Objective: Acrylic acid derivatives are frequently used as dental monomers and their cy- totoxicity towards various cell lines is well documented. This study aims to probe the structural and physicochemical attributes responsible for higher toxicity of dental mono- mers, using quantitative structure-activity relationships (QSAR) modeling approaches. Methods: A regression-based linear single-target QSAR (st-QSAR) model was developed with a comparatively small dataset containing 39 compounds, the cytotoxicity of which has been assessed over the Hela S3 cell line. By contrast, a classification-based multi-target QSAR model was developed with 138 compounds, the cytotoxicity of which has been re- ported against 18 different cell lines. Both models were set up following rigorous validation protocols confirming their statistical significance and robustness. Results: The performance of the linear mt-QSAR model, developed with various feature selection and post-selection similarity searching-based schemes, superseded that of all non-linear models produced with six machine learning methods by hyperparameter opti- mization. The final derived st-QSAR and mt-QSAR linear models are shown to be highly predictive, as well as revealing the crucial structural and physicochemical factors re- sponsible for higher cytotoxicity of the dental monomers. Significance: This study is the first attempt on unveiling the cytotoxicity of dental mono- mers over several cell lines by means of a single multi-target QSAR model. Further, such a model is ready to get widespread applicability in the screening of new monomers, judging from its almost accurate predictions over diverse experimental assay conditions.

The emergence of multi-drug resistant bacteria in the past decades has become one of the major public health issues of our time. One of the main mechanisms of resistance and persistence of bacteria is their ability to form biofilms. Quorum-sensing (QS) is one of the main mechanisms of biofilm formation. Interfering with the QS cascade constitutes a non-antibiotic strategy to reduce biofilm formation and affect bacterial resistance. In Pseudomonas aeruginosa, QS takes place by four different systems, which start with the activation of transcriptional receptor LasR. This receptor is stabilized by C12-HSL. In this work, we have optimized and employed a multi-level in silico protocol to identify promising LasR inhibitors, combining different computer aided drug design techniques such as molecular docking, virtual screening, molecular dynamics and free energy calculations. The protocol was optimized using all 21 available LasR X-ray structures, 7 docking scoring functions, and a library of 90 active and 4500 inactive compounds. The optimized protocol was used to scan 294 498 chemically distinct compounds from 5 different databases, of which 30 compounds were further studied by molecular dynamics and free energy calculations and resulted in 8 possible QS inhibitors with promising ADME properties and binding affinity.

RNA-dependent RNA polymerase (RdRp) is a potential therapeutic target for the discovery of novel antiviral agents for the treatment of life-threatening infections caused by newly emerged strains of the influenza virus. Being one of the most conserved enzymes among RNA viruses, RdRp and its inhibitors require further investigations to design novel antiviral agents. In this work, we systematically investigated the structural requirements for antiviral properties of some recently reported aryl benzoyl hydrazide derivatives through a range of in silico tools such as 2D-quantitative structure-activity relationship (2D-QSAR), 3D-QSAR, structure-based pharmacophore modeling, molecular docking and molecular dynamics simulations. The 2D-QSAR models developed in the current work achieved high statistical reliability and simultaneously afforded in-depth mechanistic interpretability towards structural requirements. The structure-based pharmacophore model developed with the docked conformation of one of the most potent compounds with the RdRp protein of H5N1 influenza strain was utilized for developing a 3D-QSAR model with satisfactory statistical quality validating both the docking and the pharmacophore modeling methodologies performed in this work. However, it is the atom-based alignment of the compounds that afforded the most statistically reliable 3D-QSAR model, the results of which provided mechanistic interpretations consistent with the 2D-QSAR results. Additionally, molecular dynamics simulations performed with the apoprotein as well as the docked complex of RdRp revealed the dynamic stability of the ligand at the proposed binding site of the receptor. At the same time, it also supported the mechanistic interpretations drawn from 2D-, 3D-QSAR and pharmacophore modeling. The present study, performed mostly with open-source tools and webservers, returns important guidelines for research aimed at the future design and development of novel anti-viral agents against various RNA viruses like influenza virus, human immunodeficiency virus-1, hepatitis C virus, corona virus, and so forth.

The low cost and high selectivity toward CO2 and H-2 of Ni-Cu catalysts for the methanol steam reforming (MSR) make them excellent candidates for the production of hydrogen from methanol. Moreover, bimetallic Ni-Cu alloy blocks the production of undesirable methane, CO, and coke. In this work, the full MSR mechanism on Ni-Cu surfaces was studied by density functional theory calculations, a step forward to explain their high activity and selectivity for that reaction. The MSR evolves on Ni-Cu surfaces mostly through the methanol decomposition on the catalytic surface followed by the water-gas shift (WGS) reaction, which converts the CO obtained from methanol decomposition to CO2 and additional H-2. Direct CO2 formation from methanol should be a minority route associated with the presence of combed surfaces in the catalysts. Finally and most importantly, the Ni-Cu alloy suppresses the formation of methane and coke while the high desorption barrier for CO species avoids its production. Overall, the information gathered in this work alongside the insights into the MSR reaction mechanism on these surfaces shall aid in the future design of improved Ni-Cu alloy-based catalysts for hydrogen production through methanol.

