Thermochemistry of Solid-state Formation, Structure, Optical, and Luminescent Properties of Complex Oxides Eu2MeO6 (Me - Mo, W), Eu2W2O9: A Combined Experimental and DFT Study.

Complex oxides Eu2MeO6 (Me - Mo, W), Eu2W2O9 were obtained by a solid-phase reaction between binary oxides. The thermodynamic and kinetic mechanisms of the reaction processes were established using a variety of physical-chemical methods. All compounds obtained in this work crystallize in the low-symmetry monoclinic system, forming complex framework structures, which determine a set of very valuable physical-chemical properties. Comparison of experimental Kubelka-Munk functions and DFT- calculated absorption spectra shows adequate agreement and reveals the origin of the fundamental absorption. In addition, the deficiency in DFT calculations in the part of mutual contribution of CTBs of Mo-O and W-O, from one side, and Eu-O contributions, from the other side, is reported. Calculations of absorption spectra are shown to be superior to band structure analysis in the determination of optical band gaps. Additionally, luminescent properties of Eu2MeO6 and Eu2W2O9 compounds were investigated. These studies provide a better understanding of the electronic and optical properties of the compounds Eu2MeO6 and Eu2W2O9, along with their potential applications in various areas.

Negative Thermal Expansion in the Polymorphic Modification of Double Sulfate ß-AEu(SO4)2 (A-Rb+, Cs+).

New polymorphic modifications of double sulfates ß-AEu(SO4)2 (A-Rb+, Cs+) were obtained by the hydrothermal method, the structure of which differs significantly from the monoclinic modifications obtained earlier by solid-state methods. According to single-crystal diffraction data, it was found that the compounds crystallize in the orthorhombic system, space group Pnna, with parameters ß-RbEu(SO4)2: a = 9.4667(4) Å, b = 13.0786(5) Å, c = 5.3760(2) Å, V = 665.61(5) Å3; ß-CsEu(SO4)2: a = 9.5278(5) Å, b = 13.8385(7) Å, c = 5.3783(3) Å, V = 709.13(7) Å3. The asymmetric part of the unit cell contains one-half Rb+/Cs+ ion, one-half Eu3+ ion, both in special sites, and one SO42- ion. Both compounds exhibit nonlinear negative thermal expansion. According to the X-ray structural analysis and theoretical calculations, the polarizing effect of the alkali metal ion has a decisive influence on the demonstration of this phenomenon. Experimental indirect band gaps of ß-Rb and ß-Cs are 4.05 and 4.11 eV, respectively, while the direct band gaps are 4.48 and 4.54 eV, respectively. The best agreement with theoretical calculations is obtained using the ABINIT package employing PAW pseudopotentials with hybrid PBE0 functional, while norm-conserving pseudopotentials used in the frame of CASTEP code and LCAO approach in the Crystal package gave worse agreement. The properties of alkali ions also significantly affect the luminescent properties of the compounds, which leads to a strong temperature dependence of the intensity of the 5D0 → 7F4 transition in ß-CsEu(SO4)2 in contrast to much weaker dependence of this kind in ß-RbEu(SO4)2.

Iodine-Chemisorption, Interpenetration and Polycatenation: Cationic MOFs and CPs from Group 13 Metal Halides and Di-Pyridyl-Linkers.

Eight cationic, two-dimensional metal-organic frameworks (MOFs) were synthesized in reactions of the group 13 metal halides AlBr3 , AlI3 , GaBr3 , InBr3 and InI3 with the dipyridyl ligands 1,2-di(4-pyridyl)ethylene (bpe), 1,2-di(4-pyridyl)ethane (bpa) and 4,4'-bipyridine (bipy). Seven of them follow the general formula 2 ∞ [MX2 (L)2 ]A, M=Al, In, X=Br, I, A- =[MX4 ]- , I- , I3 - , L=bipy, bpa, bpe. Thereby, the porosity of the cationic frameworks can be utilized to take up the heavy molecule iodine in gas-phase chemisorption vital for the capture of iodine radioisotopes. This is achieved by switching between I- and the polyiodide I3 - in the cavities at room temperature, including single-crystal-to-single-crystal transformation. The MOFs are 2D networks that exhibit (4,4)-topology in general or (6,3)-topology for 2 ∞ [(GaBr2 )2 (bpa)5 ][GaBr4 ]2 ⋅bpa. The two-dimensional networks can either be arranged to an inclined interpenetration of the cationic two-dimensional networks, or to stacked networks without interpenetration. Interpenetration is accompanied by polycatenation. Due to the cationic character, the MOFs require the counter ions [MX4 ]- , I- or I3 - counter ions in their pores. Whereas the [MX4 ]- , ions are immobile, iodide allows for chemisorption. Furthermore, eight additional coordination polymers and complexes were identified and isolated that elaborate the reaction space of the herein reported syntheses.

