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In this paper we present two currents of our research. The first goal was the geometry optimization of the structures of derivatives of the alkyl 3-azido-2,3-dideoxy-D-hexopyranosides. Then, we examine the influence of the applied quantum methods on the values of molecular features describing these structures. We use two methods for geometry optimization: the semi-empirical PM7 method and DFT B3LYP/6-31++G* method. The results of comparison parameters of descriptors indicate that there are no statistical differences obtained from both methods. Thus, we recommend the PM7 method for geometry optimization of the derivatives of the alkyl 3-azido-2,3-dideoxy-D-hexopyranosides due to its time and computer resources requirements. Another part of the research was the examination, which groups of descriptors are the most suitable for identifying the similarities in the configuration, the substituents pattern and the molecular mass of 232 examined structures. To explore relation between configuration changes of the 3-azidosaccharides and influence of these changes on the molecular characteristic, we use hierarchical cluster analysis (HCA), where WHIM, 3D-MORSE, RDF and GETAWAY descriptors were selected. Depending on the group of descriptors, molecules are divided in various ways. In general, saccharide' structures are divided into groups based on the configuration of substituents (combination of epimers) or length of the O-glycoside chain. Never before, these saccharides derivatives, were investigated by chemometric analysis. The most problematic issue in experimental and theoretical research is the configuration of substituents in pyranoside ring. Due to vast number of configurations, it is possible to obtain massive amount of diverse structures. This problem concerns us and opens opportunity in investigation the effect of configuration on the parameter of molecules., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

In this study, photocatalytic properties and in vitro cytotoxicity of 29 TiO2-based multi-component nanomaterials (i.e., hybrids of more than two composition types of nanoparticles) were evaluated using a combination of the experimental testing and supervised machine learning modeling. TiO2-based multi-component nanomaterials with metal clusters of silver, and their mixtures with gold, palladium, and platinum were successfully synthesized. Two activities, photocatalytic activity and cytotoxicity, were studied. A novel cheminformatic approach was developed and applied for the computational representation of the photocatalytic activity and cytotoxicity effect. In this approach, features of investigated TiO2-based hybrid nanomaterials were reflected by a series of novel additive descriptors for hybrid and hybrid nanostructures (denoted as "hybrid nanosctructure descriptors"). These descriptors are based on quantum chemical calculations and the Smoluchowski equation. The obtained experimental data and calculated hybrid-nanostructure descriptors were used to develop novel predictive Quantitative Structure-Activity Relationship computational models (called "nano-QSARmix"). The proposed modeling approach is an initial step in the understanding of the relationships between physicochemical properties of hybrid nanoparticles, their toxicity, and photochemical activity under UV-vis irradiation. Acquired knowledge supports the safe-by-design approaches relevant to the development of efficient hybrid nanomaterials with reduced hazardous effects.

The objective of this work was to develop a QSBR model for the prioritization of organic pollutants based on biodegradation rates from a database containing globally harmonized biodegradation tests using relevant molecular descriptors. To do this, we first categorized the chemicals into three groups (Group 1: simple aromatic chemicals with a single ring, Group 2: aromatic chemicals with multiple rings and Group3: Group 1 plus Group 2) based on molecular descriptors, estimated the first order biodegradation rate of the chemicals using rating values derived from the BIOWIN3 model, and finally developed, validated and defined the applicability domain of models for each group using a multiple linear regression approach. All the developed QSBR models complied with OECD principles for QSAR validation. The biodegradation rate in the models for the two groups (Group 2 and 3 chemicals) are associated with abstract molecular descriptors that provide little relevant practical information towards understanding the relationship between chemical structure and biodegradation rates. However, molecular descriptors associated with the QSBR model for Group 1 chemicals (R 2  = 0.89, Q 2 loo  = 0.87) provided information on properties that can readily be scrutinised and interpreted in relation to biodegradation processes. In combination, these results lead to the conclusion that QSBRs can be an alternative tool to estimate the persistence of chemicals, some of which can provide further insights into those factors affecting biodegradation., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Alzheimer's disease (AD) is a multi-factorial disease, which can be simply outlined as an irreversible and progressive neurodegenerative disorder with an unclear root cause. It is a major cause of dementia in old aged people. In the present study, utilizing the structural and biological activity information of ligands for five important and mostly studied vital targets (i.e. cyclin-dependant kinase 5, β-secretase, monoamine oxidase B, glycogen synthase kinase 3β, acetylcholinesterase) that are believed to be effective against AD, we have developed five classification models using linear discriminant analysis (LDA) technique. Considering the importance of data curation, we have given more attention towards the chemical and biological data curation, which is a difficult task especially in case of big data-sets. Thus, to ease the curation process we have designed Konstanz Information Miner (KNIME) workflows, which are made available at http://teqip.jdvu.ac.in/QSAR_Tools/ . The developed models were appropriately validated based on the predictions for experiment derived data from test sets, as well as true external set compounds including known multi-target compounds. The domain of applicability for each classification model was checked based on a confidence estimation approach. Further, these validated models were employed for screening of natural compounds collected from the InterBioScreen natural database ( https://www.ibscreen.com/natural-compounds ). Further, the natural compounds that were categorized as 'actives' in at least two classification models out of five developed models were considered as multi-target leads, and these compounds were further screened using the drug-like filter, molecular docking technique and then thoroughly analyzed using molecular dynamics studies. Finally, the most potential multi-target natural compounds against AD are suggested.

