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Peak prioritization is one of the key steps in non-target screening of environmental samples to direct the identification efforts to relevant and important features. Occurrence of chemicals is sometimes a function of time and their presence in consecutive days (trend) reveals important aspects such as discharges from agricultural, industrial or domestic activities. This study presents a validated computational framework based on deep learning conventional neural network to classify trends of chemicals over 30 consecutive days of sampling in two sampling sites (upstream and downstream of a river). From trend analysis and factor analysis, the chemicals could be classified into periodic, spill, increasing, decreasing and false trend. The developed method was validated with list of 42 reference standards (target screening) and applied to samples. 25 compounds were selected by the deep learning and identified via non-target screening. Three classes of surfactants were identified for the first time in river water and two of them were never reported in the literature. Overall, 21 new homologous series of the newly identified surfactants were tentatively identified. The aquatic toxicity of the identified compounds was estimated by in silico tools and a few compounds along with their homologous series showed potential risk to aquatic environment. © 2022 Elsevier B.V.

Introduction: Pelargonium sidoides is a member of the Geraniaceae family and it originates from the coastal regions of South Africa. In the last decades, Pelargonium sidoides root has been subjected to several surveys due to the assertion of its health benefits, such as the relief of symptoms of acute bronchitis, common cold and acute rhinosinusitis. Many studies have been conducted to reveal its naturally occurring bioactive chemicals, yet no wide-scope chemical characterisation strategies have been done using mass spectrometry. Objective: This research aimed to comprehensively characterise the chemical profile of Pelargonium sidoides root via high-resolution mass spectrometry. Methodology: The Pelargonium sidoides root was extracted by a mixture of methanol: water in the proportion of 80:20. The extraction procedure included vortexing, shaking as well as the use of an ultrasound sonication bath under 40°C. After centrifugation, the supernatant was evaporated to dryness. The dry residue was reconstituted with a mixture of methanol/water (50:50, v/v), filtered and injected into an ultra-high-pressure liquid chromatography-quadruple time-of-flight mass spectrometer. Results: Overall, 33 compounds were identified in the root using suspect and non-target screening. These compounds were originated from different classes of compounds such as amino acids, phenolic acids, α-hydroxy-acids, vitamins, polyphenols, flavonoids, coumarins, coumarins glucosides, coumarin sulphates and nucleotides. Quantitative results were provided for the identified compounds, where their reference standards were available. Conclusion: Some important compounds were elucidated, belonging to different classes of compounds such as antioxidants (coumarins and phenolic compounds), amino acids, nucleotides and vitamins revealing the importance of the bioactive content of this root. © 2021 John Wiley & Sons, Ltd.

Advances in Matrix-assisted Laser Desorption/Ionization -Time-Of-Flight Mass Spectrometry (MALDI-TOF-MS) have led to its supremacy for complex assessment of food authenticity studies, like dairy products fraud, holding promise for the discovery of potential authenticity (bio)markers. In this study, an integrated untargeted protein-based workflow in combination with advanced chemometrics is presented, to address authenticity challenges in PDO feta cheese which is legally manufactured by the mixture of sheep/goat milk. Potential markers attributed to specific animal origin were found from protein profiles acquired for authentic feta and white cheeses (prepared from cow milk), belonging to 4 kDa–18.5 kDa mass area. Rapid detection of feta cheese adulteration from cow milk was also achieved down to 1% adulteration level. The discriminative models showed high predictive ability for feta cheese authenticity (Q2 = 0.920, RMSEE = 0.053) and its adulteration (Q2 = 0.835, RMSEE = 0.121), introducing a reliable approach in routine analysis. The methodology was successfully applied in detection of cow milk in sheep yoghurt. © 2021 Elsevier Ltd

Alcohol consumption is a global healthcare problem with enormous social, economic, and clinical consequences. The liver sustains the earliest and the greatest degree of tissue injury due to chronic alcohol consumption and it has been estimated that alcoholic liver disease (ALD) accounts for almost 50% of all deaths from cirrhosis in the world. In this study, we used a modified Lieber-DeCarli (LD) diet to treat mice with alcohol and simulate chronic alcohol drinking. Using an untargeted metabolomics approach, our aim was to identify the various metabolites and pathways that are altered in the early stages of ALD. Histopathology showed minimal changes in the liver after 6 weeks of alcohol consumption. However, untargeted metabolomics analyses identified 304 metabolic features that were either up- or down-regulated in the livers of ethanol-consuming mice. Pathway analysis revealed significant alcohol-induced alterations, the most significant of which was in the FXR/RXR activation pathway. Targeted metabolomics focusing on bile acid biosynthesis showed elevated taurine-conjugated cholic acid compounds in ethanol-consuming mice. In summary, we showed that the changes in the liver metabolome manifest very early in the development of ALD, and when minimal changes in liver histopathology have occurred. Although alterations in biochemical pathways indicate a complex pathology in the very early stages of alcohol consumption, bile acid changes may serve as biomarkers of the early onset of ALD. © 2022

This study presents the photolytic and photocatalytic degradation of antineoplastic drug etoposide (ETO) in aqueous solutions under UV irradiation. Photolytic degradation of ETO was slow with insufficient mineralization. The quantum yield of ETO's photolytic degradation was calculated and ranged from 0.00029 to 0.00273 mol Einstein−1. Photocatalytic degradation was proven effective, especially at pH 4, where kobs reached 0.963 min−1. Overall, 29 TPs of photocatalysis were detected at pH 4 and 7. Structures were proposed for 23 of them, based on complementary use of low-/high- resolution MS and Quantitative Structure-Retention Relationship (QSRR) prediction models. The main proposed transformations were: addition and/or demethylation of hydroxyl groups, cyclization, dehydrogenation, full or partial deprotection of diols and the loss of sugar or 2,6-dimethoxyphenol moiety. Mineralization did not follow ETO's degradation. Toxicity assessment using Vibrio fischeri bioassay and in silico prediction model revealed the formation of partially recalcitrant and possibly toxic TPs. © 2021 Elsevier B.V.

