Modelling bioconcentration of pesticides in fish using biopartitioning micellar chromatography

Pesticides and Parabens Contaminating Aquatic Environment: Acute and Sub-Chronic Toxicity towards Early-Life Stages of Freshwater Fish and Amphibians

Pesticides and personal care products are two very important groups of contaminants posing a threat to the aquatic environment and the organisms living in it.. Therefore, this study aimed to describe the effects of widely used pesticides and parabens on aquatic non-target biota such as fish (using model organisms Danio rerio and Cyprinus carpio) and amphibians (using model organism Xenopus laevis) using a wide range of endpoints. The first part of the experiment was focused on the embryonal toxicity of three widely used pesticides (metazachlor, prochloraz, and 4-chloro-2-methyl phenoxy acetic acid) and three parabens (methylparaben, propylparaben, and butylparaben) with D. rerio, C. carpio, and X. laevis embryos. An emphasis was placed on using mostly sub-lethal concentrations that are partially relevant to the environmental concentrations of the substances studied. In the second part of the study, an embryo-larval toxicity test with C. carpio was carried out with prochloraz using concentrations 0.1, 1, 10, 100, and 1000 µg/L. The results of both parts of the study show that even the low, environmentally relevant concentrations of the chemicals tested are often able to affect the expression of genes that play either a prominent role in detoxification and sex hormone production or indicate cell stress or, in case of prochloraz, to induce genotoxicity.

Potential Toxicity Risk Assessment and Priority Control Strategy for PAHs Metabolism and Transformation Behaviors in the Environment

In this study, 16 PAHs were selected as the priority control pollutants to summarize their environmental metabolism and transformation processes, including photolysis, plant degradation, bacterial degradation, fungal degradation, microalgae degradation, and human metabolic transformation. Meanwhile, a total of 473 PAHs by-products generated during their transformation and degradation in different environmental media were considered. Then, a comprehensive system was established for evaluating the PAHs by-products' neurotoxicity, immunotoxicity, phytotoxicity, developmental toxicity, genotoxicity, carcinogenicity, and endocrine-disrupting effect through molecular docking, molecular dynamics simulation, 3D-QSAR model, TOPKAT method, and VEGA platform. Finally, the potential environmental risk (phytotoxicity) and human health risks (neurotoxicity, immunotoxicity, genotoxicity, carcinogenicity, developmental toxicity, and endocrine-disrupting toxicity) during PAHs metabolism and transformation were comprehensively evaluated. Among the 473 PAH's metabolized and transformed products, all PAHs by-products excluding ACY, CHR, and DahA had higher neurotoxicity, 152 PAHs by-products had higher immunotoxicity, and 222 PAHs by-products had higher phytotoxicity than their precursors during biological metabolism and environmental transformation. Based on the TOPKAT model, 152 PAH by-products possessed potential developmental toxicity, and 138 PAH by-products had higher genotoxicity than their precursors. VEGA predicted that 247 kinds of PAH derivatives had carcinogenic activity, and only the natural transformation products of ACY did not have carcinogenicity. In addition to ACY, 15 PAHs produced 123 endocrine-disrupting substances during metabolism and transformation. Finally, the potential environmental and human health risks of PAHs metabolism and transformation products were evaluated using metabolic and transformation pathway probability and degree of toxic risk as indicators. Accordingly, the priority control strategy for PAHs was constructed based on the risk entropy method by screening the priority control pathways. This paper assesses the potential human health and environmental risks of PAHs in different environmental media with the help of models and toxicological modules for the toxicity prediction of PAHs by-products, and thus designs a risk priority control evaluation system for PAHs.

