Assessments of Algal Toxicity and PBT Behaviour of Pesticides with No Eco-toxicological Data: Predictive Ability of QSA/(T)R Models.

In the present study, the effect of eight pesticides with no ecotoxicological data on the growth rate of Chlorella vulgaris was measured. The selected pesticides are acetochlor, acetamiprid, boscalid diphenamid, gibberellic acid, ioxynil, diclofop and 2,4,5-T. The algal toxicity (IC50 ) of boscalid could not be determined within its solubility limit. Acetamiprid, diphenamid and gibberellic acid revealed IC50 values>100 mg/L. Among the others, the order of 96-h IC50 of pesticides was found as acetochlor>ioxynil>diclofop>2,4,5-T. The IC50 values were also predicted by using four Quantitative Structure-Activity/(Toxicity) Relationship (QSA/(T)R) models selected from the literature. The predictions of the models provided by QSARINS-Chem module of QSARINS as well as those obtained in our previous studies were compared with the results of experimental algal toxicity tests that were performed in our laboratory. The QSTR model generated for the toxicity of diverse chemicals to freshwater algae was able to correctly predict the toxicity order of the pesticides tested in the present study, confirming the utility of the QSA/(T)R approach. Additionally, Persistence, Bioaccumulation and Toxicity (PBT) Index model provided via the software QSARINS was employed and boscalid and diclofop were found to be PBT chemicals based on the PBT model. The present study will be very helpful when a more holistic approach applied to understand the fate of these chemicals in the environment.

Predicting Cytotoxicity and Enzymatic Activity of Diverse Chemicals Using Goldfish Scale Tissue and Topminnow Hepatoma Cell Line-based Data.

Quantitative structure-toxicity relationship (QSTR) models were built for two in vitro endpoints: cytotoxicity and enzymatic activity of diverse chemicals to goldfish (Crassius auratus) scale tissue (GFS) and topminnow (Poeciliopsis lucida) hepatoma cell line (PLHC-1), respectively. The data sets were based on experimental cytotoxicity measured with uptake of 3-amino-7-dimethylamino-2-methylphenazine hydrochloride dye (Neutral Red assay) representing lysosomal damage and enzymatic activity measured with Ethoxyresorufin-O-deethylase (EROD) induction potency. The descriptors were calculated with DRAGON 6 and SPARTAN 10 software packages. Descriptor selection was made by 'All Subset' and Genetic Algorithm-based features implemented in QSARINS software. The proposed QSTR models validated both internally and externally. Additionally, the QSTR models generated for cytotoxicity and EROD induction potency were used to predict the relevant endpoint values for external set chemicals with structural coverage of 95.0 % and 92.1 %, respectively. A strong correlation of experimental in vivo fish lethality data with predicted in vitro cytotoxicity and EROD induction potency values for external set chemicals was found. It was concluded that the proposed QSTR models might be useful to provide an initial screening and prioritization for these diverse chemicals. Also, regarding the strong correlations between predicted in vitro and experimental in vivo data, the use of QSTR predictions as an alternative to the acute fish toxicity assessment can be claimed.

Revisiting fish toxicity of active pharmaceutical ingredients: Mechanistic insights from integrated ligand-/structure-based assessments on acetylcholinesterase.

