Toxicity of organic chemicals to Tetrahymena pyriformis: effect of polarity and ionization on toxicity

Assessing Aquatic Baseline Toxicity of Plastic-Associated Chemicals: Development and Validation of the Target Plastic Model

We developed a Target Plastic Model (TPM) to estimate the critical plastic burden of organic toxicants in five types of plastics, namely, polydimethylsiloxane (PDMS), polyoxymethylene (POM), polyacrylate (PA), low-density polyethylene (LDPE), and polyurethane ester (PU), following the Target Lipid Model (TLM) framework. By substituting the lipid-water partition coefficient in the TLM with plastic-water partition coefficients to create TPM, we demonstrated that the biomimetic nature of these plastic phases allows for the calculation of critical plastic burdens of toxicants, similar to the notion of critical lipid burdens in TLM. Following this approach, the critical plastic burdens of baseline (n = 115), less-inert (n = 73), and reactive (n = 75) toxicants ranged from 0.17 to 51.33, 0.04 to 26.62, and 1.00 × 10-6 to 6.78 × 10-4 mmol/kg of plastic, respectively. Our study showed that PDMS, PA, POM, PE, and PU are similar to biomembranes in mimicking the passive exchange of chemicals with the water phase. Using the TPM, median lethal concentration (LC50) values for fish exposed to baseline toxicants were predicted, and the results agreed with experimental values, with RMSE ranging from 0.311 to 0.538 log unit. Similarly, for the same data set of baseline toxicants, other widely used models, including the TLM (RMSE: 0.32-0.34), ECOSAR (RMSE: 0.35), and the Abraham Solvation Model (ASM; RMSE: 0.31), demonstrated comparable agreement between experimental and predicted values. For less inert chemicals, predictions were within a factor of 5 of experimental values. Comparatively, ASM and ECOSAR showed predictions within a factor of 2 and 3, respectively. The TLM based on phospholipid had predictions within a factor of 3 and octanol within a factor of 4, indicating that the TPM's performance for less inert chemicals is comparable to these established models. Unlike these methods, the TPM requires only the knowledge of plastic bound concentration for a given plastic phase to calculate baseline toxic units, bypassing the need for extensive LC50 and plastic-water partition coefficient data, which are often limited for emerging chemicals. Taken together, the TPM can provide valuable insights into the toxicities of chemicals associated with environmental plastic phases, assisting in selecting the best polymeric phase for passive sampling and designing better passive dosing techniques for toxicity experiments.

Biotoxicity dynamic change and key toxic organics identification of coal chemical wastewater along a novel full-scale treatment process

It is particularly important to comprehensively assess the biotoxicity variation of industrial wastewater along the treatment process for ensuring the water environment security. However, intensive studies on the biotoxicity reduction of industrial wastewater are still limited. In this study, the toxic organics removal and biotoxicity reduction of coal chemical wastewater (CCW) along a novel full-scale treatment process based on the pretreatment process-anaerobic process-biological enhanced (BE) process-anoxic/oxic (A/O) process-advanced treatment process was evaluated. This process performed great removal efficiency of COD, total phenol, NH4+-N and total nitrogen. And the biotoxicity variation along the treatment units was analyzed from the perspective of acute biotoxicity, genotixicity and oxidative damage. The results indicated that the effluent of pretreatment process presented relatively high acute biotoxicity to Tetrahymena thermophila. But the acute biotoxicity was significantly reduced in BE-A/O process. And the genotoxicity and oxidative damage to Tetrahymena thermophila were significantly decreased after advanced treatment. The polar organics in CCW were identified as the main biotoxicity contributors. Phenols were positively correlated with acute biotoxicity, while the nitrogenous heterocyclic compounds and polycyclic aromatic hydrocarbons were positively correlated with genotoxicity. Although the biotoxicity was effectively reduced in the novel full-scale treatment process, the effluent still performed potential biotoxicity, which need to be further explored in order to reduce environmental risk.