Nanomaterials, such as hydroxyapatite nanoparticles show a great promise for medical applications due to their unique properties at the nanoscale. However, there are concerns about the safety of using these materials in biological environments. Despite a great number of published studies of nanoobjects and their aggregates or agglomerates, the impact of their physicochemical properties (such as particle size, surface area, purity, details of structure and degree of agglomeration) on living cells is not yet fully understood. Significant differences in these properties, resulting from different manufacturing methods, are yet another problem to be taken into consideration. The aim of this work was to investigate the correlation between the properties of nanoscale hydroxyapatite from different synthesis methods and biological activity represented by the viability of four cell lines: A549, CHO, BEAS-2B and J774.1 to assess the influence of the nanoparticles on immune, reproductive and respiratory systems.

Quantitative toxicity-toxicity relationship (QTTR) models have a great potential for improving the meaning of toxicological tests conducted on simple organisms. These models allow predicting the toxicological effect of a chemical based on its known toxicological effect in different toxicity tests, even against a different organism. This fact poses a great potential for predicting the toxicity of chemicals against higher organisms based on the results against lower ones. However, the possibility of developing such models is often restricted due to the low availability of data. We present a case study of developing the QTTR model for ionic liquids in different toxicological tests against the same species, in the face of insufficient experimental data (an additional confirmation for a different species is provided in the Supporting Information). In the presented case, we use a series of quantitative structure-activity relationship (QSAR) models developed to deliver the data concerning the toxicity of ionic liquids against human HeLa and MCF-7 cancer cell lines. We use these data to develop a QTTR model with an R 2 as high as 0.8. The benefit of applying the multi-objective genetic algorithm (MOGA-a genetic algorithm allowing for selection of the best set of explanatory features for several different dependent variables at the same time) as a QSAR model feature selecting strategy is presented and discussed.

Novel microfluidic technologies allow the manufacture of in vitro organ-on-a-chip systems that hold great promise to adequately recapitulate the biophysical and functional complexity of organs found in vivo . In this study, a gut-on-a-chip model was developed aiming to study the potential cellular association and transport of food contaminants. Intestinal epithelial cells (Caco-2) were cultured on a porous polyester membrane that was tightly clamped between two glass slides to form two separate flow chambers. Glass syringes, polytetrafluoroethylene tubing and glass microfluidic chips were selected to minimize surface adsorption of the studied compounds ( i.e. highly lipophilic dioxins), during the transport studies. Confocal microscopy studies revealed that, upon culturing under constant flow for 7 days, Caco-2 cells formed complete and polarized monolayers as observed after culturing for 21 days under static conditions in Transwells. We exposed Caco-2 monolayers in the chip and Transwell to a mixture of 17 dioxin congeners (7 polychlorinated dibenzo- p -dioxins and 10 polychlorinated dibenzofurans) for 24 h. Gas chromatography-high resolution mass spectrometry was used to assess the cellular association and transport of individual dioxin congeners across the Caco-2 cell monolayers. After 24 h, the amount of transported dioxin mixture was similar in both the dynamic gut-on-a-chip model and the static Transwell model. The transport of individual congeners corresponded with their number of chlorine atoms and substitution patterns as revealed by quantitative structure-property relationship modelling. These results show that the gut-on-a-chip model can be used, as well as the traditional static Transwell system, to study the cellular association and transport of lipophilic compounds like dioxins., Competing Interests: There are no conflicts of interest to declare., (This journal is © The Royal Society of Chemistry.)

The correlations between the retention parameters of forty ampholytic, biologically active and/or pharmaceutically relevant substances (obtained for three non-polar HPLC columns at various compositions of mobile phases and pH conditions: 2.5, 7.0, 11.5) and their thirty-two physicochemical (calculated/spectral) characteristics were investigated by applying chemometric methods of analysis. In three cases (among seven cases considered), Quantitative Property-Retention Relation (QPRR) models meeting the predictive capability criteria were developed (the values of R 2 , Q 2 CV , Q 2 Ext were higher than 0.76, 0.66 and 0.67, respectively, while values of RMSE C , RMSE CV and RMSE Ext were lower than 0.51, 0.65 and 0.65 in each developed model). These models create a useful platform for predicting retention parameters of untested chemicals and, to some extent, gaining pharmaceutically valuable information on the biologically active ampholytic substances based on their properties and the conditions of chromatographic separation., (Copyright © 2018. Published by Elsevier B.V.)

Societies worldwide are investing considerable resources into the safe development and use of nanomaterials. Although each of these protective efforts is crucial for governing the risks of nanomaterials, they are insufficient in isolation. What is missing is a more integrative governance approach that goes beyond legislation. Development of this approach must be evidence based and involve key stakeholders to ensure acceptance by end users. The challenge is to develop a framework that coordinates the variety of actors involved in nanotechnology and civil society to facilitate consideration of the complex issues that occur in this rapidly evolving research and development area. Here, we propose three sets of essential elements required to generate an effective risk governance framework for nanomaterials. (1) Advanced tools to facilitate risk-based decision making, including an assessment of the needs of users regarding risk assessment, mitigation, and transfer. (2) An integrated model of predicted human behavior and decision making concerning nanomaterial risks. (3) Legal and other (nano-specific and general) regulatory requirements to ensure compliance and to stimulate proactive approaches to safety. The implementation of such an approach should facilitate and motivate good practice for the various stakeholders to allow the safe and sustainable future development of nanotechnology., (© 2017 Society for Risk Analysis.)

