Fragments quantum descriptors in classification of bio-accumulative compounds

The aim of the following research is to assess the applicability of calculated quantum properties of molecular fragments as molecular descriptors in machine learning classification task. The research is based on bio-concentration and QM9-extended databases. A number of compounds with results from quantum-chemical calculations conducted with Psi4 quantum chemistry package was also added to the quantum properties database. Classification results are compared with a baseline of random guesses and predictions obtained with the traditional RDKit generated molecular descriptors. Chosen classification metrics show that results obtained with fragments quantum descriptors fall between results from baseline and those provided by molecular descriptors widely applied in cheminformatics. According to the results, the implementation of principal component analysis, causes a drop in categorization metrics.

Intelligent consensus predictions of bioconcentration factor of pharmaceuticals using 2D and fragment-based descriptors

Bioconcentration factors (BCFs) are markers of chemical substance accumulation in organisms, and they play a significant role in determining the environmental risk of various chemicals. Experiments to obtain BCFs are expensive and time-consuming; therefore, it is better to estimate BCF early in the chemical development process. The current research aims to evaluate the ecotoxicity potential of 122 pharmaceuticals and identify possible important structural attributes using BCF as the determining feature against a group of fish species. We have calculated the theoretical 2D descriptors from the OCHEM platform and SiRMS descriptor calculating software. The regression-based quantitative structure-property relationship (QSPR) modeling was used to identify the chemical features responsible for acute fish bioconcentration. Multiple models with the "intelligent consensus" algorithm were employed for the regression-based approach improving the predictive ability of the models. To ensure the robustness and interpretability of the developed models, rigorous validation was performed employing various statistical internal and external validation metrics. From the developed models, it can be specified that the presence of large lipophilic and electronegative moieties greatly enhances the bioaccumulative potential of pharmaceuticals, whereas the hydrophilic characteristics have shown a negative impact on BCF. Furthermore, the developed models were employed to screen the DrugBank database (https://go.drugbank.com/) for assessing the BCF properties of the entire database. The evidence acquired from the modeled descriptors might be used for aquatic risk assessment in the future, with the added benefit of providing an early caution of their probable negative impact on aquatic ecosystems for regulatory purposes.

Predicting bioconcentration factor and estrogen receptor bioactivity of bisphenol a and its analogues in adult zebrafish by directed message passing neural networks

The bioconcentration factor (BCF) is a key parameter for bioavailability assessment of environmental pollutants in regulatory frameworks. The comparative toxicology and mechanism of action of congeners are also of concern. However, there are limitations to acquire them by conducting field and laboratory experiments while machinelearning is emerging as a promising predictive tool to fill the gap. In this study, the Direct Message Passing Neural Network (DMPNN) was applied to predict logBCFs of bisphenol A (BPA) and its four analogues (bisphenol AF (BPAF), bisphenol B (BPB), bisphenol F (BPF) and bisphenol S (BPS)). For the test set, the Pearson correlation coefficient (PCC) and mean square error (MSE) were 0.85 and 0.52 respectively, suggesting a good predictive performance. The predicted logBCFs values by the DMPNN ranging from 0.35 (BPS) to 2.14 (BPAF) coincided well with those by the classical EPI Suite (BCFBAF model). Besides, estrogen receptor α (ERα) bioactivity of these bisphenols was also predicted well by the DMPNN, with a probability of 97.0 % (BPB) to 99.7 % (BPAF), which was validated by the extent of vitellogenin (VTG) induction in male zebrafish as a biomarker except BPS. Thus, with little need for expert knowledge, DMPNN is confirmed to be a useful tool to accurately predict logBCF and screen for estrogenic activity from molecular structures. Moreover, a gender difference was noted in the changes of three endpoints (logBCF, ER binding affinity and VTG levels), the rank order of which was BPAF > BPB > BPA > BPF > BPS consistently, and abnormal amino acid metabolism is featured as an omics signature of abnormal hormone protein expression.

