Quantitative Structure Activity QSAR for Pesticide Regulatory Purposes 1st Edition by Emilio Benfenati ISBN 0444527109 978-0444527103

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ISBN 10:  0444527109 

ISBN 13: 978-0444527103

Author:  Emilio Benfenati 

Quantitative Structure-Activity Relationship (QSAR) for Pesticide Regulatory Purposes stems from the experience of the EC funded project DEMETRA. This project combined institutes involved in the regulatory process of pesticides, industries of the sector and scientists to develop and offer original software for the prediction of ecotoxicity of pesticides. Then to be used within the dossier preparation for pesticide registration. The basis of this book is more than three-years of research activities, discussions, studies and successful models. This experience represents a useful example not only for the case of pesticides, but also for the prediction of ecotoxicity and toxicity in general. QSAR is used to link a given property of a chemical compound with some features related to its structure. The theoretical toxicological, chemical and information technology aspects will be treated considering the regulatory issues. Innovative hybrid systems will be described, for the toxicity prediction of pesticides and related compounds, directly useful for pesticide evaluation within the Dossier preparation for pesticide registration. Five endpoints will also be discussed, addressing issues as standardisation, verification, validation, accessibility, reproducibility.The driving force for Quantitative Structure-Activity Relationship (QSAR) for Pesticide Regulatory Purposes is that all the issues of concern for end-users are analysed, discussed and solutions proposed further. An innovative feature is that, in order to offer powerful QSAR models, the book discusses and reports on integrated QSAR models, combined into a unique hybrid system.

* Assesses the needs of regulators for pesticide approval and how these needs affect QSAR models* Combines theoretical discussion with practical examples, including five worked examples of hybrid systems* Refers to original software available through the internet

Table of contents: 

CHAPTER 1
QSARs for regulatory purposes: the case for pesticide authorization
Emilio Benfenati, Mark Clook, Steven Fryday, Andy Hart

Overview of the Current Pesticide Authorization Procedure
1.1. Description of the current pesticide legislation (EU Directive 91/414/EEC)
1.2. Outline of the ecotoxicology tests required for pesticide authorization under 91/414/EEC
1.3. How frequently are certain studies submitted and how many studies are submitted to authorizers on an Annex point?
1.4. What changes are likely to occur that could alter the frequency and number of toxicity studies submitted?

Introduction on QSARs for Pesticides

Regulatory Perspectives in the use of QSARs
3.1. Current use of QSARs in regulation
3.2. Potential hurdles for using QSARs in the pesticide authorization procedure
3.3. End-user criteria for the use of QSARs in regulatory assessment

Quality Criteria for Modelling Ecotoxicity Data
4.1. Data quality and precision required
4.2. Quality criteria to be applied to ecotoxicity data used in a QSAR
4.3. Degree of precision required of QSARs for pesticide assessments

Toxicity End-Points with a High Potential to be Replaced with a QSAR Approach
5.1. Data availability
5.2. Number of animals tested
5.3. Study costs
5.4. End-points with high potential for replacement with a QSAR
5.5. Priority end-points

References

CHAPTER 2
Databases for pesticide ecotoxicity
Emilio Benfenati, Elena Boriini, Marian Craciun, Ladau Macalisi, Daniel Neagu, Alessandra Roncaglioni

Introduction
Data Availability
2.1. The EPA-OPP database
2.2. The SEEM database
2.3. The BBA database
2.4. Other databases
Selection of the Data
3.1. Key features in the choice of the database
3.2. Comparison of the data internally to the database
Data Representation for Predictive Toxicology
4.1. A public database example: DSSTox
4.2. Current toxicity database limitations
4.3. XML-based standards in chemistry and toxicology
4.4. PTxXML – a simple XML-based description in predictive toxicology
The Characteristics of the Final Data Sets
Conclusions
Acknowledgments
References
CHAPTER 3
Characterization of chemical structures
Emilio Benfenati, Moses Casalegno, Jane Cotterill, Nick Price, Moreno Spreafico, Andrey Ttorpor

Introduction
Characterization of Bi-dimensional Structures
2.1. Preprocessing of compounds in the data set
2.2. Geometrical isomers
2.3. Tautomers
Characterization of Tri-dimensional Structures
3.1. Crystallographic data
3.2. Conformational searching and energy minimization
3.3. Stereoisomers
3.4. Procedure for the quality control of the chemicals and chemical structures
Chemical Structure File Formats
4.1. Bi-dimensional descriptors
4.2. Tri-dimensional descriptors
4.3. Fragments and Residues in DEMETRA
References
CHAPTER 4
Algorithms for (Q)SAR model building
Qasim Chaudhary, Jacques Chretien, Marian Craciun, Goudeg Guo, Frank Lemke, Johanen-Adolf Müller, Daniel Neagu, Nadège Piclin, Marco Pintore, Paul Trundle