Iodine Chemisorption, Interpenetration and Polycatenation: Cationic MOFs and CPs from Group 13 Metal Halides and Dipyridyl Linkers.

Invited for the cover of this issue are Klaus Müller-Buschbaum and co-workers at Giessen University. The image depicts an aluminium-based MOF as a novel material for the capture of iodine radioisotopes from a potential gas atmosphere exposure. Read the full text of the article at 10.1002/chem.202104171.

Mixtures-Inclusive In Silico Models of Ocular Toxicity Based on United States and International Hazard Categories.

Computational modeling grounded in reliable experimental data can help design effective non-animal approaches to predict the eye irritation and corrosion potential of chemicals. The National Toxicology Program (NTP) Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) has compiled and curated a database of in vivo eye irritation studies from the scientific literature and from stakeholder-provided data. The database contains 810 annotated records of 593 unique substances, including mixtures, categorized according to UN GHS and US EPA hazard classifications. This study reports a set of in silico models to predict EPA and GHS hazard classifications for chemicals and mixtures, accounting for purity by setting thresholds of 100% and 10% concentration. We used two approaches to predict classification of mixtures: conventional and mixture-based. Conventional models evaluated substances based on the chemical structure of its major component. These models achieved balanced accuracy in the range of 68-80% and 87-96% for the 100% and 10% test concentration thresholds, respectively. Mixture-based models, which accounted for all known components in the substance by weighted feature averaging, showed similar or slightly higher accuracy of 72-79% and 89-94% for the respective thresholds. We also noted a strong trend between the pH feature metric calculated for each substance and its activity. Across all the models, the calculated pH of inactive substances was within one log10 unit of neutral pH, on average, while for active substances, pH varied from neutral by at least 2 log10 units. This pH dependency is especially important for complex mixtures. Additional evaluation on an external test set of 673 substances obtained from ECHA dossiers achieved balanced accuracies of 64-71%, which suggests that these models can be useful in screening compounds for ocular irritation potential. Negative predictive value was particularly high and indicates the potential application of these models in a bottom-up approach to identify nonirritant substances.

Exploration of the Crystal Structure and Thermal and Spectroscopic Properties of Monoclinic Praseodymium Sulfate Pr2(SO4)3.

Praseodymium sulfate was obtained by the precipitation method and the crystal structure was determined by Rietveld analysis. Pr2(SO4)3 is crystallized in the monoclinic structure, space group C2/c, with cell parameters a = 21.6052 (4), b = 6.7237 (1) and c = 6.9777 (1) Å, ß = 107.9148 (7)°, Z = 4, V = 964.48 (3) Å3 (T = 150 °C). The thermal expansion of Pr2(SO4)3 is strongly anisotropic. As was obtained by XRD measurements, all cell parameters are increased on heating. However, due to a strong increase of the monoclinic angle ß, there is a direction of negative thermal expansion. In the argon atmosphere, Pr2(SO4)3 is stable in the temperature range of T = 30-870 °C. The kinetics of the thermal decomposition process of praseodymium sulfate octahydrate Pr2(SO4)3·8H2O was studied as well. The vibrational properties of Pr2(SO4)3 were examined by Raman and Fourier-transform infrared absorption spectroscopy methods. The band gap structure of Pr2(SO4)3 was evaluated by ab initio calculations, and it was found that the valence band top is dominated by the p electrons of oxygen ions, while the conduction band bottom is formed by the d electrons of Pr3+ ions. The exact position of ZPL is determined via PL and PLE spectra at 77 K to be at 481 nm, and that enabled a correct assignment of luminescent bands. The maximum luminescent band in Pr2(SO4)3 belongs to the 3P0 → 3F2 transition at 640 nm.