In this study, the oxidative degradation of losartan (LOS), a widely administered medicine for high blood pressure by heat-activated persulfate was investigated. Increased temperature and persulfate concentration, as well as acidic conditions enhance the degradation efficiency of LOS, whose rate follows pseudo-first order kinetics. From the respective apparent rate constants in the range 40–60 °C, an apparent activation energy of 112.70 kJ/mol was computed. Radical scavenging tests demonstrated that both HO• and SO4•− contribute towards LOS degradation. LOS degradation was suppressed in real water matrices including bottled water (BW) and secondary wastewater effluent (WW), while other experiments indicated that the presence of bicarbonates and humic acid negatively affected its oxidation. Instead, the addition of chloride ions at 250 mg/L resulted in a positive effect on LOS removal. The combination of heat-activated PS with low-frequency ultrasound exhibited a synergistic effect, with the ratio S being 2.29 in BW and 1.52 in WW. Five transformation products of LOS were identified through HRMS suspect and non-target screening approaches, among which two are reported for the first time. Using the in-house risk assessment program, ToxTrAMs was revealed that most of the identified TPs present higher toxicity than LOS against Daphnia magna. © 2021

There is an increasing need for developing a strategy to quantify the newly identified substances in environmental samples, where there are not always reference standards available. The semi-quantitative analysis can assist risk assessment of chemicals and their environmental fate. In this study, a rigorously tested and system-independent semi-quantification workflow is proposed based on ionization efficiency measurement of emerging contaminants analyzed in liquid chromatography–high-resolution mass spectrometry. The quantitative structure–property relationship (QSPR)-based model was built to predict the ionization efficiency of unknown compounds which can be later used for their semi-quantification. The proposed semi-quantification method was applied and tested in real environmental seawater samples. All semi-quantification-related calculations can be performed online and free of access at http://trams.chem.uoa.gr/semiquantification/. Graphical abstract: [Figure not available: see fulltext.] © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.

There is an increasing need for comparable and harmonized retention times (tR) in liquid chromatography (LC) among different laboratories, to provide supplementary evidence for the identity of compounds in high-resolution mass spectrometry (HRMS)-based suspect and nontarget screening investigations. In this study, a rigorously tested, flexible, and less system-dependent unified retention time index (RTI) approach for LC is presented, based on the calibration of the elution pattern. Two sets of 18 calibrants were selected for each of ESI+ and ESI-based on the maximum overlap with the retention times and chemical similarity indices from a total set of 2123 compounds. The resulting calibration set, with RTI set to range between 1 and 1000, was proposed as the most appropriate RTI system after rigorous evaluation, coordinated by the NORMAN network. The validation of the proposed RTI system was done externally on different instrumentation and LC conditions. The RTI can also be used to check the reproducibility and quality of LC conditions. Two quantitative structure-retention relationship (QSRR)-based models were built based on the developed RTI systems, which assist in the removal of false-positive annotations. The applicability domains of the QSRR models allowed completing the identification process with higher confidence for substances within the domain, while indicating those substances for which results should be treated with caution. The proposed RTI system was used to improve confidence in suspect and nontarget screening and increase the comparability between laboratories as demonstrated for two examples. All RTI-related calculations can be performed online at http://rti.chem.uoa.gr/. ©

Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments. © 2021, The Author(s).

During ozonation in wastewater treatment plants, ozone reacts with emerging pollutants, which are partially removed through the secondary treatment, as long as, with their biotransformation products, triggering the formation of ozonation transformation products (TPs). Although the transformation of parent compounds (PCs) and their metabolites has been reported in the literature, the probable transformation of biotransformation products has not been investigated so far. This study evaluates the fate of citalopram (CTR) and four of its biotransformation products (DESCTR, CTRAM, CTRAC and CTROXO) during ozonation experiments. A Gaussian curve-based trend analysis was performed for the first time for the automated detection of TPs in ozone concentrations ranging from 0.06 to 12 mg/L. In total 46 ozonation TPs were detected; 7 TPs of CTR, 10 of DESCTR, 9 of CTRAM, 12 of CTRAC and 8 of CTROXO and were structurally elucidated based on their high resolution tandem mass spectra interpretation and tandem mass spectra similarity with the respective PC. Results have demonstrated that the examined compounds follow common transformation pathways in reaction with ozone and that common TPs were formed through the ozonation of different structurally-alike compounds. Moreover, the toxicity of the identified TPs was predicted with an in-house risk assessment program. © 2021 Elsevier B.V.

Use of high-resolution mass spectrometry (HRMS) including a MS calibration method has enabled simultaneous identification and quantification of knowns/unknowns. This has expanded our knowledge about the existing sample relevant chemical space in a way beyond reconciliation with a quantification task. This is largely due to fact that reference standards are not always available to achieve quantitative analysis. In this scenario, a semi-quantitative approach can fill the gap and provide a rough estimation of concentration. This research aimed to develop and compare several semi-quantification approaches based on chemical similarity or properties. The ionization efficiency scale was created for several groups of natural products. Advanced modeling approach based on a support vector machine was conducted to learn from the experimental ionization efficiency and apply it to unknowns or suspected compounds to predict their ionization efficiency in electrospray ionization mode. The developed semi-quantification workflows could be useful in most HRMS based "omics"areas, especially in natural products discovery. ©

Wine metabolomics constitutes a powerful discipline towards wine authenticity assessment through the simultaneous exploration of multiple classes of compounds in the wine matrix. Over the last decades, wines from autochthonous Greek grape varieties have become increasingly popular among wine connoisseurs, attracting great interest for their authentication and chemical characterization. In this work, 46 red wine samples from Agiorgitiko and Xinomavro grape varieties were collected from wineries in two important winemaking regions of Greece during two consecutive vintages and analyzed using ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QToF-MS). A targeted metabolomics methodology was developed, including the determination and quantification of 28 phenolic compounds from different classes (hydroxycinnamic acids, hydroxybenzoic acids, stilbenes and flavonoids). Moreover, 86 compounds were detected and tentatively identified via a robust suspect screening workflow using an in-house database of 420 wine related compounds. Supervised chemometric techniques were employed to build an accurate and robust model to discriminate between two varieties. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