Impact of organic matter on transformation during thermal remediation of pyrene-contaminated substrates

Thermal remediation (TR) is a broadly applicable technology that is effective at removing volatile and semi-volatile contaminants from soil. However, TR can be costly and inefficient in practice, with underlying removal and transformation mechanisms poorly understood. To better understand the role organic matter plays in removal, a series of experiments was performed with a humic substance, humic modified silica, and a natural soil in the presence of pyrene from 100 to 500 °C and compared to prior experiments using pure minerals. Detection of by-products confirmed that pyrene was removed by transformation in addition to volatilization. Oxidation via hydroxyl radical formation and reductive hydrogenation were both indicated as possible reaction mechanisms promoted by organic matter. Because the presence of bulk water did not impact the extent of pyrene degradation or transformation, it is hypothesized that hydroxyl radicals were produced from soil organic matter functional groups, such as carboxyl and phenol groups, and possibly bound water at elevated temperatures in dry experiments. Additionally, the average oxidation state of carbon in detected by-products increased with temperature in experiments with humic modified silica and soil but not humic substance alone, though the extent of degradation did not significantly change. This shift in oxidation state may indicate that attachment of organic matter to another surface may increase interaction between reactive species. The results of this study show that contaminant transformation in soils during TR significantly contributes to removal, even at temperatures lower than those used in traditional treatment. This information will help to guide the design and operation of TR systems, potentially reducing energy requirements and highlighting the necessity of testing for transformation by-products.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs)

Recent years have seen a substantial growth in the adoption of machine learning approaches for the purposes of quantitative structure-activity relationship (QSAR) development. Such a trend has coincided with desire to see a shifting in the focus of methodology employed within chemical safety assessment: away from traditional reliance upon animal-intensive in vivo protocols, and towards increased application of in silico (or computational) predictive toxicology. With QSAR central amongst techniques applied in this area, the emergence of algorithms trained through machine learning with the objective of toxicity estimation has, quite naturally, arisen. On account of the pattern-recognition capabilities of the underlying methods, the statistical power of the ensuing models is potentially considerable-appropriate for the handling even of vast, heterogeneous datasets. However, such potency comes at a price: this manifesting as the general practical deficits observed with respect to the reproducibility, interpretability and generalisability of the resulting tools. Unsurprisingly, these elements have served to hinder broader uptake (most notably within a regulatory setting). Areas of uncertainty liable to accompany (and hence detract from applicability of) toxicological QSAR have previously been highlighted, accompanied by the forwarding of suggestions for "best practice" aimed at mitigation of their influence. However, the scope of such exercises has remained limited to "classical" QSAR-that conducted through use of linear regression and related techniques, with the adoption of comparatively few features or descriptors. Accordingly, the intention of this study has been to extend the remit of best practice guidance, so as to address concerns specific to employment of machine learning within the field. In doing so, the impact of strategies aimed at enhancing the transparency (feature importance, feature reduction), generalisability (cross-validation) and predictive power (hyperparameter optimisation) of algorithms, trained upon real toxicity data through six common learning approaches, is evaluated.

Aquatic toxicity and aquatic ecological risk assessment of wastewater-derived halogenated phenolic disinfection byproducts

Increasing number of wastewater-derived aliphatic and phenolic disinfection byproducts (DBPs) were discharged into aquatic environment with the discharge of disinfected wastewater. However, the currently available aquatic toxicity data and the aquatic ecological risk information of them are limited, especially for wastewater-derived phenolic DBPs. In this study, we investigated the acute toxicity of 7 phenolic DBPs that selected from the typical five groups of phenolic DBPs (2,4,6-trihalo-phenols, 2,6-dihalo-4-nitrophenols, 3,5-dihalo-4-hydroxybenzaldehydes, 3,5-dihalo-4-hydroxybenzoic acids and halo-salicylic acids) and 4 aliphatic DBPs to Gobiocypris rarus and also assessed their potential aquatic ecological risk. Experimental results indicated that the half lethal concentration (LC₅₀) values of 2,4,6-trihalo-phenols and 2,6-dihalo-4-nitrophenols ranged from 1 to 10 mg/L; While that of 3,5-dihalo-4-hydroxybenzaldehydes was between 10 and 100 mg/L, and 3,5-dihalo-4-hydroxybenzoic acids and halo-salicylic acids was >100 mg/L. The toxicity mode of action (MOA) identification results from three methods suggested that no clear and consistent MOA were obtained for those 11 DBPs currently. The species-specific aquatic toxicity analysis results highlighted that no aquatic species would be considered as the most sensitive species for all 11 DBPs. However, crustacean and fish were more sensitive than that of algae for most of tested compounds. Lastly, the aquatic ecological risk assessment results of those 11 DBPs revealed that all 7 phenolic and 2 aliphatic DBPs (2-bromoacetamide and bromodichloromethane) had low aquatic ecological risk, while dichloroacetic acid and dibromoacetonitrile had high aquatic ecological risk. The low environmental concentration was the main reason why high toxic phenolic DBPs (2,4,6-trihalo-phenols and 2,6-dihalo-4-nitrophenols) exhibited low ecological risk. Their ecological risk may increase with the increases of corresponding environmental concentration. Thus, more efforts should be made to determine other potential harmful effects of those high toxic phenolic DBPs and to minimize their potential ecological risk by taking appropriate measures.