Variable Luminescence and Chromaticity of Homoleptic Frameworks of the Lanthanides together with Pyridylpyrazolates.

Homoleptic, 3D coordination polymers of the formula 33 ∞ [Ln(3-PyPz)3 ] and 3 ∞ [Ln(4-PyPz)3 ], (3-PyPz)- =3-(3-pyridyl)pyrazolate anion, (4-PyPz)- =3-(4-pyridyl)pyrazolate anion, both C8 H6 N3 - , Ln=Sm, Eu, Gd, Tb, Dy, were obtained as highly luminescent frameworks by reaction of the lanthanide metals (Ln) with the aromatic heterocyclic amine ligands 3-PyPzH and 4-PyPzH. The compounds form two isotypic series of 3D coordination polymers and exhibit fair thermal stability up to 360 °C. The luminescence properties of all ten compounds were determined in the solid state, with an antenna effect through ligand-metal energy transfer leading to high efficiency of the luminescence displayed by good quantum yields of up to 74 %. The emission is mainly based on ion-specific lanthanide-dependent intra 4 f-4 f transitions for Tb3+ : green, Dy3+ : yellow, Sm3+ : orange-red, Eu3+ : red. For the Gd3+ -containing compounds, the yellow emission of ligand triplet-based phosphorescence is observed at room temperature and 77 K. Co doping of the Gd-containing frameworks with Eu3+ and Tb3+ allow further shifting of the chromaticity towards white light emission.

Saagar-A New, Extensible Set of Molecular Substructures for QSAR/QSPR and Read-Across Predictions.

Molecular structure-based predictive models provide a proven alternative to costly and inefficient animal testing. However, due to a lack of interpretability of predictive models built with abstract molecular descriptors they have earned the notoriety of being black boxes. Interpretable models require interpretable descriptors to provide chemistry-backed predictive reasoning and facilitate intelligent molecular design. We developed a novel set of extensible chemistry-aware substructures, Saagar, to support interpretable predictive models and read-across protocols. Performance of Saagar in chemical characterization and search for structurally similar actives for read-across applications was compared with four publicly available fingerprint sets (MACCS (166), PubChem (881), ECFP4 (1024), ToxPrint (729)) in three benchmark sets (MUV, ULS, and Tox21) spanning ∼145 000 compounds and 78 molecular targets at 1%, 2%, 5%, and 10% false discovery rates. In 18 of the 20 comparisons, interpretable Saagar features performed better than the publicly available, but less interpretable and fixed-bit length, fingerprints. Examples are provided to show the enhanced capability of Saagar in extracting compounds with higher scaffold similarity. Saagar features are interpretable and efficiently characterize diverse chemical collections, thus making them a better choice for building interpretable predictive in silico models and read-across protocols.

Harnessing In Silico, In Vitro, and In Vivo Data to Understand the Toxicity Landscape of Polycyclic Aromatic Compounds (PACs).

Polycyclic aromatic compounds (PACs) are compounds with a minimum of two six-atom aromatic fused rings. PACs arise from incomplete combustion or thermal decomposition of organic matter and are ubiquitous in the environment. Within PACs, carcinogenicity is generally regarded to be the most important public health concern. However, toxicity in other systems (reproductive and developmental toxicity, immunotoxicity) has also been reported. Despite the large number of PACs identified in the environment, research attention to understand exposure and health effects of PACs has focused on a relatively limited subset, namely polycyclic aromatic hydrocarbons (PAHs), the PACs with only carbon and hydrogen atoms. To triage the rest of the vast number of PACs for more resource-intensive testing, we developed a data-driven approach to contextualize hazard characterization of PACs, by leveraging the available data from various data streams (in silico toxicity, in vitro activity, structural fingerprints, and in vivo data availability). The PACs were clustered on the basis of their in silico toxicity profiles containing predictions from 8 different categories (carcinogenicity, cardiotoxicity, developmental toxicity, genotoxicity, hepatotoxicity, neurotoxicity, reproductive toxicity, and urinary toxicity). We found that PACs with the same parent structure (e.g., fluorene) could have diverse in silico toxicity profiles. In contrast, PACs with similar substituted groups (e.g., alkylated-PAHs) or heterocyclics (e.g., N-PACs) with varying ring sizes could have similar in silico toxicity profiles, suggesting that these groups are better candidates for toxicity read-across analysis. The clusters/regions associated with certain in silico toxicity, in vitro activity, and structural fingerprints were identified. We found that genotoxicity/carcinogenicity (in silico toxicity) and xenobiotic homeostasis and stress response (in vitro activity), respectively, dominate the toxicity/activity variation seen in the PACs. The "hot spots" with enriched toxicity/activity in conjunction with availability of in vivo carcinogenicity data revealed regions of either data-poor (hydroxylated-PAHs) or data-rich (unsubstituted, parent PAHs) PACs. These regions offer potential targets for prioritization of further in vivo assessment and for chemical read-across efforts. The analysis results are searchable through an interactive web application (https://ntp.niehs.nih.gov/go/pacs_tableau), allowing for alternative hypothesis generation.

