Chemometrics and Cheminformatics in Aquatic Toxicology E-Book

Kunal Roy

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1517

Veröffentlichungsjahr: 2021

Beschreibung

CHEMOMETRICS AND CHEMINFORMATICS IN AQUATIC TOXICOLOGY

Explore chemometric and cheminformatic techniques and tools in aquatic toxicology

Chemometrics and Cheminformatics in Aquatic Toxicology delivers an exploration of the existing and emerging problems of contamination of the aquatic environment through various metal and organic pollutants, including industrial chemicals, pharmaceuticals, cosmetics, biocides, nanomaterials, pesticides, surfactants, dyes, and more. The book discusses different chemometric and cheminformatic tools for non-experts and their application to the analysis and modeling of toxicity data of chemicals to various aquatic organisms.

You’ll learn about a variety of aquatic toxicity databases and chemometric software tools and webservers as well as practical examples of model development, including illustrations. You’ll also find case studies and literature reports to round out your understanding of the subject. Finally, you’ll learn about tools and protocols including machine learning, data mining, and QSAR and ligand-based chemical design methods.

Readers will also benefit from the inclusion of:

• A thorough introduction to chemometric and cheminformatic tools and techniques, including machine learning and data mining
• An exploration of aquatic toxicity databases, chemometric software tools, and webservers
• Practical examples and case studies to highlight and illustrate the concepts contained within the book
• A concise treatment of chemometric and cheminformatic tools and their application to the analysis and modeling of toxicity data

Perfect for researchers and students in chemistry and the environmental and pharmaceutical sciences, Chemometrics and Cheminformatics in Aquatic Toxicology will also earn a place in the libraries of professionals in the chemical industry and regulators whose work involves chemometrics.

