Ecotoxicological QSARs

1. Auflage 2020
ISBN: 978-1-0716-0152-5
Verlag: Springer US

Buch, Englisch, 830 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 1569 g

Reihe: Methods in Pharmacology and Toxicology

This volume focuses on computational modeling of the ecotoxicity of chemicals and presents applications of quantitative structure–activity relationship models (QSARs) in the predictive toxicology field in a regulatory context. The extensive book covers a variety of protocols for descriptor computation, data curation, feature selection, learning algorithms, validation of models, applicability domain assessment, confidence estimation for predictions, and much more, as well as case studies and literature reviews on a number of hot topics. Written for the Methods in Pharmacology and Toxicology series, chapters include the kind of practical advice that is essential for researchers everywhere. 
Authoritative and comprehensive, Ecotoxicological QSARs is an ideal source to update readers in the field with current practices and introduce to them new developments and should therefore be very useful for researchers in academia, industries, and regulatory bodies.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Roy, Kunal

Fachgebiete

• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Pharmakologie, Toxikologie
• Geowissenschaften Umweltwissenschaften Umweltwissenschaften

Inhaltsverzeichnis

Part I: Introduction

1. Ecotoxicological Risk Assessment in the Context of Different EU Regulations

            Antonio Juan García-Fernández

2. A Brief Introduction to Quantitative Structure-Activity Relationships as Useful Tools in Predictive Ecotoxicology

            Rahul Balasaheb Aher, Kabiruddin Khan, and Kunal Roy

3. Best Practices for Constructing Reproducible QSAR Models

            Chanin Nantasenamat

4. Wildlife Sentinels for Human and Environmental Health Hazards in Ecotoxicological Risk Assessment

            Antonio Juan García-Fernández, Silvia Espín, Pilar Gómez-Ramírez, Emma Martínez-López, and Isabel Navas

Part II: Methods and Protocols

5. Importance of Data Curation in QSAR Studies Especially While Modeling Large-Size Data Sets

            Pravin Ambure and M. Nata´lia Dias Soeiro Cordeiro

6. Machine Learning and Deep Learning Methods in Ecotoxicological QSAR Modeling

            Giuseppina Gini and Francesco Zanoli

7. Use of Machine Learning and Classical QSAR Methods in Computational Ecotoxicology

            Renata P.C. Barros, Natália F. Sousa, Luciana Scotti, and Marcus T. Scotti

8. On the Relevance of Feature Selection Algorithms While Developing Non-Linear QSARs

            Riccardo Concu and M. Nata´lia Dias Soeiro Cordeiro

9. Got to Write a Classic: Classical and Perturbation-Based QSAR Methods, Machine Learning, and the Monitoring of Nanoparticles Ecotoxicity

            Ana S. Moura and M. Nata´lia Dias Soeiro Cordeiro

10. Ecotoxicological QSAR Modeling of Nanomaterials: Methods in 3D-QSARs and Combined Docking Studies for Carbon Nanostructures

            Bakhtiyor Rasulev

11. Early Prediction of Ecotoxicological Side-Effects of Pharmaceutical Impurities Based on Open-Source Non-Testing Approaches

            Anna Rita Tondo, Michele Montaruli, Giuseppe Felice Mangiatordi, and Orazio Nicolotti

12. Conformal Prediction for Ecotoxicology and Implications for Regulatory Decision Making

            Fredrik Svensson and Ulf Norinder

13. Read-Across for Regulatory Ecotoxicology

            Gulcin Tugcu, Serli Önlü, Ahmet Aydin, and Melek Türker Saçan

14. Methodological Protocol for Assessing the Environmental Footprint by Means of Ecotoxicological Tools: Wastewater Treatment Plants as an Example Case

            Roberta Pedrazzani, Pietro Baroni, Donatella Feretti, Giovanna Mazzoleni, Nathalie Steimberg, Chiara Urani, Gaia Viola, Ilaria Zerbini, Emanuele Ziliani, and Giorgio Bertanza

Part III: Case Studies and Literature Reports

15. Development of Baseline Quantitative Structure-Activity Relationships (QSARs) for the Effects of Active Pharmaceutical Ingredients (APIs) to Aquatic Species