The release of active pharmaceutical ingredients (APIs) into the environment is of great concern for aquatic ecosystem as many of these chemicals are designed to exert biological activity. Hence, their impact on non-target organisms like fish would not be surprising. In this respect, we revisited fish toxicity data of pharmaceuticals to generate linear and non-linear quantitative structure-toxicity relationships (QSTRs). We predicted fish lethality data from the validated QSTR models for 120 APIs with no experimental fish toxicity data. Toxicity of APIs on aquatic organisms is not fully characterized. Therefore, to provide a mechanistic insight for the assessment of API's toxicity to fish, the outcome of the derived QSTR models was integrated with structure-based toxicophore and molecular docking studies, utilizing the biomarker enzyme acetylcholinesterase originating from fish Torpedo californica (TcAChE). Toxicophore virtual screening of 60 chemicals with pT > 0 identified 23 hits as potential TcAChE binders with binding free energies ranging from -6.5 to -12.9 kcal/mol. The TcAChE-ligand interaction analysis revealed a good nesting of all 23 hits within TcAChE binding site through establishing strong lipophilic and hydrogen bonding interactions with the surrounding key amino acid residues. Among the chemicals passing the criteria of our integrated approach, majority of APIs belong noticeably to the Central Nervous System class. The screened chemicals displayed not only comprehensive toxicophore coverage, but also strong binding affinities according to the docking calculations, mainly due to interactions with TcAChE's key amino acid residues Tyr121, Tyr130, Tyr334, Trp84, Phe290, Phe330, Phe331, Ser122, and Ser200. Moreover, we propose here that binding of pharmaceuticals to AChE might have a potential in triggering molecular initiating events for adverse outcome pathways (AOPs), which in turn can play an important role for future screening of APIs lacking fish lethality data.

Toxicity of contaminants of emerging concern to Dugesia japonica: QSTR modeling and toxicity relationship with Daphnia magna.

Freshwater planarian Dugesia japonica has a critical ecological importance owing to its unique properties. This study presents for the first time an in silico approach to determine a priori the acute toxicity of contaminants of emerging concern towards D. japonica. Quantitative structure-toxicity/toxicity-toxicity relationship (QSTR/QTTR) models provided here allow producing reliable information using the existing data, thus, reducing the demand of in vivo and in vitro experiments, and contributing to the need for a more holistic approach to environmental safety assessment. Both models are promising for being notably simple and robust, meeting rigorous validation metrics and the OECD criteria. The QTTR model based on the available Daphnia magna data might also contribute to the US EPA Interspecies Correlation Estimation web application. Moreover, the proposed models were applied on hundreds of environmentally significant chemicals lacking experimental D. japonica toxicity data and predicted toxicity values were reported for the first time. The models presented here can be used as potential tools in toxicity assessment, screening and prioritization of chemicals and development of risk management measures in a scientific and regulatory frame.

A multipronged QSAR approach to predict algal low-toxic-effect concentrations of substituted phenols and anilines.

Environmental risk assessment procedures require acute and chronic toxicity values of hazardous chemicals. In this respect, the 96-h toxicity bioassays of nitro-, methyl-, methoxy-, chloro-, and nitrile- substituted phenols and anilines to Chlorella vulgaris were performed. Median inhibitory and low-toxic-effect concentrations were reported. Significant correlations between acute and chronic toxicities were found for the chemicals in the data set regardless of mode of action. Consequently, linear models employing theoretical and empirical descriptors were developed for the prediction of NOEC and IC20. The outcome of the study will be beneficial in the risk assessments of organic chemicals and setting water quality standards by the regulators.

On the aquatic toxicity of substituted phenols to Chlorella vulgaris: QSTR with an extended novel data set and interspecies models.

This study provides for the first time the 96-h toxicity of 16 nitro- and methyl- substituted phenols to Chlorella vulgaris. Enabling the circulation of new ecotoxicity data has expanded the previously reported toxicity data set of 30 phenols to C. vulgaris by our laboratory. In this respect, high quality, single source algal toxicity data, generated in the same laboratory according to a REACH (Registration, Evaluation, Authorization and Restriction of CHemicals) compatible endpoint, provided a sound basis to explore quantitative structure-toxicity relationship (QSTR), which can be used for regulatory purposes. Of the developed linear models on a new data set, the selected one was applied to a data set lack of toxicity values, and prediction ability of the model was discussed. Interspecies relations were sought related to Pseudokirchneriella subcapitata and Tetrahymena pyriformis. The developed models displayed decent predictivity, which can be used to predict the toxicity of untested phenols on C. vulgaris.