The ECOTOXicology Knowledgebase: A Curated Database of Ecologically Relevant Toxicity Tests to Support Environmental Research and Risk Assessment

The need for assembled existing and new toxicity data has accelerated as the amount of chemicals introduced into commerce continues to grow and regulatory mandates require safety assessments for a greater number of chemicals. To address this evolving need, the ECOTOXicology Knowledgebase (ECOTOX) was developed starting in the 1980s and is currently the world's largest compilation of curated ecotoxicity data, providing support for assessments of chemical safety and ecological research through systematic and transparent literature review procedures. The recently released version of ECOTOX (Ver 5, www.epa.gov/ecotox) provides single-chemical ecotoxicity data for over 12,000 chemicals and ecological species with over one million test results from over 50,000 references. Presented is an overview of ECOTOX, detailing the literature review and data curation processes within the context of current systematic review practices and discussing how recent updates improve the accessibility and reusability of data to support the assessment, management, and research of environmental chemicals. Relevant and acceptable toxicity results are identified from studies in the scientific literature, with pertinent methodological details and results extracted following well-established controlled vocabularies and newly extracted toxicity data added quarterly to the public website. Release of ECOTOX, Ver 5, included an entirely redesigned user interface with enhanced data queries and retrieval options, visualizations to aid in data exploration, customizable outputs for export and use in external applications, and interoperability with chemical and toxicity databases and tools. This is a reliable source of curated ecological toxicity data for chemical assessments and research and continues to evolve with accessible and transparent state-of-the-art practices in literature data curation and increased interoperability to other relevant resources. Environ Toxicol Chem 2022;41:1520-1539. © 2022 SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Dissipation and Distribution of Picarbutrazox Residue Following Spraying with an Unmanned Aerial Vehicle on Chinese Cabbage (Brassica campestris var. pekinensis)

We assessed the residual distribution and temporal trend of picarbutrazox sprayed by agricultural multicopters on Chinese cabbage and considered fortification levels and flying speeds. In plot 2, 14 days after the last spraying, the residues decreased by ~91.3% compared with those in the samples on day 0. The residues in the crops decreased by ~40.8% of the initial concentration owing to growth (dilution effect) and by ~50.6% after excluding the dilution effect. As the flight speed increased, picarbutrazox residues decreased (p < 0.05, least significant deviation [LSD]). At 2 m s-1 flight speed, the residual distribution differed from the dilution rate of the spraying solution. The average range of picarbutrazox residues at all sampling points was 0.007 to 0.486, below the limit of quantitation -0.395, 0.005-0.316, and 0.005-0.289 mg kg-1 in plots 1, 2, 3, and 4, respectively, showing significant differences (p < 0.05, LSD). These results indicated that the residual distribution of picarbutrazox sprayed by using a multicopter on the Chinese cabbages was not uniform. However, the residues were less than the maximum residue limit in all plots. Accordingly, picarbutrazox was considered to have a low risk to human health if it was sprayed on cabbage according to the recommended spraying conditions.

In silico local QSAR modeling of bioconcentration factor of organophosphate pesticides

The persistent and accumulative nature of the pesticide of indiscriminate use emerged as ecotoxicological hazards. The bioconcentration factor (BCF) is one of the key elements for environmental assessments of the aquatic compartment. Limitations of prediction accuracy of global model facilitate the use of local predictive models in toxicity modeling of emerging compounds. The BCF data of diverse organophosphate (n = 55) was collected from the Pesticide Properties Database and used as a model data set in the present study to explore physicochemical properties and structural alert concerning BCF. The structures were downloaded from Pubchem, ChemSpider database. Two splitting techniques (biological sorting and structure-based) were used to divide the whole dataset into training and test set compounds. The QSAR study was carried out with two-dimensional descriptors (2D) calculated from PaDEL by applying genetic algorithm (GA) as chemometric tools using QSARINS software. The models were statistically robust enough both internally as well as externally (Q2: 0.709-0.722, Q2 Ext: 0.717-0.903, CCC: 0.857-0.880). Overall molecular mass, presence of fused, and heterocyclic ring with electron-withdrawing groups affect the BCF value. The developed models reflected extended applicability domain (AD) and reliable predictions than the reported models for the studied chemical class. Finally, predictions of unknown organophosphate pesticides and the toxic nature of unknown organophosphate pesticides were commented on. These findings may be useful for the scientific community in prioritizing high potential pesticides of organophosphate class.