Identification and regulation of ecotoxicity of polychlorinated naphthalenes to aquatic food Chain (green algae-Daphnia magna-fish)

Polychlorinated naphthalenes (PCNs) are widely distributed in the aquatic environment and can be transmitted through the food chain, which can amplify their toxic effects on human. To inhibit their transmission in the trophic level, this study aimed to predict the joint toxicity mechanism of polychlorinated naphthalenes (PCNs) to the key organisms and control scheme of its toxicity in the aquatic food chain (green algae-Daphnia magna-fish). The toxic effect grade and mode of action (MoA) of PCNs on the food chain were first predicted to guide the establishment of toxic mechanism model. QSAR models were constructed to quantify the mechanism of aquatic toxicity due to PCNs. The results showed the PCN compounds studied were highly toxic at all the trophic levels of the aquatic food chain. The binding ability of PCNs to the aquatic organisms was the main factor causing the toxicity of PCNs in the food chain, followed by electronic parameters E_HOMO and E_LUMO. Moreover, the binding ability between PCNs and food chain receptors was related to the molecular hydrophobicity, the hydrophobicity can be changed by adjusting the ability of PCNs to be adsorbed by sediment and their chlorine substituents, while the effect of PCNs electronic parameters (E_HOMO and E_LUMO) can be adjusted by their solvation effect. In addition, the macro-control scheme of PCN-based aquatic toxicity mechanism was established, and the molecular dynamics (MD) simulation confirmed its effectiveness and accessibility. The MD simulation showed the inhibition effect of nutrition-grade toxicity in the food chain was significant when the external stimulation conditions of solvation, anaerobic dechlorination and molecular adsorption were improved, with the decrease range of 66.26-263.16%, 198.93-323.98% and 189.24-549.48%, respectively. This work reveals new insights into the mechanism of PCNs joint toxicity to aquatic ecosystem food chain and develop appropriate strategies for its ecological risk management.

Chemometric modeling of aquatic toxicity of contaminants of emerging concern (CECs) in Dugesia japonica and its interspecies correlation with daphnia and fish: QSTR and QSTTR approaches

The contaminants of emerging concern (CEC) are universally detected in surface water and soil. They can affect the wild life, and their subsequent translocation through the food chain can affect human health, which is an issue of serious concern. Very few amounts of ecotoxicological data are available on the environmental behavior and ecotoxicity of CEC, thus modeling approaches are essential to bridge the existing gap in experimental data. In this present study, we have developed quantitative structure-toxicity relationship (QSTR) models using a data set of 75 compounds for the prediction of aquatic ecotoxicity of CECs on fresh water planarian (Dugesia japonica) by partial least squares (PLS) regression algorithm using simple molecular descriptors selected by genetic algorithm approach. We also explore the correlations between toxicity against D. japonica and those against daphnia (D. magna) and fish (P. promelas), and these were improved on addition of a few molecular descriptors (B08[C-O] and B09[N-O] in case of daphnia and C-006 and H-052 in case of fish) which allowed us to develop predictive interspecies quantitative structure toxicity-toxicity relationship (QSTTR) models, allowing to extrapolate data from one endpoint to another endpoint. The QSTR (Q²_LOO ranging from 0.630 to 0.720 and R²_pred ranging from 0.723 to 0.798) and QSTTR (Q²_LOO = 0.60 and 0.67, R²_pred = 0.88 and 0.84) models have desirable statistical qualities and acceptable internal and external validation measures, meeting rigorous criteria of different validation metrics and showing acceptability for regulatory purposes as proposed by Organization for Economic Cooperation and Development (OECD). Consensus predictions were also performed based on multiple models generated in this study by using the "Intelligent Consensus Predictor" (ICP) tool to enhance the prediction quality for external set compounds.