Bacterial cellulose production by Komagataeibacter hansenii using algae-based glucose.

Bacterial cellulose (BC) is a homopolymer and it is distinguished from plant-based cellulose by its unique properties such as high purity, high crystallinity, high water-holding capacity, and good biocompatibility. Microalgae are unicellular, photosynthetic microorganisms and are known to have high protein, starch, and oil content. In this study, Chlorella vulgaris was evaluated as source of glucose for the production of BC. To increase the starch content of algae the effect of nutrient starvation (nitrogen and sulfur) and light deficiency were tested in a batch assay. The starch contents (%) were 5.27 ± 0.04, 7.14 ± 0.18, 5.00 ± 0.08, and 1.35 ± 0.04 for normal cultivation, nitrogen starvation, sulfur starvation, and dark cultivation conditions, respectively. The performance of enzymatic and acidic methods was compared for the starch hydrolysis. This study demonstrated for the first time that acid hydrolysate of algal starch can be used to substitute glucose in the fermentation medium of Komagataeibacter hansenii for BC production. Glucose was used as a control for BC production. BC production yields on dry weight basis were 1.104 ± 0.002 g/L and 1.202 ± 0.005 g/L from algae-based glucose and glucose, respectively. The characterization of both BCs produced from glucose and algae-based glucose was investigated by scanning electron microscopy and Fourier transform infrared spectroscopy. The results have shown that the structural characteristics of algae-based BC were comparable to those of glucose-based BC.

An in silico approach to cytotoxicity of pharmaceuticals and personal care products on the rainbow trout liver cell line RTL-W1.

The authors constructed novel, robust, and validated linear Quantitative Structure-Toxicity Relationship (QSTR) models in line with Organisation of Co-operation and Development (OECD) criteria using 2 cytotoxicity data sets which were obtained from the Alamar Blue and 5-carboxyfluorescein diacetate acetoxymethyl ester (CFDA-AM) assays. The data sets comprise the cytotoxic effect of structurally diverse and widely used pharmaceuticals, synthetic musks, and industrial chemicals on the rainbow trout (Oncorhynchus mykiss) liver cell line RTL-W1. Common descriptors defined the relationship between structure and cytotoxicity for both the Alamar Blue and the CFDA-AM assays which measure the metabolic activity and membrane integrity, respectively. Only the statistical parameters of the best Alamar Blue-based model were given (nTR = 13; R2 = 0.839; the root-mean-square error of the training set [RMSETR ] = 0.261; nTEST = 5; R2TEST = 0.903; RMSETEST = 0.181; CCCTEST = 0.939). The proposed QSTR model was able to predict the cytotoxicity of 101 diverse chemicals on the RTL-W1 cell line with 91% structural coverage. The authors found that in vitro-derived cytotoxicity data are promising predictors of in vivo fish toxicity and may provide an initial, rapid screening tool for acute fish toxicity assessment and reduce the need for extensive in vivo toxicity testing. Environ Toxicol Chem 2017;36:1162-1169. © 2016 SETAC.

An in silico algal toxicity model with a wide applicability potential for industrial chemicals and pharmaceuticals.

The authors modeled the 72-h algal toxicity data of hundreds of chemicals with different modes of action as a function of chemical structures. They developed mode of action-based local quantitative structure-toxicity relationship (QSTR) models for nonpolar and polar narcotics as well as a global QSTR model with a wide applicability potential for industrial chemicals and pharmaceuticals. The present study rigorously evaluated the generated models, meeting the Organisation for Economic Co-operation and Development principles of robustness, validity, and transparency. The proposed global model had a broad structural coverage for the toxicity prediction of diverse chemicals (some of which are high-production volume chemicals) with no experimental toxicity data. The global model is potentially useful for endpoint predictions, the evaluation of algal toxicity screening, and the prioritization of chemicals, as well as for the decision of further testing and the development of risk-management measures in a scientific and regulatory frame. Environ Toxicol Chem 2017;36:1012-1019. © 2016 SETAC.

