Prediction of bioconcentration factors in fish and invertebrates using machine learning

Molecular designing of potential environmentally friendly PFAS based on deep learning and generative models

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are widely used across a spectrum of industrial and consumer goods. Nonetheless, their persistent nature and tendency to accumulate in biological systems pose substantial environmental and health threats. Consequently, striking a balance between maximizing product efficiency and minimizing environmental and health risks by tailoring the molecular structure of PFAS has become a pivotal challenge in the fields of environmental chemistry and sustainable development. To address this issue, a computational workflow was proposed for designing an environmentally friendly PFAS by incorporating deep learning (DL) and molecular generative models. The hybrid DL architecture MolHGT+ based on heterogeneous graph neural network with transformer-like attention was applied to predict the surface tension, bioaccumulation, and hepatotoxicity of the molecules. Through virtual screening of the PFAS master database using MolHGT+, the findings indicate that incorporating the siloxane group and betaine fragment can effectively decrease both the bioaccumulation and hepatotoxicity of PFAS while preserving low surface tension. In addition, molecular generative models were employed to create a structurally diverse pool of novel PFASs with the aforementioned hit molecules serving as the initial template structures. Overall, our study presents a promising AI-driven method for advancing the development of environmentally friendly PFAS.

Integrated Transfer Learning and Multitask Learning Strategies to Construct Graph Neural Network Models for Predicting Bioaccumulation Parameters of Chemicals

Accurate prediction of parameters related to the environmental exposure of chemicals is crucial for the sound management of chemicals. However, the lack of large data sets for training models may result in poor prediction accuracy and robustness. Herein, integrated transfer learning (TL) and multitask learning (MTL) was proposed for constructing a graph neural network (GNN) model (abbreviated as TL-MTL-GNN model) using n-octanol/water partition coefficients as a source domain. The TL-MTL-GNN model was trained to predict three bioaccumulation parameters based on enlarged data sets that cover 2496 compounds with at least one bioaccumulation parameter. Results show that the TL-MTL-GNN model outperformed single-task GNN models with and without the TL, as well as conventional machine learning models trained with molecular descriptors or fingerprints. Applicability domains were characterized by a state-of-the-art structure-activity landscape-based (abbreviated as ADSAL) methodology. The TL-MTL-GNN model coupled with the optimal ADSAL was employed to predict bioaccumulation parameters for around 60,000 chemicals, with more than 13,000 compounds identified as bioaccumulative chemicals. The high predictive accuracy and robustness of the TL-MTL-GNN model demonstrate the feasibility of integrating the TL and MTL strategy in modeling small-sized data sets. The strategy holds significant potential for addressing small data challenges in modeling environmental chemicals.

ChemFREE: a one-stop comprehensive platform for ecological and environmental risk evaluation of chemicals in one health world

Addressing health and safety crises stemming from various environmental and ecological issues is a core focus of One Health (OH), which aims to balance and optimize the health of humans, animals, and the environment. While many chemicals contribute significantly to our quality of life when properly used, others pose environmental and ecological health risks. Recently, assessing the ecological and environmental risks associated with chemicals has gained increasing significance in the OH world. In silico models may address time-consuming and costly challenges, and fill gaps in situations where no experimental data is available. However, despite their significant contributions, these assessment models are not web-integrated, leading to user inconvenience. In this study, we developed a one-stop comprehensive web platform for freely evaluating the eco-environmental risk of chemicals, named ChemFREE (Chemical Formula Risk Evaluation of Eco-environment, available in http://chemfree.agroda.cn/chemfree/). Inputting SMILES string of chemicals, users will obtain the assessment outputs of ecological and environmental risk, etc. A performance evaluation of 2935 external chemicals revealed that most classification models achieved an accuracy rate above 0.816. Additionally, the $Q_{F1}^2$ metric for regression models ranges from 0.618 to 0.898. Therefore, it will facilitate the eco-environmental risk evaluation of chemicals in the OH world.

Bio-QSARs 2.0: Unlocking a new level of predictive power for machine learning-based ecotoxicity predictions by exploiting chemical and biological information

Practical, legal, and ethical reasons necessitate the development of methods to replace animal experiments. Computational techniques to acquire information that traditionally relied on animal testing are considered a crucial pillar among these so-called new approach methodologies. In this light, we recently introduced the Bio-QSAR concept for multispecies aquatic toxicity regression tasks. These machine learning models, trained on both chemical and biological information, are capable of both cross-chemical and cross-species predictions. Here, we significantly extend these models' applicability. This was realized by increasing the quantity of training data by a factor of approximately 20, accomplished by considering both additional chemicals and aquatic organisms. Additionally, variable test durations and associated random effects were accommodated by employing a machine learning algorithm that combines tree-boosting with mixed-effects modeling (i.e., Gaussian Process Boosting). We also explored various biological descriptors including Dynamic Energy Budget model parameters, taxonomic distances, as well as genus-specific traits and investigated the inclusion of mode-of-action information. Through these efforts, we developed Bio-QSARs for fish and aquatic invertebrates with exceptional predictive power (R squared of up to 0.92 on independent test sets). Moreover, we made considerable strides to make models applicable for a range of use cases in environmental risk assessment as well as research and development of chemicals. Models were made fully explainable by implementing an algorithmic multicollinearity correction combined with SHapley Additive exPlanations. Furthermore, we devised novel approaches for applicability domain construction that take feature importance into account. We are hence confident these models, which are available via open access, will make a significant contribution towards the implementation of new approach methodologies and ultimately have the potential to support "Green Chemistry" and "Green Toxicology".

