Can Mesoporous Silica Speed Up Degradation of Benzodiazepines? Hints from Quantum Mechanical Investigations

This work reports for the first time a quantum mechanical study of the interactions of a model benzodiazepine drug, i.e., nitrazepam, with various models of amorphous silica surfaces, differing in structural and interface properties. The interest in these systems is related to the use of mesoporous silica as carrier in drug delivery. The adopted computational procedure has been chosen to investigate whether silica–drug interactions favor the drug degradation mechanism or not, hindering the beneficial pharmaceutical effect. Computed structural, energetics, and vibrational properties represent a relevant comparison for future experiments. Our simulations demonstrate that adsorption of nitrazepam on amorphous silica is a strongly exothermic process in which a partial proton transfer from the surface to the drug is observed, highlighting a possible catalytic role of silica in the degradation reaction of benzodiazepines

Reconstructing reactivity in dynamic host-guest systems at atomistic resolution: amide hydrolysis under confinement in the cavity of a coordination cage

Spatial confinement is widely employed by nature to attain unique efficiency in controlling chemical reactions. Notable examples are enzymes, which selectively bind reactants and exquisitely regulate their conversion into products. In an attempt to mimic natural catalytic systems, supramolecular metal-organic cages capable of encapsulating guests in their cavity and of controlling/accelerating chemical reactions under confinement are attracting increasing interest. However, the complex nature of these systems, where reactants/products continuously exchange in-and-out of the host, makes it often difficult to elucidate the factors controlling the reactivity in dynamic regimes. As a case study, here we focus on a coordination cage that can encapsulate amide guests and enhance their hydrolysis by favoring their mechanical twisting towards reactive molecular configurations under confinement. We designed an advanced multiscale simulation approach that allows us to reconstruct the reactivity in such host-guest systems in dynamic regimes. In this way, we can characterize amide encapsulation/expulsion in/out of the cage cavity (thermodynamics and kinetics), coupling such host-guest dynamic equilibrium with characteristic hydrolysis reaction constants. All computed kinetic/thermodynamic data are then combined, obtaining a statistical estimation of reaction acceleration in the host-guest system that is found in optimal agreement with the available experimental trends. This shows how, to understand the key factors controlling accelerations/variations in the reaction under confinement, it is necessary to take into account all dynamic processes that occur as intimately entangled in such host-guest systems. This also provides us with a flexible computational framework, useful to build structure-dynamics-property relationships for a variety of reactive host-guest systems

Measuring Ultrasonic Characterisation to Determine the Impact of Toc and the Stress Field on Shale Gas Anisotropy

While the majority of natural gas is produced from conventional sources, there is significant growth from unconventional sources, including shale-gas reservoirs. To produce gas economically, candidate shale typically requires a range of characteristics, including a relatively high total organic carbon (TOC) content, and it must be gas mature. Mechanical and dynamic elastic properties are also important shale characteristics that are not well understood as there have been a limited number of investigations of well-preserved samples. In this study, the elastic properties of shale samples are determined by measuring wave velocities. Arrays of ultrasonic transducers are used to measure five independent wave velocities, which are used to calculate the elastic properties of the shale. The results indicated that for the shale examined in this research, P- and S-wave velocities vary depending on the isotropic stress conditions with respect to the fabric and TOC content. It was shown that the isotropic stress significantly impacts velocity. In addition, S-wave anisotropy was significantly affected by increasing stress anisotropy. Stress orientation, with respect to fabric orientation, was found to be an important influence on the degree of anisotropy of the dynamic elastic properties in the shale. Furthermore, the relationship between acoustic impedance (AI) and TOC was established for all the samples

On the interactions of melatonin/β-cyclodextrin inclusion complex: A novel approach combining efficient semiempirical extended tight-binding (xtb) results with ab initio methods

Melatonin (MT) is a molecule of paramount importance in all living organisms, due to its presence in many biological activities, such as circadian (sleep–wake cycle) and seasonal rhythms (reproduction, fattening, molting, etc.). Unfortunately, it suffers from poor solubility and, to be used as a drug, an appropriate transport vehicle has to be developed, in order to optimize its release in the human tissues. As a possible drug-delivery system, β-cyclodextrin (βCD) represents a promising scaffold which can encapsulate the melatonin, releasing when needed. In this work, we present a computational study supported by experimental IR spectra on inclusion MT/βCD complexes. The aim is to provide a robust, accurate and, at the same time, low-cost methodology to investigate these inclusion complexes both with static and dynamic simulations, in order to study the main actors that drive the interactions of melatonin with β-cyclodextrin and, therefore, to understand its release mechanism

On the interactions of melatonin/β-cyclodextrin inclusion complex: A novel approach combining efficient semiempirical extended tight-binding (xtb) results with ab initio methods

Melatonin (MT) is a molecule of paramount importance in all living organisms, due to its presence in many biological activities, such as circadian (sleep–wake cycle) and seasonal rhythms (reproduction, fattening, molting, etc.). Unfortunately, it suffers from poor solubility and, to be used as a drug, an appropriate transport vehicle has to be developed, in order to optimize its release in the human tissues. As a possible drug-delivery system, β-cyclodextrin (βCD) represents a promising scaffold which can encapsulate the melatonin, releasing when needed. In this work, we present a computational study supported by experimental IR spectra on inclusion MT/βCD complexes. The aim is to provide a robust, accurate and, at the same time, low-cost methodology to investigate these inclusion complexes both with static and dynamic simulations, in order to study the main actors that drive the interactions of melatonin with β-cyclodextrin and, therefore, to understand its release mechanism

Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments

Metals are traditionally considered hard matter. However, it is well known that their atomic lattices may become dynamic and undergo reconfigurations even well below the melting temperature. The innate atomic dynamics of metals is directly related to their bulk and surface properties. Understanding their complex structural dynamics is, thus, important for many applications but is not easy. Here, we report deep-potential molecular dynamics simulations allowing to resolve at an atomic resolution the complex dynamics of various types of copper (Cu) surfaces, used as an example, near the Hüttig (∼1/3 of melting) temperature. The development of deep neural network potential trained on density functional theory calculations provides a dynamically accurate force field that we use to simulate large atomistic models of different Cu surface types. A combination of high-dimensional structural descriptors and unsupervized machine learning allows identifying and tracking all the atomic environments (AEs) emerging in the surfaces at finite temperatures. We can directly observe how AEs that are non-native in a specific (ideal) surface, but that are, instead, typical of other surface types, continuously emerge/disappear in that surface in relevant regimes in dynamic equilibrium with the native ones. Our analyses allow estimating the lifetime of all the AEs populating these Cu surfaces and to reconstruct their dynamic interconversions networks. This reveals the elusive identity of these metal surfaces, which preserve their identity only in part and in part transform into something else under relevant conditions. This also proposes a concept of “statistical identity” for metal surfaces, which is key to understanding their behaviors and properties

Sampling Real‐Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning

Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic-resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state-of-the-art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark-field scanning transmission electron microscopy enables the acquisition of ten high-resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allow resolving the real-time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions

Impact of the Conformational Variability of Oligopeptides on the Computational Prediction of Their CD Spectra

Although successful in the structural determination of ordered biomolecules, the spectroscopic investigation of oligopeptides in solution is hindered by their complex and rapidly changing conformational ensemble. The measured circular dichroism (CD) spectrum of an oligopeptide is an ensemble average over all microstates, severely limiting its interpretation, in contrast to ordered biomolecules. Spectral deconvolution methods to estimate the secondary structure contributions in the ensemble are still mostly based on databases of larger ordered proteins. Here, we establish how the interpretation of CD spectra of oligopeptides can be enhanced by the ability to compute the same observable from a set of atomic coordinates. Focusing on two representative oligopeptides featuring a known propensity toward an α-helical and β-hairpin motif, respectively, we compare and cross-validate the structural information coming from deconvolution of the experimental CD spectra, sequence-based de novo structure prediction, and molecular dynamics simulations based on enhanced sampling methods. We find that small conformational variations can give rise to significant changes in the CD signals. While for the simpler conformational landscape of the α-helical peptide de novo structure prediction can already give reasonabl

Generation of amorphous carbon and crystallographic texture during low-temperature subseismic slip in calcite fault gouge

Identification of the nano-scale to micro-scale mechanochemical processes occurring during fault slip is of fundamental importance to understand earthquake nucleation and propagation. Here we explore the micromechanical processes occurring during fault nucleation and slip at subseismic rates (∼3 × 10−6 m s–1) in carbonate rocks. We experimentally sheared calcite-rich travertine blocks at simulated upper crustal conditions, producing a nano-grained fault gouge. Strain in the gouge is accommodated by cataclastic comminution of calcite grains and concurrent crystal-plastic deformation through twinning and dislocation glide, producing a crystallographic preferred orientation (CPO). Continued wear of fine-grained gouge particles results in the mechanical decomposition of calcite and production of amorphous carbon. We show that CPO and the production of amorphous carbon, previously attributed to frictional heating and weakening during seismic slip, can be produced at low temperature during stable slip at subseismic rates without slip weakening

The puzzling issue of silica toxicity: are silanols bridging the gaps between surface states and pathogenicity?

Background: Silica continues to represent an intriguing topic of fundamental and applied research across various scientific fields, from geology to physics, chemistry, cell biology, and particle toxicology. The pathogenic activity of silica is variable, depending on the physico-chemical features of the particles. In the last 50 years, crystallinity and capacity to generate free radicals have been recognized as relevant features for silica toxicity. The ‘surface’ also plays an important role in silica toxicity, but this term has often been used in a very general way, without defining which properties of the surface are actually driving toxicity. How the chemical features (e.g., silanols and siloxanes) and configuration of the silica surface can trigger toxic responses remains incompletely understood. Main body: Recent developments in surface chemistry, cell biology and toxicology provide new avenues to improve our understanding of the molecular mechanisms of the adverse responses to silica particles. New physicochemical methods can finely characterize and quantify silanols at the surface of silica particles. Advanced computational modelling and atomic force microscopy offer unique opportunities to explore the intimate interactions between silica surface and membrane models or cells. In recent years, interdisciplinary research, using these tools, has built increasing evidence that surface silanols are critical determinants of the interaction between silica particles and biomolecules, membranes, cell systems, or animal models. It also has become clear that silanol configuration, and eventually biological responses, can be affected by impurities within the crystal structure, or coatings covering the particle surface. The discovery of new molecular targets of crystalline as well as amorphous silica particles in the immune system and in epithelial lung cells represents new possible toxicity pathways. Cellular recognition systems that detect specific features of the surface of silica particles have been identified. Conclusions: Interdisciplinary research bridging surface chemistry to toxicology is progressively solving the puzzling issue of the variable toxicity of silica. Further interdisciplinary research is ongoing to elucidate the intimate mechanisms of silica pathogenicity, to possibly mitigate or reduce surface reactivity. Keywords: Silica, Silicosis, Lung cancer, Auto-immune diseases, Surface reactivity, Silanol, Coating, Modelling, Spectroscopy, Atomic force microscop