A critical review of effect modeling for ecological risk assessment of plant protection products

A wide diversity of plant protection products (PPP) is used for crop protection leading to the contamination of soil, water, and air, which can have ecotoxicological impacts on living organisms. It is inconceivable to study the effects of each compound on each species from each compartment, experimental studies being time consuming and cost prohibitive, and animal testing having to be avoided. Therefore, numerous models are developed to assess PPP ecotoxicological effects. Our objective was to provide an overview of the modeling approaches enabling the assessment of PPP effects (including biopesticides) on the biota. Six categories of models were inventoried: (Q)SAR, DR and TKTD, population, multi-species, landscape, and mixture models. They were developed for various species (terrestrial and aquatic vertebrates and invertebrates, primary producers, micro-organisms) belonging to diverse environmental compartments, to address different goals (e.g., species sensitivity or PPP bioaccumulation assessment, ecosystem services protection). Among them, mechanistic models are increasingly recognized by EFSA for PPP regulatory risk assessment but, to date, remain not considered in notified guidance documents. The strengths and limits of the reviewed models are discussed together with improvement avenues (multigenerational effects, multiple biotic and abiotic stressors). This review also underlines a lack of model testing by means of field data and of sensitivity and uncertainty analyses. Accurate and robust modeling of PPP effects and other stressors on living organisms, from their application in the field to their functional consequences on the ecosystems at different scales of time and space, would help going toward a more sustainable management of the environment. Graphical Abstract Combination of the keyword lists composing the first bibliographic query. Columns were joined together with the logical operator AND. All keyword lists are available in Supplementary Information at https://doi.org/10.5281/zenodo.5775038 (Larras et al. 2021).

Toward the Proactive Design of Sustainable Chemicals: Ionic Liquids as a Prime Example

The tailorable and often unique properties of ionic liquids (ILs) drive their implementation into a broad variety of seminal technologies. The modular design of ILs allows in this context a proactive selection of structures that favor environmental sustainability─ideally without compromising their technological performance. To achieve this objective, the whole life cycle must be taken into account and various aspects considered simultaneously. In this review, we discuss how the structural design of ILs affects their environmental impacts throughout all stages of their life cycles and scrutinize the available data in order to point out knowledge gaps that need further research activities. The design of more sustainable ILs starts with the selection of the most beneficial precursors and synthesis routes, takes their technical properties and application specific performance into due account, and considers its environmental fate particularly in terms of their (eco)toxicity, biotic and abiotic degradability, mobility, and bioaccumulation potential. Special emphasis is placed on reported structure-activity relationships and suggested mechanisms on a molecular level that might rationalize the empirically found design criteria.

Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook

The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds. Emerging contaminants from different industries have posed a significant hazard to the environment and public health. Given current bioremediation strategies, it is often a failure or inadequate for sustainable mitigation of hazardous pollutants. However, clear-cut vital information about biodegradation is quite incomplete from a conventional remediation techniques perspective. Lacking complete information on bio-transformed compounds leads to seeking alternative methods. Only scarce information about the transformed products and toxicity profile is available in the published literature. To fulfill this literature gap, various computational or in-silico technologies have emerged as alternating techniques, which are being recognized as in-silico approaches for bioremediation. Molecular docking, molecular dynamics simulation, and biodegradation pathways predictions are the vital part of predictive biodegradation, including the Quantitative Structure-Activity Relationship (QSAR), Quantitative structure-biodegradation relationship (QSBR) model system. Furthermore, machine learning (ML), artificial neural network (ANN), genetic algorithm (GA) based programs offer simultaneous biodegradation prediction along with toxicity and environmental fate prediction. Herein, we spotlight the feasibility of in-silico remediation approaches for various persistent, recalcitrant contaminants while traditional bioremediation fails to mitigate such pollutants. Such could be addressed by exploiting described model systems and algorithm-based programs. Furthermore, recent advances in QSAR modeling, algorithm, and dedicated biodegradation prediction system have been summarized with unique attributes.

Development of pp-LFER and QSPR models for predicting the diffusion coefficients of hydrophobic organic compounds in LDPE

Diffusion coefficient (D) is important to evaluate the performance of passive samplers and to monitor the concentration of chemicals effectively. Herein, we developed a polyparameter linear free energy relationship (pp-LFER) model and a quantitative structure-property relationship (QSPR) model for the prediction of diffusion coefficients of hydrophobic organic contaminants (HOCs) in low density polyethylene (LDPE). A dataset of 120 various chemicals was used to develop both models. The pp-LFER model was developed with two descriptors (V and E) and the statistical parameters of the model showed satisfactory results. As a further exploration of the diffusion behavior of the compounds, a QSPR model with five descriptors (ETA_Alpha, ASP-6, IC1, TDB6r and ATSC2v) was constructed with adjusted determination coefficient (R²) of 0.949 and cross-validation coefficient (Q_Loo²) of 0.941. The regression results indicated that both models had satisfactory goodness-of-fit and robustness. This study proves that pp-LFER and QSPR approaches are available for the prediction of log D values for the hydrophobic organic compounds within the applicability domain.