QSAR models for predicting acute toxicity of pesticides in rainbow trout using the CORAL software and EFSA's OpenFoodTox database

Optimal (flexible) descriptors were used to establish quantitative structure - activity relationships (QSAR) for toxicity of pesticides (n=116) towards rainbow trout. A heterogeneous set of hundreds of pesticides has been used, taken from the EFSA's chemical Hazards Database: OpenFoodTox. Optimal descriptors are preparing from simplified molecular input-line entry system (SMILES). So-called, correlation weights of different fragments of SMILES are calculating by the Monte Carlo optimization procedure where correlation coefficient between endpoint and optimal descriptor plays role of the target function. Having maximum of the correlation coefficient for the training set, one can suggest that the optimal descriptor calculated with these correlation weights can correlate with endpoint for external validation set. This approach was checked up with three different distributions into the training (≈85%) set and external validation (≈15%) set. The statistical characteristics of these models are (i) for training set correlation coefficient (r²) ranges 0.72-0.81, and root mean squared error (RMSE) ranges 0.54-1.25; (ii) for external (validation) set r² ranges 0.74-0.84; and RMSE ranges 0.64-0.75. Computational experiments have shown that presence of chlorine, fluorine, sulfur, and aromatic fragments is promoter of increase for the toxicity.

Building up a QSAR model for toxicity toward Tetrahymena pyriformis by the Monte Carlo method: A case of benzene derivatives

Data on toxicity toward Tetrahymena pyriformis is indicator of applicability of a substance in ecologic and pharmaceutical aspects. Quantitative structure-activity relationships (QSARs) between the molecular structure of benzene derivatives and toxicity toward T. pyriformis (expressed as the negative logarithms of the population growth inhibition dose, mmol/L) are established. The available data were randomly distributed three times into the visible training and calibration sets, and invisible validation sets. The statistical characteristics for the validation set are the following: r(2)=0.8179 and s=0.338 (first distribution); r(2)=0.8682 and s=0.341 (second distribution); r(2)=0.8435 and s=0.323 (third distribution). These models are built up using only information on the molecular structure: no data on physicochemical parameters, 3D features of the molecular structure and quantum mechanics descriptors are involved in the modeling process.

The discrimination of excess toxicity from baseline effect: effect of bioconcentration

Toxic ratio TR is a valuable tool in the discrimination of excess toxicity from baseline effect. Although some authors realized that internal effect concentration or critical body residual (CBR) calculated from bioconcentration factor (BCF) should be used in the TR, the effect of BCF on the discrimination of excess toxicity from baseline effect has not been investigated. In this paper, 951 acute toxicity data to fish (LC50) and 1088 BCFs were used to investigate the relationship between TR and BCF. The results showed that some compounds identified as reactive compounds exhibit excess toxicity, but some do not. BCF is closely related to TR and can significantly affect the TR value. The real excess toxicity which is used to identify reactive chemicals from baseline should be based on the toxic ratio of internal effect concentrations, rather than on the ratio of external effect concentrations, TR. The use of LC50 alone to determine TR can result in errors in TR because toxicokinetics (as estimated by the BCF) are ignored. The foundation in the discrimination of excess toxicity from baseline effect is based on the linear relationship between log BCF and hydrophobicity expressed as log KOW. However, log BCF is not linearly related with log KOW for all the compounds. The BCFs with log KOW >7 or <0 are either overestimated or underestimated by the linear baseline BCF model. Parallel lines are observed from calculated log CBR values for baseline and less inert compounds. The log BCF values overestimated or underestimated by log KOW from the baseline BCF model can result in mis-prediction and mis-classification among baseline, less inert and reactive compounds.

Comparative developmental toxicity of new aromatic halogenated DBPs in a chlorinated saline sewage effluent to the marine polychaete Platynereis dumerilii