Leseprobe

Table of Contents

Cover

Title Page

Copyright Page

Dedication Page

Preface

References

Part I: Introduction

1 Water Quality and Contaminants of Emerging Concern (CECs)

1.1 Introduction: Water Quality and Emerging Contaminants

1.2 Contaminants of Emerging Concern

1.3 Summary and Recommendations for Future Research

References

2 The Effects of Contaminants of Emerging Concern on Water Quality

2.1 Introduction

2.2 Assessing the Effects of CECs in Aquatic Life

2.3 Multiple Stressors

2.4 Conclusions

Acknowledgments

References

3 Chemometrics:

3.1 Introduction

3.2 Historic Origins

3.3 Applied Statistics

3.4 Analytical and Physical Chemistry

3.5 Scientific Computing

3.6 Development from the 1980s

3.7 A Review of the Main Methods

3.8 Experimental Design

3.9 Principal Components Analysis and Pattern Recognition

3.10 Multivariate Signal Analysis

3.11 Multivariate Calibration

3.12 Digital Signal Processing and Time Series Analysis

3.13 Multiway Methods

3.14 Conclusion

References

4 An Introduction to Chemometrics and Cheminformatics

4.1 Brief History of Chemometrics/Cheminformatics

4.2 Current State of Cheminformatics

4.3 Common Cheminformatics Tasks

4.4 Cheminformatics Toolbox

4.5 Conclusion

References

Part II: Chemometric and Cheminformatic Tools and Protocols

5 An Introduction to Some Basic Chemometric Tools

5.1 Introduction

5.2 Example Datasets

5.3 Data Analytical Methods

5.4 Results

5.5 Discussion

References

6 From Data to Models: Mining Experimental Values with Machine Learning Tools

6.1 Introduction

6.2 Data and Models

6.3 Basic Methods in Model Development with ML

6.4 More Advanced ML Methodologies

6.5 Deep Learning

6.6 Conclusions

References

Note

7 Machine Learning Approaches in Computational Toxicology Studies

7.1 Introduction

7.2 Toxicity Data Set Preparation

7.3 Machine‐Learning Techniques

7.4 Model Evaluation

7.5 Freely Available Software Tools and Open‐Source Libraries Relevant to Machine Learning

7.6 Concluding Remarks

Acknowledgment

References

8 Counter‐Propagation Neural Networks for Modeling and Read Across in Aquatic (Fish) Toxicity

8.1 Introduction

8.2 Examples of Counter‐Propagation Artificial Neural Networks in Fish Toxicity Modeling

8.3 Counter‐Propagation Artificial Neural Networks

8.4 Conclusions

References

9 Aiming High versus Aiming All:

9.1 Introduction

9.2 Multitarget QSARS and Aquatic Toxicology

9.3 Biotargets and Aqueous Environmental Assessment: Solutions and Recommendations

9.4 Future Perspectives and Conclusion

References

10 Chemometric Approaches to Evaluate Interspecies Relationships and Extrapolation in Aquatic Toxicity

10.1 Introduction

10.2 Acute Toxicity Estimation

10.3 Sublethal Toxicity Extrapolation

10.4 Discussion

10.5 Conclusions

Disclaimer

References

Part III: Case Studies and Literature Reports

11 The QSAR Paradigm to Explore and Predict Aquatic Toxicity

11.1 Introduction

11.2 Application of QSAR Methodology to Predict Aquatic Toxicity

11.3 QSAR for Narcosis – The Impact of Hydrophobicity

11.4 Excess Toxicity – Overview

11.5 Predictions of Bioconcentration Factor

11.6 Conclusions

References

12 Application of Cheminformatics to Model Fish Toxicity

12.1 Introduction

12.2 Fish Toxicities

12.3 Toxicity in Fish Families and Species

12.4 The Fathead Minnow, the Rainbow Trout, and the Bluegill

12.5 Toxicity Variations in FIT Compounds

12.6 Modeling Wide‐Range Toxicity Compounds

12.7 Further Evaluations

12.8 Alternative Approaches

12.9 Mechanisms of Action

12.10 Conclusions

Acknowledgments

References

13 Chemometric Modeling of Algal and Daphnia Toxicity

13.1 Introduction

13.2 Algae Class

13.3 Daphniidae Family

13.4 Interspecies Correlation Estimation for Algal and Daphnia Aquatic Toxicity

13.5 Conclusions

References

14 Chemometric Modeling of Algal Toxicity

14.1 Introduction

14.2 Criteria Set for the Comparison of Selected QSAR Models

14.3 Literature MLR Studies on Algae

14.4 Conclusion

References

15 Chemometric Modeling of Daphnia Toxicity

15.1 Introduction

15.2 QSTR and QSTTR Analyses

15.3 QSTR/QSTT/QSTTR Modeling of

Daphnia

Toxicity

15.4 Mechanistic Interpretations of Chemometric Models

15.5 Conclusive Remarks and Future Directions

Acknowledgment

References

16 Chemometric Modeling of Daphnia Toxicity: Quantum‐Mechanical Insights

16.1 Introduction

16.2 Quantum‐Mechanical Methods

16.3 Quantum‐Mechanical Descriptors for Daphnia Toxicity

16.4 Concluding Remarks and Future Outlook

References

17 Chemometric Modeling of Toxicity of Chemicals to Tadpoles

17.1 Introduction

17.2 Overview and Morphology of Tadpoles

17.3 Reports of Tadpole Toxicity Due Various Environmental Contaminants: What Do We Know So Far?

17.4

In silico

Models Reported for Tadpole Ecotoxicity: A Literature Review

17.5 Application of QSARs or Related Approaches in Modeling Tadpole Toxicity: A Future Perspective

17.6 Conclusion

Acknowledgment

References

18 Chemometric Modeling of Toxicity of Chemicals to Marine Bacteria

18.1 Introduction

18.2 Marine Bacteria and Their Role in Nitrogen Fixing

18.3 Marine Bacteria as Biomarkers for Ecotoxicity Estimation

18.4 Chemometric Tools Applied in Ecotoxicity Evaluation of Marine Bacteria

18.5 Conclusion

Acknowledgment

References

19 Chemometric Modeling of Pesticide Aquatic Toxicity

19.1 Introduction

19.2 QSARs Models

19.3 Conclusions

References

20 Contribution of Chemometric Modeling to Chemical Risks Assessment for Aquatic Plants: State‐of‐the‐Art

20.1 Introduction

20.2 Definition and Classification

20.3 Advantage of Aquatic Plants

20.4 Contaminants and Their Toxicity

20.5 Chemometrics for Aquatic Plants Toxicity

20.6 Review of Literature on Chemometrics for Aquatic Plants Toxicity

20.7 Conclusions

References

21 Application of 3D‐QSAR Approaches to Classification and Predictionof Aquatic Toxicity

21.1 Introduction

21.2 Principles of CAPLI 3D‐QSAR

21.3 Applications in Chemical Classification and Toxicity Prediction

21.4 Limitation and Potential Improvement

21.5 Conclusions and Recommendations

Acknowledgments

References

22 QSAR Modeling of Aquatic Toxicity of Cationic Polymers

22.1 Introduction

22.2 Materials and Methods

22.3 Results and Discussion

22.4 Conclusions

Acknowledgments

References

Part IV: Tools and Databases

23

In Silico

Platforms for Predictive Ecotoxicology

23.1 Introduction

23.2 Machine Learning and Deep Learning

23.3 Toxicity Prediction Modeling

23.4 Challenges and Future Directions

References

24 The Use and Evolution of Web Tools for Aquatic Toxicology Studies

24.1 Introduction

24.2 Methodologies Used in Aquatic Toxicology Tests

24.3 Web Tools Used in Aquatic Toxicology

24.4 Perspectives

References

25 The Tools for Aquatic Toxicology within the VEGAHUB System

25.1 Introduction

25.2 The VEGA Models

25.3 ToxRead and Read‐Across Within VEGAHUB

25.4 Prometheus and JANUS

25.5 The Future Developments

25.6 Conclusions

References

26 Aquatic Toxicology Databases

26.1 Introduction

26.2 Aquatic Toxicity

26.3 Importance of Aquatic Toxicity Databases

26.4 Characteristic of an Ideal Aquatic Toxicity Database

26.5 Aquatic Toxicology Databases

26.6 Overview and Conclusion

Acknowledgments

Conflicts of Interest

References

27 Computational Tools for the Assessment and Substitution of Biocidal Active Substances of Ecotoxicological Concern

27.1 Introduction

27.2 Database Compilation

27.3 Development of the QSAR Models

27.4 Prediction of Metabolites and their Associated Toxicity

27.5 Implementation of the

In Silico

QSARs Within VEGA and Integration with Read Across Models in ToxRead

27.6 Implementation of the LIFE‐COMBASE Decision Support System

27.7 Implementation of the LIFE‐COMBASE Mobile App

27.8 Concluding Remarks

Acknowledgments

References

28 Image Analysis and Deep Learning Web Services for Nano informatics

28.1 Introduction

28.2 NanoXtract

28.3 DeepDaph

28.4 Conclusions

Acknowledgments

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 List of emerging contaminants of concern in water environments.

Chapter 2

Table 2.1 Overview of classes, concentrations, and examples of Contaminants ...

Chapter 4

Table 4.1 List of software tools for performing cheminformatics tasks.

Table 4.2 List of selected libraries, packages, and APIs for performing chem...

Chapter 6

Table 6.1 Examples of substructures extracted by SmilesNet from molecules wh...

Table 6.2 Frequency of some substrings in the dataset.

Table 6.3 Some examples of coherence between the substring found in the mole...

Chapter 7

Table 7.1 List of freely available software tools and open‐source libraries ...

Chapter 8

Table 8.1 Read‐across example for 4‐chlorophenol.

Chapter 9

Table 9.1 Selected ecotoxicity predictive mt‐QSARs per machine learning tool...

Table 9.2 Statistical parameters and measures values

per

selected mt‐QSAR wo...

Table 9.3 Results of the models external validation

per

bio‐target type.

Chapter 10

Table 10.1 Publicly available tools and databases that can facilitate the ev...

Chapter 12

Table 12.1 A comparative overview of the fish data in EnviroTox and its subs...

Table 12.2 A description of the activity values (pAC

50

) in the most represen...

Table 12.3 DT3 evaluation results in discriminating WRCTs from NRTCs using F...

Table 12.4 DT3 evaluation in FITPOL.

Table 12.5 Consensus MoA according to Envirotox in FITL10 (used for DT3 cons...

Chapter 13

Table 13.1 Molecular descriptors, toxicity data, modeling approaches used i...

Table 13.2 The selected models developed against algae using various endpoin...

Table 13.3 Various released QSARs involving

Daphnia magna

endpoints.

Table 13.4 The selected models developed on

Daphnia magna

using various endpo...

Table 13.5 The interspecies correlation estimation selected models developed...

Chapter 14

Table 14.1 Descriptors appearing in the literature algal QSAR models and the...

Table 14.2 Literature algal MLR models, descriptors appearing in models, and...

Table 14.3 External validation metrics of QSAR models listed in Table 14.2.

Chapter 15

Table 15.1 Chemometric models reported on daphnia toxicity of ILs.

Table 15.2 Chemometric models reported on daphnia toxicity of NMs and surfac...

Table 15.3 Chemometric models reported on daphnia toxicity of agrochemicals.

Table 15.4 Chemometric models reported on daphnia toxicity of pharmaceutical...

Table 15.5 Chemometric models reported on daphnia toxicity of compounds that...

Table 15.6 Selected chemometric models reported on daphnia toxicity of diver...

Chapter 16

Table 16.1 Quantum‐mechanical descriptors commonly employed in the chemometr...

Table 16.2 Quantum‐mechanical methods for the computation of quantum‐mechani...

Table 16.3 Models utilizing quantum mechanical descriptors for Daphnia toxic...

Chapter 17

Table 17.1 Type of tadpole species so far used for toxicity evaluation of co...

Table 17.2 Toxicity endpoints and their types for studies performed on ecoto...

Table 17.3 Suitable sites available in tadpoles for studying various ecotoxi...

Table 17.4 Few important publications in the last few decades [29–83] with ...

Chapter 19

Table 19.1 Interspecies correlations of toxicity to four different aquatic ...

Chapter 20

Table 20.1 Toxic effects of contaminants on different aquatic plants.

Chapter 21

Table 21.1 Common chemical descriptors for QSAR analysis.

Table 21.2 Data sets for 3D‐QSAR development.

Table 21.3 External validation of CAPLI 3D‐QSAR combined with

in silico

site...

