CORAL: Model of Ecological Impact of Heavy Metals on Soils via the Study of Modification of Concentration of Biomolecules in Earthworms (Eisenia fetida).

The traditional application for quantitative structure-property/activity relationships (QSPRs/QSARs) in the fields of thermodynamics, toxicology or drug design is predicting the impact of molecular features using data on the measurable characteristics of substances. However, it is often necessary to evaluate the influence of various exposure conditions and environmental factors, besides the molecular structure. Different enzyme-driven processes lead to the accumulation of metal ions by the worms. Heavy metals are sequestered in these organisms without being released back into the soil. In this study, we propose a novel approach for modeling the absorption of heavy metals, such as mercury and cobalt by worms. The models are based on optimal descriptors calculated for the so-called quasi-SMILES, which incorporate strings of codes reflecting experimental conditions. We modeled the impact on the levels of proteins, hydrocarbons, and lipids in an earthworm's body caused by different combinations of concentrations of heavy metals and exposure time observed over two months of exposure with a measurement interval of 15 days.

The System of Self-Consistent Models: The Case of Henry's Law Constants.

Data on Henry's law constants make it possible to systematize geochemical conditions affecting atmosphere status and consequently triggering climate changes. The constants of Henry's law are desired for assessing the processes related to atmospheric contaminations caused by pollutants. The most important are those that are capable of long-term movements over long distances. This ability is closely related to the values of Henry's law constants. Chemical changes in gaseous mixtures affect the fate of atmospheric pollutants and ecology, climate, and human health. Since the number of organic compounds present in the atmosphere is extremely large, it is desirable to develop models suitable for predictions for the large pool of organic molecules that may be present in the atmosphere. Here, we report the development of such a model for Henry's law constants predictions of 29,439 compounds using the CORAL software (2023). The statistical quality of the model is characterized by the value of the coefficient of determination for the training and validation sets of about 0.81 (on average).

How the CORAL software can be used to select compounds for efficient treatment of neurodegenerative diseases?

Recommendations on the efficient application of CORAL software (http://www.insilico.eu/coral) to establish quantitative structure-property/activity relationships (QSPRs/QSARs) are provided. The predictive potential of the approach has been demonstrated for QSAR models developed for inhibitor concentrations (negative decimal logarithm of IC50) of derivatives of N-methyl-d-aspartate (NMDA) receptor, leucine-rich repeat kinase 2 (LRRK2), and tropomyosin receptor kinase A (TrkA). The above three protein targets are related to various neurodegenerative diseases such as Alzheimer's and Parkinson's. Each model was checked using several splits of the data for the training and the validation sets. The index of ideality of correlation (IIC) represents a tool to improve the predictive potential for an arbitrary model. However, the use of the IIC should be carried out according to rules, described in this work.

Semi-correlations combined with the index of ideality of correlation: a tool to build up model of mutagenic potential.

Mutagenicity is the ability of a substance to induce mutations. This hazardous ability of a substance is decisive from point of view of ecotoxicology. The number of substances, which are used for practical needs, grows every year. Consequently, methods for at least preliminary estimation of mutagenic potential of new substances are necessary. Semi-correlations are a special case of traditional correlations. These correlations can be named as "correlations along two parallel lines." This kind of correlation has been tested as a tool to predict selected endpoints, which are represented by only two values: "inactive/active" (0/1). Here this approach is used to build up predictive models for mutagenicity of large dataset (n = 3979). The so-called index of ideality of correlation (IIC) has been tested as a statistical criterion to estimate the semi-correlation. Three random splits of experimental data into the training, invisible-training, calibration, and validation sets were analyzed. Two models were built up for each split: the first model based on optimization without the IIC and the second model based on optimization where IIC is involved in the Monte Carlo optimization. The statistical characteristics of the best model (calculated with taking into account the IIC) n = 969; sensitivity = 0.8050; specificity = 0.9069; accuracy = 0.8648; Matthews's correlation coefficient = 0.7196 (using IIC). Thus, the use of IIC improves the statistical quality of the binary classification models of mutagenic potentials (Ames test) of organic compounds.

Role of Singlet Oxygen in the Degradation of Selected Insensitive Munitions Compounds: A Comprehensive, Quantum Chemical Investigation.

