Optimal descriptor as a translator of eclectic data into endpoint prediction: mutagenicity of fullerene as a mathematical function of conditions

A review on the structural characterization of nanomaterials for nano-QSAR models

Quantitative structure-activity relationship (QSAR) models are routinely used to predict the properties and biological activity of chemicals to direct synthetic advances, perform massive screenings, and even to register new substances according to international regulations. Currently, nanoscale QSAR (nano-QSAR) models, adapting this methodology to predict the intrinsic features of nanomaterials (NMs) and quantitatively assess their risks, are blooming. One of the challenges is the characterization of the NMs. This cannot be done with a simple SMILES representation, as for organic molecules, because their chemical structure is complex, including several layers and many inorganic materials, and their size and geometry are key features. In this review, we survey the literature for existing predictive models for NMs and discuss the variety of calculated and experimental features used to define and describe NMs. In the light of this research, we propose a classification of the descriptors including those that directly describe a component of the nanoform (core, surface, or structure) and also experimental features (related to the nanomaterial's behavior, preparation, or test conditions) that indirectly reflect its structure.

In Silico Simulation of Impacts of Metal Nano-Oxides on Cell Viability in THP-1 Cells Based on the Correlation Weights of the Fragments of Molecular Structures and Codes of Experimental Conditions Represented by Means of Quasi-SMILES

A simulation of the effect of metal nano-oxides at various concentrations (25, 50, 100, and 200 milligrams per millilitre) on cell viability in THP-1 cells (%) based on data on the molecular structure of the oxide and its concentration is proposed. We used a simplified molecular input-line entry system (SMILES) to represent the molecular structure. So-called quasi-SMILES extends usual SMILES with special codes for experimental conditions (concentration). The approach based on building up models using quasi-SMILES is self-consistent, i.e., the predictive potential of the model group obtained by random splits into training and validation sets is stable. The Monte Carlo method was used as a basis for building up the above groups of models. The CORAL software was applied to building the Monte Carlo calculations. The average determination coefficient for the five different validation sets was R² = 0.806 ± 0.061.

Machine Learning Models for Predicting Cytotoxicity of Nanomaterials

The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.

Unswerving modeling of hepatotoxicity of cadmium containing quantum dots using amalgamation of quasiSMILES, index of ideality of correlation, and consensus modeling

Liver toxicity of quantum dots varies with size, concentration, and other structural as well as experimental parameters. For modeling hepatotoxicity, the eclectic data associated with cadmium containing quantum dots have been used in the creation of quasiSMILES for their representation. The core diameter is normalized for wider applicability and the index of the ideality of correlation is applied to construct the better quantitative features toxicity relationship models. Total eight splits are created and the best model is obtained through split 1 with better prediction criteria of validation set objects. The values of all statistical criteria used in the quality determination of a QSAR model are within the specified range for all the eight toxicity models developed here. Factors like TGA ligand and 0.6-0.7 nm diameter are favorable for liver toxicity while L-cysteine ligand and 0.5-0.6 nm core diameter are helpful in the reduction of toxicity. Further, the intelligent consensus modeling process forms a total of 40 individual and 20 consensus models and the best individual and consensus models are 'Good' in MAE-based criteria. The consensus modeling enhances the prediction ability as well as the accuracy of the developed models and increases the applicability space of the built models for hepatotoxicity prediction of quantum dots.

Correlation intensity index: Building up models for mutagenicity of silver nanoparticles

Nanomaterials become significant component of economics. Consequently, nanomaterials become object of environmental sciences. There is a traditional list of endpoints which are indicators of the ecological risk. Mutagenicity is one of important component in this list. The quasi-SMILES approach, that in contrast to majority of work dedicated to modelling behaviour of nanomaterials gives possibility to consider experimental conditions as well as other circumstances which can impact the behaviour of nanomaterials is suggested. This is carried out via so-called quasi-SMILES. The quasi-SMILES is a line on of codes that contains all the above available eclectic data. Modelling process aimed to build up a model involves Correlation Intensity Index (CII) that is a new criterion of predictive potential of models. The scheme of calculation of CII is described in this work in the first time. The applying of CII together with Index of Ideality Correlation (IIC) in modelling of mutagenicity of silver nanoparticles by the Monte Carlo method using the CORAL software (http://www.insilico.eu/coral) indicates that application of the CII improves the predictive potential of these models for three random splits into the training set (75%) and validation set (25%).