Immobilized artificial membrane chromatography as a tool for the prediction of ecotoxicity of pesticides

The potential of Immobilized Artificial Membrane (IAM) chromatography to predict ecotoxicological endpoints of pesticides was investigated. For this purpose, retention factors of 39 structurally-diverse pesticides were measured on an IAM stationary phase. A representative test set of 6 pesticides was carefully selected. The training set, involving the remaining pesticides for which experimental data were available, served to establish linear IAM models with LC₅₀/EC₅₀ values in a series of aquatic organisms involving Rainbow Trout, Fathead Minnow, Bluegill Sunfish, Sheepshead Minnow, Eastern Oyster and Water Flea as well as LD₅₀ values in honey bee, compiled from literature sources. For reasons of comparison, corresponding models were derived by replacing IAM retention factors with octanol-water partition coefficients (logP). Considering the similar regression equations obtained for the 4 fish species, general models to predict toxicity in fish were established. Most models were improved upon inclusion of additional physicochemical parameters. The positive contribution of Molecular Weight to ecotoxicity along with the positive sign of hydrogen bond indices in most cases implies that toxic action is manifested mainly by accumulation on the membrane rather than through diffusion across them. IAM models are generally followed by better statistics and superior predictive performance than those based on experimental or computed logP. Predictions based on IAM chromatography were comparable or even superior with those performed by EPI Suite Software. Hence, IAM retention factors are suggested as promising indices in order to screen or rank chemicals with respect to their ecotoxicological risk, especially in the case of new entities.

Integrated Decision-tree Testing Strategies for Environmental Toxicity with Respect to the Requirements of the EU REACH Legislation

Liverpool John Moores University and FRAME recently conducted a research project sponsored by Defra on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with the REACH system. This paper focuses on the prospects for using alternative methods (both in vitro and in silico) for environmental (aquatic) toxicity testing. The manuscript reviews tests based on fish cells and cell lines, fish embryos, lower organisms, and the many expert systems and QSARs for aquatic toxicity testing. Ways in which reduction and refinement measures can be used are also discussed, including the Upper Threshold Concentration -- Step Down (UTC) approach, which has recently been retrospectively validated by ECVAM and subsequently endorsed by the ECVAM Scientific Advisory Committee (ESAC). It is hoped that the application of this approach could reduce the number of fish used in acute toxicity studies by around 65-70%. Decision-tree style integrated testing strategies are also proposed for acute aquatic toxicity and chronic toxicity (including bioaccumulation), followed by a number of recommendations for the future facilitation of aquatic toxicity testing with respect to environmental risk assessment.

Shuffling cross-validation-bee algorithm as a new descriptor selection method for retention studies of pesticides in biopartitioning micellar chromatography

Bee algorithm (BA) is an optimization algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution which can be proposed to feature selection. In this paper, shuffling cross-validation-BA (CV-BA) was applied to select the best descriptors that could describe the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides. Six descriptors were obtained using BA and then the selected descriptors were applied for model development using multiple linear regression (MLR). The descriptor selection was also performed using stepwise, genetic algorithm and simulated annealing methods and MLR was applied to model development and then the results were compared with those obtained from shuffling CV-BA. The results showed that shuffling CV-BA can be applied as a powerful descriptor selection method. Support vector machine (SVM) was also applied for model development using six selected descriptors by BA. The obtained statistical results using SVM were better than those obtained using MLR, as the root mean square error (RMSE) and correlation coefficient (R) for whole data set (training and test), using shuffling CV-BA-MLR, were obtained as 0.1863 and 0.9426, respectively, while these amounts for the shuffling CV-BA-SVM method were obtained as 0.0704 and 0.9922, respectively.

Linear and nonlinear models for predicting fish bioconcentration factors for pesticides

This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure-property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation.