DNAN (2,4-dinitroanisole), NTO (3-nitro-1,2,4-triazol-5-one), and NQ (nitroguanidine) are important energetic materials used in military applications. They may find their way to the environment during manufacturing, transportation, storage, training, and disposal. A detailed investigation of possible mechanisms for reactions of the nitrocompounds with singlet oxygen, one of the potential methods for their degradation, was performed by computational study using the PCM(Pauling)/M06-2X/6-311++G(d,p) approach. Obtained results suggest that reactivity of the investigated munitions compounds toward singlet oxygen follows the order: DNAN > NTO(anion) > NQ ≫ NTO. DNAN is involved in [4 + 2]-addition with oxygen, and further formation of diepoxide or epoxyketone through diradical intermediates have been predicted. The NTO may undergo intramolecular rearrangement with formation of peroxide compound or nitrite radical elimination, and NQ may be transformed into urea.

CORAL and Nano-QFAR: Quantitative feature - Activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co3O4, and TiO2).

Quantitative feature - activity relationships (QFAR) approach was applied to prediction of bioavailability of metal oxide nanoparticles. ZnO, CuO, Co3O4, and TiO2 nanoxides were considered. The computational model for bioavailability of investigated species is asserted. The model was calculated using the Monte Carlo method. The CORAL free software (http://www.insilico.eu/coral) was used in this study. The developed model was tested by application of three different splits of data into the training and validation sets. So-called, quasi-SMILES are used to represent the conditions of action of metal oxide nanoparticles. A new paradigm of building up predictive models of endpoints related to nanomaterials is suggested. The paradigm is the following "An endpoint is a mathematical function of available eclectic data (conditions)". Recently, the paradigm has been checked up with endpoints related to metal oxide nanoparticles, fullerenes, and multi-walled carbon-nanotubes.

Structure and electrochemical properties for complexes of nitrocompounds with inorganic ions: A theoretical approach.

Reduction and oxidation (redox) reactions are widely used for removal of nitrocompounds from contaminated soil and water. Structures and redox properties for complexes of nitrocompounds, such as 2,4,6-trinitrotoluene (TNT), 2,4-dinitrotoluene (DNT), 2,4-dinitroanisole (DNAN), and 5-nitro-2,4-dihydro-3H-1,2,4-triazol-3-one (NTO), with common inorganic ions (Na(+) , Cl(-) , NO3-) were investigated at the SMD(Pauling)/PCM(Pauling)/MPWB1K/TZVP level of theory. Atoms in molecules (AIM) theory was applied to analyze the topological properties of the bond critical points involved in the interactions between the nitrocompounds and the ions. Topological analyses show that intermolecular interactions of the types O(N)…Na(+) , C-H…Cl(-) ( ONO2-), and C…Cl(-) ( ONO2-) may be discussed as noncovalent closed-shell interactions, while N-H···Cl(-) ( ONO2-) hydrogen bonds are partially covalent in nature. Complexation causes significant decrease of redox activity of the nitrocompounds. Analysis of the reduction potentials of the complexes obtained through application of the Pourbaix diagram of an iron/water system revealed that sodium complexes of NTO might be reduced by metallic iron. © 2016 Wiley Periodicals, Inc.

In Silico Alkaline Hydrolysis of Octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine: Density Functional Theory Investigation.

HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine), an energetic material used in military applications, may be released to the environment during manufacturing, transportation, storage, training, and disposal. A detailed investigation of a possible mechanism of alkaline hydrolysis, as one of the most promising methods for HMX remediation, was performed by computational study at PCM(Pauling)/M06-2X/6-311++G(d,p) level. Obtained results suggest that HMX hydrolysis at pH 10 represents a highly exothermic multistep process involving initial deprotonation and nitrite elimination, hydroxide attachment accompanied by cycle cleavage, and further decomposition of cycle-opened intermediate to the products caused by a series of C-N bond ruptures, hydroxide attachments, and proton transfers. Computationally predicted products of HMX hydrolysis such as nitrite, 4-nitro-2,4-diazabutanal, formaldehyde, nitrous oxide, formate, and ammonia correspond to experimentally observed species. Based on computed reaction pathways for HMX decomposition by alkaline hydrolysis, the kinetics of the entire process was modeled. Very low efficiency of this reaction at pH 10 was observed. Computations predict significant increases (orders of magnitude) of the hydrolysis rate for hydrolysis reactions undertaken at pH 11, 12, and 13.

Nano-QSAR: Model of mutagenicity of fullerene as a mathematical function of different conditions.