To keep pace with its rapid development an efficient approach for the risk assessment of nanomaterials is needed. Grouping concepts as developed for chemicals are now being explored for its applicability to nanomaterials. One of the recently proposed grouping systems is DF4nanoGrouping scheme. In this study, we have developed three structure-activity relationship classification tree models to be used for supporting this system by identifying structural features of nanomaterials mainly responsible for the surface activity. We used data from 19 nanomaterials that were synthesized and characterized extensively in previous studies. Subsets of these materials have been used in other studies (short-term inhalation, protein carbonylation, and intrinsic oxidative potential), resulting in a unique data set for modeling. Out of a large set of 285 possible descriptors, we have demonstrated that only three descriptors (size, specific surface area, and the quantum-mechanical calculated property 'lowest unoccupied molecular orbital') need to be used to predict the endpoints investigated. The maximum number of descriptors that were finally selected by the classification trees (CT) was very low- one for intrinsic oxidative potential, two for protein carbonylation, and three for NOAEC. This suggests that the models were well-constructed and not over-fitted. The outcome of various statistical measures and the applicability domains of our models further indicate their robustness. Therefore, we conclude that CT can be a useful tool within the DF4nanoGrouping scheme that has been proposed before.

Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known "OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models", with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Application of predictive modeling approaches can solve the problem of missing data. Numerous studies have investigated the effects of missing values on qualitative or quantitative modeling, but only a few studies have discussed it for the case of applications in nanotechnology-related data. The present study is aimed at the development of a multi-nano-read-across modeling technique that helps in predicting the toxicity of different species such as bacteria, algae, protozoa, and mammalian cell lines. Herein, the experimental toxicity of 184 metal and silica oxide (30 unique chemical types) nanoparticles from 15 datasets is analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis is developed. In the first step, hidden patterns of toxicity among nanoparticles are identified using a combination of methods. Subsequently, the developed model based on categorization of the toxicity of the metal oxide nanoparticle outcomes is evaluated via the combination of supervised and unsupervised machine learning techniques to determine the underlying factors responsible for the toxicity.

Chemoinformatic methods, such as multivariable explorative techniques and quantitative structure-activity relationship (QSAR) modeling, allow for discovering relationships between the activity and the structure of chemical compounds. These techniques can be applied, as preliminary screening methods for designing and/or selecting new compounds with defined activity.Here we describe step by step how to preliminarily screen ionic liquids (a set of 13 ILs) and assess their cytotoxic activity against leukemia cell line IPC-81 as well as ILs' potential to inhibit acetylcholinesterase enzyme using the TRIC method (toxicity ranking index of cations) combined with the QSAR approach.

Titania-supported palladium, gold and bimetallic nanoparticles (second-generation nanoparticles) demonstrate promising photocatalytic properties. However, due to unusual reactivity, second-generation nanoparticles can be hazardous for living organisms. Considering the ever-growing number of new types of nanoparticles that can potentially contaminate the environment, a determination of their toxicity is extremely important. The main aim of presented study was to investigate the cytotoxic effect of surface modified TiO 2 -based nanoparticles, to model their quantitative nanostructure-toxicity relationships and to reveal the toxicity mechanism. In this context, toxicity tests for surface-modified TiO 2 -based nanoparticles were performed in vitro, using Gram-negative bacteria Escherichia coli and Chinese hamster ovary (CHO-K1) cells. The obtained cytotoxicity data were analyzed by means of computational methods (quantitative structure-activity relationships, QSAR approach). Based on a combined experimental and computational approach, predictive models were developed, and relationships between cytotoxicity, size, and specific surface area (Brunauer-Emmett-Teller surface, BET) of nanoparticles were discussed.

A first European Conference on Computational Nanotoxicology, CompNanoTox, was held in November 2015 in Benahavís, Spain with the objectives to disseminate and integrate results from the European modeling and database projects (NanoPUZZLES, ModENPTox, PreNanoTox, MembraneNanoPart, MODERN, eNanoMapper and EU COST TD1204 MODENA) as well as to create synergies within the European NanoSafety Cluster. This conference was supported by the COST Action TD1204 MODENA on developing computational methods for toxicological risk assessment of engineered nanoparticles and provided a unique opportunity for cross fertilization among complementary disciplines. The efforts to develop and validate computational models crucially depend on high quality experimental data and relevant assays which will be the basis to identify relevant descriptors. The ambitious overarching goal of this conference was to promote predictive nanotoxicology, which can only be achieved by a close collaboration between the computational scientists (e.g. database experts, modeling experts for structure, (eco) toxicological effects, performance and interaction of nanomaterials) and experimentalists from different areas (in particular toxicologists, biologists, chemists and material scientists, among others). The main outcome and new perspectives of this conference are summarized here.

Many technological implementations in the field of nanotechnology have involved carbon nanomaterials, including fullerenes such as the buckminsterfullerene, C 60 . The unprecedented properties of such organic nanomaterials (in particular their large surface area) gained extensive attention for their potential use as organic pollutant sorbents. Sorption interactions can be very hazardous and useful at the same time. This work investigates the influence of halogenation by bromine and/or chlorine in dibenzo- p -dioxins on their sorption ability on the C 60 fullerene surface. Halogenated dibenzo- p -dioxins (PXDDs, where X = Br or Cl) are ever-present in the environment and accidently produced in many technological processes in only approximately known quantities. If all combinatorial Br and/or Cl dioxin substitution possibilities are present in the environment, the experimental characterization and investigation of sorbent effectiveness is more than difficult. In this work, we have developed a quantitative structure-property relationship (QSPR) model (R 2 = 0.998), predicting the adsorption energy [kcal/mol] for 1,701 PXDDs adsorbed on C 60 (PXDD@C 60 ). Based on the QSPR model reported herein, we concluded that the lowest energy PXDD@C 60 complexes are those that the World Health Organization (WHO) considers to be less dangerous with respect to the aryl hydrocarbon receptor (AhR) toxicity mechanism. Therefore, the effectiveness of fullerenes as sorbent agents may be underestimated as sorption could be less effective for toxic congeners than previously believed.

