Acute Toxicity Of Chemicals Research Articles

In vivo acute and subacute toxicity assessments of Viroscope®, a polyherbal ethanolic formulation produced in Lomé, Togo

Background: Phyto-medicines have been used to treat various diseases since ancient times. However, several studies have reported the toxicity of some medicinal plant preparations. Objective: This study aimed to evaluate the in vivo acute and subacute toxicity of an ethanolic extract of medicinal plants, “VIROSCOPE®”, produced by Viroscope SARL-U in Lomé, Togo. Materials and Methods: Acute toxicity assessment was conducted according to the “OECD guideline 423 for the assessment of the acute toxicity of chemicals.” This study focused on the ready-to-use VIROSCOPE® and on the concentrated extract obtained after evaporation. Subacute toxicity was assessed according to the “OECD guideline 407 for the study of subacute toxicity of chemicals.” Control rats; VIROSCOPE®; solvents; 100, 500, and 1000 mg/kg of the dry extract of VIROSCOPE® dissolved in drinking water were tested.The mortality rate, toxicity symptoms, food and water consumption, and changes in body weight were monitored. Biochemical and haematological parameters were also measured. Furthermore, histopathological analysis of hepatic and renal cells was conducted. Results : The median lethal dose (LD50) was > 5000 mg/kg. Repeated doses of the trial drug over a long period of 28 days had no major risk. In addition, it did not induce any pathological variations in biochemical and haematological parameters. According to histopathological results, there was no significant harmful effect to liver and kidney cells. Conclusion: VIROSCOPE® can be considered safe and must be administered strictly according to the indicated dosages on its bottle.

Global classification models for predicting acute toxicity of chemicals towards Daphnia magna

Molecular descriptors reflecting structural information on hydrophobicity, reactivity, polarizability, hydrogen bond and charged groups, were used to predict the toxicity (pLC50) of chemicals towards Daphnia magna with global quantitative structure–activity/toxicity relationship (QSAR/QSTR) models. A sufficiently large dataset including 1517 chemical toxicity to Daphnia magna was divided into a training set (758 pLC50) and a test set (759 pLC50). By applying random forest algorithm, two classification models, Class Model A and Class Model B were developed, having prediction accuracy, sensitivity and specificity above 85% for Class 1 (with pLC50 ≤ 4.48) and Class 2 (with pLC50 > 4.48). The Class Model A was based on nine molecular descriptors and RF parameters of nodesize = 1, ntree = 80 and mtry = 2, and yielded accuracy of 92.3% (training set), 85.6% (test set) and 88.9% (total data set). Class Model B was based on ten descriptors and parameters, nodesize = 1, ntree = 90 and mtry = 2, produced accuracy of 88.3% (training set), 86.8% (test set) and 87.5% (total data set). The two classification models were satisfactory compared with other classification model reported in the literature, although classification models in this work dealt with more samples. Thus, the two classification models with a larger applicability domain provided efficient tools for assessing chemical aquatic toxicity towards Daphnia magna.

Classifying chemicals into toxicity categories based on LC50 values- Pros and cons

Classifying chemicals into toxicity categories based on LC50 values- Pros and cons

In silico prediction of acute chemical toxicity of biocides in marine crustaceans using machine learning