            David J. Ebbrell, Mark T.D. Cronin, Claire M. Ellison, James W. Firman, and Judith C. Madden

16. Ecotoxicological QSARs of Personal Care Products and Biocides

            Kabiruddin Khan, Hans Sanderson, and Kunal Roy

17. Computational Approaches to Evaluate Ecotoxicity of Biocides: Cases from the Project COMBASE

            Sergi Gómez-Ganau, Marco Marzo, Rafael Gozalbes, and Emilio Benfenati

18. QSAR Modeling of Dye Ecotoxicity

            Simona Funar-Timofei and Gheorghe Ilia

19. Ecotoxicological QSARs of Mixtures

            Pathan Mohsin Khan, Supratik Kar, and Kunal Roy

20. QSPR Modeling of Adsorption of Pollutants by Carbon Nanotubes (CNTs)

            Probir Kumar Ojha, Dipika Mandal, and Kunal Roy

21. Ecotoxicological QSAR Modeling of Organophosphorus and Neonicotinoid Pesticides

            Alina Bora, Luminita Crisan, Ana Borota, Simona Funar-Timofei, and Gheorghe Ilia

22. QSARs and Read-Across for Thiochemicals: A Case Study of Using Alternative Information for REACH Registrations

            Monika Nendza, Jan Ahlers, and Dirk Schwartz

23. In Silico Ecotoxicological Modeling of Pesticide Metabolites and Mixtures

            Chia Ming Chang, Chiung-Wen Chang, Fang-Wei Wu, Len Chang, and Tien-Cheng Liu

24. Combination of Read-Across and QSAR for Ecotoxicity Prediction: A Case Study of Green Algae Growth Inhibition Toxicity Data

            Ayako Furuhama

25. QSAR Approaches and Ecotoxicological Risk Assessment

            Mabrouk Hamadache, Othmane Benkortbi, Abdeltif Amrane, and Salah Hanini

 

26. Multi-Scale QSAR Approach for Simultaneous Modeling of Ecotoxic Effects of Pesticides

            Alejandro Speck-Planche

27. Quantitative Structure-Toxicity Relationship Models Based on Hydrophobicity and Electrophilicity

            Gourhari Jana, Ranita Pal, Shamik Sural, and Pratim Kumar Chattaraj

28. Environmental Toxicity (Q)SARs for Polymers as an Emerging Class of Materials in Regulatory Frameworks, with a Focus on Challenges and Possibilities Regarding Cationic Polymers

            Hans Sanderson, Kabiruddin Khan, Anna M. Brun Hansen, Kristin Connors, Monica W. Lam, Kunal Roy, and Scott Belanger

Part IV: Tools, Databases, and Web Servers

29. Ecotoxicity Databases for QSAR Modeling

            Shinjita Ghosh, Supratik Kar, and Jerzy Leszczynski

 

30. VEGAHUB for Ecotoxicological QSAR Modeling

            Emilio Benfenati and Anna Lombardo

31.Enalos Cloud Platform: Nanoinformatics and Cheminformatics Tools

            Dimitra-Danai Varsou, Andreas Tsoumanis, Antreas Afantitis, and Georgia Melagraki

32. alvaDesc: A Tool to Calculate and Analyze Molecular Descriptors and Fingerprints

            Andrea Mauri

Über Autor(innen)

Dr. Kunal Roy is a Professor in the Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India. He has been a recipient of Commonwealth Academic Staff Fellowship (University of Manchester, 2007) and Marie Curie International Incoming Fellowship (University of Manchester, 2013). The fields of his research interest are Quantitative Structure-Activity Relationship (QSAR) and Chemometric Modeling with application in Drug Design and Ecotoxicological Modeling. Dr. Roy has published about 300 research articles in refereed journals (current SCOPUS h index 40; SCOPUS Author ID 56962764800). He has also coauthored two QSAR related books (Academic Press and Springer), edited five QSAR books (Springer, Academic Press and IGI Global) and published twelve book chapters. Dr. Roy is a Co-Editor-in-Chief of Molecular Diversity (Springer Nature) and Editor-in-Chief of International Journal of Quantitative Structure-Property Relationships (IGI Global). He also serves in different capacities in the Editorial Boards of several International Journals.