In silico enhancement of azo dye adsorption affinity for cellulose fibre through mechanistic interpretation under guidance of QSPR models using Monte Carlo method with index of ideality correlation

Azo dyes are a group of chemical moieties joined by azo (-N=N-) group with potential usefulness in different industrial applications. But these dyes are not devoid of hazardous consequence because of poor affinity for the fibre and discharge into the water stream. The chemical aspects of 72 azo dyes towards cellulose fibre in terms of their affinity by QSPR have been explored in the present work. We have employed two approaches, namely balance of correlation without IIC (TF₁) and balance of correlation with IIC (TF₂), to generate 16 QSAR models from 8 splits. The determination coefficient of calibration and validation set was found higher when the QSPR models were developed using the index of ideality correlation (IIC) parameter (TF₂). The model developed with TF₂ for split 3 was considered as a prominent model because the determination coefficient of the validation set was maximum (r ² = 0.9468). The applicability domain (AD) was also analysed based on 'statistical defect', d(A) for a SMILES attribute. The mechanistic interpretation was done by identifying the SMILES attributes responsible for the promoter of endpoint increase and promoter of endpoint decrease. These SMILES attributes were applied to design 15 new dyes with higher affinity for cellulose fibre.

Pesticides, cosmetics, drugs: identical and opposite influences of various molecular features as measures of endpoints similarity and dissimilarity

The similarity is an important category in natural sciences. A measure of similarity for a group of various biochemical endpoints is suggested. The list of examined endpoints contains (1) toxicity of pesticides towards rainbow trout; (2) human skin sensitization; (3) mutagenicity; (4) toxicity of psychotropic drugs; and (5) anti HIV activity. Further applying and evolution of the suggested approach is discussed. In particular, the conception of the similarity (dissimilarity) of endpoints can play the role of a "useful bridge" between quantitative structure property/activity relationships (QSPRs/QSARs) and read-across technique.

Nanotoxicology data for in silico tools: a literature review

The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.

Practices and Trends of Machine Learning Application in Nanotoxicology

Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials

Although nanotechnology is a new and rapidly growing area of science, the impact of nanomaterials on living organisms is unknown in many aspects. In this regard, it is extremely important to perform toxicological tests, but complete characterization of all varying preparations is extremely laborious. The computational technique called quantitative structure–activity relationship, or QSAR, allows reducing the cost of time- and resource-consuming nanotoxicity tests. In this review, (Q)SAR cytotoxicity studies of the past decade are systematically considered. We regard here five classes of engineered nanomaterials (ENMs): Metal oxides, metal-containing nanoparticles, multi-walled carbon nanotubes, fullerenes, and silica nanoparticles. Some studies reveal that QSAR models are better than classification SAR models, while other reports conclude that SAR is more precise than QSAR. The quasi-QSAR method appears to be the most promising tool, as it allows accurately taking experimental conditions into account. However, experimental artifacts are a major concern in this case.

Importance of Choosing Relevant Biological End Points To Predict Nanoparticle Toxicity with Computational Approaches for Human Health Risk Assessment

Because it is impossible to assess in vitro or in vivo the toxicity of all nanoparticles available on the market on a case-by-case basis, computational approaches have been proposed as useful alternatives to predict in silico the hazard potential of engineered nanoparticles. Despite promising results, a major issue associated with these mathematical models lies in the a priori choice of the physicochemical descriptors and the biological end points. We performed a thorough bibliographic survey on the biological end points used for nanotoxicology purposes and compared them between experimental and computational approaches. They were found to be disparate: while conventional in vitro nanotoxicology assays usually investigate a large array of biological effects using eukaryotic cells (cytotoxicity, pro-inflammatory response, oxidative stress, genotoxicity), computational studies mostly focus on cell viability and also include studies on prokaryotic cells. We may thus wonder the relevance of building complex mathematical models able to predict accurately a biological end point if this latter is not the most relevant to support human health risk assessment. The choice of biological end points clearly deserves to be more carefully discussed. This could bridge the gap between experimental and computational nanotoxicology studies and allow in silico predictive models to reach their full potential.

Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials

A quasi-QSAR model was developed to predict the cell viability of human lung (BEAS-2B) and skin (HaCaT) cells exposed to 21 types of metal oxide nanomaterials. A wide range of toxicity datasets obtained from the S2NANO (www.s2nano.org) database was used. The data of descriptors representing the physicochemical properties and experimental conditions were coded to quasi-SMILES. In particular, hierarchical cluster analysis (HCA) and min-max normalization method were respectively used in assigning alphanumeric codes for numerical descriptors (e.g., core size, hydrodynamic size, surface charge, and dose) and then quasi-QSAR model performances for both methods were compared. The quasi-QSAR models were developed using CORAL software (www.insilico.eu/coral). Quasi-QSAR model built using quasi-SMILES generated by means of HCA showed better performance than the min-max normalization method. The model showed satisfactory statistical results (R_adj² for the training dataset: 0.71-0.73; R_adj² for the calibration dataset: 0.74-0.82; and R_adj² for the validation dataset: 0.70-0.76).

An assessment of applicability of existing approaches to predicting the bioaccumulation of conventional substances in nanomaterials

The experimental determination of bioaccumulation is challenging, and a number of approaches have been developed for its prediction. It is important to assess the applicability of these predictive approaches to nanomaterials (NMs), which have been shown to bioaccumulate. The octanol/water partition coefficient (K_OW ) may not be applicable to some NMs that are not found in either the octanol or water phases but rather are found at the interface. Thus the K_OW values obtained for certain NMs are shown not to correlate well with the experimentally determined bioaccumulation. Implementation of quantitative structure-activity relationships (QSARs) for NMs is also challenging because the bioaccumulation of NMs depends on nano-specific properties such as shape, size, and surface area. Thus there is a need to develop new QSAR models based on these new nanodescriptors; current efforts appear to focus on digital processing of NM images as well as the conversion of surface chemistry parameters into adsorption indices. Water solubility can be used as a screening tool for the exclusion of NMs with short half-lives. Adaptation of fugacity/aquivalence models, which include physicochemical properties, may give some insights into the bioaccumulation potential of NMs, especially with the addition of a biota component. The use of kinetic models, including physiologically based pharmacokinetic models, appears to be the most suitable approach for predicting bioaccumulation of NMs. Furthermore, because bioaccumulation of NMs depends on a number of biotic and abiotic factors, it is important to take these factors into account when one is modeling bioaccumulation and interpreting bioaccumulation results. Environ Toxicol Chem 2018;37:2972-2988. © 2018 SETAC.

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.

Comparison of conventional and advanced in vitro models in the toxicity testing of nanoparticles

Humans are exposed to a wide variety of nanoparticles (NPs) present in the environment, in consumer, health and medical products, and in food. Conventional cytotoxicity testing compared to animal testing is less expensive, faster and avoids ethical problems at the expense of a lower predictive value. New cellular models and exposure conditions have been developed to overcome the limitations of conventional cell culture and obtain more predictive data. The use of three-dimensional culture, co-culture and inclusion of mechanical stimulation can provide physiologically more relevant culture conditions. These systems are particularly relevant for oral, respiratory and intravenous exposure to NPs and it may be assumed that physiologically relevant application of the NPs can improve the predictive value of in vitro testing. Various groups have used advanced culture and exposure systems, but few direct comparisons between data from conventional cultures and from advanced systems exist. In silico models may present another option to predict human health risk by NPs without using animal studies. In the absence of validation, the question whether these alternative models provide more predictive data than conventional testing remains elusive.