Development of improved QSAR models for predicting the outcome of the in vivo micronucleus genetic toxicity assay.

All drugs entering clinical trials are expected to undergo a series of in vitro and in vivo genotoxicity tests as outlined in the International Council on Harmonization (ICH) S2 (R1) guidance. Among the standard battery of genotoxicity tests used for pharmaceuticals, the in vivo micronucleus assay, which measures the frequency of micronucleated cells mostly from blood or bone marrow, is recommended for detecting clastogens and aneugens. (Quantitative) structure-activity relationship [(Q)SAR] models may be used as early screening tools by pharmaceutical companies to assess genetic toxicity risk during drug candidate selection. Models can also provide decision support information during regulatory review as part of the weight-of-evidence when experimental data are insufficient. In the present study, two commercial (Q)SAR platforms were used to construct in vivo micronucleus models from a recently enhanced in-house database of non-proprietary study findings in mice. Cross-validated performance statistics for the new models showed sensitivity of up to 74% and negative predictivity of up to 86%. In addition, the models demonstrated cross-validated specificity of up to 77% and coverage of up to 94%. These new models will provide more reliable predictions and offer an investigational approach for drug safety assessment with regards to identifying potentially genotoxic compounds.

Analysis of model PM2.5-induced inflammation and cytotoxicity by the combination of a virtual carbon nanoparticle library and computational modeling.

Health risks induced by PM2.5 have become one of the major concerns among living populations, especially in regions facing serious pollution such as China and India. Furthermore, the composition of PM2.5 is complex and it also varies with time and locations. To facilitate our understanding of PM2.5-induced toxicity, a predictive modeling framework was developed in the present study. The core of this study was 1) to construct a virtual carbon nanoparticle library based on the experimental data to simulate the PM2.5 structures; 2) to quantify the nanoparticle structures by novel nanodescriptors; and 3) to perform computational modeling for critical toxicity endpoints. The virtual carbon nanoparticle library was developed to represent the nanostructures of 20 carbon nanoparticles, which were synthesized to simulate PM2.5 structures and tested for potential health risks. Based on the calculated nanodescriptors from virtual carbon nanoparticles, quantitative nanostructure-activity relationship (QNAR) models were developed to predict cytotoxicity and four different inflammatory responses induced by model PM2.5. The high predictability (R2 > 0.65 for leave-one-out validations) of the resulted consensus models indicated that this approach could be a universal tool to predict and analyze the potential toxicity of model PM2.5, ultimately understanding and evaluating the ambient PM2.5-induced toxicity.

Identifying Compounds with Genotoxicity Potential Using Tox21 High-Throughput Screening Assays.

