polar solvent-head (PH), tail-tail (TT) or oil-oil (OO) makes the model surfactants more lipophilic. The ANN model effectively reproduces the dependence of surfactant monolayer bending tendency on oil size, consistent with experimental observations, the larger the oil size, the higher the bending tendency toward the oil phase. The most intriguing insight derived from the ANN model here is that the effect of branching in the lipophilic tail will be enhanced by factors that make surfactants behave more lipophilic in a surfactant-oil-polar solvent (SOP) system, for rather polar-solvophilic surfactants, the effect of tail branching is negligible.

Drug-Loaded Polymeric Particulated Systems for Ophthalmic Drugs Release

Drug delivery to the anterior or posterior segments of the eye is a major challenge due to the protection barriers and removal mechanisms associated with the unique anatomical and physiological nature of the ocular system. The paper presents the preparation and characterization of drug-loaded polymeric particulated systems based on pre-emulsion coated with biodegradable polymers. Low molecular weight biopolymers (chitosan, sodium hyaluronate and heparin sodium) were selected due to their ability to attach polymer chains to the surface of the growing system. The particulated systems with dimensions of 190–270 nm and a zeta potential varying from −37 mV to +24 mV depending on the biopolymer charges have been obtained. Current studies show that particles release drugs (dexamethasone/pilocarpine/bevacizumab) in a safe and effective manner, maintaining therapeutic concentration for a longer period of time. An extensive modeling study was performed in order to evaluate the drug release profile from the prepared systems. In a multifractal paradigm of motion, nonlinear behaviors of a drug delivery system are analyzed in the fractal theory of motion, in order to correlate the drug structure with polymer. Then, the functionality of a SL(2R) type ”hidden symmetry” implies, through a Riccati type gauge, different ”synchronization modes” (period doubling, damped oscillations, quasi-periodicity and intermittency) during the drug release process. Among these, a special mode of Kink type, better reflects the empirical data. The fractal study indicated more complex interactions between the angiogenesis inhibitor Bevacizumab and polymeric structure.