Towards the Development of Global Nano-Quantitative Structure-Property Relationship Models: Zeta Potentials of Metal Oxide Nanoparticles

Zeta potential indirectly reflects a charge of the surface of nanoparticles in solutions and could be used to represent the stability of the colloidal solution. As processes of synthesis, testing and evaluation of new nanomaterials are expensive and time-consuming, so it would be helpful to estimate an approximate range of properties for untested nanomaterials using computational modeling. We collected the largest dataset of zeta potential measurements of bare metal oxide nanoparticles in water (87 data points). The dataset was used to develop quantitative structure–property relationship (QSPR) models. Essential features of nanoparticles were represented using a modified simplified molecular input line entry system (SMILES). SMILES strings reflected the size-dependent behavior of zeta potentials, as the considered quasi-SMILES modification included information about both chemical composition and the size of the nanoparticles. Three mathematical models were generated using the Monte Carlo method, and their statistical quality was evaluated (R² for the training set varied from 0.71 to 0.87; for the validation set, from 0.67 to 0.82; root mean square errors for both training and validation sets ranged from 11.3 to 17.2 mV). The developed models were analyzed and linked to aggregation effects in aqueous solutions.

Development of Theoretical Descriptors for Cytotoxicity Evaluation of Metallic Nanoparticles

Motivated by the recent development of quantitative structure-activity relationship (QSAR) methods in the area of nanotoxicology, we proposed an approach to develop additional descriptors based on results of first-principles calculations. For the evaluation of the biochemical activity of metallic nanoparticles, we consider two processes: ion extraction from the surface of a specimen to aqueous media and water dissociation on the surface. We performed calculations for a set of metals (Al, Fe, Cu, Ag, Au, and Pt). Taking into account the diversity of atomic structures of real metallic nanoparticles, we performed calculations for different models such as (001) and (111) surfaces, nanorods, and two different cubic nanoparticles of 0.6 and 0.3 nm size. Significant energy dependence of the processes from the selected model of nanoparticle suggests that for the correct description we should combine the calculations for several representative models. In addition to the descriptors of chemical activity of the metallic nanoparticles for the two studied processes, we propose descriptors for taking into account the dependence of chemical activity from the size and shape of nanoparticles. Routes to minimization of computational costs for these calculations are also discussed.

The index of ideality of correlation: A criterion of predictive potential of QSPR/QSAR models?

The index of ideality of correlation (IIC) is a new criterion of the predictive potential of quantitative structure-property/activity relationships (QSPRs/QSARs). This IIC is calculated with using of the correlation coefficient between experimental and calculated values of endpoint for the calibration set, with taking into account the positive and negative dispersions between experimental and calculated values. The mutagenicity is well-known important characteristic of substances from ecological point of view. Consequently, the estimation of the IIC for mutagenicity is well motivated. It is confirmed that the utilization of this criterion significantly improves the predictive potential of QSAR models of mutagenicity. The new criterion can be used for other endpoints.

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.

Quasi-SMILES for Nano-QSAR Prediction of Toxic Effect of Al2O3 Nanoparticles

The level of malondialdehyde (MDA) in wet tissue of different organs is utilized as a measure of toxic effect. The numerical data on the concentration of MDA in wet tissue of liver, kidneys, brain, and heart of rat is examined as the endpoint which are impacted by different dose (mg/kg), exposure time (3 and 14 days) and single oral treatment of aluminium nano-oxide (Al2O3) with 30 nm or 40 nm. An attempt to develop predictive model for this endpoint has been carried out in this work. SMILES is a traditional tool to represent molecular structure for QSPRs/QSARs. In contrast to traditional SMILES, so-called quasi-SMILES can be a tool to build up quantitative features – property / activity relationships (QFPRs/QFARs) for endpoints which are not defined by solely molecular structure, but by a group of physicochemical and/or biochemical conditions. The quasi-SMILES is the representation of the above eclectic conditions whereas the QFPR/QFAR are models of endpoints which are modified under impacts of these eclectic conditions.