Cross-Species Extrapolation of Biological Data to Guide the Environmental Safety Assessment of Pharmaceuticals-The State of the Art and Future Priorities

The extrapolation of biological data across species is a key aspect of biomedical research and drug development. In this context, comparative biology considerations are applied with the goal of understanding human disease and guiding the development of effective and safe medicines. However, the widespread occurrence of pharmaceuticals in the environment and the need to assess the risk posed to wildlife have prompted a renewed interest in the extrapolation of pharmacological and toxicological data across the entire tree of life. To address this challenge, a biological "read-across" approach, based on the use of mammalian data to inform toxicity predictions in wildlife species, has been proposed as an effective way to streamline the environmental safety assessment of pharmaceuticals. Yet, how effective has this approach been, and are we any closer to being able to accurately predict environmental risk based on known human risk? We discuss the main theoretical and experimental advancements achieved in the last 10 years of research in this field. We propose that a better understanding of the functional conservation of drug targets across species and of the quantitative relationship between target modulation and adverse effects should be considered as future research priorities. This pharmacodynamic focus should be complemented with the application of higher-throughput experimental and computational approaches to accelerate the prediction of internal exposure dynamics. The translation of comparative (eco)toxicology research into real-world applications, however, relies on the (limited) availability of experts with the skill set needed to navigate the complexity of the problem; hence, we also call for synergistic multistakeholder efforts to support and strengthen comparative toxicology research and education at a global level. Environ Toxicol Chem 2024;43:513-525. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Predicting the Accumulation of Ionizable Pharmaceuticals and Personal Care Products in Aquatic and Terrestrial Organisms

The extent to which chemicals bioaccumulate in aquatic and terrestrial organisms represents a fundamental consideration for chemicals management efforts intended to protect public health and the environment from pollution and waste. Many chemicals, including most pharmaceuticals and personal care products (PPCPs), are ionizable across environmentally relevant pH gradients, which can affect their fate in aquatic and terrestrial systems. Existing mathematical models describe the accumulation of neutral organic chemicals and weak acids and bases in both fish and plants. Further model development is hampered, however, by a lack of mechanistic insights for PPCPs that are predominantly or permanently ionized. Targeted experiments across environmentally realistic conditions are needed to address the following questions: (1) What are the partitioning and sorption behaviors of strongly ionizing chemicals among species? (2) How does membrane permeability of ions influence bioaccumulation of PPCPs? (3) To what extent are salts and associated complexes with PPCPs influencing bioaccumulation? (4) How do biotransformation and other elimination processes vary within and among species? (5) Are bioaccumulation modeling efforts currently focused on chemicals and species with key data gaps and risk profiles? Answering these questions promises to address key sources of uncertainty for bioaccumulation modeling of ionizable PPCPs and related contaminants. Environ Toxicol Chem 2024;43:502-512. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Multi-task machine learning models for simultaneous prediction of tissue-to-blood partition coefficients of chemicals in mammals

Tissue-to-blood partition coefficients (Ptb) are crucial for assessing the distribution of chemicals in organisms. Given the lack of experimental data and laborious nature of experimental methods, there is an urgent need to develop efficient predictive models. With the help of machine learning algorithms, i,e., random forest (RF), and artificial neural network (ANN), this study developed multi-task (MT) models that can simultaneously predict Ptb values for various mammalian tissues, including liver, muscle, brain, lung, and adipose. Single-task (ST) models using partial least squares regression, RF, and ANN algorithms for each endpoint were established for comparison. Overall, the performances of MT models were superior to those of ST models. The MT model using ANN algorithms showed the highest prediction accuracy with determination coefficients ranging from 0.704 to 0.886, root mean square errors between 0.223 and 0.410, and mean absolute errors ranging from 0.178 to 0.285 log units. Results showed that lipophilicity and polarizability of molecules significantly influence their partition behavior in organisms. Applicability domains (ADs) of the models were characterized by weighted molecular similarity density, and weighted inconsistency in molecular activities of structure-activity landscapes. When constrained by ADs, the models displayed enhanced predictive accuracy, making them valuable tools for the risk assessment and management of chemicals.

Probing the bioconcentration and metabolism disruption of bisphenol A and its analogues in adult female zebrafish from integrated AutoQSAR and metabolomics studies

Plenty of emerging bisphenol A (BPA) substitutes rise to wait for assessment of bioconcentration and metabolism disruption. Computational methods are useful to fill the data gap in chemical risk assessment, such as automated quantitative structure-activity relationship (AutoQSAR). It is not clear how AutoQSAR performs in predicting the bioconcentration factor (BCF) in adult zebrafish. Herein, AutoQSAR was used to predict the logBCFs of BPA, bisphenol AF (BPAF), bisphenol B, bisphenol F and bisphenol S (BPS). For the test set, a linear relationship was shown between the observed and predicted logBCFs with a slope of 0.97. The predicted logBCFs of these five bisphenols were quite close to their experimental data with a slope of 0.94, suggesting better performance than directed message passing neural networks and EPI Suite with a slope of 0.69 and 0.61, respectively. Thus, AutoQSAR is powerful in modeling logBCFs in fish with minimal time and expertise. To link bioconcentration with metabolic effects, female zebrafish were exposed to BPA, BPAF and BPS for metabolomics analysis. BPA caused a significant disturbance in amino acid metabolism, while BPAF and BPS significantly altered another three metabolic pathways, showing chemical-specific responses. BPAF with the highest logBCF elicited the strongest metabolomic responses reflected by the metabolic effect level index, followed by BPA and BPS. Thus, BPAF and BPS elicited higher or similar metabolism disruption compared with BPA in female zebrafish, respectively, reflecting consequences of bioconcentration.