Biocides are a heterogeneous group of chemical substances intended to control the growth or kill undesired organisms. Due to their extensive use, they enter marine ecosystems via non-point sources and may pose a threat to ecologically important non-target organisms. Consequently, industries and regulatory agencies have recognized the ecotoxicological hazard potential of biocides. However, the prediction of biocide chemical toxicity on marine crustaceans has not been previously evaluated. This study aims to provide in silico models capable of classifying structurally diverse biocidal chemicals into different toxicity categories and predict acute chemical toxicity (LC50) in marine crustaceans using a set of calculated 2D molecular descriptors. The models were built following the guidelines recommended by the OECD (Organization for Economic Cooperation and Development) and validated through stringent processes (internal and external validation). Six machine learning (ML) models were built and compared (linear regression: LR; support vector machine: SVM; random forest: RF; feed-forward backpropagation-based artificial neural network: ANN; decision trees: DT and naïve Bayes: NB) for regression and classification analysis to predict toxicities. All the models displayed encouraging results with high generalisability: the feed-forward-based backpropagation method showed the best results with determination coefficient R2 values of 0.82 and 0.94, respectively, for training set (TS) and validation set (VS). For classification-based modelling, the DT model performed the best with an accuracy (ACC) of 100 % and an area under curve (AUC) value of 1 for both TS and VS. These models showed the potential to replace animal testing for the chemical hazard assessment of untested biocides if they fall within the applicability domain of the proposed models. In general, the models are highly interpretable and robust, with good predictive performance. The models also displayed a trend indicating that toxicity is largely influenced by factors such as lipophilicity, branching, non-polar bonding and saturation of molecules.

A comparison of the chemo- and radiotoxicity of thorium and uranium at different enrichment grades

Uranium and thorium are heavy metals, and all of their isotopes are radioactive, so it is impossible to study chemical effects entirely independent of the radiation effects. In the present study, we tried to compare the chemo- and radiotoxicity of both metals, taking into account deterministic radiation damages reflected by acute radiation sickness and stochastic radiation damages leading to long-term health impairments (e.g., tumor induction). We made at first a literature search on acute median lethal doses that may be expected to be caused by chemical effects, as even acute radiation sickness as a manifestation of acute radiotoxicity occurs with latency. By simulations based on the biokinetic models of the International Commission on Radiological Protection and using the Integrated Modules for Bioassay Analysis software, we determined the amounts of uranium at different enrichment grades and thorium-232 leading to a short-term red bone marrow equivalent dose of 3.5 Sv considered to cause 50% lethality in humans. Different intake pathways for incorporation were considered, and values were compared to the mean lethal doses by chemotoxicity. To assess stochastic radiotoxicity, we calculated the uranium and thorium amounts leading to a committed effective dose of 200 mSv that is often considered critical. Mean lethal values for uranium and thorium are in the same order of magnitude so that the data do not give evidence for substantial differences in acute chemical toxicity. When comparing radiotoxicity, the reference units (activity in Bq or weight in g) must always be taken into account. The mean lethal equivalent dose to the red bone marrow of 3.5 Sv is reached by lower activities of thorium compared to uranium in soluble compounds. However, for uranium as well as thorium-232, acute radiation sickness is expected only after incorporation of amounts exceeding the mean lethal doses by chemotoxicity. Thus, acute radiation sickness is not a relevant clinical issue for either metal. Concerning stochastic radiation damages, thorium-232 is more radiotoxic than uranium if incorporating the same activities. Using weight units for comparison show that for soluble compounds, thorium-232 is more radiotoxic than low-enriched uranium in the case of ingestion but even more toxic than high-enriched uranium after inhalation or intravenous administration. For insoluble compounds, the situation differs as the stochastic radiotoxicity of thorium-232 ranges between depleted and natural uranium. For acute effects, the chemotoxicity of uranium, even at high enrichment grades, as well as thorium-232 exceeds deterministic radiotoxicity. Simulations show that thorium-232 is more radiotoxic than uranium expressed in activity units. If the comparison is based on weight units, the rankings depend on the uranium enrichment grades and the route of intake.

Short and long-term effects of nanobiomaterials in fish cell lines. Applicability of RTgill-W1