Periphyton bioconcentrates pesticides downstream of catchment dominated by agricultural land use

Periphyton provides important ecosystem services in aquatic environments, including supporting diverse consumers. We studied pesticide bioconcentration in periphyton in a coastal marsh on Lake Erie. The marsh is within a protected area (Rondeau Provincial Park) but receives discharge from tributaries draining intensively farmed land. Periphyton bioconcentrated 20 pesticide chemicals above levels observed in adjacent water or sediment. Average bioconcentration factors ranged from 12 times for the herbicide dicamba to 6864 times for the fungicide boscalid on a dry-weight basis. Bioconcentration factors were not linearly related to pesticides' log K_ow, log K_oc, or water solubility (simple linear regressions, p > 0.43). The removal of pesticides from ambient water represents another valuable ecosystem service provided by periphyton. However, we caution that bioconcentration of pesticides in periphyton provides a mechanism through which contemporary and legacy pesticides may enter wetland food webs.

QSPR models for bioconcentration factor (BCF): are they able to predict data of industrial interest?

The bioconcentration factor (BCF), a key parameter required by the REACH regulation, estimates the tendency for a xenobiotic to concentrate inside living organisms. In silico methods can be valid alternatives to costly data measurements. However, in the industrial context, these theoretical approaches may fail to predict BCF with reasonable accuracy. We analyzed whether models built on public data only have adequate performances when challenged to predict industrial compounds. A new set of 1129 compounds has been collected by merging publicly available datasets. Generative Topographic Mapping was employed to compare this chemical space with a set of new compounds issued from the industry. Some new chemotypes absent in the training set (such as siloxanes) have been detected. A new BCF model has been built using ISIDA (In SIlico design and Data Analysis) fragment descriptors, support vector regression and random forest machine-learning methods. It has been externally validated on: (i) collected data from the literature and (ii) industrial data. The latter also served as benchmark for the freely available tools VEGA, EPISuite, TEST, OPERA. New model performs (RMSE of 0.58 log BCF units) comparably to existing ones but benefits of an extended applicability, covering the industrial set chemical space (78% data coverage).

Structural alerts for the identification of bioaccumulative compounds

Legislators have included bioaccumulation in the evaluation of chemicals in the framework of the European Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) regulation. REACH requires information on the bioconcentration factor (BCF), which is a parameter for assessing bioaccumulation and encourages the use of a weight-of-evidence approach, including predictions from quantitative structure-activity relationships (QSARs). This study presents a novel approach, based on structural alerts, to be used as a decision-support system for the identification of substances with bioaccumulation potential. In a regulatory framework, these alerts can be integrated with other sources of information, such as experimental and in silico data, to reduce the uncertainty of the assessment, thereby supporting a weight-of-evidence approach. Moreover, the identified alerts have a direct connection with relevant structural features, thus fostering the applicability and interpretability of the approach. The structural alerts were identified on 779 chemicals annotated for their fish BCF, and the approach was then validated on 278 external molecules. The developed decision-support system allowed identification of 77% of bioaccumulative chemicals and was competitive with more complex QSAR models used in regulatory assessments. The approach is implemented in an easy-to-use workflow, provided free of charge. Integr Environ Assess Manag 2019;15:19-28. © 2018 SETAC.

Clustering pesticides according to their molecular properties, fate, and effects by considering additional ecotoxicological parameters in the TyPol method

Understanding the fate and ecotoxicological effects of pesticides largely depends on their molecular properties. We recently developed "TyPol" (Typology of Pollutants), a classification method of organic compounds based on statistical analyses. It combines several environmental (sorption coefficient, degradation half-life) and one ecotoxicological (bioconcentration factor) parameters, to structural molecular descriptors (number of atoms in the molecule, molecular surface, dipole moment, energy of orbitals, etc.). The present study attempts to extend TyPol to the ecotoxicological effects of pesticides on non-target organisms, based on data analysis from available literature and databases. It revealed that relevant ecotoxicological endpoints for terrestrial organisms (e.g., soil microorganisms, invertebrates) that support a range of ecosystemic services are lacking as compared to aquatic organisms. The availability of ecotoxicological parameters was also lower for chronic than for acute ecotoxicity endpoints. Consequently, seven parameters were included for acute (EC50, LC50) and chronic (NOEC) ecotoxicological effects for one terrestrial (Eisenia sp.) and three aquatic (Daphnia sp., algae, Lemna sp.) organisms. In this new configuration, we used TyPol to classify 50 pesticides into different clusters that gather molecules with similar environmental behaviors and ecotoxicological effects. The classification results evidenced relationships between molecular descriptors, environmental parameters, and the added ecotoxicological endpoints. This proof-of-concept study also showed that TyPol in silico classification can successfully address new scientific questions and be expanded with other parameters of interest.