Hypothesis: Different ions constituting ionic liquids (ILs) change their properties, including the Critical Micelization Concentration (CMC). It is possible to identify and quantitatively describe specific structural ions' features having influence on the micelization of ILs. Moreover, it should be possible to verify, whether the phenomenon of micelization is governed by the influence of the single ion only, rather than being a sum of both ions' mutual influence., Experimental: The qualitative and quantitative description of the structural properties responsible for micelles formation was performed with the use of the Quantitative Structure-Property Relationship (QSPR) approach. Structural features were expressed with help of the molecular GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors system. The QSPR model was properly validated and its quality and usability was additionally proven by applying it to predict the CMC for 15,000 computationally designed ILs. It was the first model to the CMC assessment for ILs., Findings: The analysis showed that longer (containing big hydrophobic domain), less spherical and not "folded" cations as well as bigger anions are the main factors causing the decrease of CMC. According to the presented model, the influence of cations and anions is independent., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Cushing's syndrome (CS) is a disease which results from excessive levels of cortisol in the human body. The disorder is associated with various signs and symptoms which are also common for the general population not suffering from compound hypersecretion. Thus, more sensitive and selective methods are required for the diagnosis of CS. This follow-up study was conducted to determine which steroid metabolites could serve as potential indicators of CS and possible subclinical hypercortisolism in patients diagnosed with so called non-functioning adrenal incidentalomas (AIs). Urine samples from negative controls ( n = 37), patients with CS characterized by hypercortisolism and excluding iatrogenic CS ( n = 16), and patients with non-functioning AIs with possible subclinical Cushing's syndrome ( n = 25) were analyzed using gas chromatography-mass spectrometry (GC/MS) and gas chromatograph equipped with flame ionization detector (GC/FID). Statistical and multivariate methods were applied to investigate the profile differences between examined individuals. The analyses revealed hormonal differences between patients with CS and the rest of examined individuals. The concentrations of selected metabolites of cortisol, androgens, and pregnenetriol were elevated whereas the levels of tetrahydrocortisone were decreased for CS when opposed to the rest of the study population. Moreover, after analysis of potential confounding factors, it was also possible to distinguish six steroid hormones which discriminated CS patients from other study subjects. The obtained discriminant functions enabled classification of CS patients and AI group characterized by mild hypersecretion of cortisol metabolites. It can be concluded that steroid hormones selected by applying urinary profiling may serve the role of potential biomarkers of CS and can aid in its early diagnosis.

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.

A sample of a nanomaterial contains a distribution of nanoparticles of various shapes and/or sizes. A scanning electron microscopy image of such a sample often captures only a fragment of the morphological variety present in the sample. In order to quantitatively analyse the sample using scanning electron microscope digital images, and, in particular, to derive numerical representations of the sample morphology, image content has to be assessed. In this work, we present a framework for extracting morphological information contained in scanning electron microscopy images using computer vision algorithms, and for converting them into numerical particle descriptors. We explore the concept of image representativeness and provide a set of protocols for selecting optimal scanning electron microscopy images as well as determining the smallest representative image set for each of the morphological features. We demonstrate the practical aspects of our methodology by investigating tricalcium phosphate, Ca 3 (PO 4 ) 2 , and calcium hydroxyphosphate, Ca 5 (PO 4 ) 3 (OH), both naturally occurring minerals with a wide range of biomedical applications., (© 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.)

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., (2016 FRAME.)

Once released into the aquatic environment, nanoparticles (NPs) are expected to interact (e.g. dissolve, agglomerate/aggregate, settle), with important consequences for NP fate and toxicity. A clear understanding of how internal and environmental factors influence the NP toxicity and fate in the environment is still in its infancy. In this study, a quantitative structure-property relationship (QSPR) approach was employed to systematically explore factors that affect surface charge (zeta potential) under environmentally realistic conditions. The nano-QSPR model developed with multiple linear regression (MLR) was characterized by high robustness [Formula: see text] and external predictivity [Formula: see text] The results clearly showed that zeta potential values varied markedly as functions of the ionic radius of the metal atom in the metal oxides, confirming that agglomeration and the extent of release of free MexOy largely depend on their intrinsic properties. A developed nano-QSPR model was successfully applied to predict zeta potential in an ionized solution of NPs for which experimentally determined values of response have been unavailable. Hence, the application of our model is possible when the values of zeta potential in the ionized solution for metal oxide nanoparticles are undetermined, without the necessity of performing more time consuming and expensive experiments. We believe that our studies will be helpful in predicting the conditions under which MexOy is likely to become problematic for the environment and human health.

In the present work, we have proposed a statistical model predicting the toxicity of ionic liquids (ILs) to Vibrio fischeri bacteria using the Quantitative Structure-Activity Relationships (QSAR) method. The model was developed with Multiple Linear Regression (MLR) technique, using the Gutman molecular topological index (GMTI), the lopping centric information index (LOC) and the number of oxygen atoms. Presented model is characterized by the good fit to the experimental data (R(2) = 0.78), high robustness (Q(2)CV = 0.72) and good predictive ability (Q(2)EXT = 0.75). This approach, with using very simple descriptors, helps to initially evaluate the toxicity of newly designed ionic liquids. The studied toxicity of ionic liquids depends mainly on their cations' structure: larger, more branched cations with long alkyl chains are more toxic than the smaller, linear ones. The presence of polar functional groups in the cation's structure reduces the toxic properties of ionic liquids. The structure of the anion has little effect on the toxicity of the studied ionic liquids. Obtained results will provide insight into the toxicity mechanisms and useful information for assessing the potential ecological risk of ionic liquids., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Background: Ionic liquids (ILs) found a variety of applications in today's chemistry. Since their properties depend on the ions constituting particular ionic liquid, it is possible to synthetize IL with desired specification, dependently on its further function. However, this task is not trivial, since knowledge regarding the influence of particular ion on the property of concern is crucial. Therefore, there is a strong need for new, fast and inexpensive methods supporting the process of ionic liquids' design, making it possible to predefine IL's properties even before the synthesis., Results: We have developed a simple tool (called Ionic Liquid PhysicoChemical predictor: ILPC) that allows for the simultaneous qualitative prediction of four physicochemical properties of ionic liquids: viscosity, n-octanol-water partition coefficient, solubility and enthalpy of fusion. By the means of Principal Component Analysis, we studied 172 ILs and defined distribution trends of those four properties, dependently on the ILs structures. We proved that the qualitative prediction of mentioned properties could be performed on the basis of most simple information we can deliver about ILs, which are their molecular formulas., Conclusions: Created tool presented in this paper allows fast, pre-synthesis screening of ILs, with the omission of any experimental steps. It can be helpful in the process of designing ILs with preferred properties. We proved that the information encrypted in molecular formula of ionic liquid could be a valuable source of knowledge regarding the IL's viscosity, n-octanol-water partition coefficient, solubility and enthalpy of fusion. Moreover, we proved that the influence of both ions, constituting the IL, on each of those four properties indicates same, additive trend.Graphical AbstractSchematic representation of ILPC performance - the exact position of the ionic liquid on the linear map is determined by its chemical structure.