A review on in silico prediction of the environmental risks posed by pharmaceutical emerging contaminants

Computer-aided (in silico) prediction has shown good potential to support the environmental risk assessment (ERA) of pharmaceutical emerging contaminants (PECs), allowing low-cost, animal-free, high-throughput screening of multiple potential risks posed by a wide variety of pharmaceuticals in the environment based on insufficient toxicity data. This review provided recent insights regarding the application of in silico approaches in prediction for environmental risks of PECs. Based on the review of 20 included articles from 8 countries published since 2018, we found that the researchers' interest and concern in this research topic were sharply aroused since 2021. Recently, in silico approaches have been widely used for the prediction of bioaccumulation and biodegradability, lethal endpoints, developmental toxicity, mutagenicity, other eco-toxicological effects such as ototoxicity and hematological toxicity, and human health hazards of exposure to PECs. Particular attention has been given to the simultaneous discernment of multiple environmental risks and health effects of PECs based on mechanistic data of pharmaceuticals using advanced bioinformatic methods such as transcriptomic analysis and network pharmacology prediction. In silico software platforms and databases used in the included studies were diversified, and there is currently no standardized and accepted in silico model for ERA of PECs. Date suggested that in silico prediction of the environmental risks posed by PECs is still in its infancy. Considerable critical challenges need to be addressed, including consideration of environmental exposure concentration for PECs, interactions among mixtures of PECs and other contaminants coexisting in environments, and development of in silico models specific to ERA of PECs.

Physiological variables in machine learning QSARs allow for both cross-chemical and cross-species predictions

A major challenge in ecological risk assessment is estimating chemical-induced effects across taxa without species-specific testing. Where ecotoxicological data may be more challenging to gather, information on species physiology is more available for a broad range of taxa. Physiology is known to drive species sensitivity but understanding about the relative contribution of specific underlying processes is still elusive. Consequently, there remains a need to understand which physiological processes lead to differences in species sensitivity. The objective of our study was to utilize existing knowledge about organismal physiology to both understand and predict differences in species sensitivity. Machine learning models were trained to predict chemical- and species-specific endpoints as a function of both chemical fingerprints/descriptors and physiological properties represented by dynamic energy budget (DEB) parameters. We found that random forest models were able to predict chemical- and species-specific endpoints, and that DEB parameters were relatively important in the models, particularly for invertebrates. Our approach illuminates how physiological properties may drive species sensitivity, which will allow more realistic predictions of effects across species without the need for additional animal testing.

Factors Determining the Susceptibility of Fish to Effects of Human Pharmaceuticals

The increasing levels and frequencies at which active pharmaceutical ingredients (APIs) are being detected in the environment are of significant concern, especially considering the potential adverse effects they may have on nontarget species such as fish. With many pharmaceuticals lacking environmental risk assessments, there is a need to better define and understand the potential risks that APIs and their biotransformation products pose to fish, while still minimizing the use of experimental animals. There are both extrinsic (environment- and drug-related) and intrinsic (fish-related) factors that make fish potentially vulnerable to the effects of human drugs, but which are not necessarily captured in nonfish tests. This critical review explores these factors, particularly focusing on the distinctive physiological processes in fish that underlie drug absorption, distribution, metabolism, excretion and toxicity (ADMET). Focal points include the impact of fish life stage and species on drug absorption (A) via multiple routes; the potential implications of fish's unique blood pH and plasma composition on the distribution (D) of drug molecules throughout the body; how fish's endothermic nature and the varied expression and activity of drug-metabolizing enzymes in their tissues may affect drug metabolism (M); and how their distinctive physiologies may impact the relative contribution of different excretory organs to the excretion (E) of APIs and metabolites. These discussions give insight into where existing data on drug properties, pharmacokinetics and pharmacodynamics from mammalian and clinical studies may or may not help to inform on environmental risks of APIs in fish.

QSAR-QSIIR-based prediction of bioconcentration factor using machine learning and preliminary application

Bioconcentration factor (BCF) is one of the important parameters for developing human health ambient water quality criteria (HHAWQC) for chemical pollutants. Traditional experimental method to obtain BCF is time-consuming and costly. Therefore, prediction of BCF by modeling has attracted much attention. QSAR (Quantitative Structure-Activity Relationship) model based on molecular descriptor is often used to predict BCF, however, in order to improve the accuracy of prediction, previous models are only applicable for prediction for a single category of substance and a single species, and cannot meet the needs of BCF prediction of pollutants lacing toxicity data. In this study, optimized 17 traditional molecular descriptor and five kinds of bioactivity descriptor were selected from more than 200 molecular descriptor and 25 kinds of biological activity descriptors. A QSAR-QSIIR (Quantitative Structure In vitro-In vivo Relationship) model suitable for multiple chemical substances and whole species is constructed by using optimized 4-MLP machine learning algorithm with selected molecular and bioactivity descriptors. The constructed model significantly improves the prediction accuracy of BCF. The R² of verification set and test set are 0.8575 and 0.7924, respectively, and the difference between predicted BCF and measured BCF is mostly less than 1.5 times. Then, BCF of BTEX in Chinese common aquatic products is predicted using the constructed QSAR-QSIIR model, and the HHAWQC of BTEX in China are derived using the predicted BCF, which provides a valuable reference for establishment of China's BTEX water quality standards.