Quantitative modeling of bioconcentration factors of carbonyl herbicides using multivariate image analysis

The bioconcentration factor (BCF) is an important parameter used to estimate the propensity of chemicals to accumulate in aquatic organisms from the ambient environment. While simple regressions for estimating the BCF of chemical compounds from water solubility or the n-octanol/water partition coefficient have been proposed in the literature, these models do not always yield good correlations and more descriptive variables are required for better modeling of BCF data for a given series of organic pollutants, such as some herbicides. Thus, the logBCF values for a set of carbonyl herbicides comprising amide, urea, carbamate and thiocarbamate groups were quantitatively modeled using multivariate image analysis (MIA) descriptors, derived from colored image representations for chemical structures. The logBCF model was calibrated and vigorously validated (r(2) = 0.79, q(2) = 0.70 and rtest(2) = 0.81), providing a comprehensive three-parameter linear equation after variable selection (logBCF = 5.682 - 0.00233 × X9774 - 0.00070 × X813 - 0.00273 × X5144); the variables represent pixel coordinates in the multivariate image. Finally, chemical interpretation of the obtained models in terms of the structural characteristics responsible for the enhanced or reduced logBCF values was performed, providing key leads in the prospective development of more eco-friendly synthetic herbicides.

in vitro and in silico determination of oral, jejunum and Caco-2 human absorption of fatty acids and polyphenols. Micellar liquid chromatography

In this investigation chosen saturated, mono- and polyunsaturated fatty acids as well as polyphenols have been analyzed. The main aim of this study was to determine oral, jejunum and Caco-2 human absorption of chosen fatty acids and polyphenols using in vitro and in silico methods. For in vitro determination of human drug absorption, the usefulness of Micellar Liquid Chromatography (MLC) with mobile phases containing different surfactants (including Brij35-Biopartitioning Micellar Chromatography (BMC)) has been confirmed. On the basis of Foley's equation, 1/k vs. CM correlations for the tested compounds have been done. Satisfactory linearity of the relationships was found over the whole eluents composition range studied with R(2) approximately 0.99 in each case. Moreover, the analyte-micelle association constants (Kma) from Foley's equation have been compared for different micellar environments, containing Brij35, SDS and CTAB as a main component of micellar mobile phases. Completely new models describing human oral as well as Caco-2 and jejunum absorption have been constructed and compared with the cited models. These models are based on the Abraham descriptors and lipophilicity parameters as well as steric descriptors. Furthermore, many different correlations between physicochemical parameters and human intestinal absorption have been done, e.g. the correlation between human jejunum permeability estimated in silico and received using LSER parameters was excellent (R(2) nearly 0.99). Chromatographic parameters have been collated with steric, electronic and physicochemical ones using QRAR (Quantitative Retention - Activity Relationships) and QSAR (Quantitative Structure - Activity Relationships) models. Moreover, retention BMC data have been compared with lipophilicity parameter logPo/w (n-octanol-water partition coefficient). The influence of lipophilicity on oral absorption (%) has been checked. The correlation between predicted oral absorption (%) and logPo/w has been done. Obtained R(2) was 0.82. On the basis of chromatographic, lipophilicity, steric and different physicochemical parameters, the principal components analysis (PCA) has been done.

Fast profiling ecotoxicity and skin permeability of benzophenone ultraviolet filters using biopartitioning micellar chromatography based on penetrable silica spheres

Penetrable silica possesses hierarchical pores, mesopores and penetrable macropores, offering fast mass transfer, satisfactory mechanical strength as well as low column pressure. In the present study, penetrable octadecyl-bonded silica (ODS) was for the first time used as biopartitioning micellar chromatography (BMC) stationary phase to profile ecotoxicity and skin permeability of benzophenone UV-filters. Mobile phase (MP) pH and concentration of polyoxyethylene(23)lauryl ether in the MP were systematically studied. Quantitative retention-activity relationships (QRARs) model was established to correlate retention factors (k) on BMC with bioconcentration factor (BCF) and transdermal rate (TR) of UV-filters. Coefficient of determination (r(2)) of the QRARs model between log BCF and log k were 0.9398-0.9753, while r(2) between TR and log k were 0.7569-0.8434, which demonstrated satisfactory predictive ability of the methodology. It was a powerful tool for fast screening by combining penetrable ODS with BMC, and avoiding column blockage often occurring in BMC.