Nanobiomaterials (NBMs) are nanostructured materials for biomedical applications that can reach aquatic organisms. The short and long-term effects of these emerging contaminants are unknown in fish. The RTgill-W1 cell line has been proposed as a model to predict the acute toxicity of chemicals to fish (OECD Test Guideline nº 249). We assessed the applicability of this cell line to study the short and long-term toxicity of 15 NBMs based on hydroxyapatites (HA), lipid (LSNP/LNP), gold, iron oxide, carbon, poly l-Lactide acid (PLLA) fibers with Ag and poly (lactide-co-glycolide) acid. Two more rainbow trout cell lines (RTL-W1, from liver, and RTS-11, from spleen) were exposed, to identify possible sensitivity differences among cells. Exposures to a range of concentrations (0.78–100 μg/mL) lasted for 24 h. Additionally, the RTgill-W1 was used to perform long-term (28 d exposure) and recovery (14 d exposure/14 d recovery) assays. Cells were exposed to the 24 h-IC20 and/or to 100 μg/mL. A triple cytotoxicity assay was conducted. After 24 h, only PLLA Fibers-Ag showed cytotoxicity (IC50 < 100 μg/mL). However, the NBMs in general provoked concentration-dependent effects after long-term exposures, except the LSNPs. A recovery of viability was only observed for AuNPs, AuNRods, Fe3O4PEG-PLGA, MgHA-Collag_Scaffolds, Ti-HA and TiHA-Alg NPs.These results evidenced the need to test the long-term toxicity of NBMs and showed differences in cytotoxicity probably associated to different mechanisms of toxic action. The RTgill-W1 was useful to screen short and long-term toxicities of NBMs and appears as a promiseful model to assess possible toxicity of NBMs in fish.

Calculation of 95% confidence interval of the median lethal dose determined by the up-and-down procedure: a letter to editor

Acute toxicity of chemicals can be determined by the up-and-down procedure (UDP) to measure the median lethal dose (LD50) in laboratory animals. An improved UPD was introduced to calculate the 95% confidence intervals (CI) for LD50 values: 95% confidence interval = LD50 + the standard error (SE) SE = SD x √2/N (SD= standard deviation of all dosages in N trials in animals). However, not all studies using the UDP report the 95% CI. Using the above mentioned equations, it is possible to calculate the 95% CI of LD50 determined by using the table of Dixon for UDP- section of the standard error 0.61. For demonstration, recalculations were done on selected LD50 values with standard deviation, standard error and 95% CI from published literature by using the above mentioned equations. These included the LD50 values of propofol and diazinon in chicks, as well as cadmium and tetramisole in mice.

Comparative evaluation of mathematical models for predicting acute toxicity of chemicals

Introduction. Considerable attention is paid to the assessment of acute toxicity of chemical compounds during oral administration due to the different rates of absorption of substances in different animal species and various experimental conditions. Given the pace of development of the chemical industry, researchers are faced with the question of accelerating the study of the properties of substances and filling data gaps. Therefore, quantitative prediction of the toxic properties of substances using mathematical models based on the structure or structural properties of compounds - quantitative structure -activity relationship (QSAR) modeling - is one of the promising areas. The purpose of this study is to create and compare the performance of the obtained mathematical models for predicting the acute toxicity of various classes of chemicals. Materials and methods. The study included four classes of pesticides (organochlorine compounds (OCs), azoles, carbamates, organophosphorus compounds (OPs) in the amount of 100 compounds with descriptors calculated by PaDEL-Descriptors software ver. 2.21. Regression models were constructed in the WEKA software, subjected to an internal validation procedure. Statistical parameters such as the mean square error (RMSE) and the coefficient of determination (r 2) were used to assess the quality of regression models. Results. To predict acute oral toxicity of OCs and OPs, it is optimal to use a model in which neural networks and the support vector method are combined, for carbamates - an ensemble model that includes linear regression and the support vector method. For substances from the azole group, it was not possible to create a model that would meet the necessary requirements: r 2&gt;0.6 for the training set and r 2 &gt;0.5 for cross-validation. Limitations. The study is limited by the number of compounds studied, the class of chemical compounds, and the area of distribution of the results obtained during modeling. Conclusion. In this study, ensemble modelling methods demonstrated the best results in predicting acute oral toxicity for OCs, carbamates, and OPs.