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

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

Bioaccumulation/bioconcentration of pharmaceutical active compounds in aquatic organisms: Assessment and factors database

There is increasing evidence that the presence of certain pharmaceuticals in the environment leads to biota exposure and constitute a potential risk for ecosystems. Bioaccumulation is an essential focus of risk assessment to evaluate at what degree emerging contaminants are a hazard both to the environment and the individuals that inhabit it. The main goals of the present review are 1) to summarize and describe the research and factors that should be taken into account in the evaluation of bioaccumulation of pharmaceuticals in aquatic organisms; and 2) to provide a database and a critical review of the bioaccumulation/bioconcentration factors (BAF or BCF) of these compounds in organisms of different trophic levels. Most studies fall into one of two categories: laboratory-scale absorption and purification tests or field studies and, to a lesser extent, large-scale, semi-natural system tests. Although in the last 5 years there has been considerable progress in this field, especially in species of fish and molluscs, research is still limited on other aquatic species like crustaceans or algae. This revision includes >230 bioconcentration factors (BCF) and >530 bioaccumulation factors (BAF), determined for 113 pharmaceuticals. The most commonly studied is the antidepressant group, followed by diclofenac and carbamazepine. There is currently no reported accumulation data on certain compounds, such as anti-cancer drugs. BCFs are highly influenced by experimental factors (notably the exposure level, time or temperature). Field BAFs are superior to laboratory BCFs, highlighting the importance of field studies for reliable assessments and in true environmental conditions. BAF data appears to be organ, species and compound-specific. The potential impact on food web transfer is also considered. Among different aquatic species, lower trophic levels and benthic organisms exhibit relatively higher uptake of these compounds.

Minimizing Experimental Testing on Fish for Legacy Pharmaceuticals

There was no regulatory requirement for ecotoxicological testing of human pharmaceuticals authorized before 2006, and many of these have little or no data available to assess their environmental risk. Motivated by animal welfare considerations, we developed a decision tree to minimize in vivo fish testing for such legacy active pharmaceutical ingredients (APIs). The minimum no observed effect concentration (NOECmin, the lowest NOEC from chronic Daphnia and algal toxicity studies), the theoretical therapeutic water concentration (TWC, calculated using the fish plasma model), and the predicted environmental concentration (PEC) were used to derive API risk quotients (PEC/NOECmin and PEC/TWC). Based on a verification data set of 96 APIs, we show that by setting a threshold value of 0.001 for both risk quotients, the need for in vivo fish testing could potentially be reduced by around 35% without lowering the level of environmental protection. Hence, for most APIs, applying an assessment factor of 1000 (equivalent to the threshold of 0.001) to NOECmin substituted reliably for NOECfish, and TWC acted as an effective safety net for the others. In silico and in vitro data and mammalian toxicity data may further support the final decision on the need for fish testing.

Active Pharmaceutical Ingredient Uptake by Zebrafish (Danio rerio) Oct2 (slc22a2) Transporter Expressed in Xenopus laevis Oocytes

Uptake of active pharmaceutical ingredients (APIs) across the gill epithelium of fish is via either a passive or facilitated transport process, with the latter being more important at the lower concentrations more readily observed in the environment. The solute carrier (SLC) 22A family, which includes the organic cation transporter OCT2 (SLC22A2), has been shown in mammals to transport several endogenous chemicals and APIs. Zebrafish oct2 was expressed in Xenopus oocytes and the uptake of ranitidine, propranolol, and tetraethylammonium characterized. Uptake of ranitidine and propranolol was time- and concentration-dependent with a k_m and V_max for ranitidine of 246 µM and 45 pmol/(oocyte × min) and for propranolol of 409 µM and 190 pmol/(oocyte × min), respectively. Uptake of tetraethylammonium (TEA) was inhibited by propranolol, amantadine, and cimetidine, known to be human OCT2 substrates, but not quinidine or ranitidine. At external media pH 7 and 8 propranolol uptake was 100-fold greater than at pH 6; pH did not affect ranitidine or TEA uptake. It is likely that cation uptake is driven by the electrochemical gradient across the oocyte. Uptake kinetics parameters, such as those derived in the present study, coupled with knowledge of transporter localization and abundance and API metabolism, can help derive pharmacokinetic models. Environ Toxicol Chem 2022;41:2993-2998. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Affinity of Compounds for Phosphatydylcholine-Based Immobilized Artificial Membrane-A Measure of Their Bioconcentration in Aquatic Organisms

The BCF (bioconcentration factor) of solutes in aquatic organisms is an important parameter because many undesired chemicals enter the ecosystem and affect the wildlife. Chromatographic retention factor log k_w^IAM obtained from immobilized artificial membrane (IAM) HPLC chromatography with buffered, aqueous mobile phases and calculated molecular descriptors obtained for a group of 120 structurally unrelated compounds were used to generate useful models of log BCF. It was established that log k_w^IAM obtained in the conditions described in this study is not sufficient as a sole predictor of bioconcentration. Simple, potentially useful models based on log k_w^IAM and a selection of readily available, calculated descriptors and accounting for over 88% of total variability were generated using multiple linear regression (MLR), partial least squares (PLS) regression and artificial neural networks (ANN). The models proposed in the study were tested on an external group of 120 compounds and on a group of 40 compounds with known experimental log BCF values. It was established that a relatively simple MLR model containing four independent variables leads to satisfying BCF predictions and is more intuitive than PLS or ANN models.