The Partnership for Chemicals Risk Assessment (PARC) is currently under development as a joint research and innovation programme to strengthen the scientific basis for chemical risk assessment in the EU. The plan is to bring chemical risk assessors and managers together with scientists to accelerate method development and the production of necessary data and knowledge, and to facilitate the transition to next-generation evidence-based risk assessment, a non-toxic environment and the European Green Deal. The NORMAN Network is an independent, well-established and competent network of more than 80 organisations in the field of emerging substances and has enormous potential to contribute to the implementation of the PARC partnership. NORMAN stands ready to provide expert advice to PARC, drawing on its long experience in the development, harmonisation and testing of advanced tools in relation to chemicals of emerging concern and in support of a European Early Warning System to unravel the risks of contaminants of emerging concern (CECs) and close the gap between research and innovation and regulatory processes. In this commentary we highlight the tools developed by NORMAN that we consider most relevant to supporting the PARC initiative: (i) joint data space and cutting-edge research tools for risk assessment of contaminants of emerging concern; (ii) collaborative European framework to improve data quality and comparability; (iii) advanced data analysis tools for a European early warning system and (iv) support to national and European chemical risk assessment thanks to harnessing, combining and sharing evidence and expertise on CECs. By combining the extensive knowledge and experience of the NORMAN network with the financial and policy-related strengths of the PARC initiative, a large step towards the goal of a non-toxic environment can be taken. © 2020, The Author(s).

Food science continually requires the development of novel analytical methods to prevent fraudulent actions and guarantee food authenticity. Greek table olives, one of the most emblematic and valuable Greek national products, are often subjected to economically motivated fraud. In this work, a novel ultra-high-performance liquid chromatography–quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS) analytical method was developed to detect the mislabeling of Greek PDO Kalamata table olives, and thereby establish their authenticity. A non-targeted screening workflow was applied, coupled to advanced chemometric techniques such as Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) in order to fingerprint and accurately discriminate PDO Greek Kalamata olives from Kalamata (or Kalamon) type olives from Egypt and Chile. The method performance was evaluated using a target set of phenolic compounds and several validation parameters were calculated. Overall, 65 table olive samples from Greece, Egypt, and Chile were analyzed and processed for the model development and its accuracy was validated. The robustness of the chemometric model was tested using 11 Greek Kalamon olive samples that were produced during the following crop year, 2018, and they were successfully classified as Greek Kalamon olives from Kalamata. Twenty-six characteristic authenticity markers were indicated to be responsible for the discrimination of Kalamon olives of different geographical origins. © 2020 by the authors.

The sonochemical degradation of trimethoprim (TMP), a widely used antibiotic, in various water matrices was investigated. The effect of several parameters, such as initial TMP concentration (0.5–3 mg/L), actual power density (20–60 W/L), initial solution pH (3–10), inorganic ions, humic acid and water matrix on degradation kinetics was examined. The pseudo-first order degradation rate of TMP was found to increase with increasing power density and decreasing pH, water complexity (ultrapure water > bottled water > secondary wastewater) and initial TMP concentration. TMP degradation is accompanied by the formation of several transformation products (TPs) as evidenced by LC-QToF-MS analysis. Nine such TPs were successfully identified and their time-trend profiles during degradation were followed. An in silico toxicity evaluation was performed showing that several TPs could potentially be more toxic than the parent compound towards Daphnia magna, Pimephales promelas and Pseudokirchneriella subcapitata. © 2020 Elsevier B.V.

This study presents the development and validation of a comprehensive quantitative target methodology for the analysis of 2316 emerging pollutants in water based on Ultra-Performance Liquid Chromatography Quadrupole-Time-Of-Flight Mass Spectrometry (UPLC-Q-ToF-HRMS/MS). Target compounds include pesticides, pharmaceuticals, drugs of abuse, industrial chemicals, doping compounds, surfactants and transformation products, among others. The method was validated for 195 analytes, chosen to be representative of the chemical space of the target list, enabling the assessment of the performance of the method. The method involves a generic sample preparation based on mixed mode solid phase extraction, a UPLC-QTOF-MS/MS screening method using Data Independent Acquisition (DIA) mode, which provides MS and MS/MS spectra simultaneously and an elaborate strong post-acquisition evaluation of the data. The processing method was optimized to provide a successful identification rate >95 % and to minimize the number of false positive results (< 5 %). Decision limit (CCα) and detection capability (CCβ) were also introduced in the validation scheme to provide more realistic metrics on the performance of a HRMS-based wide-scope screening method. A new system of identification points (IPs) based on the one described in the Commission Decision 2002/657/EC was applied to communicate the confidence level in the identification of the analytes. This system considers retention time, mass accuracy, isotopic fit and fragmentation; taking full advantage of the capacities of the HRMS instruments. Finally, 398 contaminants were detected and quantified in real wastewater. © 2019 Elsevier B.V.

Pomegranate juice is one of the most popular fruit juices, is well-known as a "superfood", and plays an important role in healthy diets. Due to its constantly growing demand and high value, pomegranate juice is often targeted for adulteration, especially with cheaper substitutes such as apple and red grape juice. In the present study, the potential of applying a metabolomics approach to trace pomegranate juice adulteration was investigated. A novel methodology based on high-resolution mass spectrometric analysis was developed using targeted and untargeted screening strategies to discover potential biomarkers for the reliable detection of pomegranate juice adulteration from apple and red grape juice. Robust classification and prediction models were built with the use of unsupervised and supervised techniques (principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA)), which were able to distinguish pomegranate juice adulteration to a level down to 1%. Characteristic m/z markers were detected, indicating pomegranate juice adulteration, and several marker compounds were identified. The results obtained from this study clearly demonstrate that Mass Spectrometry (MS)-based metabolomics have the potential to be used as a reliable screening tool for the rapid determination of food adulteration. © 2019 MDPI Multidisciplinary Digital Publishing Institute. All rights reserved.

Background: The Max Weaver Dye Library (MWDL) from North Carolina State University is a repository of around 98,000 synthetic dyes. Historically, the uses for these dyes included the coloration of textiles, paper, packaging, cosmetic and household products. However, little is reported about their ecotoxicological properties. It is anticipated that prediction models could be used to help provide this type information. Thus, the purpose of this work was to determine whether a recently developed QSAR (quantitative structure–activity relationships) model, based on ACO-SVM techniques, would be suitable for this purpose. Results: We selected a representative subset of the MWDL, composed of 15 dyes, for testing under controlled conditions. First, the molecular structure and purity of each dye was confirmed, followed by predictions of their solubility and pKa to set up the appropriate test conditions. Only ten of the 15 dyes showed acute toxicity in Daphnia, with EC50 values ranging from 0.35 to 2.95 mg L−1. These values were then used to determine the ability of the ACO-SVM model to predict the aquatic toxicity. In this regard, we observed a good prediction capacity for the 10 dyes, with 90% of deviations within one order of magnitude. The reasons for this outcome were probably the high quality of the experimental data, the consideration of solubility limitations, as well as the high purity and confirmed chemical structures of the tested dyes. We were not able to verify the ability of the model to predict the toxicity of the remaining 5 dyes, because it was not possible to determine their EC50. Conclusions: We observed a good prediction capacity for the 10 of the 15 tested dyes of the MWDL, but more dyes should be tested to extend the existing training set with similar dyes, to obtain a reliable prediction model that is applicable to the full MWDL. © 2019, The Author(s).