Suitability and Sensitivity of Golden Grey Mullet Chelon auratus (Risso, 1810) as a Reference Fish Species for Ecotoxicity Tests in the Black Sea

In recent years, hydrocarbon exploration and production operations have intensified in the Black Sea. Alongside growth in exploration and production activities, the influence of chemical usage across multiple industrial sectors within the Black Sea environment has become increasingly interesting. The aim of this research was to define a protocol for determining the acute toxicity of chemicals using the golden grey mullet, Chelon auratus (Risso, 1810), a native pelagic fish species of the Black Sea. Juvenile golden grey mullets were exposed for 96 h, under semi-static conditions, to dilutions of the reference toxicant 3,5-Dichlorophenol. Results from three reference toxicity tests (LC50 = 1.25 mg/L, 1.739 mg/L, and 1.409 mg/L) indicated that C. auratus is of moderate sensitivity when compared to literature values from EPAs Ecotox database. The protocol described within is intended to ensure Black Sea native organisms are represented by standard hazard assessment practices.

BeeToxAI: An artificial intelligence-based web app to assess acute toxicity of chemicals to honey bees

Chemically induced toxicity is the leading cause of recent extinction of honey bees. In this regard, we developed an innovative artificial intelligence-based web app (BeeToxAI) for assessing the acute toxicity of chemicals to Apis mellifera. Initially, we developed and externally validated QSAR models for classification (external set accuracy ∼91%) through the combination of Random Forest and molecular fingerprints to predict the potential for chemicals to cause acute contact toxicity and acute oral toxicity to honey bees. Then, we developed and externally validated regression QSAR models (R2 = 0.75) using Feedforward Neural Networks (FNNs). Afterward, the best models were implemented in the publicly available BeeToxAI web app (http://beetoxai.labmol.com.br/). The outputs of BeeToxAI are: toxicity predictions with estimated confidence, applicability domain estimation, and color-coded maps of relative structure fragment contributions to toxicity. As an additional assessment of BeeToxAI performance, we collected an external set of pesticides with known bee toxicity that were not included in our modeling dataset. BeeToxAI classification models were able to predict four out of five pesticides correctly. The acute contact toxicity model correctly predicted all of the eight pesticides. Here we demonstrate that BeeToxAI can be used as a rapid new approach methodology for predicting acute toxicity of chemicals in honey bees.

A Systematic Review to Compare Chemical Hazard Predictions of the Zebrafish Embryotoxicity Test With Mammalian Prenatal Developmental Toxicity.

Originally developed to inform the acute toxicity of chemicals on fish, the zebrafish embryotoxicity test (ZET) has also been proposed for assessing the prenatal developmental toxicity of chemicals, potentially replacing mammalian studies. Although extensively evaluated in primary studies, a comprehensive review summarizing the available evidence for the ZET’s capacity is lacking. Therefore, we conducted a systematic review of how well the presence or absence of exposure-related findings in the ZET predicts prenatal development toxicity in studies with rats and rabbits. A two-tiered systematic review of the developmental toxicity literature was performed, a review of the ZET literature was followed by one of the mammalian literature. Data were extracted using DistillerSR, and study validity was assessed with an amended SYRCLE's risk-of-bias tool. Extracted data were analyzed for each species and substance, which provided the basis for comparing the 2 test methods. Although limited by the number of 24 included chemicals, our results suggest that the ZET has potential to identify chemicals that are mammalian prenatal developmental toxicants, with a tendency for overprediction. Furthermore, our analysis confirmed the need for further standardization of the ZET. In addition, we identified contextual and methodological challenges in the application of systematic review approaches to toxicological questions. One key to overcoming these challenges is a transition to more comprehensive and transparent planning, conduct and reporting of toxicological studies. The first step toward bringing about this change is to create broad awareness in the toxicological community of the need for and benefits of more evidence-based approaches.