The use of biopartitioning micellar chromatography and immobilized artificial membrane column for in silico and in vitro determination of blood-brain barrier penetration of phenols

Biopartitioning Micellar Chromatography (BMC) is a mode of micellar liquid chromatography that uses C18 stationary phases and micellar mobile phases of Brij35 under adequate experimental conditions and can be useful to mimic human drug absorption, blood-brain barrier distribution or partitioning processes in biological systems. BMC system can be useful in constructing good predictive models because the characteristics of the BMC system are similar to biological barriers and extracellular fluids. Immobilized Artificial Membrane (IAM) chromatography uses stationary phase which consists of a monolayer of phosphatidylcholine covalently immobilized on an inert silica support. IAM columns are thought to mimic very closely a membrane bilayer and are used in a HPLC system with a physiological buffer as eluent. In this paper the usefulness of BMC and IAM system for in silico and in vitro determination of blood-brain barrier (BBB) penetration of phenols has been demonstrated. The most important pharmacokinetic parameters of brain have been obtained for the determination of BBB penetration, i.e. BBB permeability - surface area product (PS), usually given as a logPS, brain/plasma equilibration rate (log(PS×fu,brain)) and fraction unbound in plasma (Fu). Moreover, the relationships between retention of eighteen phenols and different parameters of molecular size, lipophilicity and BBB penetration were studied. Extrapolated to pure water values of the logarithms of retention factors (logkw) have been compared with the corresponding octanol-water partition coefficient (logPo-w) values of the solutes. In addition, different physicochemical parameters from Foley's equation for BMC system have been collated with the chromatographic data. The Linear Solvation Energy Relationship (LSER) using Abraham model for the describing of phenols penetration across BBB has been used. Four equations were developed as a multiple linear regression using retention data from IAM and BMC system (QRAR models) and molecular volume parameter (Vm) and Abraham descriptors to correlate the logBB values. Moreover, in order to establish the relationships between different variables, the principal components analysis (PCA) has been done. The results of PCA were obtained using chromatographic data from IAM and BMC systems as well as from the structures of tested phenols. The four parameters: logkwIAM(exp), logkwBMC(exp), analyte-micelle association constant (Kma) and logPo-w have been checked.

Bioconcentration of pesticides in zebrafish eleutheroembryos (Danio rerio)

The feasibility of a bioaccumulation test based on the use of zebrafish eleutheroembryos as an alternative to adult-individual-based approaches for REACH application has been evaluated for three test compounds, chlorpyrifos, dicofol and atrazine. Following the OECD 305 guidelines, zebrafish eleutheroembryos (72 h after hatching, hpf) were separately exposed to the investigated pesticides at two nominal concentrations below 1% of its corresponding LC(50). The uptake experiments lasted for 48 h. Then, the exposure medium was replaced by a non-contaminated medium for depuration experiments (up to 72 h). Zebrafish eleutheroembryos (larvae 144 hpf, i.e. at the end of the depuration step) and their corresponding exposure media was sampled at ten different times during each experiment and the concentration of the investigated pesticide determined in both the organisms and in the exposure medium. The experimentally determined pesticide accumulation profiles in the eleutheroembryos demonstrated that atrazine has a very fast accumulation kinetic, reaching steady sate (SS) within 24h. Chlorpyrifos and dicofol did not reach the SS within the 48-h uptake experiments although they exhibit higher accumulations than the former pesticide. Two toxicokinetic models were used to calculate the bioconcentration factor (BCF) of the studied pesticide in zebrafish eleutheroembryos. In the former, the BCF was calculated under SS conditions (BCF(SS)). The second was used when the compounds did not reach the SS during the uptake experiment (BCF(k)). Log BCF values of 3.55 and 3.84 for chlorpyrifos; 0.6 and 1.17 for atrazine, and 3.90 for dicofol were experimentally calculated at selected exposure concentrations. These values have been compared with those reported in related bioaccumulation studies and official databases.