Hydrophilic interaction liquid chromatography (HILIC) and reversed phase LC (RPLC) coupled to high resolution mass spectrometry (HRMS) are widely used for the identification of suspects and unknown compounds in the environment. For the identification of unknowns, apart from mass accuracy and isotopic fitting, retention time (tR) and MS/MS spectra evaluation is required. In this context, a novel comprehensive workflow was developed to study the tR behavior of large groups of emerging contaminants using Quantitative Structure-Retention Relationships (QSRR). 682 compounds were analyzed by HILIC-HRMS in positive Electrospray Ionization mode (ESI). Moreover, an extensive dataset was built for RPLC-HRMS including 1830 and 308 compounds for positive and negative ESI, respectively. Support Vector Machines (SVM) was used to model the tR data. The applicability domains of the models were studied by Monte Carlo Sampling (MCS) methods. The MCS method was also used to calculate the acceptable error windows for the predicted tR from various LC conditions. This paper provides validated models for predicting tR in HILIC/RPLC-HRMS platforms to facilitate identification of new emerging contaminants by suspect and non-target HRMS screening, and were applied for the identification of transformation products (TPs) of emerging contaminants and biocides in wastewater and sludge. © 2018 Elsevier B.V.

A platform for archiving liquid chromatography high-resolution mass spectrometry (LC-HRMS) data was developed for the retrospective suspect screening of thousands of environmental pollutants with the ambition of becoming a European and possibly global standard. It was termed Digital Sample Freezing Platform (DSFP) and incorporates all the recent developments in the HRMS screening methods within the NORMAN Network. In the workflow, raw mass spectral data are converted into mzML, then mass spectral and chromatographic information on thousands of peaks of each sample is extracted into Data Collection Templates. The ‘digitally frozen’ samples can be retrospectively screened for the presence of virtually any compound amenable to LC–MS using a combination of information on its (i) exact mass, (ii) predicted retention time window in the chromatogram, (iii) isotopic fit and (iv) qualifier fragment ions. Its potential was demonstrated on monitoring of 670 antibiotics and 777 REACH chemicals from the Joint Black Sea Surveys (JBSS). © 2019 The Authors

Almost all licit and illicit drugs consumed by the society end up either unchanged or as a mixture of metabolites in the sewage systems. The analysis of influent wastewater samples and the estimation of drug consumption is the field of wastewater-based epidemiology (WBE). A new trend of WBE is the estimation of the consumption of New Psychoactive Substances (NPS), which are legal replacements of established narcotic and psychotropic drugs with slightly modified chemical structures and similar or new effects. To investigate the occurrence of NPS, 30 composite daily influent wastewater samples from the wastewater treatment plant (WWTP) of Athens (Greece) were collected in a four-year sampling campaign (2015–2018). A generic four-sorbent solid-phase extraction (SPE) sample preparation protocol able to retain compounds with wide physicochemical properties was used. Extracts were analyzed by liquid-chromatography coupled to quadrupole-time-of-flight mass spectrometry (LC-QTOF-MS) using target screening for 278 NPS and suspect screening for 451 NPS. Target screening method was validated for a subset of 49 representative NPS and illicit drugs with similar structures with the NPS. 24 NPS and related compounds were detected by target screening and two compounds were tentatively identified based on mass accuracy, prediction of retention time using in-house QSRR prediction models, isotopic pattern and HRMS/MS fragmentation, whereas the excreted mass loads were also calculated. The results indicated an occasional and low occurrence of NPS in wastewater during the week and over the years, whereas the estimation of the exact sources and the evaluation of the patterns in wastewater were critically discussed. © 2019 Elsevier B.V.

Untargeted analysis of a composite house dust sample has been performed as part of a collaborative effort to evaluate the progress in the field of suspect and nontarget screening and build an extensive database of organic indoor environment contaminants. Twenty-one participants reported results that were curated by the organizers of the collaborative trial. In total, nearly 2350 compounds were identified (18%) or tentatively identified (25% at confidence level 2 and 58% at confidence level 3), making the collaborative trial a success. However, a relatively small share (37%) of all compounds were reported by more than one participant, which shows that there is plenty of room for improvement in the field of suspect and nontarget screening. An even a smaller share (5%) of the total number of compounds were detected using both liquid chromatography–mass spectrometry (LC-MS) and gas chromatography–mass spectrometry (GC-MS). Thus, the two MS techniques are highly complementary. Most of the compounds were detected using LC with electrospray ionization (ESI) MS and comprehensive 2D GC (GC×GC) with atmospheric pressure chemical ionization (APCI) and electron ionization (EI), respectively. Collectively, the three techniques accounted for more than 75% of the reported compounds. Glycols, pharmaceuticals, pesticides, and various biogenic compounds dominated among the compounds reported by LC-MS participants, while hydrocarbons, hydrocarbon derivatives, and chlorinated paraffins and chlorinated biphenyls were primarily reported by GC-MS participants. Plastics additives, flavor and fragrances, and personal care products were reported by both LC-MS and GC-MS participants. It was concluded that the use of multiple analytical techniques was required for a comprehensive characterization of house dust contaminants. Further, several recommendations are given for improved suspect and nontarget screening of house dust and other indoor environment samples, including the use of open-source data processing tools. One of the tools allowed provisional identification of almost 500 compounds that had not been reported by participants. [Figure not available: see fulltext.]. © 2019, The Author(s).