Pre-selection and assessment of green organic solvents by clustering chemometric tools

The study presents the result of the application of chemometric tools for selection of physicochemical parameters of solvents for predicting missing variables - bioconcentration factors, water-octanol and octanol-air partitioning constants. EPI Suite software was successfully applied to predict missing values for solvents commonly considered as "green". Values for logBCF, logK_OW and logK_OA were modelled for 43 rather nonpolar solvents and 69 polar ones. Application of multivariate statistics was also proved to be useful in the assessment of the obtained modelling results. The presented approach can be one of the first steps and support tools in the assessment of chemicals in terms of their greenness.

Relationship between acute and chronic toxicity for prevalent organic pollutants in Vibrio fischeri based upon chemical mode of action

Chemicals show diverse modes of action (MOAs) in aquatic organisms depending upon acute and chronic toxicity evaluations. Here, toxicity data for Vibrio fischeri involving 52 compounds for acute and chronic toxicity were used to determine the congruence of acute and chronic toxicity for assessing MOAs. Using toxic ratios, most of the compounds categorized into MOAs that included baseline, less inert or reactive compounds with acute toxicity were also categorized as baseline, less inert or reactive compounds with chronic toxicity. However, significantly different toxic effects were observed with acute and chronic toxicity for the reactive and specific-acting compounds. The acute-chronic toxic ratios were smaller and less variable for the baseline and less inert compounds, but were greater and more variable for the reactive and specific-acting compounds. Baseline and less inert compounds share same MOAs, but reactive and specific-acting compounds have different MOAs between acute and chronic toxicity. Bioconcentration processes cannot reach an equilibrium for highly hydrophilic and ionized compounds with short-term exposure, resulting in lower toxicity compared to long-term exposure. Pronounced differences for the antibiotics were not only due to the difference in bioconcentration, but also due to a predicted difference in MOAs during acute and chronic exposures.

Membrane partitioning of ionic liquid cations, anions and ion pairs - Estimating the bioconcentration potential of organic ions

Recent efforts have been directed towards better understanding the persistency and toxicity of ionic liquids (ILs) in the context of the "benign-by-design" approach, but the assessment of their bioaccumulation potential remains neglected. This paper reports the experimental membrane partitioning of IL cations (imidazolium, pyridinium, pyrrolidinium, phosphonium), anions ([C(CN)₃]^-, [B(CN)₄]^-, [FSO₂)₂N]^-, [(C₂F₅)₃PF₃]^-, [(CF₃SO₂)₂N]^-) and their combinations as a measure for estimating the bioconcentration factor (BCF). Both cations and anions can have a strong affinity for phosphatidylcholine bilayers, which is mainly driven by the hydrophobicity of the ions. This affinity is often reflected in the ecotoxicological impact. Our data revealed that the bioconcentration potential of IL cations and anions is much higher than expected from octanol-water-partitioning based estimations that have recently been presented. For some ILs, the membrane-water partition coefficient reached levels corresponding to BCFs that might become relevant in terms of the "B" (bioaccumulation potential) classification under REACH. However, this preliminary estimation need to be confirmed by in vivo bioconcentration studies.

Investigating the mechanisms of bioconcentration through QSAR classification trees

This paper proposes a scheme to predict whether a compound (1) is mainly stored within lipid tissues, (2) has additional storage sites (e.g., proteins), or (3) is metabolized/eliminated with a reduced bioconcentration. The approach is based on two validated QSAR (Quantitative Structure-Activity Relationship) trees, whose salient features are: (a) descriptor interpretability and (b) simplicity. Trees were developed for 779 organic compounds, the TGD approach was used to quantify the lipid-driven bioconcentration, and a refined machine-learning optimization procedure was applied. We focused on molecular descriptor interpretation, which allowed us to gather new mechanistic insights into the bioconcentration mechanisms.