Application of multi-objective optimization in the study of anti-breast cancer candidate drugs

In the development of anti-breast cancer drugs, the quantitative structure-activity relationship model of compounds is usually used to select potential active compounds. However, the existing methods often have problems such as low model prediction performance, lack of overall consideration of the biological activity and related properties of compounds, and difficulty in directly selection candidate drugs. Therefore, this paper constructs a complete set of compound selection framework from three aspects: feature selection, relationship mapping and multi-objective optimization problem solving. In feature selection part, a feature selection method based on unsupervised spectral clustering is proposed. The selected features have more comprehensive information expression ability. In the relationship mapping part, a variety of machine learning algorithms are used for comparative experiments. Finally, the CatBoost algorithm is selected to perform the relationship mapping between each other, and better prediction performance is achieved. In the multi-objective optimization part, based on the analysis of the conflict relationship between the objectives, the AGE-MOEA algorithm is improved and used to solve this problem. Compared with various algorithms, the improved algorithm has better search performance.

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.

Predicting the Bioconcentration Factor in Fish from Molecular Structures

The bioconcentration factor (BCF) is one of the metrics used to evaluate the potential of a substance to bioaccumulate into aquatic organisms. In this work, linear and non-linear regression QSARs were developed for the prediction of log BCF using different computational approaches, and starting from a large and structurally heterogeneous dataset. The new MLR-OLS and ANN regression models have good fitting with R2 values of 0.62 and 0.70, respectively, and comparable external predictivity with R2ext 0.64 and 0.65 (RMSEext of 0.78 and 0.76), respectively. Furthermore, linear and non-linear classification models were developed using the regulatory threshold BCF >2000. A class balanced subset was used to develop classification models which were applied to chemicals not used to create the QSARs. These classification models are characterized by external and internal accuracy up to 84% and 90%, respectively, and sensitivity and specificity up to 90% and 80%, respectively. QSARs presented in this work are validated according to regulatory requirements and their quality is in line with other tools available for the same endpoint and dataset, with the advantage of low complexity and easy application through the software QSAR-ME Profiler. These QSARs can be used as alternatives for, or in combination with, existing models to support bioaccumulation assessment procedures.

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).

Research themes in machine learning applications in supply chain management using bibliometric analysis tools

Purpose

This paper conducts a Systematic Literature Review (SLR) of Machine Learning (ML) in Supply Chain Management through bibliometric and network analysis, the authors are able to grasp key features of the contemporary literature. The study makes use of state-of-the-art analytical framework based on a unified approach to reveal insights from the present body of knowledge and the potentials for future research developments.

Design/methodology/approach

Unlike standard literature reviews, in SLR, a structured approach is followed. The approach enables utilizing contemporary tools and software packages such as R-package “bibliometrix” and Gephi for exploratory and visual analytics. A number of clustering methods are employed to form clusters. Later, multivariate analysis methodologies are adopted to determine the dominant clusters for the influential co-cited references.

Findings

Using contemporary tools from Bibliometric Analysis (BA), the authors identify in an exploratory analysis, the influential authors, sources, regions, affiliations and papers. In addition, the use of network analysis tools reveals research clusters, topological analysis, key research topics, interrelation and authors’ collaboration along with their patterns. Finally, the optimum number of clusters computed for cluster analysis is decided using a systematic procedure based on multivariate analysis such as k-means and factor analysis.

Originality/value

Modern-day supply chains increasingly depend on developing superior insights from large amounts of data available from diverse sources in unstructured and semi-structured formats. In order to maintain a competitive edge, the supply chains need to perform speedy analysis of big data using efficient tools that provide real-time decision-making insights. Such an analysis necessitates automated processing using intelligent ML algorithms. Through a BA followed by a detailed data visualization in a network analysis enabled grasping key features of the contemporary literature. The analysis is based on 155 documents from the period 2008 to 2018 selected using a systematic selection procedure.

Classification-based QSARs for predicting dietary biomagnification in fish

The assessment of bioaccumulation is an important step to describe the environmental behaviour and the potential risk due to exposure to potentially hazardous chemicals. In the last two decades, several in silico tools have been made available to predict bioconcentration, which is commonly used to assess bioaccumulation in risk assessment frameworks all over the world. However, only a few QSAR studies address the prediction of the biomagnification factor (BMF), which describes the accumulation of chemicals into organisms due to exposure through the diet. No classification models are currently available to this end. In this work, we developed classification QSARs to predict classes based on dietary biomagnification, using three different classifiers (i.e. LDA, ANN and RF). We started from a recently published dataset that includes more than 300 curated dietary BMF values measured in fish. The new models have high-quality performances (accuracy in fitting: from 94 to 96%; accuracy in prediction from 84 to 86%). The good performances of the here proposed QSARs confirm the quality of the original input data and highlight the importance of data curation and data sharing to support the development of new in silico approaches to assist risk assessment and chemicals screening.