Quantitative Nanostructure-Activity Relationship Models for the Risk Assessment of NanoMaterials

In the last few decades, nanotechnology has been deeply established into human's everyday life with a great number of applications in cosmetics, textiles, electronics, optics, medicine, and many more. Although nanotechnology applications are rapidly increasing, the toxicity of some nanomaterials to living organisms and the environment still remains unknown and needs to be explored. The traditional toxicological evaluation of nanoparticles with the wide range of types, shapes, and sizes often involves expensive and time-consuming procedures. An efficient and cheap alternative is the development and application of predictive computational models using Quantitative Nanostructure-Activity Relationship (QNAR) methods. Towards this goal, researchers are mainly focused on the adverse effects of metal oxides and carbon nanotubes, but to date, QNAR studies are rare mainly because of the limited number of available organized datasets. In this chapter, recent studies for predictive QNAR models for the risk assessment of nanomaterials are reported and the perspectives of computational nanotoxicology that deeply relies on the intense collaboration between experimental and computational scientists are discussed.

Compilation of Data and Modelling of Nanoparticle Interactions and Toxicity in the NanoPUZZLES Project

The particular properties of nanomaterials have led to their rapidly increasing use in diverse fields of application. However, safety assessment is not keeping pace and there are still gaps in the understanding of their hazards. Computational models predicting nanotoxicity, such as (quantitative) structure-activity relationships ((Q)SARs), can contribute to safety evaluation, in line with general efforts to apply alternative methods in chemical risk assessment. Their development is highly dependent on the availability of reliable and high quality experimental data, both regarding the compounds' properties as well as the measured toxic effects. In particular, "nano-QSARs" should take the nano-specific characteristics into account. The information compiled needs to be well organized, quality controlled and standardized. Integrating the data in an overarching, structured data collection aims to (a) organize the data in a way to support modelling, (b) make (meta)data necessary for modelling available, and (c) add value by making a comparison between data from different sources possible.Based on the available data, specific descriptors can be derived to parameterize the nanomaterial-specific structure and physico-chemical properties appropriately. Furthermore, the interactions between nanoparticles and biological systems as well as small molecules, which can lead to modifications of the structure of the active nanoparticles, need to be described and taken into account in the development of models to predict the biological activity and toxicity of nanoparticles. The EU NanoPUZZLES project was part of a global cooperative effort to advance data availability and modelling approaches supporting the characterization and evaluation of nanomaterials.

Comparing the CORAL and Random Forest Approaches for Modelling the In Vitro Cytotoxicity of Silica Nanomaterials

Nanotechnology is one of the most important technological developments of the 21st century. In silico methods to predict toxicity, such as quantitative structure–activity relationships (QSARs), promote the safe-by-design approach for the development of new materials, including nanomaterials. In this study, a set of cytotoxicity experimental data corresponding to 19 data points for silica nanomaterials were investigated, to compare the widely employed CORAL and Random Forest approaches in terms of their usefulness for developing so-called ‘nano-QSAR’ models. ‘External’ leave-one-out cross-validation (LOO) analysis was performed, to validate the two different approaches. An analysis of variable importance measures and signed feature contributions for both algorithms was undertaken, in order to interpret the models developed. CORAL showed a more pronounced difference between the average coefficient of determination (R2) for training and for LOO (0.83 and 0.65 for training and LOO, respectively), compared to Random Forest (0.87 and 0.78 without bootstrap sampling, 0.90 and 0.78 with bootstrap sampling), which may be due to overfitting. With regard to the physicochemical properties of the nanomaterials, the aspect ratio and zeta potential were found to be the two most important variables for Random Forest, and the average feature contributions calculated for the corresponding descriptors were consistent with the clear trends observed in the data set: less negative zeta potential values and lower aspect ratio values were associated with higher cytotoxicity. In contrast, CORAL failed to capture these trends.