Single and mixture toxicity of pharmaceuticals and chlorophenols to freshwater algae Chlorella vulgaris.

Organisms in the aquatic environment are exposed to a variety of substances of numerous chemical classes. The unintentional co-occurrence of pharmaceuticals and other contaminants of emerging concern may pose risk to non-target organisms. In this study, individual and binary mixture toxicity experiments of selected pharmaceuticals (ibuprofen and ciprofloxacin) and chlorophenols (2.4-dichlorophenol (2,4-DCP) and 3-chlorophenol (3-CP)) have been performed with freshwater algae Chlorella vulgaris. All experiments have been carried out according to the 96-h algal growth inhibition test OECD No. 201. Binary mixture tests were conducted using proportions of the respective IC50s in terms of toxic unit (TU). The mixture concentration-response curve was compared to predicted effects based on both the concentration addition (CA) and the independent action (IA) model. Additionally, the Combination Index (CI)-isobologram equation method was used to assess toxicological interactions of the binary mixtures. All substances individually tested had a significant effect on C. vulgaris population density and revealed IC50 values <100mgL(-1) after exposure period of 96-h. The toxic ranking of these four compounds to C. vulgaris was 2,4-DCP>ciprofloxacin>3-CP>ibuprofen. Generally, it can be concluded from this study that toxic mixture effects of all tested chemicals to C. vulgaris are higher than the individual effect of each mixture component. It could be demonstrated that IC50 values of the tested mixtures predominately lead to additive effects. The CA model is appropriate to estimate mixture toxicity, while the IA model tends to underestimate the joint effect. The CI-isobologram equation method predicted the mixtures accurately and elicited synergism at low effect levels for the majority of tested combinations.

Comparative performance of descriptors in a multiple linear and Kriging models: a case study on the acute toxicity of organic chemicals to algae.

This study presents quantitative structure-toxicity relationship (QSTR) models on the toxicity of 91 organic compounds to Chlorella vulgaris using multiple linear regression (MLR) and Kriging techniques. The molecular descriptors were calculated using SPARTAN and DRAGON programs, and descriptor selection was made by "all subset" method available in the QSARINS software. MLR and Kriging models developed with the same descriptors were compared. In addition to these models, Kriging method was used for descriptor selection, and model development. The selected descriptors showed the importance of hydrophobicity, molecular weight and atomic ionization state in describing the toxicity of a diverse set of chemicals to C. vulgaris. A QSTR model should be associated with appropriate measures of goodness-of-fit, robustness, and predictivity in order to be used for regulatory purpose. Therefore, while the internal performances (goodness-of-fit and robustness) of the models were determined by using a training set, the predictive abilities of the models were determined by using a test set. The results of the study showed that while MLR method is easier to apply, the Kriging method was more successful in predicting toxicity.

Assessment and modeling of the novel toxicity data set of phenols to Chlorella vulgaris.

This study provides for the first time the 96 h toxicity of 30 phenols to Chlorella vulgaris. Analysis of the novel data set revealed that the algal toxicity of polar narcotics and respiratory uncouplers was correlated strongly to the pH corrected hydrophobicity parameter, Log D, demonstrating the importance of ionization in the C. vulgaris test system. Compounds expected to act by more reactive mechanisms were shown to have toxicity in excess of that predicted by Log D and were successfully modelled using the activation energy index (AEI). Three global quantitative structure-activity relationships (QSARs) were constructed using the C. vulgaris data set and validated externally using a data set retrieved from literature comprising the toxicity of 58 compounds to freshwater alga Pseudokirchneriella subcapitata. Results revealed that the response-surface model was highly interpretable and provided acceptable predictions for polar narcotics and respiratory uncouplers, though it lacked the reliability to predict the toxicity of reactive phenols. In two other externally validated QSAR models, a WHIM (Weighted Holistic Invariant Molecular) descriptor, namely, Tm (T total size index/weighted by atomic masses), revealed promising results that could be used to predict algal toxicity of compounds other than phenols such as anilines.