Chapter 22

Table 22.1 Blinded cationic polymer dataset [1].

Chapter 23

Table 23.1 QSAR models for predicting adverse ecotoxicity outcomes.

Chapter 24

Table 24.1 Descriptions of the available web tools for use in aquatic toxic...

Chapter 25

Table 25.1 The list of the models for aquatic toxicity present in VEGA.

Table 25.2 The parameters used within VEGA to measure the applicability dom...

Chapter 26

Table 26.1 Types of frequently performed aquatic toxicity tests.

Table 26.2 OECD testing guidelines for different species under aquatic toxi...

Table 26.3 Majorly employed toxicity parameters in aquatic toxicology.

Table 26.4 Major search parameter and filtering option under the ECOTOX dat...

Table 26.5 Major features of the ERED database.

Table 26.6 Sources of ecotoxicological data, physico‐chemical information, ...

Table 26.7 Details of MOA, chemical class, and number of chemicals along wi...

Chapter 27

Table 27.1 Complete list of metabolic reactions and SMART strings.

Chapter 28

Table 28.1 Image descriptors calculated using the NanoXtract web service, a...

List of Illustrations

Chapter 2

Figure 2.1 Number of manuscripts published with either “Contaminant of emerg...

Figure 2.2 Contaminants of emerging concern (CECs) commonly include new comp...

Figure 2.3 Measured environmental concentrations (ng l

−1

) for a range ...

Chapter 5

Figure 5.1 Notation used in principal components analysis (PCA). The observa...

Figure 5.2 PCA derives a model that fits the data as well as possible in the...

Figure 5.3 Some terminology often used in conjunction with multivariate regr...

Figure 5.4 Loading plot of the PCA model based on the eight

Y

‐variables of E...

Figure 5.5 Correlation matrix of the eight

Y

‐variables of Example 1. The cor...

Figure 5.6 Score plot of the PCA model based on the eight

Y

‐variables of Exa...

Figure 5.7 (a) (above) and (b) (below): Loading plots of the PLS (above) and...

Figure 5.8 (a) (above) and (b) (below): Column plots of the OPLS model loadi...

Figure 5.9 Overview of the individual R2Y/Q2Y‐values of the multiresponse QS...

Figure 5.10 PLS

t

1

/

u

1

score plot of Example 1. No strong outliers are found ...

Figure 5.11 PLS weight plot of the multiresponse QSAR model of Example 1. Fo...

Figure 5.12 (a) (above) and (b) (below): Scatter plots in the first two fact...

Figure 5.13 PLS regression coefficients for the first response variable of...

Figure 5.14 Response contour plot for the first response variable of Example...

Chapter 6

Figure 6.1 Evolution of AI methods.

Figure 6.2 Different representations of clusters: Table representation, Venn...

Figure 6.3 An example of covering rules.

Figure 6.4 Dataset and its functional groups, the support (number of occurre...

Figure 6.5 Couples and triples of groups.

Figure 6.6 The perceptron neural net.

Figure 6.7 Different models represented as points in a ROC diagram. The best...

Figure 6.8 Model error as a function of model complexity. Bias decreases wit...

Figure 6.9 REC curves of three models; in the box the AOC value is in parent...

Figure 6.10 Classifiers fusion, on the left, and classifiers selection on th...

Figure 6.11 Constructing the alerts in SARpy.

Figure 6.12 The architecture of SARpy.

Figure 6.13 Applying SARpy in steps to reduce the wrongly predicted molecule...

Figure 6.14 The maximum margin hyperplane for a 2 class problem. The element...

Figure 6.15 A schematic representation of a feedforward neural network. Neur...

Figure 6.16 The sigmoid function.

Figure 6.17 The ReLU function.

Figure 6.18 A net before and after dropout.

Figure 6.19 Basic structure of a CNN. The input image is fed to the network ...

Figure 6.20 RNN as a loop and as a deep network.

Figure 6.21 Fragments of interest found by Toxception. The pictures have the...

Figure 6.22 Examples of substrings of interest found by SmilesNet.

Chapter 7

Figure 7.1 Standard

k

‐means clustering algorithm.

Figure 7.2 Demonstration of agglomerative hierarchical clustering algorithm ...

Figure 7.3 A sample scatter plot illustrating the simple linear regression w...

Figure 7.4 Logistic regression curves. (a) Plot of logarithm of the odds (lo...

Figure 7.5 Linear discriminant analysis: projecting the data into newly form...

Figure 7.6

k

‐Nearest neighbor algorithm for classification.

Figure 7.7 A simple example showing the usage of “naïve Bayes” technique for...

Figure 7.8 Simple example illustrating a random forest classifier employed f...

Figure 7.9 Illustration of hard margin and soft margin support vector machin...

Figure 7.10 Support vector machines using polynomial kernel function of degr...

Figure 7.11 A simplified artificial neural networks architecture with an inp...

Figure 7.12 Demonstration of semi‐supervised learning to build a classifier ...

Chapter 8

Figure 8.1 Elements of CPNN model optimization using parallel implementation...

Figure 8.2 Section of Kohonen top‐map showing chlorinated compounds. The top...

Figure 8.3 A scheme of counter‐propagation neural network. An input object (...

Chapter 10

Figure 10.1 Approaches for interspecies extrapolation of acute toxicity: (a)...

Figure 10.2 Omics‐based approaches for adverse outcome pathway (AOP)‐based i...

Figure 10.3 Examples of two different approaches can be used to investigate ...

Chapter 11

Figure 11.1 Steps and tasks to build a QSAR/QSTR model.

Figure 11.2 Multiplicity in aquatic toxicity QSAR/QSTR landscape.

Chapter 12

Figure 12.1 Histograms showing the distributions of FIT compounds in terms o...

Figure 12.2 An overview of the top 10 taxonomic families in terms of the num...

Figure 12.3 Venn diagram between

Pimephales promelas

(Pp; fathead minnow),

O

...

Figure 12.4 Mean compound pAC

50

plotted against the corresponding pAC

50

rang...

Figure 12.5 Molecular descriptors sorted according to the variable importanc...

Figure 12.6 DT3 classification tree for WRTCs (wide‐range toxicity compounds...

Figure 12.7 Boxplot showing the relative distribution of WRTCs and NRTCs in ...

Figure 12.8 Chemical depictions of close representatives pairs of NRTC and W...

Chapter 13

Figure 13.1 Workflow scheme.

Figure 13.2 Classification of QSAR models for acute aquatic toxicity.

Figure 13.3 Daphnia species examples and the most two preferred organisms fo...

Chapter 14

Figure 14.1 Freshwater algae,

Chlorella vulgaris

, under the continuous light...

Chapter 17

Figure 17.1 Schematic representation of tadpole with its anatomy.

Figure 17.2 A line plot representing an exponential rise in the literature o...

Chapter 18

Figure 18.1 Schematic representation of nitrification process in marine envi...

Figure 18.2 Examples of some simple organic chemicals evaluated for their ha...

Figure 18.3 Examples of some widely used ionic liquids (ILs) in the industry...

Chapter 20

Figure 20.1 Class types of the aquatic plants.

Figure 20.2 Major contaminants removed by aquatic plants.

Chapter 21

Figure 21.1 Flowchart of the iterative procedure for CAPLI 3D‐QSAR model dev...