Writing assistant scoring system for English second language learners based on machine learning

To reduce the workload of paper evaluation and improve the fairness and accuracy of the evaluation process, a writing assistant scoring system for English as a Foreign Language (EFL) learners is designed based on the principle of machine learning. According to the characteristics of the data processing process and the advantages and disadvantages of the Browser/Server (B/S) structure, the equipment structure design of the project online evaluation teaching auxiliary system is further optimized. The panda method is used to read the data, the clean method is used to realize the data preprocessing, the model test is carried out, the cross validation method is selected, the data is divided in advance, and the process of programming the problem scoring system is further optimized, the automatic scoring technology is constructed by English teaching recognition module, feature extraction module and scoring module, the table structure of programming problems is designed, the auxiliary evaluation program of English writing is designed, and the design of writing auxiliary scoring system is completed. The analysis of the experimental results shows that the accuracy of the system is close to 90%, and the total average difference is 0.56. The system can normally take out a variety of test papers. Considering the subjectivity of manual scoring and the impact of key code setting on scoring, the carefully set key code can effectively improve the scoring accuracy of the system. The scoring strategy of the automatic scoring system is effective and the scoring effect is good, and it can be used in practical application.

Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach

Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks.

Use of a large dataset to develop new models for estimating the sorption of active pharmaceutical ingredients in soils and sediments

Information on the sorption of active pharmaceutical ingredients (APIs) in soils and sediments is needed for assessing the environmental risks of these substances yet these data are unavailable for many APIs in use. Predictive models for estimating sorption could provide a solution. The performance of existing models is, however, often poor and most models do not account for the effects of soil/sediment properties which are known to significantly affect API sorption. Therefore, here, we use a high-quality dataset on the sorption behavior of 54 APIs in 13 soils and sediments to develop new models for estimating sorption coefficients for APIs in soils and sediments using three machine learning approaches (artificial neural network, random forest and support vector machine) and linear regression. A random forest-based model, with chemical and solid descriptors as the input, was the best performing model. Evaluation of this model using an independent sorption dataset from the literature showed that the model was able to predict sorption coefficients of 90% of the test set to within a factor of 10 of the experimental values. This new model could be invaluable in assessing the sorption behavior of molecules that have yet to be tested and in landscape-level risk assessments.

Effect of Water pH on the Uptake of Acidic (Ibuprofen) and Basic (Propranolol) Drugs in a Fish Gill Cell Culture Model

Water pH is predicted to affect the uptake of ionizable pharmaceuticals in fish. The current study used an in vitro primary fish gill cell culture system to assess the effect of pH values in the range of 4.5-8.75 on the uptake rates of the base propranolol (pKa 9.42) and the acid ibuprofen (pKa 4.59). The rate-limiting step in the uptake was the diffusive supply flux of the unionized form from the water to the apical membrane, with subsequent rapid transfer across the epithelium. Computed uptake rate based on the unionized fraction best described the uptake of propranolol and ibuprofen over the range of pH values 5-8 and 6-8.75, respectively. For ibuprofen, the computed uptake rate overestimated the uptake below pH 6 where the unionized fraction increased from 4% at pH 6 to 55% at pH 4.5. As the unionized fraction increased, the uptake rate plateaued suggesting a saturation of the transport process. For both drugs, large variations in the uptake occur with only small fluctuations in pH values. This occurs between pH values 6 and 8, which is the pH range acceptable in regulatory test guidelines and seen in most of our freshwaters.

Multicompartment and cross-species monitoring of contaminants of emerging concern in an estuarine habitat

The fate of many chemicals in the environment, particularly contaminants of emerging concern (CEC), have been characterised to a limited extent with a major focus on occurrence in water. This study presents the characterisation, distribution and fate of multiple chemicals including pharmaceuticals, recreational drugs and pesticides in surface water, sediment and fauna representing different food web endpoints in a typical UK estuary (River Colne, Essex, UK). A comparison of contaminant occurrence across different benthic macroinvertebrates was made at three sites and included two amphipods (Gammarus pulex &Crangon crangon), a polychaete worm (Hediste diversicolor) and a gastropod (Peringia ulvae). Overall, multiple contaminants were determined in all compartments and ranged from; Collapse

Key Words

• Environmental risk
• Invertebrate
• Occurrence
• Pesticides
• Pharmaceuticals
• Sediment

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MESH Headings

• Animals
• Ecosystem
• Environmental Monitoring
• Pesticides/analysis
• Rivers
• Water Pollutants, Chemical/analysis

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Grants

• MR/S019669/1 Medical Research Council

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Affiliation(s)

Thomas H Miller Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, Kingston Lane, UB8 3PH, UK; Department of Analytical, Environmental & Forensic Sciences, School of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London, SE1 9NH, UK.
Keng Tiong Ng Department of Analytical, Environmental & Forensic Sciences, School of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London, SE1 9NH, UK; Environmental Research Group, School of Public Health, Faculty of Medicine, Imperial College London, UK
Aaron Lamphiere School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, Essex, CO43SQ, UK
Tom C Cameron School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, Essex, CO43SQ, UK
Nicolas R Bury School of Science, Technology and Engineering, University of Suffolk, James Hehir Building, University Avenue, Ipswich, Suffolk, IP3 0FS, UK; Suffolk Sustainability, University of Suffolk, Waterfront Building, Neptune Quay, Ipswich, IP4 1QJUK, UK
Leon P Barron Department of Analytical, Environmental & Forensic Sciences, School of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London, SE1 9NH, UK; Environmental Research Group, School of Public Health, Faculty of Medicine, Imperial College London, UK