Feasibility of using interspecies relationships for integration of human and environmental hazard assessment

In this paper we have explored the possibilities and difficulties of integrating data between ERA and HRA, in particular how toxicity in fish could inform the toxicity in other (aquatic) vertebrates and how their relative sensitivity can be used to perform the risk assessment of chemical more efficiently. With respect to acute toxicity of chemicals with a nonspecific mode of action, the sensitivity of fish seems to correlate well with that of rats. For substances interfering via a specific mode of action, extrapolation between species might be feasible provided that the molecular features of the receptors involved are known. Overall, fish species appear to be rather sensitive to chemicals interacting with the hypothalamic–pituitary–gonadal/thyroid axes. Fish could therefore be a good model to identify estrogenic and androgenic and thyroid active substances and to determine whether or not developmental and/or reproductive effects in vertebrates via these mode of actions can be expected. It is therefore recommended for hazard assessment of endocrine disruptors to include histopathology of the thyroid gland and analysis of thyroid hormones into a fish reproduction assay, to reduce testing on other aquatic vertebrates.

Multistate models of developmental toxicity: Application to valproic acid-induced malformations in the zebrafish embryo

For the determination of acute toxicity of chemicals in zebrafish (Danio rerio) embryos, the OECD test guideline 236, relative to the Fish Embryo Toxicity Test (FET), stipulates a dose-response analysis of four lethal core endpoints and a quantitative characterization of abnormalities including their time-dependency. Routinely, the data are analyzed at the different observation times separately. However, observations at a given time strongly depend on the previous effects and should be analyzed jointly with them. To solve this problem, we developed multistate models for occurrence of developmental malformations and live events in zebrafish embryos exposed to eight concentrations of valproic acid (VPA) the first five days of life. Observations were recorded daily per embryo. We statistically infer on model structure and parameters using a numerical Bayesian framework. Hatching probability rate changed with time and we compared five forms of its time-dependence; a constant rate, a piecewise constant rate with a fixed hatching time at 48 h post fertilization, a piecewise constant rate with a variable hatching time, as well as a Hill and Gaussian form. A piecewise constant function of time adequately described the hatching data. The other transition rates were conditioned on the embryo body concentration of VPA, obtained using a physiologically-based pharmacokinetic model. VPA impacted mostly the malformation probability rate in hatched and non-hatched embryos. Malformation reversion probability rates were lowered by VPA. Direct mortality was low at the concentrations tested, but increased linearly with internal concentration. The model makes full use of data and gives a finer grain analysis of the teratogenic effects of VPA in zebrafish than the OECD-prescribed approach. We discuss the use of the model for obtaining toxicological reference values suitable for inter-species extrapolation. A general result is that complex multistate models can be efficiently evaluated numerically.

Sensitivity of Zebrafish (Danio rerio) Embryos to Hospital Effluent Compared to Daphnia magna and Aliivibrio fischeri

The Fish Embryo Acute Toxicity (FET) Test was adopted by the Organisation for Economic Co-operation and Development as OECD TG 236 in 2013. The test has been designed to determine acute toxicity of chemicals on embryonic stages of fish and proposed as an alternative method to the Fish Acute Toxicity Test performed according to OECD TG 203. In recent years fish embryos were used not only in the assessment of toxicity of chemicals but also for environmental and wastewater samples. In our study we investigated the acute toxicity of treated wastewater from seven hospitals in the Czech Republic. Our main purpose was to compare the suitability and sensitivity of zebrafish embryos with the sensitivity of two other aquatic organisms commonly used for wastewater testing - Daphnia magna and Aliivibrio fischeri. For the aim of this study, in addition to the lethal endpoints of the FET test, sublethal effects such as delayed heartbeat, lack of blood circulation, pericardial and yolk sac edema, spinal curvature and pigmentation failures were evaluated. The comparison of three species demonstrated that the sensitivity of zebrafish embryos is comparable or in some cases higher than the sensitivity of D. magna and A. fischeri. The inclusion of sublethal endpoints caused statistically significant increase of the FET test efficiency in the range of 1-12 %. Based on our results, the FET test, especially with the addition of sublethal effects evaluation, can be considered as a sufficiently sensitive and useful additional tool for ecotoxicity testing of the acute toxicity potential of hospital effluents.