Figure 21.2 Conformations of His524 in ERα (PDB IDs: 2YJA, 4IVY, and 4IWC). ...

Figure 21.3 Geometric criteria used to identify interactions between protein...

Figure 21.4 Two dimensional description of 3D‐pharmacophore map for AChE. In...

Figure 21.5 Description of hydrophobic interactions on the protein–ligand co...

Figure 21.6 Comparison of the predicted binding modes of (a) donepezil (1, A...

Figure 21.7 Analysis of quantitative contributions of interactions to toxici...

Chapter 22

Figure 22.1 PQ6 structure.

Figure 22.2 Distribution of PQ6 aquatic toxicities.

Figure 22.3 VIP plot of the PLS model for prediction of cationic polymer acu...

Figure 22.4 Loading plot of the PLS model for prediction of cationic polymer...

Figure 22.5 Randomization plot (unitless) of the PLS model for prediction of...

Figure 22.6 AD plot of training set compounds of the PLS model generated for...

Figure 22.7 AD plot of test set compounds of the PLS model generated for pre...

Figure 22.8 VIP plot of the PLS model for prediction of cationic polymer acu...

Figure 22.9 Loading plot of the PLS model for prediction of cationic polymer...

Figure 22.10 Randomization plot of the PLS model for prediction of cationic ...

Figure 22.11 AD plot of the PLS model generated for prediction of cationic p...

Figure 22.12 VIP plot of the PLS model for prediction of cationic polymer ch...

Figure 22.13 Loading plot of the PLS model for prediction of the of cationic...

Figure 22.14 Randomization plot of the PLS model for prediction of cationic ...

Figure 22.15 AD plot of the PLS model generated for prediction of the polyme...

Figure 22.16 Experimental acute fish toxicity and predicted acute

D. magna

t...

Figure 22.17 Experimental fish toxicity and predicted acute green algae acut...

Figure 22.18 Experimental acute green algae toxicity and predicted daphnia t...

Chapter 23

Figure 23.1

In silico

modeling frameworks: support vector machine, random fo...

Figure 23.2 Schematic of the procedure of machine learning‐based ecotoxicity...

Chapter 24

Figure 24.1 Representation of the Ambit platform. Available in: http://ambit...

Figure 24.2 Representation of the OCHEM platform. Available in: https://oche...

Figure 24.3 Representation of the ECHA platform. Available in: https://echa....

Figure 24.4 Representation of the REACH platform. Available in: https://echa...

Figure 24.5 Representation of the OECD platform. Available in: https://www.o...

Chapter 26

Figure 26.1 Word cloud of discussed aquatic toxicology databases in the pres...

Figure 26.2 Toxicity search of 2,3,4,6‐tetrachlorophenol to channel catfish ...

Figure 26.3 Screenshot of the ECOTOX database.

Figure 26.4 Screenshot of different search options under the ERED database....

Chapter 27

Figure 27.1 Statistics of animals sacrificed annually in EU for scientific a...

Figure 27.2 Welcome screen to the on line LIFE‐COMBASE Decision Support Syst...

Figure 27.3 Database search engine request screen.

Figure 27.4 Database search engine results’ screen.

Figure 27.5 Metabolites research screen.

Figure 27.6 Aquatic toxicity prediction screen.

Figure 27.7 Radical replacer module.

Chapter 28

Figure 28.1 An ENM released to the environment undergoes transformation by r...

Figure 28.2 The NanoXtract user‐interface and uploading environment. The use...

Figure 28.3 (a) The user defines the image scale by ticking the “Activate Ca...

Figure 28.4 NanoXtract stepwise computational workflow for image processing ...

Figure 28.5 Stepwise image processing for the extraction of image descriptor...

Figure 28.6 End‐result image following (a) image processing and (b) statisti...

Figure 28.7 Schematic representation of the parameters needed for image desc...

Figure 28.8 Calculation of the mean values and standard deviations of 18 ima...

Figure 28.9 Areas of interest identified, studied and classified through Dee...

Figure 28.10 Predictive deep learning workflow for the classification of Dap...

Figure 28.11 Image uploading and metadata import in the DeepDaph online tool...

Figure 28.12 Acquired results following the analysis of a daphnid exposed to...

Guide

Cover Page

Title Page

Copyright Page

Dedication Page

Preface

Table of Contents

Begin Reading

Index

Wiley End User License Agreement

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Chemometrics and Cheminformatics in Aquatic Toxicology

Edited by

Kunal Roy

Professor in the Department of Pharmaceutical TechnologyJadavpur UniversityKolkata, India

This first edition first published 2022© 2022John Wiley & Sons, Inc.

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Preface

Chemometric tools are statistical methods used to design or select optimal procedures and experiments and to provide maximum chemical information by analyzing data obtained from application of computer and information sciences to a range of problems in the field of chemistry (the latter field being known as Cheminformatics). Aquatic Toxicology deals with both laboratory and field studies that increase the understanding of the impact of harmful substances (both natural and synthetic chemicals) on aquatic organisms and ecosystems. In recent years, the toxicology literature has witnessed a great extent of successful application of Chemometric and Cheminformatic tools in modeling and analyzing data obtained from the field of Aquatic Toxicology.

Metal and organic contaminants are constantly released into aquatic systems from several sources including industrial and domestic sewage discharges, mining, farming, electronic waste, climate change events like floods, etc. [1]. The pollutants dissolved in water are subsequently absorbed by aquatic organisms representing three trophic levels, i.e. vertebrates (fish), invertebrates (crustaceans such as Daphnia), and plants (algae) inducing a wide range of biological effects, including being lethal at higher concentrations. They could induce toxic effects disturbing organisms' growth, metabolism, or reproduction with consequences to the entire trophic chain, including on humans. Some of the contaminants or their metabolites are not completely removed in wastewater treatment systems, and therefore could persist long enough to enter drinking water systems. Human exposure could then occur from consumption of water or by consumption of aquatic organisms such as fish which have accumulated pollutant residues with biomagnification [2–5]. Biomagnification is the increasing concentration of a substance in the tissues of tolerant organisms at successively higher levels in a food chain, which can occur as a result of persistence, food chain energetics or low or nonexistent rate of internal degradation or excretion of the substance. Different chemometric and cheminformatic tools are used for classification, pattern recognition, and clustering of aquatic toxicity data. Classification and regression based models can be developed based on graded and quantitative aquatic toxicity data, respectively, and these may help in data gap filling for toxicity of compounds which have remained untested so far. Interspecies correlation may also bridge data gaps, especially for higher level organisms. In silico modeling techniques are also in consonance with the recommendation of regulatory bodies such as Organization for Economic cooperation and Development (OECD) and regulations such as Registration, Authorization, and Restriction of Chemicals (REACH) in the European Union [6].

This volume introduces to the readers the existing and emerging problems of contamination of the aquatic environment due to various metal and organic pollutants (including industrial chemicals, pharmaceuticals, cosmetics, biocides, nanomaterials, pesticides, surfactants, dyes, etc.) and the resultant effects on water quality, chemical threat to the aquatic organisms and consequent effects on human health. The book discusses different chemometric and cheminformatic tools for the nonexperts and their application in analyzing and modeling toxicity data of chemicals with respect to various aquatic organisms. Multispecies aquatic toxicity modeling demonstrates the data gap filling in absence of toxicity data for a particular aquatic species. Various aquatic toxicity databases and chemometric software tools and webservers are also covered. This volume has four distinct sections.

