Predicting the Release Mechanism of Amorphous Solid Dispersions: A Combination of Thermodynamic Modeling and In Silico Molecular Simulation.

Background: During the dissolution of amorphous solid dispersion (ASD) formulations, the drug load (DL) often impacts the release mechanism and the occurrence of loss of release (LoR). The ASD/water interfacial gel layer and its specific phase behavior in connection with DL strongly dictate the release mechanism and LoR of ASDs, as reported in the literature. Thermodynamically driven liquid-liquid phase separation (LLPS) and/or drug crystallization at the interface are the key phase transformations that drive LoR.

Complexation Mechanisms of Aqueous Amylose: Molecular Dynamics Study Using 3-Pentadecylphenol.

Molecular dynamics (MD) simulations of linear amylose fragments containing 10 to 40 glucose units were used to study the complexation of the prototypical compound, 3-pentadecylphenol (PDP)─a natural product with surfactant-like properties─in aqueous solution. The amylose-PDP binding leverages mainly hydrophobic interactions together with excluded volume effects. It was found that while the most stable complexes contained PDP inside the helical structure of the amylose in the expected guest-host (inclusion) complexation manner, at higher temperatures, the commonly observed PDP-amylose complexes often involved more nonspecific interactions than inclusion complexation.

Nanoscale Simulation of Plastic Contaminants Migration in Packaging Materials and Potential Leaching into Model Food Fluids.

Polymers are the most commonly used packaging materials for nutrition and consumer products. The ever-growing concern over pollution and potential environmental contamination generated from single-use packaging materials has raised safety questions. Polymers used in these materials often contain impurities, including unreacted monomers and small oligomers.

Advancing material property prediction: using physics-informed machine learning models for viscosity.

In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To address this, we integrate physics-informed descriptors from molecular dynamics (MD) simulations to enhance the accuracy and interpretability of ML models.

Molecular-Scale Exploration of Mechanical Properties and Interactions of Poly(lactic acid) with Cellulose and Chitin.

Poly(lactic acid) (PLA), one of the pillars of the current overarching displacement trend switching from fossil- to natural-based polymers, is often used in association with polysaccharides to increase its mechanical properties. However, the use of PLA/polysaccharide composites is greatly hampered by their poor miscibility, whose underlying nature is still vastly unexplored. This work aims to shed light on the interactions of PLA and two representative polysaccharide molecules (cellulose and chitin) and reveal structure-property relationships from a fundamental perspective using atomistic molecular dynamics.

Development of scalable and generalizable machine learned force field for polymers.

Understanding and predicting the properties of polymers is vital to developing tailored polymer molecules for desired applications. Classical force fields may fail to capture key properties, for example, the transport properties of certain polymer systems such as polyethylene glycol. As a solution, we present an alternative potential energy surface, a charge recursive neural network (QRNN) model trained on DFT calculations made on smaller atomic clusters that generalizes well to oligomers comprising larger atomic clusters or longer chains.

Drug Aggregation of Sparingly-Soluble Ionizable Drugs: Molecular Dynamics Simulations of Papaverine and Prostaglandin F2α.

Aggregation in aqueous solution can have important implications on both the exposure of a drug and its pharmaceutical manufacturability. However, the drug aggregates formed can be very small and, thus, difficult to interrogate experimentally. On the other hand, at higher supersaturations where larger aggregates are supported, the chemical system is inherently metastable and therefore likewise challenging to study from an experimental standpoint.

Exploring the Effects of Wetting and Free Fatty Acid Deposition on an Atomistic Hair Fiber Surface Model Incorporating Keratin-Associated Protein 5-1.

The complex development of cosmetic and medical formulations relies on an ever-growing accuracy of predictive models of hair surfaces. Hitherto, modeling efforts have focused on the description of 18-methyl eicosanoic acid (18-MEA), the primary fatty acid covalently attached to the hair surface, without explicit modeling of the protein layer. Herein, the molecular details of the outermost surface of the human hair fiber surface, also called the F-layer, were studied using molecular dynamics (MD) simulations.

Shearing friction behaviour of synthetic polymers compared to a functionalized polysaccharide on biomimetic surfaces: models for the prediction of performance of eco-designed formulations.

The substitution of natural, bio-based and/or biodegradable polymers for those of petrochemical origin in consumer formulations has become an active area of research and development as the sourcing and destiny of material components becomes a more critical factor in product design. These polymers often differ from their petroleum-based counterparts in topology, raw material composition and solution behaviour. Effective and efficient reformulation that maintains comparable cosmetic performance to existing products requires a deep understanding of the differences in frictional behaviour between polymers as a function of their molecular structure.

A Molecular Dynamics Study of Monomer Melt Properties of Cyanate Ester Monomer Melt Properties.

The objective of this work was to computationally predict the melting temperature and melt properties of thermosetting monomers used in aerospace applications. In this study, we applied an existing voids method by Solca. to examine four cyanate ester monomers with a wide range of melting temperatures.

Molecular-Level Examination of Amorphous Solid Dispersion Dissolution.

Amorphous solid dispersions (ASDs) are commonly used to orally deliver small-molecule drugs that are poorly water-soluble. ASDs consist of drug molecules in the amorphous form which are dispersed in a hydrophilic polymer matrix. Producing a high-performance ASD is critical for effective drug delivery and depends on many factors such as solubility of the drug in the matrix and the rate of drug release in aqueous medium (dissolution), which is linked to bioperformance.

Nucleation and polymorph selection in a model colloidal fluid.

Understanding and controlling polymorph selection is important because the structure of a crystal is critical to its properties. We have explored polymorph selection during nucleation in the hard-core repulsive Yukawa fluid. This model has two polymorphs (bcc and fcc), the relative stability of which can be adjusted by changing the potential parameters.