Quantitative structure-activity relationships (QSARs) using the novel marine algal toxicity data of phenols.

The present study reports for the first time in its entirety the toxicity of 30 phenolic compounds to marine alga Dunaliella tertiolecta. Toxicity of polar narcotics and respiratory uncouplers was strongly correlated to hydrophobicity as described by the logarithm of the octanol/water partition coefficient (Log P). Compounds expected to act by more reactive mechanisms, particularly hydroquinones, were shown to have toxicity in excess of that predicted by Log P. A quality quantitative structure-activity relationship (QSAR) was obtained with Log P and a 2D autocorrelation descriptor weighted by atomic polarizability (MATS3p) only after the removal of hydroquinones from the data set. In an attempt to model the whole data set including hydroquinones, 3D descriptors were included in the modeling process and three quality QSARs were developed using multiple linear regression (MLR). One of the most significant results of the present study was the superior performance of the consensus MLR model, obtained by averaging the predictions from each individual linear model, which provided excellent prediction accuracy for the test set (Q(test)²=0.94). The four-parameter Counter Propagation Artificial Neural Network (CP ANN) model, which was constructed using four out of six descriptors that appeared in the linear models, also provided an excellent external predictivity (Q(test)²=0.93). The proposed algal QSARs were further tested in their predictivity using an external set comprising toxicity data of 44 chemicals on freshwater alga Pseudokirchneriella subcapitata. The two-parameter global model employing a 3D descriptor (Mor24m) and a charge-related descriptor (C(ortho)) not only had high external predictivity (Q(ext)²=0.74), but it also had excellent external data set coverage (%97).

First toxicity data of chlorophenols on marine alga Dunaliella tertiolecta: correlation of marine algal toxicity with hydrophobicity and interspecies toxicity relationships.

The toxicity of phenol and 13 chlorinated phenols to the marine alga Dunaliella tertiolecta is presented for the first time. The newly generated marine algal toxicity data was found to correlate strongly with the widely used hydrophobicity parameter-the logarithm of the n-octanol-water partition coefficient (log K(OW)). Interspecies relationships using the new marine algal toxicity data of chlorophenols with the previously published data on bacterium (Vibrio fischeri), protozoan (Tetrahymena pyriformis), daphnid (Daphnia magna), freshwater alga (Pseudokirchneriella subcapitata), and fish (Pimephales promelas) revealed promising results that could be exploited in extrapolations using freshwater data to predict marine algal toxicity.

Water quality and heavy metal monitoring in water and sediment samples of the Küçükçekmece Lagoon, Turkey (2002-2003).

Physical and chemical parameters have been analyzed in water samples from a brackish water lagoon, Küçükçekmece, located on the western outskirts of Istanbul. Samples were collected every two months for a year from nine sampling stations. Of the parameters measured, temperature, pH, salinity, nitrate and phosphate showed changes when compared with the previously published data. The lagoon was found eutrophic as it was reported previously. Sulphate and COD levels were higher when compared with the standards established by the Turkish Water Pollution and Control Regulation. Additionally, concentrations of heavy metals (Cd, Co, Cr, Cu, Mn, Ni, Pb, Zn) in water and bottom sediments were measured and compared with the standards established by the Turkish Water Pollution and Control Regulation and with the previously published data. The results were analysed statistically with respect to location and any relationships between the concentration of the elements in corresponding water and sediment samples were examined. Principal Component Analysis of water samples allowed us to discriminate three areas affected mainly by heavy metal contamination, possibly due to industrial, commercial and/or urban activities. Generally, the concentrations of the heavy metals were higher at stations near the three estuaries, suggesting a direct influence of the three creeks on the pollution of the Küçükçekmece Lagoon. Although elevated levels of Cd were recorded in several water samples, it was not detected in sediment. On the other hand, a particularly high level of Cr pollution was recorded most of the water and sediment samples.