The first section gives an introduction to contaminants of emerging concern and their impact on the water quality in the first two chapters (Chapter 1 contributed by García‐Fernández et al., and Chapter 2 contributed by Schoenfuss) followed by an introduction to Chemometrics and Cheminfornatics in the next two chapters (Chapter 3 contributed by Brereton, and Chapter 4 contributed by Nantasenamat).

The next section deals with different chemometric and cheminformatic tools. The first chapter of this section authored by Eriksson and others discusses different chemometric tools. The second chapter (contributed by Gini and Benfenati) and the third chapter (authored by Ambure and colleagues) of this section deal with machine learning tools while the fourth chapter (contributed by Vračko and Drgan) focuses on counter‐propagation artificial neural network modeling and read‐across. Moura and Cordeiro have discussed in the next chapter multi‐target quantitative structure–activity relationship (QSAR) models while Raimondo and Colleagues have highlighted interspecies toxicity modeling in the last chapter of this section.

The third section deals with Case Studies and Literature Reports. Tsopelas and Tsantili‐Kakoulidou, in the first chapter of this section, discuss the role of QSAR in the prediction of aquatic toxicity. Ilia and colleagues contribute the second chapter of this section on application of cheminformatics to model fish toxicity and the third chapter on chemometric modeling of daphnia and algal toxicity. Modeling of algal and daphnia toxicity is also covered by the next two chapters contributed by Saçan and Colleagues and Halder and Cordeiro, respectively. Application of quantum mechanical approach in modeling daphnia toxicity is discussed by Reenu and Vikas in the next chapter. Khan and Roy have covered chemometric modeling of chemical toxicity to tadpoles and marine bacteria in the next two chapters. Bora and Funar‐Timofei discuss chemometric modeling of pesticide aquatic toxicity in the ninth chapter of this section. The next chapter contributed by Amrane and Colleagues deals with chemometric modeling of toxicity to aquatic plants. Lee and Barron cover application of 3D‐QSAR approaches to classification and prediction of aquatic toxicity in the eleventh chapter of the section. The last chapter of the section is contributed by Sanderson and colleagues, and it deals with ecotoxicological QSAR modeling of cationic polymers.

The last section of this book deals with Tools and Databases. Lee and Sung in the first chapter of this section discuss in silico platforms for predictive toxicology. Scotti and colleagues contribute the next chapter on the use and evolution of web tools for aquatic toxicology studies. Benfenati and colleagues cover the VEGAHUB system for aquatic toxicology in the next chapter. Another chapter on aquatic toxicity databases is contributed by Kar and Leszczynski. Gozalbes and colleagues present the computational tools for the ecotoxicological assessment of biocidal compounds developed under the COMBASE project. The last chapter of the book on image analysis and deep learning web services for nanoinformatics is contributed by Afantitis and colleagues.

This collection of 28 chapters presents the current status and recent developments in the applications of chemometrics and cheminformatics in predictive aquatic toxicology modeling, especially in the context of different EU regulations. This book will certainly update the readers of the field with current practices and introduce to them newer developments, and hence should be very useful for the researchers in academia, industries, and regulatory bodies.

References

1

Gheorghe, S., Stoica, C., Vasile, G.G. et al. (2017). Metals toxic effects in aquatic ecosystems: modulators of water quality. In:

Water Quality

(ed. H. Tutu). IntechOpen

https://doi.org/10.5772/65744

.

2

Klatte, S., Schaefer, H.‐C., and Hempel, M. (2017). Pharmaceuticals in the environment – a short review on options to minimize the exposure of humans, animals and ecosystems.

Sustainable Chemistry and Pharmacy

5: 61–66.

3

Cunningham, V.L., Binks, S.P., and Olson, M.J. (2009). Human health risk assessment from the presence of human pharmaceuticals in the aquatic environment.

Regulatory Toxicology and Pharmacology

53: 39–45.

4

Mansour, F., Al‐Hindi, M., Saad, W., and Salam, D. (2016). Environmental risk analysis and prioritization of pharmaceuticals in a developing world context.

Science of the Total Environment

557–558: 31–43.

5

Singh, K.P., Gupta, S., Kumar, A., and Mohan, D. (2014). Multispecies QSAR modeling for predicting the aquatic toxicity of diverse organic chemicals for regulatory toxicology.

Chemical Research in Toxicology

27 (5): 741–753.

6

Kar, S., Sanderson, H., Roy, K. et al. (2020). Ecotoxicological assessment of pharmaceuticals and personal care products using predictive toxicology approaches.

Green Chemistry

22: 1458–1516.

Kolkata, India

Kunal Roy

2020

Part IIntroduction

1Water Quality and Contaminants of Emerging Concern (CECs)

Antonio Juan García-Fernández1, Silvia Espín1, Pilar Gómez-Ramírez1, Pablo Sánchez-Virosta2, and Isabel Navas1

1 Toxicology and Risk Assessment Group, Department of Health Sciences, Faculty of Veterinary, Biomedical Research Institute of Murcia (IMIB-Arrixaca), University of Murcia, Murcia, Spain

2 Toxicology Group, Department of Health Sciences, Faculty of Veterinary, University of Murcia, Murcia, Spain

1.1 Introduction: Water Quality and Emerging Contaminants

Water is a scarce resource globally and the pollution‐driven deterioration of its quality further limits its availability. Freshwater pollution is mainly caused by the discharge of large amounts of untreated or insufficiently treated wastewater into aquatic environments, like aquifers, rivers, lakes, and coastal waters. Therefore, water quality is an important concern around the planet and during the twenty‐first century will represent one of the main challenges for our societies. This challenge will be of huge proportions in both industrialized and developing countries. However, water quality is actually not just a health issue. According to UNESCO, public health, sustainable livelihoods, gender equality, poverty reduction, conservation of the ecosystems, and equitable social and economic developments are highly dependent on water quality [1]. This should justify why in 2010 the United Nations General Assembly (RES/64/292) recognized access to clean and safe drinking water and sanitation as a human right for our health and well‐being.

The water quality survey carried out in 2002 by the US Geological Survey revealed the omnipresence of pharmaceuticals and hormones in the environment [2]. Almost 30 years before, organic compounds including pharmaceuticals had already been identified in domestic wastewaters by gas chromatography/mass spectrometry (GC/MS) [3]. However, the significant increase in the number of studies on the so‐called emerging contaminants (ECs) has only been possible thanks to the technological advances in high‐performance liquid chromatography/mass spectrometry (HPLC/MS) during the last two decades, achieving sufficient sensitivity to quantify organic compounds at concentrations below μg l−1[4].