Odor threshold prediction by means of the Monte Carlo method

A large set of organic compounds (n=906) has been used as a basis to build up a model for the odor threshold (mg/m(3)). The statistical characteristics of the best model are the following: n=523, r(2)=0.647, RMSE=1.18 (training set); n=191, r(2)=0.610, RMSE=1.03, (calibration set); and n=192, r(2)=0.686, RMSE=1.06 (validation set). A mechanistic interpretation of the model is presented as the lists of statistical promoters of the increase and decrease in the odor threshold.

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.

Quasi-SMILES and nano-QFAR: united model for mutagenicity of fullerene and MWCNT under different conditions

Simplified molecular input-line entry system (SMILES) are a tool to represent molecular features of various compounds. Quasi-SMILES is a tool to represent various eclectic features of interaction between complex substances and bio targets (cells, organs, organisms). The construction and the application of quasi-SMILES in order to build up a model for prediction of mutagenicity of fullerene and multi-walled carbon-nanotubes (MWCNTs) are described in this work: instead of paradigm "endpoint is a mathematical function of molecular structure", the paradigm "endpoint is a mathematical function of eclectic data (features)" is used.

CORAL: model for no observed adverse effect level (NOAEL)

The in vivo repeated dose toxicity (RDT) test is intended to provide information on the possible risk caused by repeated exposure to a substance over a limited period of time. The measure of the RDT is the no observed adverse effect level (NOAEL) that is the dose at which no effects are observed, i.e., this endpoint indicates the safety level for a substance. The need to replace in vivo tests, as required by some European Regulations (registration, evaluation authorization and restriction of chemicals) is leading to the searching for reliable alternative methods such as quantitative structure-activity relationships (QSAR). Considering the complexity of the RDT endpoint, for which data quality is limited and depends anyway on the study design, the development of QSAR for this endpoint is an attractive task. Starting from a dataset of 140 organic compounds with NOAEL values related to oral short term toxicity in rats, we developed a QSAR model based on optimal descriptors calculated with simplified molecular input-line entry systems and the graph of atomic orbitals by the Monte Carlo method, using CORAL software. Three different splits into the training, calibration, and validation sets are studied. The mechanistic interpretation of these models in terms of molecular fragment with positive or negative contributions to the endpoint is discussed. The probabilistic definition for the domain of applicability is suggested.

Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes

Available on the Internet, the CORAL software (http://www.insilico.eu/coral) has been used to build up quasi-quantitative structure-activity relationships (quasi-QSAR) for prediction of mutagenic potential of multi-walled carbon-nanotubes (MWCNTs). In contrast with the previous models built up by CORAL which were based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) the quasi-QSARs based on the representation of conditions (not on the molecular structure) such as concentration, presence (absence) S9 mix, the using (or without the using) of preincubation were encoded by so-called quasi-SMILES. The statistical characteristics of these models (quasi-QSARs) for three random splits into the visible training set and test set and invisible validation set are the following: (i) split 1: n=13, r(2)=0.8037, q(2)=0.7260, s=0.033, F=45 (training set); n=5, r(2)=0.9102, s=0.071 (test set); n=6, r(2)=0.7627, s=0.044 (validation set); (ii) split 2: n=13, r(2)=0.6446, q(2)=0.4733, s=0.045, F=20 (training set); n=5, r(2)=0.6785, s=0.054 (test set); n=6, r(2)=0.9593, s=0.032 (validation set); and (iii) n=14, r(2)=0.8087, q(2)=0.6975, s=0.026, F=51 (training set); n=5, r(2)=0.9453, s=0.074 (test set); n=5, r(2)=0.8951, s=0.052 (validation set).

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).