Exposure of Dunaliella tertiolecta to lead and aluminum: toxicity and effects on ultrastructure.

The growth response of the marine alga Dunaliella tertiolecta to different concentrations of lead and aluminum was investigated. Both metals had a stimulatory effect at low concentration and an inhibitory effect at high concentration (hormesis). The IC25 values of lead are 8.43, 7.29, and 6.74 mg L-1 for 24, 48, and 72 h, respectively. The corresponding values for aluminum are 30.54, 22.42, and 18.16 mg L-1. Although it seems that the two metals are not directly toxic to the alga at the concentrations found in the environment, as implied by the IC25 values and the environmental concentrations of the metals, low concentrations of both metals, alone and in combination, affected the ultrastructure. The growth of batch-grown cells exposed to 0.5 mg L-1 lead and aluminum, alone and combined, during the 24-h exponential phase was investigated. The same cells were also examined under an electron microscope to determine the biological effects of the two metals on the ultrastructure. The most obvious effects of lead were disrupted thylakoidal membranes, accumulated polyphosphate bodies and vacuoles, and lead precipitates on the cell surface. These ultrastructural alterations were partially present in aluminum-treated and lead-aluminum-treated cells. In joint exposure, the most important change was the lysis of the cell membrane. Aluminum and lead seem to act synergistically on the cell membrane leading to cell membrane lysis.

QSPR analysis of the toxicity of aromatic compounds to the algae (Scenedesmus obliquus).

The quantitative structure-property relationship (QSPR) model was developed for the 50% effective inhibition concentration (48h - EC(50)) of 36 selected substituted benzenes for the algae Scenedesmus obliquus by the application of the Characteristic Root Index (CRI) model. To increase the predictive power of the CRI-based model, the following semi-empirical molecular descriptors calculated by the quantum chemical PM3 method were included: the energy of the highest occupied molecular orbital (E(HOMO)), the energy of the lowest unoccupied molecular orbital (E(LUMO)), and the dipole moment (mu). A two-descriptor model with a correlation coefficient of r=0.926 was developed without the outliers from multiple regression analysis [-logEC(50)=0.494 (+/-0.072) CRI-0.798 (+/-0.063) E(LUMO)+1.985 (+/-0.169)]. E(LUMO) was the most important parameter, followed by the CRI. E(LUMO) reflects electronic properties, whereas the CRI reflects hydrophobicity, molecular size, and branching. The statistical robustness of the developed model was validated by the modified jackknife test. The predictive accuracy of the proposed model was compared with the recently published study in which a toxicity model was developed for the same algae. Because of its high statistical significance, the validated model has been used to predict -logEC(50) values of compounds for which there are no experimental measurements.

QSPR study on the bioconcentration factors of nonionic organic compounds in fish by characteristic root index and semiempirical molecular descriptors.

The characteristic root index (CRI) was modeled together with four semiempirical molecular descriptors, namely-energies of the highest occupied and the lowest unoccupied molecular orbital (E(HOMO) and E(LUMO)), heat of formation (DeltaH(f)), and dipole moment (micro)-to predict the fish bioconcentration factor (BCF) of 122 nonionic organic compounds. The best fit equation found by "forward multiple linear regression" showed that the topology based CRI was the most important parameter. The addition of quantum chemical descriptors made only a slight improvement in the predictive capability of the Quantitative Structure-Property Relationship (QSPR) model. The CRI was followed by E(HOMO). A two-parameter equation with a correlation coefficient of r = 0.921 was obtained for a diverse set of nonionic organic chemicals. Statistical robustness of the developed model was validated by modified jackknife tests where random deletion of a class of compounds and specific deletion of a set of compounds were both performed. The predictive accuracy of the proposed model was compared with the commonly used K(ow) model and recently published studies in which BCF models were developed. Particular emphasis has been made to clearly define the boundaries for the application of the alternative developed model as well as the quality of estimates.