Broadly speaking, ECs could be defined as any chemical product, whether synthetic or natural, or any microorganism with potential to induce adverse health effects on human and the environment, and for which there are no controls or regulations on their presence in the environment. Therefore, ECs constitute a heterogeneous class of pollutants, which includes different groups of contaminants such as pharmaceuticals, personal care products (PCPs), pesticides, flame retardants, industrial additives, surfactants, plasticizers, nanomaterials, and mycotoxins, among others (Table 1.1). Metabolites and natural hormones should also be included. In addition, it should be kept in mind that ECs suffer different processes (biodegradation, oxidation, hydrolysis, chemical reduction, and photolysis) generating other transformed products that, in some cases, are more toxic or more persistent than the parent compound [4, 5]. There are many technological obstacles to remove them with immediate effectiveness from wastewater treatment plants (WWTPs), releasing them at residual concentrations into surface and groundwater. This is a cause of concern because some of them may trigger the development of antibiotic resistance in pathogen bacteria, chronic toxicity, and endocrine disruption in humans and aquatic wildlife [1, 6].

Table 1.1 List of emerging contaminants of concern in water environments.

Group of contaminant

Type of compound

Examples

Pharmaceuticals

Human and veterinary antibiotics

Trimethoprim, ciprofloxacin, enrofloxacin, erythromycin, amoxicillin, lincomycin, sulfamethoxazole, chloramphenicol, triclosan

Analgesics, anti‐inflammatory drugs

Ibuprofen, diclofenac, paracetamol, codein, acetaminophen, acetylsalicylic acid, fenoprofen, ketoprofen, paracetamol, phenazone, and propyphenazone

Psychiatric drugs

Diazepam, carbamazepine, primidone, salbutamol, fluoxetine

β‐Blockers

Metoprolol, propranolol, timolol, atenolol, sotalol

Lipid regulators

Bezafibrate, clofibric acid, fenofibric acid, etofibrate, gemfibrozil

X‐ray contrasts

Iopromide, iopamidol, diatrizoate

Hormones and steroids

Estradiol, estrone, estriol, diethylstilbestrol (DES)

Personal care products (PCPs)

UV filters

4‐Methyl‐benzylidene‐camphor (4MBC), ethylhexyl‐methoxycinnamate (EHMC), benzophenone‐3 (BP‐3), octocrylene (OC)

Fragrances

Nitro (e.g. ketone or xylene), polycyclic (e.g. galaxolide, tonalide, celestolide, phantolide), macrocyclic, and alicyclic musks

Preservatives

Parabens (alkyl esters of

p

‐hydroxybenzoic acid)

Antiseptics

Triclosan, triclocarban, chlorophene

Insect repellents

N

,

N

‐Diethyl‐

m

‐toluamide (DEET)

Nanomaterials

Metals and metal oxides, carbonaceous, silicon nanomaterials, organics, ceramics, polymers

Silver (Ag), titanium dioxide (TiO

2

), zinc oxide (ZnO), carbon‐based nanomaterials

Plasticizers

Phthalates, bisphenol A (BPA)

Surfactants and surfactant metabolites

Alkylphenol ethoxylates, 4‐nonylphenol, 4‐octylphenol, alkylphenol carboxylates

Flame retardants

Chlorinated flame retardants

Polychlorinated biphenyls, dechlorane plus, polychlorinated dibenzodioxins, and polychlorinated dibenzofurans

Brominated flame retardants

Polybrominated diphenyl ethers (PBDEs): polybrominated biphenyls (PBBs), TBBPA, HBCD, decabromodiphenyl ethane or 1,2‐bis (pentabromodiphenyl) ethane (DBDPE), 1,2‐bis (2,4,6‐tribromophenoxy) ethane (BTBPE), 2‐ethylhexyl‐2,3,4,5‐tetrabromobenzoate (TBB or EHTBB), bis(2‐ethylhexyl)‐3,4,5,6‐tetrabromo‐phthalate (TBPH or BEHTBP), tetrabromobisphenol A–bis (2,3‐dibromopropylether) (TBBPA–DBPE), and hexachlorocyclopentadienyldibromo‐cyclooctane (HCDBCO)

Fluorinated flame retardants

Perfluorotoctane sulfonate (PFOS), perfluoroctanoic acid (PFOA)

Organophosphate flame retardants and plastizicers

Tris(2‐chloroethyl) phosphate (TCEP), tris(2‐chloro‐1‐methylethyl) phosphate (TCPP), and tris(1,3‐dichloro‐2‐propyl) phosphate (TDCP)

Industrial additives and agents

Volatile organic carbons (VOCs)

CHCl

3

, trichloroethylene (TCE) and tetrachloroethylene (PERC)

Polycyclic aromatic hydrocarbons (PAHs)

Indeno[1,2,3‐

cd

]pyrene, benz[

a

]anthracene, dibenz[

a

,

h

]anthracene, benzo[

a

]pyrene (BaP), chrysene, benzo[

b

]fluoranthene, and benzo[

k

]fluoranthene

Siloxanes (silicones)

Octamethylcyclotetrasiloxane (D4) and decamethylcyclopentasiloxane (D5)

Gasoline additives

Dialkyl ethers, methyl‐

t

‐butyl ether (MTBE)

Anticorrosives and antifouling agents

Benzotriazoles (1

H

‐benzotriazole (BTri), (4‐methyl‐1

H

‐benzotriazole (4‐TTri), 5‐methyl‐1

H

‐benzotriazole (5‐TTri), the mixture of 4‐ and 5‐methyl‐1

H

‐benzotriazole (TTri), 5,6‐dimethyl‐1

H

‐benzotriazole (XTri) and 5‐chloro‐1

H

‐benzotriazole (CBT) and benzothiazoles (benzothiazole (BTH), 2‐hydroxy‐benzothiazole (2‐OH‐BTH), 2‐amino‐benzothiazole (2‐NH2‐BTH), 2‐methyl‐benzothiazole (2‐Me‐BTH), 2‐methyl‐thio‐benzothiazole (2‐Me‐S‐BTH), 2‐mercapto‐benzothiazole(2‐SH‐BTH), 2‐thiocyanomethylthio‐benzothiazole (2‐SCNMeS‐BTH), and 2‐benzothiazole‐sulfonic acid (2‐SO

3

H‐BTH), organotins, zinc pyrithione (ZnPT), antifouling biocides (irgarol, diuron, sea‐nine 211, dichlofluanid, chlorothalonil, thiram, busan, densil, pyridine–triphenylborane, capsaicin, econea, medetomidine, and tolylfluanid)

Chelating agents (EDTA), aromatic sulfonates

Natural emerging contaminants

Mycotoxins

Trichothecenes: Deoxynivalenol or vomitoxin (DON), deepoxy‐deoxynivalenol (DOM‐1), 3‐acetyl‐deoxynivalenol (3AcDON), 15‐acetyl‐deoxynivalenol (15AcDON), nivalenol (NIV), and T2‐toxin. Zearalenone (ZEN) and its metabolite β‐ZEN. Fumonisins: fumonisin B1 (FB1), B2 (FB2), and B3 (FB3). Beauvericin (BEA)

Phytotoxins

Phenolic acids, quinones, benzoxazinones, terpenoids, glycoalkaloids, glucosinolates, isothiocyanates, phytosterols, flavonoids, coumestans, lignans, and chalcones

These contaminants usually have an anthropogenic origin, with special relevance in discharges from urban and industrial WWTPs, livestock production, and aquaculture. Urban wastewater collects the discharges of ECs from households, hospitals, and veterinary establishments, so a very important part of the burden of ECs are pharmaceuticals and PCPs. On the other hand, industrial wastewater receives a more specific load depending on the industries located in each area, being the pharmaceutical and chemical ones especially relevant.