Are In Silico Approaches Applicable As a First Step for the Prediction of e-Liquid Toxicity in e-Cigarettes?

Recent studies have raised concerns about e-cigarette liquid inhalation toxicity by reporting the presence of chemicals with European Union CLP toxicity classification. In this scenario, the regulatory context is still developing and is not yet up to date with vaping current reality. Due to the paucity of toxicological studies, robust data regarding which components in e-liquids exhibit potential toxicities, are still inconsistent. In this study we applied computational methods for estimating the toxicity of poorly studied chemicals as a useful tool for predicting the acute toxicity of chemicals contained in e-liquids. The purpose of this study was 3-fold: (a) to provide a lower tier assessment of the potential health concerns associated with e-liquid ingredients, (b) to prioritize e-liquid ingredients by calculating the e-tox index, and (c) to estimate acute toxicity of e-liquid mixtures. QSAR models were generated using QSARINS software to fill the acute toxicity data gap of 264 e-liquid ingredients. As a second step, the potential acute toxicity of e-liquids mixtures was evaluated. Our preliminary data suggest that a computational approach may serve as a roadmap to enable regulatory bodies to better regulate e-liquid composition and to contribute to consumer health protection.

Development of a hybrid Bayesian network model for predicting acute fish toxicity using multiple lines of evidence

A hybrid Bayesian network (BN) was developed for predicting the acute toxicity of chemicals to fish, using data from fish embryo toxicity (FET) testing in combination with other information. This model can support the use of FET data in a Weight-of-Evidence (WOE) approach for replacing the use of ju-venile fish. The BN predicted correct toxicity intervals for 69%–80% of the tested substances. The model was most sensitive to components quantified by toxicity data, and least sensitive to compo-nents quantified by expert knowledge. The model is publicly available through a web interface. Fur-ther development of this model should include additional lines of evidence, refinement of the discre-tisation, and training with a larger dataset for weighting of the lines of evidence. A refined version of this model can be a useful tool for predicting acute fish toxicity, and a contribution to more quantitative WOE approaches for ecotoxicology and environmental assessment more generally.

Repeatability and Reproducibility of the RTgill-W1 Cell Line Assay for Predicting Fish Acute Toxicity.

Predicting fish acute toxicity of chemicals in vitro is an attractive alternative method to the conventional approach using juvenile and adult fish. The rainbow trout (Oncorhynchus mykiss) cell line assay with RTgill-W1 cells has been designed for this purpose. It quantifies cell viability using fluorescent measurements for metabolic activity, cell- and lysosomal-membrane integrity on the same set of cells. Results from over 70 organic chemicals attest to the high predictive capacity of this test. We here report on the repeatability (intralaboratory variability) and reproducibility (interlaboratory variability) of the RTgill-W1 cell line assay in a round-robin study focusing on 6 test chemicals involving 6 laboratories from the industrial and academic sector. All participating laboratories were able to establish the assay according to preset quality criteria even though, apart from the lead laboratory, none had previously worked with the RTgill-W1 cell line. Concentration-response modeling, based on either nominal or geometric mean-derived measured concentrations, yielded effect concentrations (EC50) that spanned approximately 4 orders of magnitude over the chemical range, covering all fish acute toxicity categories. Coefficients of variation for intralaboratory and interlaboratory variability for the average of the 3 fluorescent cell viability measurements were 15.5% and 30.8%, respectively, which is comparable to other fish-derived, small-scale bioassays. This study therefore underlines the robustness of the RTgill-W1 cell line assay and its accurate performance when carried out by operators in different laboratory settings.

Extending the concept of predicting fish acute toxicity in vitro to the intestinal cell line RTgutGC.