Using seawater for toilet flushing may introduce high levels of bromide and iodide into a city's sewage treatment works, and result in the formation of brominated and iodinated disinfection byproducts (DBPs) during chlorination to disinfect sewage effluents. In a previous study, the authors' group has detected the presence of many brominated DBPs and identified five new aromatic brominated DBPs in chlorinated saline sewage effluents. The presence of brominated DBPs in chlorinated saline effluents may pose adverse implications for marine ecology. In this study, besides the detection and identification of another seven new aromatic halogenated DBPs in a chlorinated saline sewage effluent, their developmental toxicity was evaluated using the marine polychaete Platynereis dumerilii. For comparison, the developmental toxicity of some commonly known halogenated DBPs was also examined. The rank order of the developmental toxicity of 20 halogenated DBPs was 2,5-dibromohydroquinone > 2,6-diiodo-4-nitrophenol ≥ 2,4,6-triiodophenol > 4-bromo-2-chlorophenol ≥ 4-bromophenol > 2,4-dibromophenol ≥ 2,6-dibromo-4-nitrophenol > 2-bromo-4-chlorophenol > 2,6-dichloro-4-nitrophenol > 2,4-dichlorophenol > 2,4,6-tribromophenol > 3,5-dibromo-4-hydroxybenzaldehyde > bromoform ≥ 2,4,6-trichlorophenol > 2,6-dibromophenol > 2,6-dichlorophenol > iodoacetic acid ≥ tribromoacetic acid > bromoacetic acid > chloroacetic acid. On the basis of developmental toxicity data, a quantitative structure-activity relationship (QSAR) was established. The QSAR involved two physical-chemical property descriptors (log P and pKa) and two electronic descriptors (the lowest unoccupied molecular orbital energy and the highest occupied molecular orbital energy) to indicate the transport, biouptake, and biointeraction of these DBPs. It can well predict the developmental toxicity of most of the DBPs tested.

Prediction of acute aquatic toxicity in Tetrahymena pyriformis--'Eco-Derek', a knowledge-based system approach

A 'proof-of-concept' version of a software tool for making transparent predictions of acute aquatic toxicity has been developed. It is primarily limited to semi-quantitative predictions in one species, the ciliated protozoan, Tetrahymena pyriformis. A freely available system, 'Eco-Derek', was derived by adapting a well-established, knowledge-based structure-activity and reasoning platform (Derek for Windows, Lhasa Limited). The Derek reasoning code was modified to express potency rather than confidence. Structure-activity relationship (SAR) development utilised a curated version of a published dataset, supplemented with the CADASTER Challenge datasets. Forty-five structural alerts were produced. The dependence on log P was examined for each alert and entered into the system as qualitative reasoning rules specifying the predicted potency as Very Low, Low, Moderate, High or Very High. Evaluation studies showed: (a) moderate accuracy for the training set but low accuracy for an external test set; (b) non-linearity in the toxicity-log P relationship for chemicals without identified structural alerts; (c) insufficient differentiation of substituent effects in some of the reactivity-based structural alerts resulting in too few chemicals predicted with Very High toxicity; and (d) the need for additional structural alerts covering polar narcosis and less common reactive or metabolically activated chemical functionality.

Comparison of Tetrahymena pyriformis toxicity based on hydrophobicity, polarity, ionization and reactivity of class-based compounds

A toxicity data set containing the toxicities of 970 hydrophobic, polar and ionizable, nitro substituted and α,β-unsaturated compounds to Tetrahymena pyriformis was classified into different groups based on the structure and substituted functional groups. Polar, ionizable and reactive compounds exhibit greater toxicity as compared with the non-polar hydrophobic compounds. Step-by-step analysis was carried out between the toxicity and descriptors representing hydrophobicity, polarity/polarizability, ionization and reactivity of compounds. Significant relationships were developed between the toxicity and these descriptors for the compounds. The models developed are simple, interpretable and transparent, using a small number of descriptors that may reflect the interactions of chemicals with the biological macromolecules at the target sites. Hydrophobic parameter log P reflects bio-uptake process compounds. Polarity/polarizability descriptor S reflects the interaction of hydrophilic residues of polar chemicals with biological macromolecules. The fractions of ionized (F (i)) and neutral (F (0)) forms calculated from pK (a) reflect the interactions of ionizable compounds with the macromolecules and effect of ionization of ionizable compounds on the bio-uptake process, respectively. A successful single model was developed by using the descriptors log P, S, F (i) and log F (0) for non-polar, polar and ionizable compounds.

