Density Functional Theory Transition-State Modeling for the Prediction of Ames Mutagenicity in 1,4 Michael Acceptors

Assessing the safety of new chemicals, without introducing the need for animal testing, is a task of great importance. The Ames test, a widely used bioassay to assess mutagenicity, can be an expensive, wasteful process with animal-derived reagents. Existing in silico methods for the prediction of Ames test results are traditionally based on chemical category formation and can lead to false positive predictions. Category formation also neglects the intrinsic chemistry associated with DNA reactivity. Activation energies and HOMO/LUMO energies for thirty 1,4 Michael acceptors were calculated using a model nucleobase and were further used to predict the Ames test result of these compounds. The proposed model builds upon existing work and examines the fundamental toxicant-target interactions using density functional theory transition-state modeling. The results show that Michael acceptors with activation energies <20.7 kcal/mol and LUMO energies < -1.85 eV are likely to act as direct mutagens upon exposure to DNA

Adsorption in action: Molecular dynamics as a tool to study adsorption at the surface of fine plastic particles in aquatic environments

The presence of microscopic fine plastic particles (FPPs) in aquatic environments continues to be a societal issue of great concern. Further, the adsorption of pollutants and other macromolecules onto the surface of FPPs is a well-known phenomenon. To establish the adsorption behavior of pollutants and the adsorption capacity of different plastic materials, batch adsorption experiments are typically carried out, wherein known concentrations of a pollutant are added to a known amount of plastic. These experiments can be time-consuming and wasteful by design, and in this work, an alternative theoretical approach to considering the problem is reviewed. As a theoretical tool, molecular dynamics (MD) can be used to probe and understand adsorbent-adsorbate interactions at the molecular scale while also providing a powerful visual picture of how the adsorption process occurs. In recent years, numerous studies have emerged that used MD as a theoretical tool to study adsorption on FPPs, and in this work, these studies are presented and discussed across three main categories: (i) organic pollutants, (ii) inorganic pollutants, and (iii) biological macromolecules. Emphasis is placed on how MD-calculated interaction energies can align with experimental data from batch adsorption experiments, and particular consideration is given to how MD can complement existing approaches. This work demonstrates that MD can provide significant insight into the adsorption behavior of different pollutants, but modern approaches are lacking a generalized formula for theoretically predicting adsorption behavior. With more data, MD could be used as a robust, initial assessment tool for the prioritization of chemical pollutants in the context of the microplastisphere, meaning that less time-consuming and potentially wasteful experiments would need to be carried out. With additional refinement, modern simulations will facilitate an improved understanding of chemical adsorption in aquatic environments

Machine learning activation energies of chemical reactions

Application of machine learning (ML) to the prediction of reaction activation barriers is a new and exciting field for these algorithms. The works covered here are specifically those in which ML is trained to predict the activation energies of homogeneous chemical reactions, where the activation energy is given by the energy difference between the reactants and transition state of a reaction. Particular attention is paid to works that have applied ML to directly predict reaction activation energies, the limitations that may be found in these studies, and where comparisons of different types of chemical features for ML models have been made. Also explored are models that have been able to obtain high predictive accuracies, but with reduced datasets, using the Gaussian process regression ML model. In these studies, the chemical reactions for which activation barriers are modeled include those involving small organic molecules, aromatic rings, and organometallic catalysts. Also provided are brief explanations of some of the most popular types of ML models used in chemistry, as a beginner's guide for those unfamiliar

Comparing the Performances of Force Fields in Conformational Searching of Hydrogen-Bond-Donating Catalysts

Here, we compare the relative performances of different force fields for conformational searching of hydrogen-bond-donating catalyst-like molecules. We assess the force fields by their predictions of conformer energies, geometries, low-energy, nonredundant conformers, and the maximum numbers of possible conformers. Overall, MM3, MMFFs, and OPLS3e had consistently strong performances and are recommended for conformationally searching molecules structurally similar to those in this study

Reactivity prediction in aza-Michael additions without transition state calculations: The Ames test for mutagenicity

Animal testing remains a contentious ethical issue in predictive toxicology. Thus, a fast, versatile, low-cost quantum chemical model is presented for predicting the risk of Ames mutagenicity in a series of 1,4 Michael acceptor type compounds. This framework eliminates the need for transition state calculations, and uses an intermediate structure to probe the reactivity of aza-Michael acceptors. This model can be used in a variety of settings e.g., the design of targeted covalent inhibitors and polyketide biosyntheses

The message on the bottle:Rethinking plastic labelling to better encourage sustainable use

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordPlastic pollution continues to worsen globally in volume and complexity. The complexity in plastic production, use and disposal is significant, highlighting the importance of clear communication to consumers. Yet despite this, poor plastic labelling is clear, evident from poor waste management metrics even in the most equipped countries. Plastic labelling must change to contribute to a holistic intervention on global plastic mismanagement. Discussion on this topic leads to three key recommendations: 1. An accurate and clear “sustainability scale” to empower consumers to make decisions informed by environmental and human health implications; 2. Directions for appropriate disposal action in the region of purchase; 3. A comprehensive list of plastic composition, including additives.Natural Environment Research Council (NERC)QUEX InstituteQueensland Health, AustraliaMinderoo Foundation, Australi

Photochemical fingerprinting Is a sensitive probe for the detection of synthetic cannabinoid receptor agonists; toward robust point-of-care detection

With synthetic cannabinoid receptor agonist (SCRA) use still prevalent across Europe and structurally advanced generations emerging, it is imperative that drug detection methods advance in parallel. SCRAs are a chemically diverse and evolving group, which makes rapid detection challenging. We have previously shown that fluorescence spectral fingerprinting (FSF) has the potential to provide rapid assessment of SCRA presence directly from street material with minimal processing and in saliva. Enhancing the sensitivity and discriminatory ability of this approach has high potential to accelerate the delivery of a point-of-care technology that can be used confidently by a range of stakeholders, from medical to prison staff. We demonstrate that a range of structurally distinct SCRAs are photochemically active and give rise to distinct FSFs after irradiation. To explore this in detail, we have synthesized a model series of compounds which mimic specific structural features of AM-694. Our data show that FSFs are sensitive to chemically conservative changes, with evidence that this relates to shifts in the electronic structure and cross-conjugation. Crucially, we find that the photochemical degradation rate is sensitive to individual structures and gives rise to a specific major product, the mechanism and identification of which we elucidate through density-functional theory (DFT) and time-dependent DFT. We test the potential of our hybrid “photochemical fingerprinting” approach to discriminate SCRAs by demonstrating SCRA detection from a simulated smoking apparatus in saliva. Our study shows the potential of tracking photochemical reactivity via FSFs for enhanced discrimination of SCRAs, with successful integration into a portable device

Abstracts from the NIHR INVOLVE Conference 2017

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