Rapid quantification of pharmaceuticals and pesticides in passive samplers using ultra high performance liquid chromatography coupled to high resolution mass spectrometry

The presence of both pharmaceuticals and pesticides in the aquatic environment has become a well-known environmental issue during the last decade. An increasing demand however still exists for sensitive and reliable monitoring tools for these rather polar contaminants in the marine environment. In recent years, the great potential of passive samplers or equilibrium based sampling techniques for evaluation of the fate of these contaminants has been shown in literature. Therefore, we developed a new analytical method for the quantification of a high number of pharmaceuticals and pesticides in passive sampling devices. The analytical procedure consisted of extraction using 1:1 methanol/acetonitrile followed by detection with ultra-high performance liquid chromatography coupled to high resolution and high mass accuracy Orbitrap mass spectrometry. Validation of the analytical method resulted in limits of quantification and recoveries ranging between 0.2 and 20 ng per sampler sheet and between 87.9 and 105.2%, respectively. Determination of the sampler-water partition coefficients of all compounds demonstrated that several pharmaceuticals and most pesticides exert a high affinity for the polydimethylsiloxane passive samplers. Finally, the developed analytical methods were used to measure the time-weighted average (TWA) concentrations of the targeted pollutants in passive samplers, deployed at eight stations in the Belgian coastal zone. Propranolol, carbamazepine and seven pesticides were found to be very abundant in the passive samplers. These obtained long-term and large-scale TWA concentrations will contribute in assessing the environmental and human health risk of these emerging pollutants.

QSAR model for the prediction of bio-concentration factor using aqueous solubility and descriptors considering various electronic effects

The in silico modelling of bio-concentration factor (BCF) is of considerable interest in environmental sciences, because it is an accepted indicator for the accumulation potential of chemicals in organisms. Numerous QSAR models have been developed for the BCF, and the majority utilize the octanol/water partition coefficient (log P) to account for the penetration characteristics of the chemicals. The present work used descriptors from a variety of software packages for the development of a multi-linear regression model to estimate BCF. The modelled data set of 473 diverse compounds covers a wide range of log BCF values. In the proposed QSAR model, most of the variation is described by the calculated solubility in water. Other contributing descriptors describe, for instance, hydrophobic surface area, hydrogen bonding and other electronic effects. The model was validated internally by using a variety of statistical approaches. Two external validations were also performed. For the former validation, a subset from the same data source was used. The 2nd external validation was based on an independent data set collected from different resources. All validations showed the consistency of the model. The applicability domain of the model was discussed and described and a thorough outlier analysis was performed.

The usefulness of an artificial membrane accumulation index for estimation of the bioconcentration factor of organophosphorus pesticides

The bioconcentration factor (BCF) is an important ecotoxicological parameter that describes the accumulation of chemicals in organisms. As many studies have reported, logBCF has good correlation with the logarithm of the 1-octanol/water partition coefficient, logP(oct), for chemicals that have logP(oct) values below about 6 and are not significantly metabolized in organisms. In this study we measured a membrane accumulation index, that is, the membrane partition (P(M)) of organophosphorus pesticides using the parallel artificial membrane permeation assay (PAMPA) to clarify whether this index is useful for the estimation of BCF. As a result, logP(M) had good correlation with logP(oct) except for iprobenfos and edifenphos. Furthermore, logBCF in five kinds of fish, especially male guppy correlated better with logP(M) than logP(oct). The results indicate that logP(M) is a better index for predicting logBCF than logP(oct). We have already reported that PAMPA permeability coefficients could predict human oral absorption of compounds, including hydrophobic chemicals and agrochemicals. Since it is expected that both human oral absorption and BCF can be estimated simultaneously using PAMPA, PAMPA is useful for exposure estimation to humans of chemicals in the environment.