Acute toxicity of chlorobenzenes in tetrahymena: estimated by microcalorimetry and mechanism

The toxicity of chlorobenzenes to Tetrahymena growth metabolism was studied by microcalorimetry. The growth constant (k), peak time (T) and generation times (T(G)) were calculated. IC(50) of chlorobenzenes was obtained through the kinetic parameters. The results suggested that the order of toxicity was 1,2,4-trichlorobenzene>o-dichlorobenzene>p-dichlorobenzene>m-dichlorobenzene>chlorobenzene. ATR-FTIR spectra revealed that amide groups and PO(2)(-) of the phospholipid phospho-diester, both in the hydrophobic end exposed to the outer layer, were the easiest to be damaged. The relationship between IC(50) and chemicals structure parameters (E(LUMO), E(HOMO), logK(OW), ∑Q(R), ΔQ(πR) and ΔE), indicated that the more chlorine atoms were substituted, the greater the toxicity was. Chlorobenzenes have toxicity of non-polar narcosis. Their toxicity is proportional to their concentrations at the site of action, and caused by membrane perturbation.

Application of a computational model for Michael addition reactivity in the prediction of toxicity to Tetrahymena pyriformis

A computational model to predict acute aquatic toxicity to the ciliate Tetrahymena pyriformis has been developed. A general prediction of toxicity can be based on three consecutive steps: 1. Identification of a potential reactive mechanism via structural alerts; 2. Confirmation and quantification of (bio)chemical reactivity; 3. Establishing a relationship between calculated reactivity and toxicity. The method described herein uses a combination of a reactive toxicity (RT) model, including computed kinetic rate constants for adduct formation (log k) via a Michael acceptor mechanism of action, and baseline toxicity (BT), modelled by hydrophobicity (octanol-water partition coefficient). The maximum of the RT and BT values defines acute toxicity for a particular compound. The reactive toxicity model is based on site-specific steric and quantum chemical ground state electronic properties. The performance of the model was examined in terms of predicting the toxicity of 106 potential Michael acceptor compounds covering several classes of compounds (aldehydes, ketones, esters, heterocycles). The advantages of the computational method are described. The method allows for a closer and more transparent mechanistic insight into the molecular initiating events of toxicological endpoints.

QSTR with extended topochemical atom (ETA) indices. 14. QSAR modeling of toxicity of aromatic aldehydes to Tetrahymena pyriformis

Aldehydes are a toxic class of chemicals causing severe health hazards. In this background, quantitative structure-toxicity relationship (QSTR) models have been developed in the present study using Extended Topochemical Atom (ETA) indices for a large group of 77 aromatic aldehydes for their acute toxicity against the protozoan ciliate Tetrahymena pyriformis. The ETA models have been compared with those developed using various non-ETA topological indices. Attempt was also made to include the n-octanol/water partition coefficient (logK(o/w)) as an additional descriptor considering the importance of hydrophobicity in toxicity prediction. Thirty different models were developed using different chemometric tools. All the models have been validated using internal validation and external validation techniques. The statistical quality of the ETA models was found to be comparable to that of the non-ETA models. The ETA models have shown the important effects of steric bulk, lipophilicity, presence of electronegative atom containing substituents and functionality of the aldehydic oxygen to the toxicity of the aldehydes. The best ETA model (without using logK(o/w)) shows encouraging statistical quality (Q(int)(2)=0.709,Q(ext)(2)=0.744). It is interesting to note that some of the topological models reported here are better in statistical quality than previously reported models using quantum chemical descriptors.

Toxicity of organic pollutants to seven aquatic organisms: effect of polarity and ionization

The toxicity of organic chemicals to Vibrio fischeri, river bacteria, algae, Daphnia magna and fishes were analysed. The results showed that the toxicity of chemicals to narcotics was dependent on hydrophobicity. A single model for both polar and non-polar narcotics was developed by inclusion of a polarity descriptor as well as the hydrophobic parameter. The highly hydrophobic polar narcotics could be treated as non-polar narcotics because their polar functional group(s) make(s) a relatively small contribution to polarity as compared with their hydrophobicity. In order to investigate the toxic mechanism of action for reactive compounds, the response-surface approach was used to develop models derived from easily calculated descriptors. The stepwise analysis selected the octanol/water partition coefficient and a polarity descriptor to parameterize bio-uptake and reactivity, respectively, for seven species. Benzoic acids can be easily absorbed into the unicellular bacteria, but this is not the case for multicellular D. magna and fish. Their toxicity to V. fischeri is much higher than that to D. magna and carp. Regression analysis was performed based on the model that we developed for ionizable compounds. Good correlations were observed by introducing the correction factor for ionizable compounds. The toxic mechanisms are discussed.