Genotoxicity is a critical component of a comprehensive toxicological profile. The Tox21 Program used five quantitative high-throughput screening (qHTS) assays measuring some aspect of DNA damage/repair to provide information on the genotoxic potential of over 10 000 compounds. Included were assays detecting activation of p53, increases in the DNA repair protein ATAD5, phosphorylation of H2AX, and enhanced cytotoxicity in DT40 cells deficient in DNA-repair proteins REV3 or KU70/RAD54. Each assay measures a distinct component of the DNA damage response signaling network; >70% of active compounds were detected in only one of the five assays. When qHTS results were compared with results from three standard genotoxicity assays (bacterial mutation, in vitro chromosomal aberration, and in vivo micronucleus), a maximum of 40% of known, direct-acting genotoxicants were active in one or more of the qHTS genotoxicity assays, indicating low sensitivity. This suggests that these qHTS assays cannot in their current form be used to replace traditional genotoxicity assays. However, despite the low sensitivity, ranking chemicals by potency of response in the qHTS assays revealed an enrichment for genotoxicants up to 12-fold compared with random selection, when allowing a 1% false positive rate. This finding indicates these qHTS assays can be used to prioritize chemicals for further investigation, allowing resources to focus on compounds most likely to induce genotoxic effects. To refine this prioritization process, models for predicting the genotoxicity potential of chemicals that were active in Tox21 genotoxicity assays were constructed using all Tox21 assay data, yielding a prediction accuracy up to 0.83. Data from qHTS assays related to stress-response pathway signaling (including genotoxicity) were the most informative for model construction. By using the results from qHTS genotoxicity assays, predictions from models based on qHTS data, and predictions from commercial bacterial mutagenicity QSAR models, we prioritized Tox21 chemicals for genotoxicity characterization.

Chemistry-Wide Association Studies (CWAS): A Novel Framework for Identifying and Interpreting Structure-Activity Relationships.

Quantitative structure-activity relationships (QSAR) models are often seen as a "black box" because they are considered difficult to interpret. Meanwhile, qualitative approaches, e.g., structural alerts (SA) or read-across, provide mechanistic insight, which is preferred for regulatory purposes, but predictive accuracy of such approaches is often low. Herein, we introduce the chemistry-wide association study (CWAS) approach, a novel framework that both addresses such deficiencies and combines advantages of statistical QSAR and alert-based approaches. The CWAS framework consists of the following steps: (i) QSAR model building for an end point of interest, (ii) identification of key chemical features, (iii) determination of communities of such features disproportionately co-occurring more frequently in the active than in the inactive class, and (iv) assembling these communities to form larger (and not necessarily chemically connected) novel structural alerts with high specificity. As a proof-of-concept, we have applied CWAS to model Ames mutagenicity and Stevens-Johnson Syndrome (SJS). For the well-studied Ames mutagenicity data set, we identified 76 important individual fragments and assembled co-occurring fragments into SA both replicative of known as well as representing novel mutagenicity alerts. For the SJS data set, we identified 29 important fragments and assembled co-occurring communities into SA including both known and novel alerts. In summary, we demonstrate that CWAS provides a new framework to interpret predictive QSAR models and derive refined structural alerts for more effective design and safety assessment of drugs and drug candidates.

Developing Enhanced Blood-Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling.

PURPOSE: Experimental Blood-Brain Barrier (BBB) permeability models for drug molecules are expensive and time-consuming. As alternative methods, several traditional Quantitative Structure-Activity Relationship (QSAR) models have been developed previously. In this study, we aimed to improve the predictivity of traditional QSAR BBB permeability models by employing relevant public bio-assay data in the modeling process. METHODS: We compiled a BBB permeability database consisting of 439 unique compounds from various resources. The database was split into a modeling set of 341 compounds and a validation set of 98 compounds. Consensus QSAR modeling workflow was employed on the modeling set to develop various QSAR models. A five-fold cross-validation approach was used to validate the developed models, and the resulting models were used to predict the external validation set compounds. Furthermore, we used previously published membrane transporter models to generate relevant transporter profiles for target compounds. The transporter profiles were used as additional biological descriptors to develop hybrid QSAR BBB models. RESULTS: The consensus QSAR models have R(2) = 0.638 for five-fold cross-validation and R(2) = 0.504 for external validation. The consensus model developed by pooling chemical and transporter descriptors showed better predictivity (R(2) = 0.646 for five-fold cross-validation and R(2) = 0.526 for external validation). Moreover, several external bio-assays that correlate with BBB permeability were identified using our automatic profiling tool. CONCLUSIONS: The BBB permeability models developed in this study can be useful for early evaluation of new compounds (e.g., new drug candidates). The combination of chemical and biological descriptors shows a promising direction to improve the current traditional QSAR models.