Comparison of Toxicities to Vibrio fischeri and Fish Based on Discrimination of Excess Toxicity from Baseline Level

Investigations on the relationship of toxicities between species play an important role in the understanding of toxic mechanisms to environmental organisms. In this paper, the toxicity data of 949 chemicals to fish and 1470 chemicals to V. fischeri were used to investigate the modes of action (MOAs) between species. The results show that although there is a positive interspecies correlation, the relationship is poor. Analysis on the excess toxicity calculated from toxic ratios (TR) shows that many chemicals have close toxicities and share the same MOAs between the two species. Linear relationships between the toxicities and octanol/water partition coefficient (log KOW) for baseline and less inert compounds indicate that the internal critical concentrations (CBRs) approach a constant both to fish and V. fischeri for neutral hydrophobic compounds. These compounds share the same toxic mechanisms and bio-uptake processes between species. On the other hand, some hydrophilic compounds exhibit different toxic effects with greatly different log TR values between V. fischeri and fish species. These hydrophilic compounds were identified as reactive MOAs to V. fischeri, but not to fish. The interspecies correlation is improved by adding a hydrophobic descriptor into the correlation equation. This indicates that the differences in the toxic ratios between fish and V. fischeri for these hydrophilic compounds can be partly attributed to the differences of bioconcentration between the two species, rather than the differences of reactivity with the target macromolecules. These hydrophilic compounds may more easily pass through the cell membrane of V. fischeri than the gill and skin of fish, react with the target macromolecules and exhibit excess toxicity. The compounds with log KOW > 7 exhibiting very low toxicity (log TR < -1) to both species indicate that the bioconcentration potential of a chemical plays a very important role in the identification of excess toxicity and MOAs.

Comparison between bioconcentration factor (BCF) data provided by industry to the European Chemicals Agency (ECHA) and data derived from QSAR models

The bioconcentration factor (BCF) is the ratio of the concentration of a chemical in an organism to the concentration in the surrounding environment at steady state. It is a valuable indicator of the bioaccumulation potential of a substance. BCF is an essential environmental property required for regulatory purposes within the Registration, Evaluation, Authorization and restriction of Chemicals (REACH) and Globally Harmonized System (GHS) regulations. In silico models for predicting BCF can facilitate the risk assessment for aquatic toxicology and reduce the cost and number of animals used. The aim of the present study was to examine the correlation of BCF data derived from the dossiers of registered chemicals submitted to the European Chemical Agency (ECHA) with the results of a battery of Quantitative Structure-Activity Relationship (QSAR). After data pruning, statistical analysis was performed using the predictions of the selected models. Results in terms of R(2) had low rating around 0.5 for the pruned dataset. The use of the model applicability domain index (ADI) led to an improvement of the performance for compounds falling within it. The variability of the experimental data and the use of different parameters to define the applicability domain can influence the performance of each model. All available information should be adapted to the requirements of the regulation to obtain a safe decision.

Integrating QSAR and read-across for environmental assessment

Read-across and QSAR have different traditions and drawbacks. We address here two main questions: (1) How do we solve the issue of the subjectivity in the evaluation of data and results, which may be particularly critical for read-across, but may have a role also for the QSAR assessment? (2) How do we take advantage of the results of both approaches to support each other? The QSAR model starts from the training set. The presence of similar chemicals with property values close to that predicted can support the result. The approach in read-across is the opposite. The assessment is focused on the few substances similar to the target. The data quality of the similar chemicals is fundamental. A risk is poor standardization in the definition of 'similarity', because different approaches may be applied. Inspired by the principles of high transparency and reproducibility, a new program for read-across, called ToxRead, has been developed and made freely available ( www.toxgate.eu ). The output of ToxRead can be compared and integrated with the output of QSAR, within a weight-of-evidence strategy. We discuss the evaluation and integration of ToxRead and QSAR with examples of the assessment of bioconcentration factors of chemicals.

Study of enrichment factors for six β-blockers in aliphatic alcohols by hollow-fiber liquid-phase microextraction

The selectivity of a suitable organic solvent is key for extraction in liquid-phase microextraction experiments. Nevertheless, the screening process remains a daunting task. Our research aimed to study the relationship between extraction efficiency and extraction solvents, analytes, and finally select the appropriate extraction solvent. In the present article, β-blockers and six extraction solvents were chosen as the models and hollow-fiber liquid-phase microextraction was conducted. The relationship was built by statistical analysis on the data. Factors affecting extraction efficiency including the logarithms of the octanol/water partition coefficient (logPo/w ) of analytes, acid dissociation constants, the logarithms of the octanol/water partition coefficient of solvents and pH of the sample solution were investigated. The results showed that a low water solubility of extraction solvent is the foundation to ensure higher extraction efficiency. Moreover, when ΔlogPo/w > 0, a higher extraction efficiency is observed at lower ΔlogPo/w , on the contrary, when ΔlogPo/w < 0, extraction efficiency is higher as the absolute value of ΔlogPo/w becomes greater. Finally, the relationship between enrichment factor and extraction solvents, analytes was established and a helpful guidance was provided for the selection of an optimal solvent to obtain the best extraction efficiency by liquid-phase microextraction.