This work focuses on determining the influence of both ionic-liquid (IL) type and redox couple concentration on Seebeck coefficient values of such a system. The quantitative structure-property relationship (QSPR) and read-across techniques are proposed as methods to identify structural features of ILs (mixed with LiI/I2 redox couple), which have the most influence on the Seebeck coefficient (Se ) values of the system. ILs consisting of small, symmetric cations and anions with high values of vertical electron binding energy are recognized as those with the highest values of Se . In addition, the QSPR model enables the values of Se to be predicted for each IL that belongs to the applicability domain of the model. The influence of the redox-couple concentration on values of Se is also quantitatively described. Thus, it is possible to calculate how the value of Se will change with changing redox-couple concentration. The presence of the LiI/I2 redox couple in lower concentrations increases the values of Se , as expected., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Nanotechnology is of increasing significance. Curation of nanomaterial data into electronic databases offers opportunities to better understand and predict nanomaterials' behaviour. This supports innovation in, and regulation of, nanotechnology. It is commonly understood that curated data need to be sufficiently complete and of sufficient quality to serve their intended purpose. However, assessing data completeness and quality is non-trivial in general and is arguably especially difficult in the nanoscience area, given its highly multidisciplinary nature. The current article, part of the Nanomaterial Data Curation Initiative series, addresses how to assess the completeness and quality of (curated) nanomaterial data. In order to address this key challenge, a variety of related issues are discussed: the meaning and importance of data completeness and quality, existing approaches to their assessment and the key challenges associated with evaluating the completeness and quality of curated nanomaterial data. Considerations which are specific to the nanoscience area and lessons which can be learned from other relevant scientific disciplines are considered. Hence, the scope of this discussion ranges from physicochemical characterisation requirements for nanomaterials and interference of nanomaterials with nanotoxicology assays to broader issues such as minimum information checklists, toxicology data quality schemes and computational approaches that facilitate evaluation of the completeness and quality of (curated) data. This discussion is informed by a literature review and a survey of key nanomaterial data curation stakeholders. Finally, drawing upon this discussion, recommendations are presented concerning the central question: how should the completeness and quality of curated nanomaterial data be evaluated?

In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase "correlation does not imply causation" reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal analysis of multivariate data. Methods of causal discovery have been applied for broader physical insight into mechanisms of action and interpretation of the developed nano-QSAR models. Previously developed nano-QSAR models for toxicity of 17 nano-sized metal oxides towards E. coli bacteria have been validated by means of the causality criteria. Using the descriptors confirmed by the causal technique, we have developed new models consistent with the straightforward causal-reasoning account. It was proven that causal inference methods are able to provide a more robust mechanistic interpretation of the developed nano-QSAR models.

Synthesis of novel nanoparticles should always be accompanied by a comprehensive assessment of risk to human health and to ecosystem. Application of in silico models is encouraged by regulatory authorities to fill the data gaps related to the properties of nanoparticles affecting the environment and human health. Interspecies toxicity correlations provide a tool for estimation of contaminant's sensitivity with known levels of uncertainty for a diverse pool of species. We propose here first interspecies cytotoxicity correlation models between Escherichia coli (prokaryotic system) and human keratinocyte cell line (HaCaT) (eukaryotic system) to assess the discriminatory features for cytotoxicity of metal oxide nanoparticles. The nano-QTTR models can be employed for extrapolating cytotoxicity to E. coli and human keratinocyte cell line (HaCaT) for metal nanoparticles when the data for the other species are available. Informative illustrations of the contributing mechanisms of toxic action of the metal oxide nanoparticles to the HaCaT cell line as well as to the E. coli are identified from the developed nano quantitative toxicity-toxicity relationship (nano-QTTR) models., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Ionic liquids (ILs) form a wide group of compounds characterized by specific properties that allow using ILs in different fields of science and industry. Regarding that the growing production and use of ionic liquids increase probability of their emission to the environment, it is important to estimate the ability of these compounds to spread in the environment. One of the most important parameters that allow evaluating environmental mobility of compound is n-octanol/water partition coefficient (KOW). Experimental measuring of the KOW values for a large number of compounds could be time consuming and costly. Instead, computational predictions are nowadays being used more often. The paper presents new Quantitative Structure-Property Relationship (QSPR) model that allows predicting the logarithmic values of KOW for 335 ILs, for which the experimentally measured values had been unavailable. We also estimated bioaccumulation potential and point out which group of ILs could have negative impact on environment., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Computational techniques, such as Quantitative Structure-Property Relationship (QSPR) modeling, are very useful in predicting physicochemical properties of various chemicals. Building QSPR models requires calculating molecular descriptors and the proper choice of the geometry optimization method, which will be dedicated to specific structure of tested compounds. Herein, we examine the influence of the ionic liquids' (ILs) geometry optimization methods on the predictive ability of QSPR models by comparing three models. The models were developed based on the same experimental data on density collected for 66 ionic liquids, but with employing molecular descriptors calculated from molecular geometries optimized at three different levels of the theory, namely: (1) semi-empirical (PM7), (2) ab initio (HF/6-311+G*) and (3) density functional theory (B3LYP/6-311+G*). The model in which the descriptors were calculated by using ab initio HF/6-311+G* method indicated the best predictivity capabilities ([Formula: see text] = 0.87). However, PM7-based model has comparable values of quality parameters ([Formula: see text] = 0.84). Obtained results indicate that semi-empirical methods (faster and less expensive regarding CPU time) can be successfully employed to geometry optimization in QSPR studies for ionic liquids.