Seven-day composite effluent samples from a German monitoring campaign including 33 conventional wastewater treatment plants (WWTP) were analyzed for linear alkylbenzene sulfonates (LAS) and alkyl ethoxysulfates (AES) and were screened by wide-scope suspect screening for 1564 surfactants and their transformation products (TPs) by UHPLC-ESI-QTOF-MS. Corresponding seven-day composite influent samples of selected WWTPs showed high influent concentrations as well as very high removal rates for LAS and AES. However, average total LAS and AES effluent concentrations were still 14.4 μg/L and 0.57 μg/L, respectively. The LAS-byproducts di-alkyl tetralin sulfonates (DATSs), the TPs sulfophenyl alkyl carboxylic acids (SPACs) and sulfo-tetralin alkyl carboxylic acids (STACs) reached maximum effluent concentrations of 19 μg/L, 17 μg/L and 5.3 μg/L, respectively. In many cases the sum of the concentration of all LAS-related byproducts and TPs surpassed the concentration of the precursors. High concentrations of up to 7.4 μg/L were found for 41 polyethylenoglycol homologs. Quantified surfactants and their TPs and by-products together accounted for concentrations up to 82 μg/L in WWTP effluents. To determine the risk of individual surfactants and their mixtures, single homologs were grouped by a “weighted carbon number approach” to derive normalized Predicted No-Effect Concentrations (PNEC), based on experimental ecotoxicity data from existing risk assessments, complemented by suitable Quantitative Structure-Activity Relationships (QSAR) predictions. Predicted Environmental Concentrations (PEC) were derived by dividing effluent concentrations of surfactants by local dilution factors. Risks for all analyzed surfactants were below the commonly accepted PEC/PNEC ratio of 1 for single compounds, while contributions to mixture toxicity effects from background levels of LAS and DATS cannot be excluded. Maximum LAS concentrations exceeded half of its PNEC, which may trigger country-wide screening to investigate potential environmental risks. © 2019 Elsevier B.V.

Background: A mixture of aspirin and dipyridamole has been proved to significantly reduce the stroke recurrence. As the use of mixture of aspirin and dipyridamole is increased, the accurate concentration of each component should be determined to avoid side effects and to improve the treatment process in pharmaceutical preparation. Objective: The goal of the present study is the simultaneous determination of aspirin and dipyridamole mixtures in pharmaceuticals using the chemometrics methods. Methods: The simultaneous spectrophotometric determination of aspirin and dipyridamole was carried out using the genetic algorithm as feature selection, coupled with partial least squares for regression analysis. Results: The linearity range of calibration curve was obtained over the range of 30-250 and 1-20 μg·mL-1 for aspirin and dipyridamole, respectively. The results indicated that the developed methods are of high accuracy and can be used as a simple and fast techniques for analyzing of these compounds. Conclusion: The developed techniques are simple and with high precision and accuracy can be used in real samples for pharmaceutical formulation. © 2018 Bentham Science Publishers.

An optimized and validated LC-ESI-QTOF-MS method with an integrated non-target screening workflow was applied in the investigation of the metabolomic profile of 51 Greek monovarietal extra virgin olive oils (EVOOs) from the varieties: Manaki, Ladoelia, Koroneiki, Amfissis, Chalkidikis and Kolovi. Data processing was carried out with the R language and XCMS package. A local database consisting of 1608 compounds naturally occurring in different organs of Olea Europa L. was compiled in order to accelerate the identification workflow. The preliminary examination of the distribution of EVOOs toward their cultivars was achieved by Principal Component Analysis (PCA). Ant Colony Optimization-Random Forest (ACO-RF) was developed to prioritize over 250 features and to establish a classification tree. Apigenin, vanillic acid, luteolin 7-methyl ether and oleocanthal were suggested as the markers responsible for the classification of Greek EVOOs’ cultivars. © 2018 Elsevier Ltd

The discrimination of organic and conventional production has been a critical topic of public discussion and constitutes a scientific issue. It remains a challenge to establish a correlation between the agronomical practices and their effects on the composition of olive oils, especially the phenolic composition, since it defines their organoleptic and nutritional value. Thus, a liquid chromatography-electrospray ionization-quadrupole time of flight tandem mass spectrometric method was developed, using target and suspect screening workflows, coupled with advanced chemometrics for the identification of phenolic compounds and the discrimination between organic and conventional extra virgin olive oils. The method was optimized by one-factor design and response surface methodology to derive the optimal conditions of extraction (methanol/water (80:20, v/v), pure methanol, or acetonitrile) and to select the most appropriate internal standard (caffeic acid or syringaldehyde). The results revealed that extraction with methanol/water (80:20, v/v) was the optimum solvent system and syringaldehyde 1.30 mg L−1 was the appropriate internal standard. The proposed method demonstrated low limits of detection in the range of 0.002 (luteolin) to 0.028 (tyrosol) mg kg−1. Then, it was successfully applied in 52 olive oils of Kolovi variety. In total, 13 target and 24 suspect phenolic compounds were identified. Target compounds were quantified with commercially available standards. A novel semi-quantitation strategy, based on chemical similarity, was introduced for the semi-quantification of the identified suspects. Finally, ant colony optimization-random forest model selected luteolin as the only marker responsible for the discrimination, during a 2-year study. [Figure not available: see fulltext.]. © 2017, Springer-Verlag Berlin Heidelberg.

Quantitative structure-activity relationship (QSAR) study has been employed for predicting the inhibitory activities of the Hepatitis C virus (HCV) NS5B polymerase inhibitors. A data set consisted of 72 compounds was selected, and then different types of molecular descriptors were calculated. The whole data set was split into a training set (80% of the dataset) and a test set (20% of the dataset) using principle component analysis. The stepwise (SW) and the genetic algorithm (GA) techniques were used as variable selection tools. Multiple linear regression method was then used to linearly correlate the selected descriptors with inhibitory activities. Several validation technique including leave-one-out and leave-group-out cross-validation, Y-randomization method were used to evaluate the internal capability of the derived models. The external prediction ability of the derived models was further analyzed using modified r2, concordance correlation coefficient values and Golbraikh and Tropsha acceptable model criteria's. Based on the derived results (GA-MLR), some new insights toward molecular structural requirements for obtaining better inhibitory activity were obtained. © 2016,Leibniz Research Centre for Working Environment and Human Factors.All rights reserved.