Comparison of toxicity of class-based organic chemicals to algae and fish based on discrimination of excess toxicity from baseline level

Toxicity data to fish and algae were used to investigate excess toxicity between species. Results show that chemicals exhibiting excess toxicity to fish also show excess toxicity to algae for most of the compounds. This indicates that they share the same mode of action between species. Similar relationships between logKOW and toxicities to fish and algae for baseline and less inert compounds suggest that they have similar critical body residues in the two species. Differences in excess toxicity for some compounds suggest that there is a difference of physiological structure and metabolism between fish and algae. Some reactive compounds (e.g. polyamines) exhibit greater toxic effects for algae than those for fish because of relatively low bio-uptake potential of these hydrophilic compounds in fish as compared with that in algae. Esters exhibiting greater toxicity in fish than that in algae indicate that metabolism can affect the discrimination of excess toxicity from baseline level. Algae growth inhibition is a very good surrogate for fish lethality. This is not only because overall toxicity sensitivity to algae is greater than that to fish, but also the excess toxicity calculated from algal toxicity can better reflect reactivity of compounds with target molecules than fish toxicity.

Investigation on the relationship between bioconcentration factor and distribution coefficient based on class-based compounds: The factors that affect bioconcentration

Bioconcentration factor (BCF) is one of the most important parameters in the assessment of the potential hazard of new compounds in aquatic ecosystems. However, the factors that influence the estimation of BCFs for a large variety of chemicals have not been systemically investigated in the literature. In this paper, a large BCF data set containing 1088 nonionic and ionic organic compounds was used to study the relationship between BCF and molecular descriptors and influencing factors. Step-by-step analysis on the class-based compounds showed that nonlinear Gaussian and Sigmoid equations could well describe relationships between logBCF and distribution coefficient for the compounds over a wide range of structures and chloro or/and bromo substituted aromatics, respectively. The quality of fit from the nonlinear models is better than the BCFBAF method from the Epi Suite program for the class-based compounds. Systemic prediction deviations have been observed for some types of compounds. The reasons for systemic deviations for these compounds can be attributed to the difference in bioconcentration mechanism for hydrophilic compounds, transformation for hydroxyphenols and three-membered rings, physical barrier for long chain and large polycyclic compounds, difference in determining methods of BCF (kinetic and steady-state), bioavailability for highly hydrophobic compounds and accuracy of BCF measurements for compounds with extremely high or low BCFs. These factors are important and should be considered in any reliable bioconcentration prediction.

TyPol - a new methodology for organic compounds clustering based on their molecular characteristics and environmental behavior

Following legislation, the assessment of the environmental risks of 30000-100000 chemical substances is required for their registration dossiers. However, their behavior in the environment and their transfer to environmental components such as water or atmosphere are studied for only a very small proportion of the chemical in laboratory tests or monitoring studies because it is time-consuming and/or cost prohibitive. Therefore, the objective of this work was to develop a new methodology, TyPol, to classify organic compounds, and their degradation products, according to both their behavior in the environment and their molecular properties. The strategy relies on partial least squares analysis and hierarchical clustering. The calculation of molecular descriptors is based on an in silico approach, and the environmental endpoints (i.e. environmental parameters) are extracted from several available databases and literature. The classification of 215 organic compounds inputted in TyPol for this proof-of-concept study showed that the combination of some specific molecular descriptors could be related to a particular behavior in the environment. TyPol also provided an analysis of similarities (or dissimilarities) between organic compounds and their degradation products. Among the 24 degradation products that were inputted, 58% were found in the same cluster as their parents. The robustness of the method was tested and shown to be good. TyPol could help to predict the environmental behavior of a "new" compound (parent compound or degradation product) from its affiliation to one cluster, but also to select representative substances from a large data set in order to answer some specific questions regarding their behavior in the environment.

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.