An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology

Analysis of trends in nanotoxicology data and the development of data driven models for nanotoxicity is facilitated by the reporting of data using a standardised electronic format. ISA-TAB-Nano has been proposed as such a format. However, in order to build useful datasets according to this format, a variety of issues has to be addressed. These issues include questions regarding exactly which (meta)data to report and how to report them. The current article discusses some of the challenges associated with the use of ISA-TAB-Nano and presents a set of resources designed to facilitate the manual creation of ISA-TAB-Nano datasets from the nanotoxicology literature. These resources were developed within the context of the NanoPUZZLES EU project and include data collection templates, corresponding business rules that extend the generic ISA-TAB-Nano specification as well as Python code to facilitate parsing and integration of these datasets within other nanoinformatics resources. The use of these resources is illustrated by a "Toy Dataset" presented in the Supporting Information. The strengths and weaknesses of the resources are discussed along with possible future developments.

A quasi-QSPR modelling for the photocatalytic decolourization rate constants and cellular viability (CV%) of nanoparticles by CORAL

Most quantitative structure-property/activity relationships (QSPRs/QSARs) predict various endpoints related to organic compounds. Gradually, the variety of organic compounds has been extended to inorganic, organometallic compounds and polymers. However, the so-called molecular descriptors cannot be defined for super-complex substances such as different nanomaterials and peptides, since there is no simple and clear representation of their molecular structure. Some possible ways to define approaches for a predictive model in the case of super-complex substances are discussed. The basic idea of the approach is to change the traditionally used paradigm 'the endpoint is a mathematical function of the molecular structure' with another paradigm 'the endpoint is a mathematical function of available eclectic information'. The eclectic data can be (i) conditions of a synthesis, (ii) technological attributes, (iii) size of nanoparticles, (iv) concentration, (v) attributes related to cell membranes, and so on. Two examples of quasi-QSPR/QSAR analyses are presented and discussed. These are (i) photocatalytic decolourization rate constants (DRC) (10(-5)/s) of different nanopowders; and (ii) the cellular viability under the effect of nano-SiO(2).

Optimal nano-descriptors as translators of eclectic data into prediction of the cell membrane damage by means of nano metal-oxides

Systematization of knowledge on nanomaterials has become a necessity with the fast growth of applications of these species. Building up predictive models that describe properties (both beneficial and hazardous) of nanomaterials is vital for computational sciences. Classic quantitative structure-property/activity relationships (QSPR/QSAR) are not suitable for investigating nanomaterials because of the complexity of their molecular architecture. However, some characteristics such as size, concentration, and exposure time can influence endpoints (beneficial or hazardous) related to nanoparticles and they can therefore be involved in building a model. Application of the optimal descriptors calculated with the so-called correlation weights of various concentrations and different exposure times are suggested in order to build up a predictive model for cell membrane damage caused by a series of nano metal-oxides. The numerical data on correlation weights are calculated by the Monte Carlo method. The obtained results are in good agreement with the experimental data.

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<r(2)training<0.92 and 0.67<r(2)validation<0.83.

Molecular classification of pesticides including persistent organic pollutants, phenylurea and sulphonylurea herbicides

Pesticide residues in wine were analyzed by liquid chromatography–tandem mass spectrometry. Retentions are modelled by structure–property relationships. Bioplastic evolution is an evolutionary perspective conjugating effect of acquired characters and evolutionary indeterminacy–morphological determination–natural selection principles; its application to design co-ordination index barely improves correlations. Fractal dimensions and partition coefficient differentiate pesticides. Classification algorithms are based on information entropy and its production. Pesticides allow a structural classification by nonplanarity, and number of O, S, N and Cl atoms and cycles; different behaviours depend on number of cycles. The novelty of the approach is that the structural parameters are related to retentions. Classification algorithms are based on information entropy. When applying procedures to moderate-sized sets, excessive results appear compatible with data suffering a combinatorial explosion. However, equipartition conjecture selects criterion resulting from classification between hierarchical trees. Information entropy permits classifying compounds agreeing with principal component analyses. Periodic classification shows that pesticides in the same group present similar properties; those also in equal period, maximum resemblance. The advantage of the classification is to predict the retentions for molecules not included in the categorization. Classification extends to phenyl/sulphonylureas and the application will be to predict their retentions.