The experimental data on the bacterial reverse mutation test (under various conditions) on C60 nanoparticles for the cases (i) TA100, and (ii) WP2uvrA/pkM101 are examined as endpoints. By means of the optimal descriptors calculated with the Monte Carlo method a mathematical model of these endpoints has been built up. The models are a mathematical function of eclectic data such as (i) dose (g/plate); (ii) metabolic activation (i.e. with mix S9 or without mix S9); and (iii) illumination (i.e. darkness or irradiation). The eclectic data on different conditions were represented by so-called quasi-SMILES. In contrast to the traditional SMILES which are representation of molecular structure, the quasi-SMILES are representation of conditions by sequence of symbols. The calculations were carried out with the CORAL software, available on the Internet at http://www.insilico.eu/coral. The main idea of the suggested descriptors is the accumulation of all available eclectic information in the role of logical and digital basis for building up a model. The computational experiments have shown that the described approach can be a tool to build up models of mutagenicity of fullerene under different conditions.

Organic compound composition in soil and sediments collected in Jackson, Mississippi.

The aim of our study was to identify organic pollutants found in soil and sediment samples collected within the Jackson, MS metropolitan area. The chemical characterization of the organic compound fractions in soil and sediment samples was carried out by separating the organic fraction using column chromatography (CC) and quantitatively analyzing the polycyclic aromatic hydrocarbons (PAHs), n-alkanes and other organic compounds using gas chromatography-electron impact mass spectrometry (GC-MS). Fifty-six compounds were identified and quantified in the soil samples and 33 compounds were identified and quantified in the sediment samples. The PAHs, n-alkanes and other organic compound profiles in the soil and sediment samples were compared. The percentage contents of the organic compounds in the soil samples were very diverse (from traces to 12.44 ± 1.47%). The compounds present in the highest concentrations were n-alkanes: n-C31 (12.44 ± 1.47%), n-C29 (11.64 ± 1.21%), and n-C33 (8.95 ± 1.08%). The components occurring in smaller quantities (from 1% to 5%) were 2 PAHs (fluoranthene 1.28 ± 0.25%, pyrene 1.16 ± 0.20%), 10 n-alkanes from n-C21 (1.25 ± 0.29%) to n-C32 (2.67 ± 0.52%) and 11 other compounds (e.g., 2-pentanol, 4-methyl (3.33 ± 0.44%), benzyl butyl phthalate (4.25 ± 0.59%), benzenedicarboxylic acid (1.14 ± 0.08%), ethane, 1,1-diethoxy (3.15 ± 0.41) and hexadecanoic acid (2.52 ± 0.34). The soil samples also contained 30 compounds present in concentrations <1% (e.g., anthracene (0.13 ± 0.04%), n-C20 (0.84 ± 0.21%) and acetic acid (0.12 ± 0.04%). The compounds present in the highest concentrations in the sediment samples were PAHs: pyrene (7.73 ± 1.15%) and fluoranthene (6.23 ± 1.07%) and n-alkanes: n-C31 (6.74 ± 1.21%), n-C29 (6.65 ± 0.98%) and n-C27 (6.13 ± 1.09%). The remaining organic compounds were present in smaller quantities (< 5%).

Are the reduction and oxidation properties of nitrocompounds dissolved in water different from those produced when adsorbed on a silica surface? A DFT M05-2X computational study.

The reduction and oxidation properties of four nitrocompounds (trinitrotoluene [TNT], 2,4-dinitrotoluene, 2,4-dinitroanisole, and 5-nitro-2,4-dihydro-3H-1,2,4-triazol-3-one [NTO]) dissolved in water as compared with the same properties for compounds adsorbed on a silica surface were studied. To consider the influence of adsorption, cluster models were developed at the M05/tzvp level. A hydroxylated silica (001) surface was chosen to represent a key component of soil. The PCM(Pauling) and SMD solvation models were used to model water bulk influence. The following properties were analyzed: electron affinity, ionization potential, reduction Gibbs free energy, oxidation Gibbs free energy, and reduction and oxidation potentials. It was found that adsorption and solvation decrease gas phase electron affinity, ionization potential, and Gibbs free energy of reduction and oxidation, and thus, promote redox transformation of nitrocompounds. However, in case of solvation, the changes are more significant than for adsorption. This means that nitrocompounds dissolved in water are easier to transform by reduction or oxidation than adsorbed ones. Among the considered compounds, TNT was found to be the most reactive in an electron attachment process and the least reactive for an electron detachment transformation. During ionization, a deprotonation of adsorbed NTO was found to occur.

QSAR model as a random event: A case of rat toxicity.