Linear and non-linear relationships between bioconcentration and hydrophobicity: theoretical consideration

A non-linear relationship (e.g. Gaussian-type) between measured bioconcentration factor (BCF) and octanol/water partition coefficient (K(OW)) was noted many years ago. Many studies have focused on the cause of the breakdown in the log BCF/log K(OW) curve for highly hydrophobic chemicals with log K(OW)>6. However, there has been little investigation on the theoretical background of this feature for highly hydrophilic chemicals. In this paper, the cause of linear and non-linear relationships between log BCF and log K(OW) has been investigated on the basis of the partitioning-based mechanism for classified non-ionic and ionisable compounds. For highly hydrophilic compounds, lipid tissue in fish is not the major storage site of chemicals. Uptake from other tissues/organs plays a much more important role than the lipid content, leading to a variation of measured log BCF around 0.5. For hydrophobic chemicals with 0.5<log K(OW)<6, hydrophobicity is the principal driving force of bioconcentration and log BCF increases with increasing log K(OW). The log BCF/log K(OW) curve breaks down for highly hydrophobic chemicals with log K(OW)>6. The main reason for this is attributed to the reduced bioavailability of chemicals in water. A linear solvation energy relationship shows that the bioconcentration increases with increasing molecular size by increasing the dispersion interactions between the chemical and lipid content. Bioconcentration decreases with increasing the basicity of hydrophobic compounds by increasing the H-bonding of chemicals with water. Principal component analysis shows that the octanol/water system is the closest system, but not an ideal surrogate, to describe the bioconcentration for hydrophobic compounds as compared with other solvent/water partition systems.

A new quantitative structure-property relationship model to predict bioconcentration factors of polychlorinated biphenyls (PCBs) in fishes using E-state index and topological descriptors

A quantitative structure-property relationship (QSPR) study for predicting the logarithm of bioconcentration factors (LogBCF) of polychlorinated biphenyls (PCBs) is presented in this work. For this, the descriptors were obtained using only the Simplified Molecular Input Line Entry System (SMILES) strings in the free web server Parameter Client. The model was built using the Partial Least Squares (PLS) regression method. The best model presented five descriptors (one E-state index and four topological descriptors) and a high quality for fit, internal, and external predictions. The leave-N-out (LNO) cross validation and the y-randomization test showed the model is robust and has no shown chance correlation. With a second test set, the model was compared to other models and presented a root mean square error (RMSE) very close to the best model. The mechanistic interpretation was corroborated by other works in the literature and by the descriptors' theory. Thus, the results meet the five Organization for Economic Co-operation and Development (OECD) principles for validation of QSA(P)R models, and it is expected the model can effectively predict the BCF values in fishes of the PCB congeners without highly reliable experimental BCF.

Introducing catastrophe-QSAR. Application on modeling molecular mechanisms of pyridinone derivative-type HIV non-nucleoside reverse transcriptase inhibitors

The classical method of quantitative structure-activity relationships (QSAR) is enriched using non-linear models, as Thom's polynomials allow either uni- or bi-variate structural parameters. In this context, catastrophe QSAR algorithms are applied to the anti-HIV-1 activity of pyridinone derivatives. This requires calculation of the so-called relative statistical power and of its minimum principle in various QSAR models. A new index, known as a statistical relative power, is constructed as an Euclidian measure for the combined ratio of the Pearson correlation to algebraic correlation, with normalized t-Student and the Fisher tests. First and second order inter-model paths are considered for mono-variate catastrophes, whereas for bi-variate catastrophes the direct minimum path is provided, allowing the QSAR models to be tested for predictive purposes. At this stage, the max-to-min hierarchies of the tested models allow the interaction mechanism to be identified using structural parameter succession and the typical catastrophes involved. Minimized differences between these catastrophe models in the common structurally influential domains that span both the trial and tested compounds identify the "optimal molecular structural domains" and the molecules with the best output with respect to the modeled activity, which in this case is human immunodeficiency virus type 1 HIV-1 inhibition. The best molecules are characterized by hydrophobic interactions with the HIV-1 p66 subunit protein, and they concur with those identified in other 3D-QSAR analyses. Moreover, the importance of aromatic ring stacking interactions for increasing the binding affinity of the inhibitor-reverse transcriptase ligand-substrate complex is highlighted.

Screening for low aquatic bioaccumulation (2): physico-chemical constraints

Physico-chemical properties related to the bioavailability of xenobiotics in aquatic environments have been tested for their ability to identify chemicals with low bioconcentration potential. Cut-offs in lipophilicity (log K(OW) < 3 or > 10), solubility and volatility (log Henry constant <-11 [atm (mol L(-1))(-1)]), degradability (ready biodegradability, hydrolysis) and ionisation (>5% ionisation at pH 7) have been adopted and combined into a decision tree based on 382 industrial chemicals. The five-parameter classification scheme was externally validated with 49 pesticides and successfully confirmed with 83 bioaccumulative compounds. The applicability domain of the model has been described in terms of chemical classes (excluding polybrominated compounds (>4 Br), organometallics, compounds with perfluorinated fragments, substances with an acyclic alkyl moiety (chain length > C7) and thiols) and ranges of physico-chemical properties. The present tool allows to securely de-prioritize more than 50% chemicals of low concern with regard to the B criterion (BCF < 2000). Bioassays with compounds with these physico-chemical constraints may be waived because testing may be technically not possible and does not appear scientifically necessary in persistent, bioaccumulative, toxic (PBT) substances and risk assessments.