In any case, these compounds reach aquatic ecosystems in different ways, including effluents of WWTPs or directly from water activities (e.g. gasolines, sunscreen products), having been detected at a global scale in WWTP effluents [7], surface and groundwater, and even in drinking water [8–11].

Although the ECs concentrations vary depending on the type of water, concentrations in surface waters are usually lower than those detected in WWTPs [6], mainly due to dilution processes, but also to natural processes such as volatilization, biodegradation, sorption, or photolysis.

According to Petrie et al. [12], several factors influence concentrations of ECs in wastewater:

Sampling

: inappropriate sampling strategies have been considered as one of the main weaknesses of reported occurrence data.

Spatial variation

: climate associated to each geographical area, including rainfall distribution and temperatures, affects dilution processes of ECs in aquatic environments.

Intra‐day variation

: for example, levels of antibiotics in rivers are usually higher early in the morning (7:00–9:00, a.m.), mainly due to their concentration in urine overnight.

Inter‐day variation

: concentrations of illicit drugs in influents of wastewater are generally higher at weekends than at working days.

Seasonality

: for example, concentrations of antihistamines are higher in spring, but, on the contrary, levels of anti‐cough and decongestant medicines are higher during the cold months of the year, and especially in winter.

Some occasional events, such as festivals, local fairs or exam periods, can induce concentration peaks of certain type of drugs.

In general, these contaminants provoke few acute toxicity events as they are found at low concentrations in aquatic ecosystems. However, significant reproductive effects have been observed after long‐term exposures to mixtures of very low doses of pharmaceuticals [13]. Currently, there are a great number of analytical and experimental tools available to detect and assess toxicity of emerging pollutants in aquatic compartments, including WWTPs. This allows to link chemical measurements with the toxicity exhibited by a sample [14].

In summary, slightly modifying the Noguera‐Oviedo and Aga's proposal [4], research on ECs in aquatic ecosystems have taught us six lessons:

No part of the world is free from ECs.

There is no single water treatment capable of removing all ECs.

Metabolites and transformation products should be taken into account in terms of toxicity.

Demonstration of effects and toxicity induced by ECs need unconventional tests.

In spite of modern and advanced analytical tools, the target of detecting and quantifying ECs in water may not be achieved.

The extent and consequences of the risks associated to mixtures of ECs is still far from being known.

1.2 Contaminants of Emerging Concern

1.2.1 Pharmaceuticals

Pharmaceuticals are natural or man‐made chemicals used in human and veterinary drugs. Their consumption in developed countries is very high, rising up to 50–150 g per capita with an annual global mean of 15 g [15]. More than 600 active pharmaceutical ingredients (APIs) have been identified in the environment, mainly in sewage effluents and surface waters, but also in groundwater and soils [16]. Their presence in drinking water is of particular concern for the scientific community, mainly due to the ignorance of their effects after chronic exposure to low doses [4]. However, they are present in all types of waters, being, together with PCPs, the most frequently detected group of ECs in surface and groundwater [17]. In groundwater, the most frequently detected pharmaceuticals are non‐steroideal anti‐inflammatory drugs (NSAIDs) (ibuprofen, diclofenac, salicylic acid, and ketoprofen), analgesics (paracetamol, phenazone, and propyphenazone), antibiotics (lincomycin, triclosan, sulfamethoxazole, and erythromycin), barbiturates (primidone), antiepileptics (carbamazepine), X‐ray contrast agents (iopamidol), and clofibric acid (a metabolite of clofibrate, a lipid regulator) [6, 17]. On the other hand, the most frequently detected pharmaceuticals in drinking waters are meprobamate, atenolol, sulfamethoxazole, carbamazepine, ibuprofen, naproxen, gemfibrozil, phenytoin, estrone, and trimethoprim [18, 19].

The first step to understand the toxic effects of pharmaceuticals in the environment is to identify the different sources and entry routes into the environment [16]. The main point sources of pharmaceuticals as ECs in aquatic environment are [6, 16]:

Pharmaceutical industry

: residual pharmaceuticals in industrial wastes, in spite of

Good Manufacturing Practices

(

GMP

) and storm water runoff carrying them.

Hospital wastewater outflows

: discharges containing pharmaceuticals, X‐ray contrast, solvents, disinfectants, and expired medicines. Some of these substances (e.g. iopromide, iopamidol, diatrizoate) are highly persistent in aquatic environments and are also detected in groundwater.

Livestock production and veterinary medicine

: pharmaceuticals are highly present in waste discharges. In the case of intensive livestock farming, veterinary medicines, particularly antibiotics, are of special concern and they have to be exclusively prescribed for veterinary use. Urine and feces of livestock animals contaminate soil and surrounding surface and groundwater. Some of these drugs are sulfamethazine, frequently detected in groundwater in the United States and Europe, as well as tylosin and monensin

[17]

.

Domestic outflows

: these mainly include unused and expired medicines and metabolites eliminated in urine.

Agriculture

: Sludge from

sewage treatment plant

s (

STP

s) is frequently used as fertilizer, and antibiotics have been used in the United States to control bacteria in flowers and fruits.

Aquaculture

: Antibiotics have been employed for both preventive and therapeutic purposes.

Human and animal metabolism of pharmaceuticals results in new molecules (metabolites) due to structural changes of the parent compounds mostly by oxidation, hydrolysis, or reduction, being then eliminated, mainly by urine, together with the unchanged fraction of the parent chemical. In this sense, antibiotics, one of the most frequently used pharmaceutical classes, are poorly metabolized, being released from the body in an unchanged form at rates from 25% to 75% after ingestion [20]. Once in the environment, pharmaceuticals and metabolites may suffer both transformation and removal processes like sorption, dilution, or degradation, among others [6]. Both pharmaceuticals and their transformation products can persist in the environment [21].

Although the presence of pharmaceuticals and related compounds in the environment, and especially in aquatic ecosystems, is unquestionable, they are considered as micro‐pollutants because their usual concentrations are in most cases very low, ranging between μg l−1 and ng l−1. In this sense, the typical concentrations detected in drinking water (below 0.05 μg l−1) are considerably lower than the lowest therapeutic doses [6]. According to the World Health Organization (WHO), the exposure to individual pharmaceuticals at this level may not be a direct threat to human health, which explains the initial lack of guidelines regulating their presence in the environment [22]. A first positive step toward regulating pharmaceuticals in water environments was already taken by the European Union approving the Directive 2013/39/EU of the European Parliament amending Directives 2000/60/EC and 2008/105/EC regarding priority substances for water policy. Article 8c of this Directive treats the specific provisions for pharmaceutical substances in water [23].

Some studies have suggested that pharmaceuticals acting, for example, on cardiovascular (as propranolol) or central nervous systems (for example, antiepileptic drugs as carbamazepine or clofibrate), as well as antibiotics, may pose high risk to the environment [24]. In this sense, the presence of antibiotics in aquatic environments may cause considerable impacts, mainly due to the generation and transmission of resistances among microorganisms [25]. This is especially relevant in treated wastewater effluents as they can disrupt the microbial community structure and, thus, alter the microbial populations as well as their ecological function within their ecosystem. Therefore, antibiotics are a cause of concern considering the high risks associated to their presence in the aquatic environment. In addition, effects on human health related to antibiotic resistances are also being a cause of special attention worldwide [26].