Quantitative structure-property/activity relationships (QSPRs/QSARs) can be used to predict physicochemical and/or biochemical behavior of substances which were not studied experimentally. Typically predicted values for chemicals in the training set are accurate since they were used to build the model. QSPR/QSAR models must be validated before they are used in practice. Unfortunately, the majority of the suggested approaches of the validation of QSPR/QSAR models are based on consideration of geometrical features of clusters of data points in the plot of experimental versus calculated values of an endpoint. We believe these geometrical criteria can be more useful if they are analyzed for several splits into the training and test sets. In this way, one can estimate the reproducibility of the model with various splits and better evaluate model reliability. The probability of the correct prediction of an endpoint for external validation set (in the series of the above-mentioned splits) can provide an useful way to evaluate the domain of applicability of the model.

Structure and Redox Properties of 5-Amino-3-nitro-1H-1,2,4-triazole (ANTA) Adsorbed on a Silica Surface: A DFT M05 Computational Study.

A cluster approximation was applied at the M05/tzvp level to model an adsorption of 5-amino-3-nitro-1H-1,2,4-triazole (ANTA) on the (001) surface of α-quartz. Structures of the obtained ANTA-silica complexes confirm a nearly parallel orientation of the nitro compound toward the surface. The atoms in molecules (AIM) method was applied to analyze binding between ANTA and the silica surface. Attachment or loss of an electron was found to lead to a significant deviation from coplanarity in the complexes and to a strengthening of a hydrogen bonding. Redox properties of the adsorbed ANTA were compared with those of gas-phase and hydrated species by calculation of the ionization potential, electron affinity, oxidation and reduction Gibbs free energies, and oxidation and reduction potentials. It was shown that the adsorbed ANTA has a lower ability to undergo redox transformations as compared to that of the hydrated one.

Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions.

The Monte Carlo technique has been used to build up quantitative structure-activity relationships (QSARs) for prediction of dark cytotoxicity and photo-induced cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli (minus logarithm of lethal concentration for 50% bacteria pLC50, LC50 in mol/L). The representation of nanoparticles include (i) in the case of the dark cytotoxicity a simplified molecular input-line entry system (SMILES), and (ii) in the case of photo-induced cytotoxicity a SMILES plus symbol '^'. The predictability of the approach is checked up with six random distributions of available data into the visible training and calibration sets, and invisible validation set. The statistical characteristics of these models are correlation coefficient 0.90-0.94 (training set) and 0.73-0.98 (validation set).

Optimal descriptor as a translator of eclectic information into the prediction of membrane damage: the case of a group of ZnO and TiO2 nanoparticles.

The development of quantitative structure-activity relationships for nanomaterials needs representation of molecular structure of extremely complex molecular systems. Obviously, various characteristics of nanomaterial could impact associated biochemical endpoints. Following features of TiO2 and ZnO nanoparticles (n=42) are considered here: (i) engineered size (nm); (ii) size in water suspension (nm); (iii) size in phosphate buffered saline (PBS, nm); (iv) concentration (mg/L); and (v) zeta potential (mV). The damage to cellular membranes (units/L) is selected as an endpoint. Quantitative features-activity relationships (QFARs) are calculated by the Monte Carlo technique for three distributions of data representing values associated with membrane damage into the training and validation sets. The obtained models are characterized by the following average statistics: 0.78

QSAR models for inhibitors of physiological impact of Escherichia coli that leads to diarrhea.

Quantitative structure - activity relationships (QSARs) developed to evaluate percentage of inhibition of STa-stimulated (Escherichia coli) cGMP accumulation in T84 cells are calculated by the Monte Carlo method. This endpoint represents a measure of biological activity of a substance against diarrhea. Statistical quality of the developed models is quite good. The approach is tested using three random splits of data into the training and test sets. The statistical characteristics for three splits are the following: (1) n=20, r(2)=0.7208, q(2)=0.6583, s=16.9, F=46 (training set); n=11, r(2)=0.8986, s=14.6 (test set); (2) n=19, r(2)=0.6689, q(2)=0.5683, s=17.6, F=34 (training set); n=12, r(2)=0.8998, s=12.1 (test set); and (3) n=20, r(2)=0.7141, q(2)=0.6525, s=14.7, F=45 (training set); n=11, r(2)=0.8858, s=19.5 (test set). Based on the proposed here models hypothetical compounds which can be useful agents against diarrhea are suggested.

Receptor- and ligand-based study of fullerene analogues: comprehensive computational approach including quantum-chemical, QSAR and molecular docking simulations.