Big data in chemical toxicity research: the use of high-throughput screening assays to identify potential toxicants.

High-throughput screening (HTS) assays that measure the in vitro toxicity of environmental compounds have been widely applied as an alternative to in vivo animal tests of chemical toxicity. Current HTS studies provide the community with rich toxicology information that has the potential to be integrated into toxicity research. The available in vitro toxicity data is updated daily in structured formats (e.g., deposited into PubChem and other data-sharing web portals) or in an unstructured way (papers, laboratory reports, toxicity Web site updates, etc.). The information derived from the current toxicity data is so large and complex that it becomes difficult to process using available database management tools or traditional data processing applications. For this reason, it is necessary to develop a big data approach when conducting modern chemical toxicity research. In vitro data for a compound, obtained from meaningful bioassays, can be viewed as a response profile that gives detailed information about the compound's ability to affect relevant biological proteins/receptors. This information is critical for the evaluation of complex bioactivities (e.g., animal toxicities) and grows rapidly as big data in toxicology communities. This review focuses mainly on the existing structured in vitro data (e.g., PubChem data sets) as response profiles for compounds of environmental interest (e.g., potential human/animal toxicants). Potential modeling and mining tools to use the current big data pool in chemical toxicity research are also described.

Critical evaluation of human oral bioavailability for pharmaceutical drugs by using various cheminformatics approaches.

PURPOSE: Oral bioavailability (%F) is a key factor that determines the fate of a new drug in clinical trials. Traditionally, %F is measured using costly and time-consuming experimental tests. Developing computational models to evaluate the %F of new drugs before they are synthesized would be beneficial in the drug discovery process. METHODS: We employed Combinatorial Quantitative Structure-Activity Relationship approach to develop several computational %F models. We compiled a %F dataset of 995 drugs from public sources. After generating chemical descriptors for each compound, we used random forest, support vector machine, k nearest neighbor, and CASE Ultra to develop the relevant QSAR models. The resulting models were validated using five-fold cross-validation. RESULTS: The external predictivity of %F values was poor (R(2) = 0.28, n = 995, MAE = 24), but was improved (R(2) = 0.40, n = 362, MAE = 21) by filtering unreliable predictions that had a high probability of interacting with MDR1 and MRP2 transporters. Furthermore, classifying the compounds according to the %F values (%F < 50% as "low", %F ≥ 50% as 'high") and developing category QSAR models resulted in an external accuracy of 76%. CONCLUSIONS: In this study, we developed predictive %F QSAR models that could be used to evaluate new drug compounds, and integrating drug-transporter interactions data greatly benefits the resulting models.

Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers.

Compared to the current knowledge on cancer chemotherapeutic agents, only limited information is available on the ability of organic compounds, such as drugs and/or natural products, to prevent or delay the onset of cancer. In order to evaluate chemical chemopreventive potentials and design novel chemopreventive agents with low to no toxicity, we developed predictive computational models for chemopreventive agents in this study. First, we curated a database containing over 400 organic compounds with known chemoprevention activities. Based on this database, various random forest and support vector machine binary classifiers were developed. All of the resulting models were validated by cross validation procedures. Then, the validated models were applied to virtually screen a chemical library containing around 23,000 natural products and derivatives. We selected a list of 148 novel chemopreventive compounds based on the consensus prediction of all validated models. We further analyzed the predicted active compounds by their ease of organic synthesis. Finally, 18 compounds were synthesized and experimentally validated for their chemopreventive activity. The experimental validation results paralleled the cross validation results, demonstrating the utility of the developed models. The predictive models developed in this study can be applied to virtually screen other chemical libraries to identify novel lead compounds for the chemoprevention of cancers.

Hybrid in silico models for drug-induced liver injury using chemical descriptors and in vitro cell-imaging information.