Although there are no regulations for concentrations of pharmaceuticals in the environment, there are some for pharmaceuticals in human food. Recently, a survey of 47 antibiotics in seafood in the United States was published [27] reporting that five antibiotics (virginiamycin, oxytetracycline, sulfadimethoxine, ormetoprim, and 4‐epioxytetracycline) were detected at doses ranging between 0.3 and 8.6 ng g−1 (fresh weight). Although they were below US regulatory limits in seafood, chronic exposure could trigger antibiotic resistance genes (ARG) [4] with the corresponding risks to human health.

In addition to ARG, some antibiotics, together with other pharmaceuticals such as sex hormones, veterinary growth hormones, and glucocorticoids are known for their endocrine disrupting properties [4, 6]. This is especially important for chronic exposure, because there is still much uncertainty concerning the impact of ECs in scenarios of long‐term exposure at low doses on human, terrestrial, and aquatic wildlife and ecosystems [28]. Moreover, a majority of the scientific research focuses on certain groups such as antineoplastics, antibiotics, or hormones, whereas other groups such as anti‐ulcerants and some of the non‐prescribed drugs with a potential to impact the environment negatively have not received the due attention yet. In this sense, different aquatic organisms have been found to be susceptible to a variety of pharmaceuticals (including antineoplastics, antibiotics, cardiovascular drugs, and hormones), being sex hormones and antibiotics considered as the most dangerous compounds for both human and aquatic life health [29].

1.2.2 Personal Care Products

PCPs represent a group of heterogeneous emerging pollutants of concern due to their extensive use and ubiquity in water environments. This group of compounds includes a wide variety of substances mainly used as ultraviolet (UV) filters, fragrances, preservatives in cosmetics, antiseptics in creams, soaps and toothpaste, and insect repellents. Current wastewater treatment systems are not efficiently removing some PCPs [30], and a variety of them may persist in ecosystems and can be accumulated in wildlife, inducing a wide range of adverse effects in organisms (e.g. endocrine disruption, reproductive and neurotoxic alterations, growth and development impairment) [31]. They are detected in different water compartments worldwide, including influent/effluent wastewaters, groundwater, rivers, lakes, marine environments, and sediments [4, 12,31–35]. However, less attention has been placed on these compounds compared with other groups of contaminants.

UV filters are the active ingredients in sunscreen products and other cosmetics, and they are also used in different products (e.g. textile, plastics, packages, and paints). There are many different filters, and some of the most frequently investigated are 4‐methyl‐benzylidene‐camphor (4MBC), ethylhexyl‐methoxycinnamate (EHMC), benzophenone‐3 (BP‐3), and octocrylene (OC). UV filters show environmental stability and lipophilicity, and some reports suggest that they can bioaccumulate at similar levels than some legacy organochlorine compounds (dichlorodiphenyltrichloroethane, DDT and polychlorinated biphenyls, PCBs) [31]. UV filters have been found in different organisms such as crustaceans, molluscs, fish, birds, and marine mammals [31,36–38]. The potential for trophic magnification of some UV filters (e.g. BP3, EHMC, and OC) has been recently observed in freshwater ecosystems [38, 39]. These compounds have shown estrogenic activity and effects on growth, behavior, and reproduction [31, 40, 41], but further research is needed to evaluate additional environmental concentration data and related effects in different wild species.

Fragrances area broadly used in different products (e.g. soaps, lotions, perfumes, deodorants, and detergents), and are divided in different groups including nitro, polycyclic, macrocyclic, and alicyclic musks. Nitro musks (e.g. ketone or xylene) are persistent and some of them have been found to be weakly estrogenic. Some compounds in this group are banned or restricted in their use due to their toxicity [42]. Synthetic polycyclic musks are frequently used (e.g. galaxolide, tonalide, celestolide, and phantolide), being galaxolide and tonalide the fragrances most widely used. They are water soluble and persistent, they can be accumulated in sediments and have high bioaccumulation potential in aquatic species. Their toxicity varies depending on the species; growth and development‐related effects are the main alterations reported [31]. Some evidences suggest that they may produce endocrine disruption [43]. Effects associated to long‐term exposure to fragrances due to industrial and domestic discharges need further evaluation in aquatic organisms. Different studies show that these chemicals are not efficiently removed by conventional WWTPs and are present in their effluents and receiving aquatic ecosystems. In addition, galaxolide may biotransform into galaxolidone in activated sludge WWTPs [44, 45], and the concentrations of this product may increase in the final effluent [46].

Parabens (alkyl esters of p‐hydroxybenzoic acid) are chemicals with antimicrobial preservative properties that are widely used in cosmetics (e.g. lotions, creams, and shampoos), but also in pharmaceuticals, beverages, and food. Some studies have identified these chemicals in surface water. The parabens most commonly used in PCPs are methyl‐, ethyl‐, propyl‐, and butyl‐paraben. Studies seem to indicate that increased chain length and chlorination increase the toxicity of parabens [31]. Although additional studies are required to assess concentrations in the environment and elucidate chronic effects, results show that some parabens may cause negative effects on aquatic wildlife, including estrogenic responses at low concentrations, while effects on spermatogenesis have also been reported [31, 47].

Two antiseptics used in soaps, toothpaste, creams, and deodorants are triclosan and triclocarban. They are frequently detected in surface water worldwide. These chemicals, as well as the methyl derivative of triclosan (methyl triclosan) are lipophilic, and although some contradictions are found in the literature regarding triclosan bioaccumulation capacity, these compounds seem to bioaccumulate in aquatic biota. Algae seem to be the most sensitive trophic group to triclosan toxicity and algal growth being the main endpoint affected. Algae, invertebrates, and fish seem to be more sensitive to triclosan than mammals [48]. Triclosan has shown to induce oxidative and genetic damage, and can affect development, behavior, reproduction, and survival. In addition, some studies show its potential for endocrine disruption [31,48–50].

N,N‐Diethyl‐m‐toluamide (DEET) is the active ingredient most commonly used in insect repellents worldwide. This compound is relatively persistent and has been detected in WWTPs effluents and surface waters. However, it has a low bioconcentration factor suggesting low accumulation capacity in aquatic organisms [31]. Acute toxicity studies have reported that DEET is slightly toxic to some aquatic organisms [31, 51]. Published results on chronic toxicity show that DEET can affect growth and survival, as well as biochemistry (e.g. cholesterol) [31, 51, 52]. The toxicity data available has indicated that exposure to DEET poses higher toxicity to some algae than other aquatic species, and crustaceans were more sensitive than insects and fish [51].

1.2.3 Nanomaterials

The use of nanomaterials into different commercial products (e.g. PCPs, clothing, cosmetics, sporting goods, home and garden products, electronics, food and beverage, medicine, construction, etc.) has rapidly increased [53]. Nanomaterials include nanoparticles defined as having at least one dimension of 1–100 nm of length and displaying novel properties different from those of molecules or material of the same composition [54]