<jats:p>This chapter brings important novel insights and perspectives to the urging contemporary debate on public hygienist policies. The authors intend to explore how an episode of history of science can be used to explore the struggles of universal pandemic responses. The focus will be on the inception of science-based legislation, created to deal with public health emergencies, and their communication and social acceptance. They argue if any of the symptoms of science misinformation and a weak science foundation of legislative action identified in the 2020 coronavirus pandemic can be identified in an early 20th-century outbreak of bubonic plague in Portugal. They present a national legislative policy timeline towards the pandemic effort in the form of consolidated legislative responses to fight Porto's emerging pandemic in 1899. They also provide future studies on science-based policy with newfound material, aiding the characterization of the communication and eventual harmonization of concerted responses in preempting the spread of pandemics. </jats:p>

<jats:title>ABSTRACT</jats:title><jats:p>The modulation of drug efflux by P-glycoprotein (P-gp, ABCB1) represents one of the most promising approaches to overcome multidrug resistance (MDR) in cancer cells, however the mechanisms of drug specificity and signal-transmission are still poorly understood, hampering the development of more selective and efficient P-gp modulators. In this study, the impact of four P-gp mutations (G185V, G830V, F978A and ΔF335) on drug-binding and efflux-related signal-transmission mechanism was comprehensively evaluated in the presence of ligands within the drug-binding pocket (DBP), which are experimentally related with changes in their drug efflux profiles. The severe repacking of the transmembrane helices (TMH), induced by mutations and exacerbated by the presence of ligands, indicates that P-gp is sensitive to perturbations in the transmembrane region. Alterations on drug-binding were also observed as a consequence of the TMH repacking, but were not always correlated with alterations on ligands binding mode and/or binding affinity. Finally, and although all P-gp variants <jats:italic>holo</jats:italic> systems showed considerable changes in the intracellular coupling helices/nucleotide-binding domain (ICH-NBD) interactions, they seem to be primarily induced by the mutation itself rather than by the presence of ligands within the DBP. The data further suggest that the changes in drug efflux experimentally reported are mostly related with changes on drug specificity rather than effects on signal-transmission mechanism. We also hypothesize that an increase in the drug-binding affinity may also be related with the decreased drug efflux, while minor changes in binding affinities are possibly related with the increased drug efflux observed in transfected cells.</jats:p>

Conventional in silico modeling is often viewed as 'one-target' or 'single-task' computer-aided modeling since it mainly relies on forecasting an endpoint of interest from similar input data. Multitasking or multitarget in silico modeling, in contrast, embraces a set of computational techniques that efficiently integrate multiple types of input data for setting up unique in silico models able to predict the outcome(s) relating to various experimental and/or theoretical conditions. The latter, specifically, based upon the Box-Jenkins moving average approach, has been applied in the last decade to several research fields including drug and materials design, environmental sciences, and nanotechnology. The present review discusses the current status of multitasking computer-aided modeling efforts, meanwhile describing both the existing challenges and future opportunities of its underlying techniques. Some important applications are also discussed to exemplify the ability of multitasking modeling in deriving holistic and reliable in silico classification-based models as well as in designing new chemical entities, either through fragment-based design or virtual screening. Focus will also be given to some software recently developed to automate and accelerate such types of modeling. Overall, this review may serve as a guideline for researchers to grasp the scope of multitasking computer-aided modeling as a promising in silico tool.

In this study, the full reaction mechanism for N2O hydrogenation on silver doped Au(210) surfaces was investigated in order to clarify the experimental observations. Density functional theory (DFT) calculations were used to state the most favorable reaction paths for individual steps involved in the N2O hydrogenation. From the DFT results, the activation energy barriers, rate constants and reaction energies for the individual steps were determined, which made it possible to elucidate the most favorable reaction mechanism for the global catalytic process. It was found that the N2O dissociation occurs in surface regions where silver atoms are present, while hydrogen dissociation occurs in pure gold regions of the catalyst or in regions with a low silver content. Likewise, N2O dissociation is the rate determining step of the global process, while water formation from O adatoms double hydrogenation and N-2 and H2O desorptions are reaction steps limited by low activation energy barriers, and therefore, the latter are easily carried out. Moreover, water formation occurs in the edges between the regions where hydrogen and N2O are dissociated. Interestingly, a good dispersion of the silver atoms in the surface is necessary to avoid catalyst poison by O adatoms accumulation, which are strongly adsorbed on the surface.