Integrated decision-tree testing strategies for environmental toxicity with respect to the requirements of the EU REACH legislation

Liverpool John Moores University and FRAME recently conducted a research project sponsored by Defra on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with the REACH system. This paper focuses on the prospects for using alternative methods (both in vitro and in silico) for environmental (aquatic) toxicity testing. The manuscript reviews tests based on fish cells and cell lines, fish embryos, lower organisms, and the many expert systems and QSARs for aquatic toxicity testing. Ways in which reduction and refinement measures can be used are also discussed, including the Upper Threshold Concentration--Step Down (UTC) approach, which has recently been retrospectively validated by ECVAM and subsequently endorsed by the ECVAM Scientific Advisory Committee (ESAC). It is hoped that the application of this approach could reduce the number of fish used in acute toxicity studies by around 65-70%. Decision-tree style integrated testing strategies are also proposed for acute aquatic toxicity and chronic toxicity (including bioaccumulation), followed by a number of recommendations for the future facilitation of aquatic toxicity testing with respect to environmental risk assessment.

Biopartitioning micellar chromatography to predict blood to lung, blood to liver, blood to fat and blood to skin partition coefficients of drugs

Biopartitioning micellar chromatography (BMC), a mode of micellar liquid chromatography that uses micellar mobile phases of Brij35 in adequate experimental conditions, has demonstrated to be useful in mimicking the drug partitioning process into biological systems. In this paper, the usefulness of BMC for predicting the partition coefficients from blood to lung, blood to liver, blood to fat and blood to skin is demonstrated. PLS2 and multiple linear regression (MLR) models based on BMC retention data are proposed and compared with other ones reported in bibliography. The proposed models present better or similar descriptive and predictive capability.

Biopartitioning micellar chromatography: An alternative high-throughput method for assessing the ecotoxicity of anilines and phenols

An investigation of the use of the chromatographic retention (log k) as an in vitro approach for modelling the toxicity to Fathead Minnows of anilines and phenols is developed. A data set of 65 compounds with available experimental toxicity data was used. Log k data at three pH values were used for the compounds classification and two groups or 'MODEs' were identified. For one 'MODE' a quantitative retention-activity relationship (QRAR) model was calculated. Finally, it was used to estimate the toxicity to Fathead minnows of anilines and phenols for which experimental data are not available. These estimations were compared to those obtained from another toxicity (to Tetrahymena pyriformis) data set and those estimated from a U.S. EPA QSAR approach (ECOSAR software) to decide on the toxicity level according to the Directive 3/21/EEC.

Quantitative structure-(chromatographic) retention relationships

Since the pioneering works of Kaliszan (R. Kaliszan, Quantitative Structure-Chromatographic Retention Relationships, Wiley, New York, 1987; and R. Kaliszan, Structure and Retention in Chromatography. A Chemometric Approach, Harwood Academic, Amsterdam, 1997) no comprehensive summary is available in the field. Present review covers the period of 1996-August 2006. The sources are grouped according to the special properties of kinds of chromatography: Quantitative structure-retention relationship in gas chromatography, in planar chromatography, in column liquid chromatography, in micellar liquid chromatography, affinity chromatography and quantitative structure enantioselective retention relationships. General tendencies, misleading practice and conclusions, validation of the models, suggestions for future works are summarized for each sub-field. Some straightforward applications are emphasized but standard ones. The sources and the model compounds, descriptors, predicted retention data, modeling methods and indicators of their performance, validation of models, and stationary phases are collected in the tables. Some important conclusions are: Not all physicochemical descriptors correlate with the retention data strongly; the heat of formation is not related to the chromatographic retention. It is not appropriate to give the errors of Kovats indices in percentages. The apparently low values (1-3%) can disorient the reviewers and readers. Contemporary mean interlaboratory reproducibility of Kovats indices are about 5-10 i.u. for standard non polar phases and 10-25 i.u. for standard polar phases. The predictive performance of QSRR models deteriorates as the polarity of GC stationary phase increases. The correlation coefficient alone is not a particularly good indicator for the model performance. Residuals are more useful than plots of measured and calculated values. There is no need to give the retention data in a form of an equation if the numbers of compounds are small. The domain of model applicability of models should be given in all cases.