Over the past decade, the application of liquid chromatography-high resolution mass spectroscopy (LC-HRMS) has been growing extensively due to its ability to analyze a wide range of suspected and unknown compounds in environmental samples. However, various criteria, such as mass accuracy and isotopic pattern of the precursor ion, MS/MS spectra evaluation, and retention time plausibility, should be met to reach a certain identification confidence. In this context, a comprehensive workflow based on computational tools was developed to understand the retention time behavior of a large number of compounds belonging to emerging contaminants. Two extensive data sets were built for two chromatographic systems, one for positive and one for negative electrospray ionization mode, containing information for the retention time of 528 and 298 compounds, respectively, to expand the applicability domain of the developed models. Then, the data sets were split into training and test set, employing k-nearest neighborhood clustering, to build and validate the models' internal and external prediction ability. The best subset of molecular descriptors was selected using genetic algorithms. Multiple linear regression, artificial neural networks, and support vector machines were used to correlate the selected descriptors with the experimental retention times. Several validation techniques were used, including Golbraikh-Tropsha acceptable model criteria, Euclidean based applicability domain, modified correlation coefficient (rm2), and concordance correlation coefficient values, to measure the accuracy and precision of the models. The best linear and nonlinear models for each data set were derived and used to predict the retention time of suspect compounds of a wide-scope survey, as the evaluation data set. For the efficient outlier detection and interpretation of the origin of the prediction error, a novel procedure and tool was developed and applied, enabling us to identify if the suspect compound was in the applicability domain or not. © 2016 American Chemical Society.

Three-dimensional quantitative structure–activity relationship (3D-QSAR) modelling was conducted on a series of leucine-rich repeat kinase 2 (LRRK2) antagonists using CoMFA and CoMSIA methods. The data set, which consisted of 37 molecules, was divided into training and test subsets by using a hierarchical clustering method. Both CoMFA and CoMSIA models were derived using a training set on the basis of the common substructure-based alignment. The optimum PLS model built by CoMFA and CoMSIA provided satisfactory statistical results (q2 = 0.589 and r2 = 0.927 and q2 = 0.473 and r2 = 0.802, respectively). The external predictive ability of the models was evaluated by using seven compounds. Moreover, an external evaluation set with known experimental data was used to evaluate the external predictive ability of the porposed models. The statistical parameters indicated that CoMFA (after region focusing) has high predictive ability in comparison with standard CoMFA and CoMSIA models. Molecular docking was also performed on the most active compound to investigate the existence of interactions between the most active inhibitor and the LRRK2 receptor. Based on the obtained results and CoMFA contour maps, some features were introduced to provide useful insights for designing novel and potent LRRK2 inhibitors. © 2016 Informa UK Limited, trading as Taylor & Francis Group.

This study focuses on the effect of experimental parameters on the removal of ranitidine (RAN) during ozonation and the identification of the formed transformation products (TPs). The influence of pH value, the initial concentrations, the inorganic and the organic matter on RAN's removal were evaluated. Results indicated high reactivity of RAN with molecular aqueous ozone. Initial ozone concentration and pH were proven the major process parameters. Alkaline pH values promoted degradation and overall mineralization. Dissolved organic matter reacts competitively to RAN with the oxidants (ozone and/or radicals), influencing the target compound's removal. The presence of inorganic ions in the matrix did not seem to affect RAN ozonation. A total of eleven TPs were identified and structurally elucidated, with the complementary use of both Reversed Phase (RP) and Hydrophilic Interaction Liquid Chromatography (HILIC) quadrupole time of flight tandem mass spectrometry (Q-ToF-MS/MS). Most of the TPs (TP-304, TP-315b, TP-299b, TP-333, TP-283) were generated by the attack of ozone at the double bond or the adjacent secondary amine, with the abstraction of NO 2 moiety, forming TPs with an aldehyde group and an imine bond. Oxidized derivatives with a carboxylic group (TP-315a, TP-331a, TP-331b, TP-299a) were also formed. RAN S-oxide was identified as an ozonation TP (TP-330) and its structure was confirmed through the analysis of a reference standard. TP-214 was also produced during ozonation, through the CN bond rupture adjacent to the NO 2 moiety. HILIC was used complementary to RP, either for the separation and identification of TPs with isomeric structures that may have been co-eluted in RPLC or for the detection of new TPs that were not eluted in the RP chromatographic system. Retention time prediction was used as a supporting tool for the identification of TPs and results were in accordance with the experimental ones in both RP and HILIC. © 2016 Elsevier B.V.

Citalopram (CTR) is a worldwide highly consumed antidepressant which has demonstrated incomplete removal by conventional wastewater treatment. Despite its global ubiquitous presence in different environmental compartments, little is known about its behaviour and transformation processes during wastewater treatment. The present study aims to expand the knowledge on fate and transformation of CTR during the biological treatment process. For this purpose, batch reactors were set up to assess biotic, abiotic and sorption losses of this compound. One of the main objectives of the study was the identification of the formed transformation products (TPs) by applying suspect and non-target strategies based on liquid chromatography quadrupole-time-of-flight mass spectrometry (LC-QTOF-MS). The complementary use of reversed phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) for the identification of polar TPs, and the application of in-house developed quantitative structure-retention relationship (QSRR) prediction models, in addition to the comprehensive evaluation of the obtained MS/MS spectra, provided valuable information to support identification. In total, fourteen TPs were detected and thirteen of them were tentatively identified. Four compounds were confirmed (N-desmethylCTR, CTR amide, CTR carboxylic acid and 3-oxo-CTR) through the purchase of the corresponding reference standard. Probable structures based on diagnostic evidence were proposed for the additional nine TPs. Eleven TPs are reported for the first time. A transformation pathway for the biotransformation of CTR was proposed. The presence of the identified TPs was assessed in real wastewater samples through retrospective analysis, resulting in the detection of five compounds. Finally, the potential ecotoxicological risk posed by CTR and its TPs to different trophic levels of aquatic organisms was evaluated by means of risk quotients. © 2016 Elsevier Ltd