In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure-Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure-Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Comparative Molecular Similarity Indices Analysis, CoMSIA analysis employed for nanomaterials) have been briefly summarized. Both approaches were compared according to the selected criteria, including: efficiency, type of experimental data, class of nanomaterials, time required for calculations and computational cost, difficulties in the interpretation. Taking into account the advantages and limitations of each method, we provide the recommendations for nano-QSAR modellers and QSAR model users to be able to determine a proper and efficient methodology to investigate biological activity of nanoparticles in order to describe the underlying interactions in the most reliable and useful manner.

The capability of reproducing the open circuit voltages (V(oc)) of 15 representative C60 fullerene derivatives was tested using the selected quantum mechanical methods (B3LYP, PM6, and PM7) together with the two one-electron basis sets. Certain theoretical treatments (e.g. PM6) were found to be satisfactory for preliminary estimates of the open circuit voltages (V(oc)), whereas the use of the B3LYP/6-31G(d) approach has been proven to assure highly accurate results. We also examined the structural similarity of 19 fullerene derivatives by employing principle component analysis (PCA). In order to express the structural features of the studied compounds we used molecular descriptors calculated with semi-empirical (PM6 and PM7) and density functional (B3LYP/6-31G(d)) methods separately. In performing PCA, we noticed that semi-empirical methods (i.e. PM6 and PM7) seem satisfactory for molecules, in which one can distinguish the aromatic and the aliphatic parts in the cyclopropane ring of PCBM (phenyl-C61-buteric acid methyl ester) and they significantly overestimate the energy of the highest occupied molecular orbital (E(HOMO)). The use of the B3LYP functional, however, is recommended for studying methanofullerenes, which closely resemble the structure of PCBM, and for their modifications.

The production of nanomaterials increases every year exponentially and therefore the probability these novel materials that they could cause adverse outcomes for human health and the environment also expands rapidly. We proposed two types of mechanisms of toxic action that are collectively applied in a nano-QSAR model, which provides governance over the toxicity of metal oxide nanoparticles to the human keratinocyte cell line (HaCaT). The combined experimental-theoretical studies allowed the development of an interpretative nano-QSAR model describing the toxicity of 18 nano-metal oxides to the HaCaT cell line, which is a common in vitro model for keratinocyte response during toxic dermal exposure. The comparison of the toxicity of metal oxide nanoparticles to bacteria Escherichia coli (prokaryotic system) and a human keratinocyte cell line (eukaryotic system), resulted in the hypothesis that different modes of toxic action occur between prokaryotic and eukaryotic systems.

Creating suitable chemical categories and developing read-across methods, supported by quantum mechanical calculations, can be an effective solution to solving key problems related to current scarcity of data on the toxicity of various nanoparticles. This study has demonstrated that by applying a nano-read-across, the cytotoxicity of nano-sized metal oxides could be estimated with a similar level of accuracy as provided by quantitative structure-activity relationship for nanomaterials (nano-QSAR model(s)). The method presented is a suitable computational tool for the preliminary hazard assessment of nanomaterials. It also could be used for the identification of nanomaterials that may pose potential negative impact to human health and the environment. Such approaches are especially necessary when there is paucity of relevant and reliable data points to develop and validate nano-QSAR models.

Regulation for nanomaterials is urgently needed, and the drive to adopt an intelligent testing strategy is evident. Such a strategy will not only provide economic benefits but will also reduce moral and ethical concerns arising from animal testing. For regulatory purposes, such an approach is promoted by REACH, particularly the use of quantitative structure-activity relationships [(Q)SAR] as a tool for the categorisation of compounds according to their physicochemical and toxicological properties. In addition to compounds, (Q)SAR has also been applied to nanomaterials in the form of nano(Q)SAR. Although (Q)SAR in chemicals is well established, nano(Q)SAR is still in early stages of development and its successful uptake is far from reality. This article aims to identify some of the pitfalls and challenges associated with nano-(Q)SARs in relation to the categorisation of nanomaterials. Our findings show clear gaps in the research framework that must be addressed if we are to have reliable predictions from such models. Three major barriers were identified: the need to improve quality of experimental data in which the models are developed from, the need to have practical guidelines for the development of the nano(Q)SAR models and the need to standardise and harmonise activities for the purpose of regulation. Of these three, the first, i.e. the need to improve data quality requires immediate attention, as it underpins activities associated with the latter two. It should be noted that the usefulness of data in the context of nano-(Q)SAR modelling is not only about the quantity of data but also about the quality, consistency and accessibility of those data.