CHAPTER 5
Hybrid systems
Nicolas Amaury, Emilio Benfenati, Severin Buntharu, Antonio Chana, Marian Craciun, Jacques R. Chretien, Giuseppina Gini, Goudeg Guo, Frank Lemke, Viorel Mirica, Johanen-Adolf Müller, Daniel Neagu, Marco Pintore, Silvia Augustina Stoica, Paul Trundle

Introduction: Goals of the Hybrid Systems
Our Hybrid Approach for Quantitative Structure-Activity Relationship
Gating Networks
3.1. Introduction
3.2. Gating networks for predictive toxicology – a new approach based on descriptors clustering
3.3. Hybrid neural fuzzy systems
3.4. Gating networks as HISs – a data-driven approach
Multi-Classifier Systems
4.1. Approaches for multi-classifier systems
4.2. An architecture of MCS
4.3. Classifiers
4.4. Combination Methods
4.5. Distributed multi-classifier systems
Neural ik- and Ek-Based Systems – Introduction of the Prototype NIKE
5.1. Experiment 1
5.2. Experiment 2
Rule-Based Systems
Self-Organizing Statistical Learning Networks
Conclusions
References
CHAPTER 6
Validation of the models
Emilio Benfenati, Jacques R. Chretien, Giuseppina Gini, Nadège Piclin, Marco Pintore, Alessandra Roncaglioni

Introduction
Selection of the Training and Test Sets
Internal Validation and Robustness
External Validation

CHAPTER 7
Results of DEMETRA models
Nicolas Amaury, Emilio Benfenati, Elena Boriani, Moisé Casalegno, Antonio Chana, Qasim Chaudhary, Jacques R. Chretien, Jane Cotterill, Frank Lemke, Nadège Piclin, Marco Pintore, Chiara Porello, Nicholas Price, Alessandra Roncaglioni, Andrey Toropov

Overview of Results with the Regression Approach
Overview of the Prediction Results Obtained by Classification Methods
2.1. Data sets and toxicity intervals
2.2. Descriptors selection and classification results
2.3. Conclusions about classification results
Overview of Results of Local Models
3.1. Chemical classes
Overview of Results Obtained with the Hybrid Models
4.1. Hybrid model for rainbow trout
4.2. Outliers and the applicability domain
4.3. Hybrid model for water flea (Daphnia magna)
4.4. Hybrid model for quail: oral exposure
4.5. Hybrid model for quail: dietary exposure
4.6. Hybrid model for acute contact toxicity of honey bee
Conclusions
Acknowledgments
References
CHAPTER 8
The quality criteria of the DEMETRA models for regulatory purposes
Emilio Benfenati

The OECD Guidelines for QSAR Models
1.1. Introduction
1.2. The identification of the regulation
1.3. The criteria for the endpoint selection
1.4. The model utility
1.5. The endpoint selection: identification of the guidelines
1.6. The accuracy of the toxicity data to the guidelines
1.7. The check of quality in the OECD documents
1.8. The definition of the model components. OECD principle number 2: an unambiguous algorithm
1.9. The selection of the toxicity values of the data set
1.10. The characterization of the uncertainty of the experimental data
1.11. The chemical structures
1.12. The chemical descriptors
1.13. The algorithms
1.14. The performances of the model
1.15. The reproducibility of the models
1.16. The false-negative issue
1.17. The applicability domain
1.18. The quality control
1.19. The use of the model

The Specificity of the QSAR Models for Regulatory Purposes

The Probabilistic Meaning of the Model, the Prediction of the Effect, and the Prediction of the Mechanism
3.1. The probabilistic nature of the models
3.2. The mechanistic basis of the models
3.3. The final model and the ways to obtain it

The Benefits of the DEMETRA Models

Future Perspectives
References

CHAPTER 9
The use of the DEMETRA models
Emilio Benfenati, Marian Craciun, Daniel Neagu

Introduction
The Users of the DEMETRA Models
Ownership of the Software
Using DEMETRA Models
Chemical Restrictions of the DEMETRA Models
The Format for Model Presentation for DEMETRA: HISML
References

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