Food analysis is continuously requiring the development of more robust, efficient, and cost-effective food authentication analytical methods to guarantee the safety, quality, and traceability of food commodities with respect to legislation and consumer demands. Hence, a novel reversed-phase ultra high performance liquid chromatography–electrospray ionization quadrupole time of flight tandem mass spectrometry analytical method was developed that uses target, suspect, and nontarget screening strategies coupled with advanced chemometric tools for the investigation of the authenticity of extra virgin olive oil. The proposed method was successfully applied in real olive oil samples for the identification of markers responsible for the sensory profile. The proposed target analytical method includes the determination of 14 phenolic compounds and demonstrated low limits of detection ranging from 0.015 μg mL-1 (apigenin) to 0.039 μg mL-1 (vanillin) and adequate recoveries (96–107 %). A suspect list of 60 relevant compounds was compiled, and suspect screening was then applied to all the samples. Semiquantitation of the suspect compounds was performed with the calibration curves of target compounds having similar structures. Then, a nontarget screening workflow was applied with the aim to identify additional compounds so as to differentiate extra virgin olive oils from defective olive oils. Robust classification-based models were built with the use of supervised discrimination techniques, partial least squares–discriminant analysis and counterpropagation artificial neural networks, for the classification of olive oils into extra virgin olive oils or defective olive oils. Variable importance in projection scores were calculated to select the most significant features that affect the discrimination. Overall, 51 compounds were identified and suggested as markers, among which 14, 26, and 11 compounds were identified by target, suspect, and nontarget screening respectively. Retrospective analysis was also performed and identified 19 free fatty acids. [Figure not available: see fulltext.] © 2016, Springer-Verlag Berlin Heidelberg.

Parkinson’s disease affects millions of people around the world. Recently, adenosine A2A receptor antagonists have been identified as a drug target for the treatment of Parkinson’s disease. Consequently, there is an immediate need to develop new classes of A2A receptor antagonists. In the present analysis, three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on a series of pyrimidines, using comparative molecular field analysis (CoMFA). The best prediction was obtained with a CoMFA standard model (q2 = 0.475, r2 = 0.977) and a CoMFA region focusing model (q2 = 0.637, r2 = 0.976) combined with steric and electrostatic fields. The structural insights derived from the contour maps helped to better interpret the structure–activity relationships. Also, to understand the structure–activity correlation of A2A receptor antagonists, we have carried out molecular docking analysis. Based on the results obtained from the present 3D-QSAR and docking studies, we have identified some key features for increasing the activity of compounds, which have been used to design new A2A receptor antagonists. The newly designed molecules showed high activity with the obtained models. © 2015 Taylor & Francis.

The predictive analysis based on quantitative structure activity relationships (QSAR) on benzimidazolepyrrolidinyl amides as prolylcarboxypeptidase (PrCP) inhibitors was performed. Molecules were represented by chemical descriptors that encode constitutional, topological, geometrical, and electronic structure features. The hierarchical clustering method was used to classify the dataset into training and test subsets. The important descriptors were selected with the aid of the genetic algorithm method. The QSAR model was constructed, using the multiple linear regressions (MLR), and its robustness and predictability were verified by internal and external cross-validation methods. Furthermore, the calculation of the domain of applicability defines the area of reliable predictions. The root mean square errors (RMSE) of the training set and the test set for GA-MLR model were calculated to be 0.176, 0.279 and the correlation coefficients (R 2) were obtained to be 0.839, 0.923, respectively. The proposed model has good stability, robustness and predictability when verified by internal and external validation. [Figure not available: see fulltext.] © 2015 Indian Academy of Sciences.

Quantitative structure property relationship study of Fullerene derivatives was studied to predict the power conversion efficiency of compounds as polymer solar cell acceptors. The data set was split into the training and test set by employing hierarchal cluster technique. The most relevant descriptors were selected using the genetic algorithm (GA) method. The predictive ability of the constructed model was evaluated using Y-randomization test, cross-validation and test set compounds. The GA-MLR model was built based on six molecular descriptors, and it revealed appropriate statistical results. The results suggested that some quantum-chemical descriptors play significant effects on increasing the PCE values. © 2014 The Korean Society of Industrial and Engineering Chemistry.

A quantitative structure-property relation study was performed on the solubility of C60 and C70 fullerene derivatives. Topological and geometrical as well as quantum mechanical energy-related and charge distribution-related descriptors, generated from CODESSA, were calculated to define the molecule structures requirement for measuring their correlations with solubility. The best four variables among the other subsets were selected by the genetic algorithm variable subset selection procedure. Modeling of the relationship between selected molecular descriptors and solubility data was achieved by multiple linear regression method (R2train = 0.801, R2test = 0.792, Q2LOO = 0.716, Q2BOOT = 0.674). The robustness and the predictive performance of the proposed model were verified using both internal (cross-validation by leave one out, bootstrap, Y-scrambling) and external statistical validations (external test set by splitting the original data set into training and test sets by k-nearest neighbor (kNN) classification method). Further, the external predictive power of the developed model was examined by considering modified r2 and concordance correlation coefficient values. The reactivity, the polar interactions, the electron-electron repulsion energy, the electronuclear attraction energy, the nuclear-nuclear repulsion energy, and the rotational-vibrational energies were the main independent factors contributing to the solubility of the fullerenes. © 2015 Elsevier B.V. All rights reserved.

In the present work, a molecular modeling study was carried out using 2D and 3D quantitative structure-activity relationships for the various series of compounds known as B-RafV600E inhibitors. For 2D-QSAR analysis, a linear model was developed by MLR based on GA-OLS with appropriate results (Formula presented.)= 0.796, (Formula presented.)= 0.827), which was validated by several external validation techniques. To perform a 3D-QSAR analysis, CoMFA and CoMSIA methods were used. The selected CoMFA model could provide reliable statistical values (Formula presented.) = 0.683, r2=0.947) based on the training set in the biases of the selected alignment. Using the same selected alignment, a statistically reliable CoMSIA model, out of thirty-one different combinations, was also obtained (Formula presented.)= 0.645, r2=0.897). The predictive accuracy of the derived models was rigorously evaluated with the external test set of nineteen compounds based on several validation techniques. Molecular docking simulations and pharmacophore analyses were also performed to derive the true conformations of the most potent inhibitors with B-Raf$$^{\mathrm{V600E}}$$V600E kinase. © 2015, Springer International Publishing Switzerland.

