Machine Learning Allowed Interpreting Toxicity of a Fe-Doped CuO NM Library Large Data Set─An Environmental In Vivo Case Study.

The wide variation of nanomaterial (NM) characters (size, shape, and properties) and the related impacts on living organisms make it virtually impossible to assess their safety; the need for modeling has been urged for long. We here investigate the custom-designed 1-10% Fe-doped CuO NM library. Effects were assessed using the soil ecotoxicology model (Oligochaeta) in the standard 21 days plus its extension (49 days).

NanoConstruct: A web application builder of ellipsoidal nanoparticles for the investigation of their crystal growth, stability, and the calculation of atomistic descriptors.

NanoConstruct is a state-of-the-art computational tool that enables a) the digital construction of ellipsoidal neutral energy minimized nanoparticles (NPs) in vacuum through its graphical user-friendly interface, and b) the calculation of NPs atomistic descriptors. It allows the user to select NP's shape and size by inserting its ellipsoidal axes and rotation angle while the NP material is selected by uploading its Crystallography Information File (CIF). To investigate the stability of materials not yet synthesised, NanoConstruct allows the substitution of the chemical elements of an already synthesized material with chemical elements that belong into the same group and neighbouring rows of the periodic table.

ASCOT: A web tool for the digital construction of energy minimized Ag, CuO, TiO spherical nanoparticles and calculation of their atomistic descriptors.

ASCOT (an acronym derived from Ag-Silver, Copper Oxide, Titanium Oxide) is a user-friendly web tool for digital construction of electrically neutral, energy-minimized spherical nanoparticles (NPs) of Ag, CuO, and TiO (both Anatase and Rutile forms) in vacuum, integrated into the Enalos Cloud Platform (https://www.enaloscloud.novamechanics.

Predicting electrophoretic mobility of TiO, ZnO, and CeO nanoparticles in natural waters: The importance of environment descriptors in nanoinformatics models.

Natural and engineered nanoparticles (NPs) entering the environment are influenced by many physicochemical processes and show various behavior in different systems (e.g., natural waters showing different characteristics).

A novel role of KEAP1/PGAM5 complex: ROS sensor for inducing mitophagy.

When ROS production exceeds the cellular antioxidant capacity, the cell needs to eliminate the defective mitochondria responsible for excessive ROS production. It has been proposed that the removal of these defective mitochondria involves mitophagy, but the mechanism of this regulation remains unclear. Here, we demonstrate that moderate mitochondrial superoxide and hydrogen peroxide production oxidates KEAP1, thus breaking the interaction between this protein and PGAM5, leading to the inhibition of its proteasomal degradation.

Machine learning and materials modelling interpretation of toxicological response to TiO nanoparticles library (UV and non-UV exposure).

Assessing the risks of nanomaterials/nanoparticles (NMs/NPs) under various environmental conditions requires a more systematic approach, including the comparison of effects across many NMs with identified different but related characters/descriptors. Hence, there is an urgent need to provide coherent (eco)toxicological datasets containing comprehensive toxicity information relating to a diverse spectra of NPs characters. These datasets are test benches for developing holistic methodologies with broader applicability.

Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?

Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications.

Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform.

A literature curated dataset containing 24 distinct metal oxide (MeO) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MeO NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MeO NPs.

ANAMMOX-denitrification biomass in microbial fuel cell to enhance the electricity generation and nitrogen removal efficiency.

The inoculum biomass was collected from a pilot-scale (3 m process tank) nitritation-anaerobic ammonium oxidation (ANAMMOX) (deammonification moving bed biofilm (DeaMBBR)) reactor demonstrating the highest total nitrogen removal rate (TNRR) of 0.33 kg N m day. This biomass was used for inoculating the anodic chamber of a microbial fuel cell (MFC) to investigate the capacity of DeaMBBR biomass to act as an exo-electrogenic consortia.

NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for nanosafety assessment.

Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals.

Fe-Doped ZnO nanoparticle toxicity: assessment by a new generation of nanodescriptors.

In the search for novel tools to combat cancer, nanoparticles (NPs) have attracted a lot of attention. Recently, the controlled release of cancer-cell-killing metal ions from doped NPs has shown promise, but fine tuning of dissolution kinetics is required to ensure specificity and minimize undesirable toxic side-effects. Theoretical tools to help in reaching a proper understanding and finally be able to control the dissolution kinetics by NP design have not been available until now.

In Silico Design of Optimal Dissolution Kinetics of Fe-Doped ZnO Nanoparticles Results in Cancer-Specific Toxicity in a Preclinical Rodent Model.

Cancer cells have unique but widely varying characteristics that have proven them difficult to be treated by classical therapeutics and calls for novel and selective treatment options. Nanomaterials (NMs) have been shown to display biological effects as a function of their chemical composition, and the extent and exact nature of these effects can vary between different biological environments. Here, ZnO NMs are doped with increasing levels of Fe, which allows to finely tune their dissolution rate resulting in significant differences in their biological behavior on cancer or normal cells.

Identification of several high-risk HPV inhibitors and drug targets with a novel high-throughput screening assay.

Human papillomaviruses (HPVs) are oncogenic viruses that cause numerous different cancers as well as benign lesions in the epithelia. To date, there is no effective cure for an ongoing HPV infection. Here, we describe the generation process of a platform for the development of anti-HPV drugs.

An Integrated Data-Driven Strategy for Safe-by-Design Nanoparticles: The FP7 MODERN Project.