Biopartitioning micellar chromatography to predict mutagenicity of aromatic amines

Mutagenicity is a toxicity endpoint associated with the chronic exposure to chemicals. Aromatic amines have considerable industrial and environmental importance due to their widespread use in industry and their mutagenic capacity. Biopartitioning micellar chromatography (BMC), a mode of micellar liquid chromatography that uses micellar mobile phases of Brij35 in adequate experimental conditions, has demonstrated to be useful in mimicking the drug partitioning process into biological systems. In this paper, the usefulness of BMC for predicting mutagenicity of aromatic amines is demonstrated. A multiple linear regression (MLR) model based on BMC retention data is proposed and compared with other ones reported in bibliography. The proposed model present better or similar descriptive and predictive capability.

Micellar liquid chromatography with dodecyl dimethyl betaine as an in vitro method for prediction of protein-drug binding

With the accelerating development of new drugs, there is a high need for rapid and simple screening technologies. In this paper, a new in vitro method, dodecyl dimethyl betaine (BS-12) micellar liquid chromatography (MLC) was presented for prediction of protein-drug binding based on the similar property of BS-12 micelles to protein. The predictive possibility of this method was validated by comparing the retention factors of drugs (antidiabetic and antibacterial drugs) on C18 modified by different surfactants with those on the protein column. Through the investigation of the concentration and pH effect on the retention of the drugs in BS-12 MLC, quantitative retention-protein binding relationships were established according to the retention factors in 0.2 M BS-12 (pH 7.4) MLC and those on the protein column. According to the relationships established, the protein binding of seven drugs for psychiatric disorders, six potential drugs for antibiotics and four commercial antibiotics were predicted. The results were consistent with those on the BSA column very well. This indicated, BS-12 MLC was a simple, fast and reproducible method to predict protein-drug binding.

Counter-current chromatography using hexane/surfactant-containing water solvent systems

We developed a n-hexane/surfactant-containing water solvent system in counter-current chromatography (CCC) in order to separate hydrophobic compounds. By using the upper phase as the mobile phase, we have separated steroid samples. Retention times of steroids progesterone and delta4-androstene-3,17-dione increased slightly by increasing the concentration below the critical micellar concentration (CMC) of surfactant sodium 1-heptanesulfonate. However, the retention times increased drastically while the SHS concentrations were above the CMC. The partition of these two steroids in the two phases was significantly dependent on the interaction with micelles. Aromatic hydrocarbons were not retained by the lower phase no matter what the surfactant concentrations were. Their hydrophobic interaction with n-hexane greatly exceeded that with the micellar solution. The retention times of esters, however, were only slightly affected by the surfactant addition even above the CMC. The weaker interaction between esters and the micellar solution was probably due to their higher polarity. The micellar solvent systems provide an alternative way for hydrophobic sample separations in CCC, but the performance is limited.

Quantitative structure–property relationships for pesticides in biopartitioning micellar chromatography

The retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides was studied by quantitative structure-property relationships (QSPR) method. Heuristic method (HM) and support vector machine (SVM) method were used to build linear and nonlinear models, respectively. Compared the results of these two methods, those obtained by the SVM model are much better. For the test set, a predictive correlation coefficient (R) of 0.9755 and root-mean-square (RMS) error of 0.1403 were obtained. The proposed QSPR models, both by HM and SVM, contain the same descriptors that agree with the classical Abraham parameters of well-known linear solvation energy relationships (LSER).