Many metal oxide nanoparticles are able to cause persistent stress to live organisms, including humans, when discharged to the environment. To understand the mechanism of metal oxide nanoparticles' toxicity and reduce the number of experiments, the development of predictive toxicity models is important. In this study, performed on a series of nanoparticles, the comparative quantitative-structure activity relationship (nano-QSAR) analyses of their toxicity towards E. coli and HaCaT cells were established. A new approach for representation of nanoparticles' structure is presented. For description of the supramolecular structure of nanoparticles the "liquid drop" model was applied. It is expected that a novel, proposed approach could be of general use for predictions related to nanomaterials. In addition, in our study fragmental simplex descriptors and several ligand-metal binding characteristics were calculated. The developed nano-QSAR models were validated and reliably predict the toxicity of all studied metal oxide nanoparticles. Based on the comparative analysis of contributed properties in both models the LDM-based descriptors were revealed to have an almost similar level of contribution to toxicity in both cases, while other parameters (van der Waals interactions, electronegativity and metal-ligand binding characteristics) have unequal contribution levels. In addition, the models developed here suggest different mechanisms of nanotoxicity for these two types of cells.

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

Nanotechnology has evolved as a frontrunner in the development of modern science. Current studies have established toxicity of some nanoparticles to human and environment. Lack of sufficient data and low adequacy of experimental protocols hinder comprehensive risk assessment of nanoparticles (NPs). In the present work, metal electronegativity (χ), the charge of the metal cation corresponding to a given oxide (χox), atomic number and valence electron number of the metal have been used as simple molecular descriptors to build up quantitative structure-toxicity relationship (QSTR) models for prediction of cytotoxicity of metal oxide NPs to bacteria Escherichia coli. These descriptors can be easily obtained from molecular formula and information acquired from periodic table in no time. It has been shown that a simple molecular descriptor χox can efficiently encode cytotoxicity of metal oxides leading to models with high statistical quality as well as interpretability. Based on this model and previously published experimental results, we have hypothesized the most probable mechanism of the cytotoxicity of metal oxide nanoparticles to E. coli. Moreover, the required information for descriptor calculation is independent of size range of NPs, nullifying a significant problem that various physical properties of NPs change for different size ranges., (Copyright © 2014 Elsevier Inc. All rights reserved.)

As experimental evaluation of the safety of nanoparticles (NPs) is expensive and time-consuming, computational approaches have been found to be an efficient alternative for predicting the potential toxicity of new NPs before mass production. In this background, we have developed here a regression-based nano quantitative structure-activity relationship (nano-QSAR) model to establish statistically significant relationships between the measured cellular uptakes of 109 magnetofluorescent NPs in pancreatic cancer cells with their physical, chemical, and structural properties encoded within easily computable, interpretable and reproducible descriptors. The developed model was rigorously validated internally as well as externally with the application of the principles of Organization for Economic Cooperation and Development (OECD). The test for domain of applicability was also carried out for checking reliability of the predictions. Important fragments contributing to higher/lower cellular uptake of NPs were identified through critical analysis and interpretation of the developed model. Considering all these identified structural attributes, one can choose or design safe, economical and suitable surface modifiers for NPs. The presented approach provides rich information in the context of virtual screening of relevant NP libraries., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The composition of the cuticular and internal lipids of larvae and pupae of Lucilia sericata was studied using chromatographic techniques. The lipids from both stages of L. sericata had similar free fatty acid (FFA) profiles and also contained alcohols and cholesterol. The range of the number of C-atoms detected for these classes of compounds was to some extent similar in larvae and pupae, but the relative amounts of each class differed between stages. Saturated as well as unsaturated FFAs with even and odd numbered C-atom chains were present in both cuticular and internal lipids. The alcohol fractions of L. sericata were represented by free, straight-chain primary alcohols containing an even number of C-atoms. The lipid composition of male and female L. sericata adults and the hydrocarbon composition of all stages of L. sericata had previously been analyzed. To have a full overview of the lipid composition and to identify similarities or dissimilarities between the individual lipid fractions in this insect species, two-way hierarchical cluster analysis (HCA) was performed using also the data from these previous publications. The content of FFA 18 : 1 (n-9) was noticed to be very high in the cuticular fractions of larvae and pupae as well as in all internal fractions (male, female, larvae, and pupae) and low in the cuticular fractions of male and female imago. The contents of FFAs 16 : 0 and 16 : 1 (n-9), cholesterol, and the n-alkanes n-C31 , n-C29 , n-C27 , n-C25 , and n-C23 varied between particular fractions, whereas the amounts of other compounds were similar in all fractions., (Copyright © 2014 Verlag Helvetica Chimica Acta AG, Zürich.)

We propose a new metric for long-range transport potential (LRTP), GIF, based on source-receptor analyses and evaluate the LRTP and persistence of a wide variety of chlorinated and brominated organic compounds using GIF and overall persistence (POV), respectively. We calculated GIF and POV using our global 3D dynamic multimedia model (FATE). Physicochemical properties were obtained from quantitative structure-property relationship (QSPR) models. The FATE-QSPR combined model enabled us to systematically investigate the LRTP and persistence of a wide variety of chemical substances. On average, the estimated GIF and POV for chlorinated compounds were larger than those for their brominated counterparts, with the largest and smallest values found for polychlorinated biphenyls and polybrominated dibenzodioxins, respectively. We also compared GIF with four differently defined LRTP metrics and two LRTP metrics obtained from a simple model. The results of our analyses indicate that the LRTP ranks can differ considerably among LRTP metrics, the differences being dependent on the governing environmental processes, relevant physicochemical properties, and multimedia model.

Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool to predict various endpoints for various substances. The "classic" QSPR/QSAR analysis is based on the representation of the molecular structure by the molecular graph. However, simplified molecular input-line entry system (SMILES) gradually becomes most popular representation of the molecular structure in the databases available on the Internet. Under such circumstances, the development of molecular descriptors calculated directly from SMILES becomes attractive alternative to "classic" descriptors. The CORAL software (http://www.insilico.eu/coral) is provider of SMILES-based optimal molecular descriptors which are aimed to correlate with various endpoints. We analyzed data set on nanoparticles uptake in PaCa2 pancreatic cancer cells. The data set includes 109 nanoparticles with the same core but different surface modifiers (small organic molecules). The concept of a QSAR as a random event is suggested in opposition to "classic" QSARs which are based on the only one distribution of available data into the training and the validation sets. In other words, five random splits into the "visible" training set and the "invisible" validation set were examined. The SMILES-based optimal descriptors (obtained by the Monte Carlo technique) for these splits are calculated with the CORAL software. The statistical quality of all these models is good., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Nanotechnology that develops novel materials at size of 100nm or less has become one of the most promising areas of human endeavor. Because of their intrinsic properties, nanoparticles are commonly employed in electronics, photovoltaic, catalysis, environmental and space engineering, cosmetic industry and - finally - in medicine and pharmacy. In that sense, nanotechnology creates great opportunities for the progress of modern medicine. However, recent studies have shown evident toxicity of some nanoparticles to living organisms (toxicity), and their potentially negative impact on environmental ecosystems (ecotoxicity). Lack of available data and low adequacy of experimental protocols prevent comprehensive risk assessment. The purpose of this review is to present the current state of knowledge related to the risks of the engineered nanoparticles and to assess the potential of efficient expansion and development of new approaches, which are offered by application of theoretical and computational methods, applicable for evaluation of nanomaterials., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Congeners are molecules based on the same carbon skeleton but are different by the number of substituents and/or a substitution pattern. Examples are 1-chloronaphthalene, 1,4-dichloronaphthalene, and 1,3,8-trichloronaphthalene. Various persistent organic pollutants (POPs) exist in the environment as families of congeners. Very large numbers of possible congeners make their experimental characterization and risk assessment unfeasible. Computational high-throughput and quantitative structure-property relationship (QSPR) modeling has been limited by the lack of tools and approaches facilitating analysis of such POP families. We present a comprehensive approach that enables modeling of extremely large congeneric libraries. The approach involves three steps: (1) combinatorial generation of a library of congeners, (2) quantum chemical characterization of each structure at the PM6 semiempirical level to obtain molecular descriptors, and (3) analysis of the information generated in step 2. In steps 1-3, we employ combinatorial, computational, and cheminformatics techniques, respectively. Therefore, this hybrid approach is named "Combinatorial × Computational × Cheminformatics", or just abbreviated as C(3) (or C-cubed) approach. We demonstrate the usefulness of this approach by generating and characterizing Br- and Cl-substituted congeneric families of 23 typical POPs. The analysis of the resulting set of 1 840 951 congeners that includes Cl-, Br-, and mixed Br/Cl-substituted species, proves that, based on structural similarities defined by the molecular descriptors' values, the existing QSPR models developed originally for Cl- and Br-substituted congeners can be applied also to mixed Br/Cl-substituted ones. Thus, the C(3) approach may serve as a tool for exploring structural applicability domains of the existing QSPR models for congeneric sets.

Convenient to apply and available on the Internet software CORAL (http://www.insilico.eu/CORAL) has been used to build up quantitative structure-activity relationships (QSAR) for prediction of cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli (minus logarithm of concentration for 50% effect pEC50). In this study six random splits of the data into the training and test set were examined. It has been shown that the CORAL provides a reliable tool that could be used to build up a QSAR of the pEC50., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Official OECD recommendations give the highest priority to application of purely empirical phys/chem data (partition coefficients, environmental half-live times etc.) in multimedia mass balance modeling of environmental overall persistence and long-range transport for potentially hazardous chemicals. We have demonstrated that the replacement of the empirical data with those predicted by employing Quantitative Structure-Property Relationships (QSPR) technique did not significantly decrease the performance of The Tool 2.0--the OECD multimedia mass balance model. To prove this, we compared each other the output results (overall persistence -P(OV); characteristic traveling distance -CTD and transport efficiency TE) obtained from 6 of multimedia models. The models utilized combinations of experimentally determined and QSPR-predicted values of the partition coefficients and half-live times. For predicting phys/chem data, we utilized 2 QSPRs developed in our laboratory and the EPI Suite package (US EPA). We did not observe any statistically significant (p<0.05) differences between the models. This conclusion is important, because it leads to reducing time and costs of laboratory studies required during the risk assessment procedure. Moreover, regarding the obtained results, we proposed to replace the single-threshold approaches established by majority of international regulations to screen substances for persistence, bioaccumulation and long-range transport potential with the approaches taking into account uncertainty of the results and/or probability of passing a given threshold., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Congeners are molecules based on the same carbon skeleton but different by the number of substituents and/or a substitution pattern. Various Persistent Organic Pollutants (POPs) exist in the environment as families of halogen substituted congeners and/or their hydroxyl and methoxy substituted derivatives. Numbers of possible congeners resulting from substitution of a parent POP molecule with only one type of chemical group are generally available. At the same time, numbers of mixed-substituent congeners have not been counted and presented yet, although there is an increasing interest in such as is the increasing number of research articles presenting results on already identified Cl-/Br-mixed type congeners and/or their HO-/CH(3)O-mixed metabolites. We have enumerated and counted possible mixed-substituent congeners of common POPs. This article presents the obtained numbers for congener families of benzene, naphthalene, biphenyl, diphenyl ether, dibenzo-p-dioxin, dibenzofuran, anthracene, pyrene and others and obtained by substitution of up to five chemical group types., (Published by Elsevier Ltd.)