The development and implementation of safe-by-design strategies is key for the safe development of future generations of nanotechnology enabled products. The safety testing of the huge variety of nanomaterials that can be synthetized is unfeasible due to time and cost constraints. Computational modeling facilitates the implementation of alternative testing strategies in a time and cost effective way.

Robust Modeling and Scaffold Hopping: Case Study Based on HIV Reverse Transcriptase Inhibitors Type-1 Data.

Background: Human immunodeficiency virus type 1 (HIV-1) is the causative agent of AIDS occurs across mucosal surfaces or by direct inoculation.

Objective: The objective of this study was to consider chemically diverse scaffold sets of HIV-1 Reverse Transcriptase Inhibitors (HIV-1 RTI) subjected to ideal oriented QSAR with large descriptor space.

Method: We generated a four-parameter QSAR model based on 111 data points, which provided an optimum prediction of HIV-1 RTI for overall 367 experimentally measured compounds.

Macrocyclic peptidomimetics with antimicrobial activity: synthesis, bioassay, and molecular modeling studies.

Novel, cyclic peptidomimetics were synthesized by facile acylation reactions using benzotriazole chemistry. Microbiological testing of the synthesized compounds revealed an exceptionally high activity against Candida albicans with a minimum inhibitory concentration (MIC) two orders of magnitude lower than the MIC of the antifungal reference drug amphotericin B. A strikingly high activity was also observed against three Gram-negative bacterial strains (Pseudomonas aeruginosa, Klebsiella pneumoniae and Proteus vulgaris), two of which are known human pathogens.

Theoretical modeling of HPV: QSAR and novodesign with fragment approach.

Structure-activity relationships in a data set of HPV6-E1 helicase ATPase inhibitors were investigated based on two different sets of descriptors. Statistically significant four parameter Quantitative Structure-Activity Relationships (QSAR) models were constructed and validated in both cases (R(2)=0.849; R(2) cv=0.

Subchronic Oral and Inhalation Toxicities: a Challenging Attempt for Modeling and Prediction.

The article deals with a challenging attempt to model and predict "difficult" properties as long-term subchronic oral and inhalation toxicities (90 days) using nonlinear QSAR approach. This investigation is one of the first to tackle such multicomplex properties where we have employed nonlinear models based on artificial neural network for the prediction of NOAEL (no observable adverse effect level). Despite the complex nature of the NOAEL property based on in vivo rat experiments, the successful models can be used as alternative tools to non-animal tests for the initial assessment of these chronic toxicities.

Fragment-based development of HCV protease inhibitors for the treatment of hepatitis C.

A novel computational technology based on fragmentation of the chemical compounds has been used for the fast and efficient prediction of activities of prospective protease inhibitors of the hepatitis C virus. This study spans over a discovery cycle from the theoretical prediction of new HCV NS3 protease inhibitors to the first cytotoxicity experimental tests of the best candidates. The measured cytotoxicity of the compounds indicated that at least two candidates would be suitable further development of drugs.

Application of the QSPR approach to the boiling points of azeotropes.

CODESSA Pro derivative descriptors were calculated for a data set of 426 azeotropic mixtures by the centroid approximation and the weighted-contribution-factor approximation. The two approximations produced almost identical four-descriptor QSPR models relating the structural characteristic of the individual components of azeotropes to the azeotropic boiling points. These models were supported by internal and external validations.

Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks.

An investigation of cell-penetrating peptides (CPPs) by using combination of Artificial Neural Networks (ANN) and Principle Component Analysis (PCA) revealed that the penetration capability (penetrating/non-penetrating) of 101 examined peptides can be predicted with accuracy of 80%-100%. The inputs of the ANN are the main characteristics classifying the penetration. These molecular characteristics (descriptors) were calculated for each peptide and they provide bio-chemical insights for the criteria of penetration.

Estimating the toxicities of organic chemicals in activated sludge process.

The experimental logEC50 toxicity values of 104 compounds causing bioluminescent repression of the bacterium strain Pseudomonas isolated from an industrial wastewater were studied. Using the Best Multilinear Regression method implemented in CODESSA PRO, models with up to 8 theoretical descriptors were obtained. Utilizing a rigorous descriptor selection and validation procedure a reliable QSAR model with four parameters was selected as best.

Skin permeation rate as a function of chemical structure.

Multilinear and nonlinear QSAR models were built for the skin permeation rate (Log K(p)) of a set of 143 diverse compounds. Satisfactory models were obtained by three approaches applied: (i) CODESSA PRO, (ii) Neural Network modeling using large pools of theoretical molecular descriptors, and (iii) ISIDA modeling based on fragment descriptors. The predictive abilities of the models were assessed by internal and external validations.

QSPR analysis for infinite dilution activity coefficients of organic compounds.

A quantitative structure-property relationship study of the infinite-dilution activity coefficients for a set of 38 organic compounds in ionic liquids such as 1-methyl-3-ethylimidazolium bis((trifluoromethyl)sulfonyl)imide, 1,2-dimethyl-3-ethylimidazolium bis((trifluoromethyl)-sulfonyl)imide, and 4-methyl-N-butylpyridinium tetrafluoroborate. QSPR study was carried out using the CODESSA PRO program. A general three-parameter QSPR model was obtained.

Aqueous biphasic systems. Partitioning of organic molecules: a QSPR treatment.

The partitioning of 29 small organic probes in a PEG-2000/(NH4)2SO4 biphasic system was investigated using a quantitative structure-property relationship (QSPR) approach. A three-descriptor equation with the squared correlation coefficient (R2) of 0.97 for the partition coefficient (log D) was obtained.