Drug-induced liver injury (DILI) is a major adverse drug reaction that accounts for one-third of post-marketing drug withdrawals. Several classifiers for human hepatotoxicity using chemical descriptors with limited prediction accuracies have been published. In this study, we developed predictive in silico models based on a set of 156 DILI positive and 136 DILI negative compounds for DILI prediction. First, models based on a chemical descriptor (CDK, Dragon and MOE) and in vitro cell-imaging endpoints [human hepatocyte imaging assay technology (HIAT) descriptors] were built using random forest (RF) and five-fold cross-validation procedure. Then three hybrid models were built using HIAT and a single type of chemical descriptors. Generally, the models based only on chemical descriptors were poor, with a correct classification rate (CCR) around 0.60 when the default threshold value (i.e. threshold = 0.50) was used. The hybrid models afforded a CCR of 0.73 with a specificity of 0.74 and a better true positive rate (sensitivity of 0.71), which is crucial in drug toxicity screening for the purpose of patient safety. The benefit of hybrid models was even more drastic when stricter classification thresholds were employed (e.g. CCR would be 0.83 when double thresholds (non-toxic < 0.40 and toxic > 0.60) were used for the hybrid model). We have developed rigorously validated hybrid models which can be used in virtual screening of lead compounds with potential hepatotoxicity. Our study also showed a chemical structure and in vitro biological data can be complementary in enhancing the prediction accuracy of human hepatotoxicity and can afford rational mechanistic interpretation.

Hazard and risk characterization of 56 structurally diverse PFAS using a targeted battery of broad coverage assays using six human cell types.

Per- and poly-fluoroalkyl substances (PFAS) are extensively used in commerce leading to their prevalence in the environment. Due to their chemical stability, PFAS are considered to be persistent and bioaccumulative; they are frequently detected in both the environment and humans. Because of this, PFAS as a class (composed of hundreds to thousands of chemicals) are contaminants of very high concern. Little information is available for the vast majority of PFAS, and regulatory agencies lack safety data to determine whether exposure limits or restrictions are needed. Cell-based assays are a pragmatic approach to inform decision-makers on potential health hazards; therefore, we hypothesized that a targeted battery of human in vitro assays can be used to determine whether there are structure-bioactivity relationships for PFAS, and to characterize potential risks by comparing bioactivity (points of departure) to exposure estimates. We tested 56 PFAS from 8 structure-based subclasses in concentration response (0.1-100 µM) using six human cell types selected from target organs with suggested adverse effects of PFAS - human induced pluripotent stem cell (iPSC)-derived hepatocytes, neurons, and cardiomyocytes, primary human hepatocytes, endothelial and HepG2 cells. While many compounds were without effect; certain PFAS demonstrated cell-specific activity highlighting the necessity of using a compendium of in vitro models to identify potential hazards. No class-specific groupings were evident except for some chain length- and structure-related trends. In addition, margins of exposure (MOE) were derived using empirical and predicted exposure data. Conservative MOE calculations showed that most tested PFAS had a MOE in the 1-100 range; ∼20% of PFAS had MOE<1, providing tiered priorities for further studies. Overall, we show that a compendium of human cell-based models can be used to derive bioactivity estimates for a range of PFAS, enabling comparisons with human biomonitoring data. Furthermore, we emphasize that establishing structure-bioactivity relationships may be challenging for the tested PFAS.

Efficient general procedure to access a diversity of gold(0) particles and gold(I) phosphine complexes from a simple HAuCl4 source. Localization of homogeneous/heterogeneous system's interface and field-emission scanning electron microscopy study.

Soluble gold precatalysts, aimed for homogeneous catalysis, under certain conditions may form nanoparticles, which dramatically change the mechanism and initiate different chemistry. The present study addresses the question of designing gold catalysts, taking into account possible interconversions and contamination at the homogeneous/heterogeneous system's interface. It was revealed that accurate localization of boundary experimental conditions for formation of molecular gold complexes in solution versus nucleation and growth of gold particles opens new opportunities for well-known gold chemistry. Within the developed concept, a series of practical procedures was created for efficient synthesis of soluble gold complexes with various phosphine ligands (R3P)AuCl (90-99% yield) and for preparation of different types of gold materials. The effect of the ligand on the particles growth in solution has been observed and characterized with high-resolution field-emission scanning electron microscopy (FE-SEM) study. Two unique types of nanostructured gold materials were prepared: hierarchical agglomerates and gold mirror composed of ultrafine smoothly shaped particles.