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A Review on Prediction Models for Pesticide Use, Transmission, and Its Impacts

The lure of increased productivity and crop yield has caused the imprudent use of pesticides in great quantity that has unfavorably affected environmental health. Pesticides are chemicals intended for avoiding, eliminating, and mitigating any pests that affect the crop. Lack of awareness, improper management, and negligent disposal of pesticide containers have led to the permeation of pesticide residues into the food chain and other environmental pathways, leading to environmental degradation. Sufficient steps must be undertaken at various levels to monitor and ensure judicious use of pesticides. Development of prediction models for optimum use of pesticides, pesticide management, and their impact would be of great help in monitoring and controlling the ill effects of excessive use of pesticides. This paper aims to present an exhaustive review of the prediction models developed and modeling strategies used to optimize the use of pesticides.

Publishing Environmental Assessment and Management Science: Crossing the Hurdles

Benefits accrue to scientists, resource managers, companies, and policymakers when environmental scientists publish in peer-reviewed journals. However, environmental scientists and practitioners face challenges, including the sometimes low value placed on journal articles, institutional vested interests in outcomes, and the changing priorities of employers and project sponsors. Confidentiality agreements can also lead scientists to assume publication is not an option. Case studies may be viewed by potential authors as too routine for peer-reviewed journals. On the basis of 30 years of experience, we suggest that publishing hurdles can be overcome and that environmental scientists have a range of options. The topics of manuscripts can include not only results from case studies and perspectives based on them but also byproducts of assessments, including definitions, plans, monitoring methods and models, and decision frameworks. Environmental scientists have unique opportunities to move science forward with their practical knowledge if they can move across the institutional, logistical, data-related, and content-related hurdles.

Theoretical study of fluorinated bioisosteres of organochlorine compounds as effective and eco-friendly pesticides

Chlordane is a worldwide banned organochlorine insecticide because of its hazard to animal and human health. It is also a persistent organic pollutant, which can affect either the soil or the aquatic life. The same applies to other chlorinated cyclodiene insecticides, such as dieldrin and aldrin. In turn, organofluorine compounds have a widespread use in agriculture. Therefore, density functional calculations and docking studies showed that the bioisosteric replacement of chlorines in the above-mentioned compounds by fluorines improves some physicochemical parameters used to estimate the toxicity and environmental risk of these compounds, as well as the ligand-enzyme (GABA_A receptor-chloride channel complex) interactions related to their insecticidal activity. This work is an effort to provide an improved new class of organofluorine pesticides.

Examining plant uptake and translocation of emerging contaminants using machine learning: Implications to food security

When water and solutes enter the plant root through the epidermis, organic contaminants in solution either cross the root membranes and transport through the vascular pathways to the aerial tissues or accumulate in the plant roots. The accumulation of contaminants in plant roots and edible tissues is measured by root concentration factor (RCF) and fruit concentration factor (FCF). In this paper, 1) a neural network (NN) was applied to model RCF based on physicochemical properties of organic compounds, 2) correlation and significance of physicochemical properties were assessed using statistical analysis, 3) fuzzy logic was used to examine the simultaneous impacts of significant compound properties on RCF and FCF, 4) a clustering algorithm (k-means) was used to identify unique groups and discover hidden relationships within contaminants in various parts of the plants. The physicochemical cutoffs achieved by fuzzy logic for the RCF and the FCF were compared versus the cutoffs for compounds that crossed the plant root membranes and found their way into transpiration stream (measured by transpiration stream concentration factor, TSCF). The NN predicted the RCF with improved accuracy compared to mechanistic models. The analysis indicated that log K_ow, molecular weight, and rotatable bonds are the most important properties for predicting the RCF. These significant compound properties are positively correlated with RCF while they are negatively correlated with TSCF. Comparing the relationships between compound properties in various plant tissues showed that compounds detected in the edible parts have physicochemical cutoffs that are more like the compounds crossing the plant root membranes (into xylem tissues) than the compounds accumulating in the plant roots, with clear relationships to food security. The cluster analysis placed the contaminants into three meaningful groups that were in agreement with the results of fuzzy logic.

Vision of a near future: Bridging the human health-environment divide. Toward an integrated strategy to understand mechanisms across species for chemical safety assessment

There is a growing recognition that application of mechanistic approaches to understand cross-species shared molecular targets and pathway conservation in the context of hazard characterization, provide significant opportunities in risk assessment (RA) for both human health and environmental safety. Specifically, it has been recognized that a more comprehensive and reliable understanding of similarities and differences in biological pathways across a variety of species will better enable cross-species extrapolation of potential adverse toxicological effects. Ultimately, this would also advance the generation and use of mechanistic data for both human health and environmental RA. A workshop brought together representatives from industry, academia and government to discuss how to improve the use of existing data, and to generate new NAMs data to derive better mechanistic understanding between humans and environmentally-relevant species, ultimately resulting in holistic chemical safety decisions. Thanks to a thorough dialogue among all participants, key challenges, current gaps and research needs were identified, and potential solutions proposed. This discussion highlighted the common objective to progress toward more predictive, mechanistically based, data-driven and animal-free chemical safety assessments. Overall, the participants recognized that there is no single approach which would provide all the answers for bridging the gap between mechanism-based human health and environmental RA, but acknowledged we now have the incentive, tools and data availability to address this concept, maximizing the potential for improvements in both human health and environmental RA.