QSAR modelling of bioconcentration factor using hydrophobicity, hydrogen bonding and topological descriptors

Bioconcentration factor (BCF) is an important step in the uptake of environmental pollutants in the food chain. It is expensive and time-consuming to measure, so predictive methods are of value. We have used an artificial neural network QSAR approach involving descriptors for hydrophobicity, hydrogen bonding and molecular topology, obtained from commercially available software, to predict the fish BCF values of a diverse data set of 624 chemicals. The training set statistics were: r²= 0.765, q²= 0.763, s = 0.610, and those of the external test set were: r²= 0.739, s = 0.627. The model complies with the OECD Principles for the Validation of (Q)SARs.

Screening for low aquatic bioaccumulation. 1. Lipinski's 'Rule of 5' and molecular size

Aquatic bioconcentration factors are critical in PBT assessment of industrial chemicals under REACH. Reliable indicators based on physico-chemical properties and molecular attributes of chemicals with low bioconcentration potential have been searched to de-prioritize non-accumulative chemicals in order to avoid unnecessary biotests that do not produce risk-relevant information. Developed to screen drug candidates, Lipinski's 'Rule of 5' identifies chemicals with poor oral absorption based on criteria in partitioning, molecular weight and hydrogen bonding. This parameter ensemble has been supplemented with molecular diameter and tested for its adequacy to filter chemicals with low bioconcentration potential. Perhaps (not) surprisingly, the application of the 'Rule of 5' fails to protectively identify non-accumulative compounds because other processes dominate the uptake in aquatic environments as compared with oral absorption. No robust evidence was found for cut-offs in bioconcentration related to molecular size. However, pragmatic thresholds in molecular weight (>650 g mol(-1)) and lipophilicity (log K(OW) < 3 or > 10) have been verified to securely de-prioritize 30-40% of chemicals of low concern with regard to the B criterion.

Evaluation of liposome-water partitioning for predicting bioaccumulation potential of hydrophobic organic chemicals

Considering the importance of bioaccumulation factors (BAFs) in risk assessment of chemicals and the ethical issues and complexity of the determination of these factors in standard tests with living organisms, there is a need for alternative approaches for predicting bioaccumulation. In this study, liposome-water partitioning coefficients as determined by using solid-phase microextraction (SPME) were evaluated for the cause of assessing bioaccumulation potential of hydrophobic organic chemicals (HOCs). To this end, the SPME method was mapped (in terms of mass balance, mode of spiking, kinetics, and reproducibility) and validated against literature data. Furthermore, the robustness of liposomes as partitioning phase was investigated (in terms of chemical loading, and pH and ionic strength of the medium), and finally liposome-water partition coefficients (K(lipw)) determined for polycyclic aromatic hydrocarbons (PAHs; 4.5 < logK(ow) < 7.2) were compared with literature BAF values for several aquatic species. The results indicated that (i) SPME is a valid, fast, and reproducible method for measuring K(lipw) values; (ii) liposomes provide a very robust partitioning phase; and (iii) K(lipw) values agreed very well with literature PAH BAF values. SPME-derived K(lipw) values therefore seem a very promising predictor of bioaccumulation potential of HOCs. By including model- or in vitro-derived biotransformation rates, bioaccumulation potential estimates might be converted into surrogate BAFs, thereby extending the applicability of K(lipw) values to metabolizable chemicals and species with more advanced biotransformation capacity.

Quantum-SAR extension of the spectral-SAR algorithm: application to polyphenolic anticancer bioactivity

Aiming to assess the role of individual molecular structures in the molecular mechanism of ligand-receptor interaction correlation analysis, the recent Spectral-SAR approach is employed to introduce the Quantum-SAR (QuaSAR) “wave” and “conversion factor” in terms of difference between inter-endpoint inter-molecular activities for a given set of compounds; this may account for inter-conversion (metabolization) of molecular (concentration) effects while indicating the structural (quantum) based influential/detrimental role on bio-/eco- effect in a causal manner rather than by simple inspection of measured values; the introduced QuaSAR method is then illustrated for a study of the activity of a series of flavonoids on breast cancer resistance protein.