Two and Three-dimensional quantitative structure-activity relationship (2D, 3D-QSAR) study was performed for some pyrazole-thiazolinone derivatives as EGFR kinase inhibitors using the CoMFA, CoMSIA and GA-MLR methods. The utilized data set was split into training and test set based on hierarchical clustering technique. From the five CoMSIA descriptors, electrostatic field presented the highest correlation with the activity. The statistical parameters for the CoMFA (r2=0.862, q2=0.644) and CoMSIA (r2=0.851, q2=0.740) were obtained for the training set with the common substructure-based alignment. The obtained parameters indicated the superiority of the CoMSIA model over the CoMFA model. A test set consisted of seven compounds was used to evaluate the proposed models. The results of contour maps which were presented by each method lead to some insights for increasing the inhibition activity of compounds. The 2D-QSAR model was built based on three descriptors selected by genetic algorithm and showed high predictive ability (R2train= 0.843, Q2 LOO=0.787). Molecular docking study was also performed to understand the type interactions presented in binding site of the receptor and ligand. The developed models in parallel with molecular docking can be employed to design and derive novel compounds with the potent EGFR inhibitory activity. © 2015 Bentham Science Publishers.

Three-dimensional quantitative structure-activity relationship was developed for series of compounds such as malonyl-CoA decarboxylase antagonists (MCD) using the CoMFA and CoMSIA methods. The statistical parameters for CoMFA (q2=0.558, r2=0.841) and CoMSIA (q2= 0.615, r2 = 0.870) models were derived based on 38 compounds as training set on the basis of the selected alignment. The external predictive abilities of the built models were evaluated by using the test set of nine compounds. From obtained results, the CoMSIA method was found to have highly predictive capability in comparison with CoMFA method. Based on the given results by CoMSIA and CoMFA contour maps, some features that can enhance the activity of compounds as MCD antagonists were introduced and used to design new compounds with a better inhibition activity. © 2015 Bentham Science Publishers.

An integrated workflow based on liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer (LC-QTOF-MS) was developed and applied to detect and identify suspect and unknown contaminants in Greek wastewater. Tentative identifications were initially based on mass accuracy, isotopic pattern, plausibility of the chromatographic retention time and MS/MS spectral interpretation (comparison with spectral libraries, in silico fragmentation). Moreover, new specific strategies for the identification of metabolites were applied to obtain extra confidence including the comparison of diurnal and/or weekly concentration trends of the metabolite and parent compounds and the complementary use of HILIC. Thirteen of 284 predicted and literature metabolites of selected pharmaceuticals and nicotine were tentatively identified in influent samples from Athens and seven were finally confirmed with reference standards. Thirty four nontarget compounds were tentatively identified, four were also confirmed. The sulfonated surfactant diglycol ether sulfate was identified along with others in the homologous series (SO4C2H4(OC2H4)xOH), which have not been previously reported in wastewater. As many surfactants were originally found as nontargets, these compounds were studied in detail through retrospective analysis. © 2015 American Chemical Society.

In this work, a quantitative structure-activity relationship (QSAR) model was used to predict the ACK1 inhibitory activities. A data set of 37 compounds with known ACK1 inhibitory activities was used. The data set was divided into two subsets of training and test sets, based on hierarchical clustering technique. Genetic algorithm was applied to select the respective variables to build the model in the next step. Multiple linear regressions (MLR) were employed to give the QSAR model. The squared cross-validated correlation coefficient for leave-one-out (QLOO2) of 0.712 and squared correlation coefficient (Rtrain2) of 0.806 were obtained for the training set compounds by GA-MLR model. The given model performed a good stability and predictability when it was verified by internal and external validation. The predicted results from this study can lead to design of better and potent ACK1 inhibitors. © 2014 King Saud University.

In this study, the quantitative structure-Activity relationship (QSAR) model for some pyrazole/imidazole amide derivatives as mGlu5 inhibitors was developed. The data set was split into the training and test subsets, randomly. The most relevant variables were selected using the genetic algorithm (GA) variable selection method. Multiple linear regression (MLR) method was used as a linear model to predict the activity of mGlu5 inhibitors based on compounds in training set. The external set of nine compounds selected out of 47 compounds, and used to evaluate the predictive ability of QSAR model. The built model could give high statistical quantities (R train 2 = 0.837, Q 2 = 0.759, R test 2 = 0.919) in which proved that the GA-MLR model was a useful tool to predict the inhibitory activity of pyrazole/imidazole amide derivatives. The results suggested that the atomic masses, atomic van der Waals volumes, atomic electronegativities, and the number of imines (aromatic) are the most important independent factors that contribute to the mGlu5 inhibition activity of pyrazole/imidazole amides derivatives. © 2013 Springer Science+Business Media New York.

In this present study, three-dimensional quantitative structure-activity relationship (3D-QSAR) and 2D-QSAR analyses were performed on the series of compounds Hepatitis C Virus NS5B polymerase inhibitors using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and stepwise multiple linear regression (SW-MLR) approaches. A CoMFA model with good predictive ability was generated based on training set of 39 compounds and showed satisfactory statistical results (q2= 0.600, r2 = 0.871). To improve the contribution of points for next analyses, CoMFA (after region focusing) was employed in biases of similar alignment and showed appropriate predictive results (q2 = 0.691, r2 = 0.889). A reliable CoMSIA model out of 31 different combinations with the higher "leave-one-out'"cross-validation correlation coefficients (q2) were obtained and indicated suitable statistical results (q2 = 0.664, r2 = 0.911). An external test set of nine compounds were used to evaluate the predictive ability of generated models. The 2D-QSAR model was built with the four descriptors selected by stepwise technique and presented high predictive ability (Rtrain2 = 0.833, Rtest2 = 0.773, QLOO2 = 0.758, QBOOT2 = 0.736). The derived contour maps from each model were assessed to identify the significant structural features required for improving biological activity so as to design potent HCV NS5B polymerase inhibitors. © 2014 American Chemical Society.

In this work, a quantitative structure-activity relationship study was developed to predict the NaV1.7 antagonist activities. A data set consisted of 36 compounds with known NaV1.7 antagonist activities was split into two subsets of training set and test set using hierarchical clustering technique. To select the most respective descriptors among the pool of descriptors, genetic algorithm was applied. The model based on selected descriptors through genetic algorithm (GA) was built by employing multiple linear regression (MLR) method. The squared correlation coefficient (R 2 train) of 0.813, squared cross-validated correlation coefficient for leave-one-out (Q2LOO) of 0.699 and root mean square error of 0.214 were calculated for the training set compounds by GA-MLR model. The proposed model performed good predictive ability when it was verified by internal and external validation tests. The results of predictive model can lead to design better compounds with high NaV1.7 antagonist activities. © Springer Science+Business Media New York 2013.