Neural computational model GrowthEstimate: A model for studying living resources through digestive efficiency

The neural computational model GrowthEstimate is introduced with focusing on new perspectives for the practical estimation of weight specific growth rate (SGR, % day-1). It is developed using recurrent neural networks of reservoir computing type, for estimating SGR based on the known data of three key biological factors relating to growth. These factors are: (1) weight (g) for specifying the age of the growth stage; (2) digestive efficiency through the pyloric caecal activity ratio of trypsin to chymotrypsin (T/C ratio) for specifying genetic differences in food utilization and growth potential, basically resulting from food consumption under variations in food quality and environmental conditions; and (3) protein growth efficiency through the condition factor (CF, 100 × g cm-3), as higher dietary protein level affecting higher skeletal growth (length) and resulting in lower CF. The computational model was trained using four datasets of different salmonids with size variations. It was evaluated with 15% of each dataset, resulting in an acceptable range of SGR outputs. Additional tests with different species indicated similarity between the estimated SGR outputs and the real SGR values, and the same ranking of wild population growth. The developed model GrowthEstimate is exceptionally useful for the precise and comparable growth estimation of living resources at individual levels, especially in natural ecosystems where the studied individuals, environmental conditions, food availability and consumption rates cannot be controlled. It is a revelation and will help to minimize uncertainty in wild stock assessment process. This will improve our knowledge in nutritional ecology, through the biochemical effects of climate change and environmental impact on the growth performance quality of aquatic living resources in the wild, as well as in aquaculture. The original GrowthEstimate software is available at GitHub repository (https://github.com/RungruangsakTorrissenManoonpong/GrowthEstimate). All other relevant data are within the paper. It will be improved for generality for future use, and required co-operations of the biodata collections of different species from different climate zones. Therefore, a co-operation will be available.

Biomonitoring of pesticides, pharmaceuticals and illicit drugs in a freshwater invertebrate to estimate toxic or effect pressure

Multiple classes of environmental contaminants have been found in aquatic environments, globally. Understanding internalised concentrations in the organism could further improve the risk assessment process. The present study is concerned with the determination of several contaminant classes (107 compounds) in Gammarus pulex collected from 15 sites covering 5 river catchments across Suffolk, UK. Quantitative method performance was acceptable for 67 compounds including pharmaceuticals, pesticides, illicit drugs and drugs of abuse. A total of 56 compounds were detectable and ranged from Collapse

Key Words

• Environmental risk assessment
• Exposome
• Pesticides
• Pharmaceuticals

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MESH Headings

• Animals
• Environmental Monitoring/methods
• Fresh Water
• Humans
• Illicit Drugs/chemistry
• Illicit Drugs/toxicity
• Invertebrates/chemistry
• Pesticides/chemistry
• Pesticides/toxicity
• Toxicity Tests
• Water Pollutants, Chemical/chemistry

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Grants

• MR/S019669/1 Medical Research Council

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Affiliation(s)

Thomas H Miller Department of Analytical, Environmental & Forensic Sciences, School of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London SE1 9NH, UK.
Keng Tiong Ng Department of Analytical, Environmental & Forensic Sciences, School of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London SE1 9NH, UK
Samuel T Bury St Olaves Grammer School, Goddington Lane, Orpington, BR6 9SH, UK
Sophie E Bury Department of Pyschology, Royal Holloway, University of London, Egham, Surrey TW20 0EX, UK
Nicolas R Bury School of Science, Technology and Engineering, University of Suffolk, James Hehir Building, University Avenue, Ipswich, Suffolk IP3 0FS, UK; Suffolk Sustainability Institute, University of Suffolk, Waterfront Building, Neptune Quay, Ipswich IP4 1QJ, UK
Leon P Barron Department of Analytical, Environmental & Forensic Sciences, School of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London SE1 9NH, UK

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Exploiting machine learning for end-to-end drug discovery and development

A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.

The Use of Molecular Descriptors To Model Pharmaceutical Uptake by a Fish Primary Gill Cell Culture Epithelium

Modeling approaches such as quantitative structure-activity relationships (QSARs) use molecular descriptors to predict the bioavailable properties of a compound in biota. However, these models have mainly been derived based on empirical data for lipophilic neutral compounds and may not predict the uptake of ionizable compounds. The majority of pharmaceuticals are ionizable, and freshwaters can have a range of pH values that affect speciation. In this study, we assessed the uptake of 10 pharmaceuticals (acetazolamide, beclomethasone, carbamazepine, diclofenac, gemfibrozil, ibuprofen, ketoprofen, norethindrone, propranolol, and warfarin) with differing modes of action and physicochemical properties (p K_a, log S, log D, log K_ow, molecular weight (MW), and polar surface area (PSA)) by an in vitro primary fish gill cell culture system (FIGCS) for 24 h in artificial freshwater. Principal component analysis (PCA) and partial least-squares (PLS) regression was used to determine the molecular descriptors that influence the uptake rates. Ionizable drugs were taken up by FIGCS; a strong positive correlation was observed between log S and the uptake rate, and a negative correlation was observed between p K_a, log D, and MW and the uptake rate. This approach shows that models can be derived on the basis of the physicochemical properties of pharmaceuticals and the use of an in vitro gill system to predict the uptake of other compounds. There is a need for a robust and validated model for gill uptake that could be used in a tiered risk assessment to prioritize compounds for experimental testing.