Fullerene and its derivatives have potential antiviral activity due to their specific binding interactions with biological molecules. In this study fullerene derivatives were investigated by the synergic combination of three approaches: quantum-mechanical calculations, protein-ligand docking and quantitative structure-activity relationship methods. The protein-ligand docking studies and improved structure-activity models have been able both to predict binding affinities for the set of fullerene-C60 derivatives and to help in finding mechanisms of fullerene derivative interactions with human immunodeficiency virus type 1 aspartic protease, HIV-1 PR. Protein-ligand docking revealed several important molecular fragments that are responsible for the interaction with HIV-1 PR. In addition, a density functional theory method has been utilized to identify the optimal geometries and predict physico-chemical parameters of the studied compounds. The 5-variable GA-MLRA based model showed the best predictive ability (r(2)training = 0.882 and r(2)test = 0.738), with high internal and external correlation coefficients.

Development of Self-Consistency Models of Anticancer Activity of Nanoparticles under Different Experimental Conditions Using Quasi-SMILES Approach.

Algorithms of the simulation of the anticancer activity of nanoparticles under different experimental conditions toward cell lines A549 (lung cancer), THP-1 (leukemia), MCF-7 (breast cancer), Caco2 (cervical cancer), and hepG2 (hepatoma) have been developed using the quasi-SMILES approach. This approach is suggested as an efficient tool for the quantitative structure-property-activity relationships (QSPRs/QSARs) analysis of the above nanoparticles. The studied model is built up using the so-called vector of ideality of correlation. The components of this vector include the index of ideality of correlation (IIC) and the correlation intensity index (CII). The epistemological component of this study is the development of methods of registration, storage, and effective use of experimental situations that are comfortable for the researcher-experimentalist in order to be able to control the physicochemical and biochemical consequences of using nanomaterials. The proposed approach differs from the traditional models based on QSPR/QSAR in the following respects: (i) not molecules but experimental situations available in a database are considered; in other words, an answer is offered to the question of how to change the plot of the experiment in order to achieve the desired values of the endpoint being studied; and (ii) the user has the ability to select a list of controlled conditions available in the database that can affect the endpoint and evaluate how significant the influence of the selected controlled experimental conditions is.

The validation of predictive potential via the system of self-consistent models: the simulation of blood-brain barrier permeation of organic compounds.

CONTEXT: To apply the quantitative relationships "structure-endpoint" approach, the reliability of prediction is necessary but sometimes challenging to achieve. In this work, an attempt is made to accomplish the reliability of forecasts by creating a set of random partitions of data into training and validation sets, followed by constructing random models. A system of random models for a helpful approach should be self-consistent, giving a similar or at least comparable statistical quality of the predictions for models obtained using different splits of available data into training and validation sets. METHOD: The carried out computer experiments aimed at obtaining blood-brain barrier permeation models showed that, in principle, can be used such an approach (the Monte Carlo optimization of the correlation weights for different molecular features) for the above purpose taking advantage of specific algorithms to optimize the modelling steps with applying of new statistical criteria such as the index of ideality of correlation (IIC) and the correlation intensity index (CII). The results so obtained are good and better than what was reported previously. The suggested approach to validation of models is non-identic to traditionally applied manners of the checking up models. The concept of validation can be used for arbitrary models (not only for models of the blood-brain barrier).

In vitro and in silico study of mixtures cytotoxicity of metal oxide nanoparticles to Escherichia coli: a mechanistic approach.

Metal oxide nanoparticles (MONPs) are commonly found in the aquatic and terrestrial systems as chemical mixtures. Assessment of cytotoxicity associated with single and combination of MONPs can truly identify the concerned environmental risk. Thus, using Escherichia coli as a test model, in vitro cytotoxicity of 6 single MONPs, 15 binary and 20 tertiary mixtures with equitoxic ratios was evaluated following standard bioassay protocols. Assessment of oxidative stress suggested that the production of reactive oxygen species (ROS) was negligible, and the release of metal zinc ions played an important role in the toxicity of MONP mixtures. From our experimental data points, seven quantitative structure-activity relationships (QSARs) models were developed to model the cytotoxicity of these MONPs, based on our created periodic table-based descriptors and experimentally analyzed Zeta-potential. Two strategic approaches i.e. pharmacological and mathematical hypotheses were considered to identify the mixture descriptors pool for modeling purposes. The stringent validation criteria suggested that the model (Model M4) developed with mixture descriptors generated by square-root mole contribution outperformed the other six models considering validation criteria. While considering the pharmacological approach, the 'independent action' generated descriptor pool offered the best model (Model M2), which firmly confirmed that each MONP in the mixture acts through 'independent action' to induce cytotoxicity to E. coli instead of fostering an additive, antagonistic or synergistic effect among MONPs. The total metal electronegativity in a specific metal oxide relative to the number of oxygen atoms and metal valence was associated with a positive contribution to cytotoxicity. At the same time, the core count, which gives a measure of molecular bulk and Zeta potential, had a negative contribution to cytotoxicity.