Testing chemicals for fish acute toxicity is a legal requirement in many countries as part of environmental risk assessment. To reduce the numbers of fish used, substantial efforts have been focussed on alternative approaches. Prominently, the cell viability assay with the rainbow trout (Oncorhynchus mykiss) gill cell line, RTgill-W1, has been recognized, owing to its high predictive power and robustness. Like gills, the intestine is considered a major site of chemical uptake and biotransformation but, in contrast to gills, is expected to be exposed to rather hydrophobic chemicals, which enter the fish via food. In the present study, we therefore aimed to extend the cell bioassay to the rainbow trout epithelial cell line from intestine, RTgutGC. Using 16 hydrophobic and volatile chemicals from the fragrance palette, we showed that also the RTgutGC cell line can be used to predict fish acute toxicity of chemicals and yields intra-laboratory variability in line with other bioassays. By comparing the RTgutGC toxicity to a study employing the RTgill-W1 assay on the same group of chemicals, a fragrance specific relationship was established which reflects an almost perfect 1:1 relationship between in vitro and in vivo toxicity results. Thus, both cell lines can be used to predict fish acute toxicity, either by using the obtained in vivo-in vitro relationship or by taking the in vitro results at face value. We moreover demonstrate the derivation of non-toxic concentrations for downstream applications which rely on a healthy cell state, such as the assessment of biotransformation or chemical transfer.

QSAR for baseline toxicity and classification of specific modes of action of ionizable organic chemicals in the zebrafish embryo toxicity test

The fish embryo toxicity (FET) test with the zebrafish Danio rerio is widely used to assess the acute toxicity of chemicals thereby serving as animal alternative to the acute fish toxicity test. The minimal toxicity of neutral chemicals in the FET can be predicted with a previously published Quantitative Structure-Activity Relationship (QSAR) based on the liposome-water partition coefficient Klipw. Such a QSAR may serve to plan toxicity testing and to evaluate whether an observed effect is caused by a specific mode of action (MoA). The applicability domain of this QSAR was extended to ionizable organic chemicals (IOC) without any modification of slope and intercept simply by replacing the Klipw with the speciation-corrected liposome-water distribution ratio (Dlipw(pH)) as descriptor for the uptake into the embryo. FET LC50 values of IOCs were extracted from an existing FET database and published literature. IOCs were selected that are present concomitantly as neutral and charged, species, i.e., acids with an acidity constant pKa <10 and bases with pKa>5. IOCs were grouped according to their putative MoA of acute aquatic toxicity. The toxic ratios (TR) in the FET were derived by of the experimental FET-LC50 in comparison with the baseline toxicity QSAR. Baseline toxicants were confirmed to align well with the FET baseline toxicity QSAR (TR < 10). Chemicals identified to act as specific or reactive chemicals with the toxic ratio analysis in the FET test (TR > 10) were generally consistent with MoA classification for acute fish toxicity with a few exceptions that were suspected to have had issues with the stability of the pH during testing. One critical aspect for the effect analysis of ionizable chemicals is the pH, since the difference between pH and pKa determines the speciation and thereby the Dlipw(pH).

Development of classification models for predicting chronic toxicity of chemicals to Daphnia magna and Pseudokirchneriella subcapitata

ABSTRACTBoth the acute toxicity and chronic toxicity data on aquatic organisms are indispensable parameters in the ecological risk assessment priority chemical screening process (e.g. persistent, bioaccumulative and toxic chemicals). However, most of the present modelling actions are focused on developing predictive models for the acute toxicity of chemicals to aquatic organisms. As regards chronic aquatic toxicity, considerable work is needed. The major objective of the present study was to construct in silico models for predicting chronic toxicity data for Daphnia magna and Pseudokirchneriella subcapitata. In the modelling, a set of chronic toxicity data was collected for D. magna (21 days no observed effect concentration (NOEC)) and P. subcapitata (72 h NOEC), respectively. Then, binary classification models were developed for D. magna and P. subcapitata by employing the k-nearest neighbour method (k-NN). The model assessment results indicated that the obtained optimum models had high accuracy, sensitivity and specificity. The model application domain was characterized by the Euclidean distance-based method. In the future, the data gap for other chemicals within the application domain on their chronic toxicity for D. magna and P. subcapitata could be filled using the models developed here.