Quantitative Structure-Activity Relationships (QSAR) for Pesticide Regulatory Purposes

505 Pages·2007·7.25 MB·English

by  Emilio Benfenati and Emilio Benfenati (Eds.)

xi
Forewords
THE DEMETRA PROJECT: AN INNOVATIVE CONTRIBUTION
TO REGULATORY QSAR
Within DEMETRA, we wanted to produce software for a specific application:
the prediction of ecotoxicological properties of pesticides. This application-driven
approach is very different from most of the thousands of QSAR methods so far
published, in its very premises. We wanted to develop software to be used by
industries and regulators, so the first action was to identify their needs and the
related constrains, to be applied to the software. As a consequence, two major
differences compared to all the other models resulted:
1) the targets organisms to be modelled (i.e. the toxicity endpoints) have been
decided not by the modellers (as typically done in most of the QSAR
publishedstudies)butbytheusers,accordingtoaseriesofcriteriathathave
been deeply studied and clearly defined by users. The criteria have been
applied to identify where there was more utility for a predictive model, to
reduce the costs, the use of animals and to gain a maximum benefit of the
QSAR model. This activity is, of course, very specific, but in our opinion
should be done in all the cases where a QSAR model is developed to a
specific purpose and not as a general theoretical tool.
2) The QSAR models have been developed and optimized according to the
specific criteria defined with the users and not only according to generic
mathematicalfeatures,i.e.thesoobtainedQSARmodelsencodethedesired
features. This refers for instance to the quality and source of the data and to
the careful check for the presence of the so-called false negatives: indeed,
what it has primarily to be avoided is to define not toxic a compound
which vice versa is toxic, because this may result in serious environmental
problems.
In terms of the model, this careful definition of the constrains mainly
involves the input and the output of the model, because the users have to define
whatisimportantasoutputandwhatisreliableasinput.Wehavetorememberthat
the final target of the QSAR model for toxicity prediction is the risk assessment
of chemical compounds and that, if the related issues are not fully addressed, the
model can be perfect, but it will not be used. In other words, the QSAR model is
only a segment in a more complex process, and if this is not taken into account,
the necessary links with the real world are lost.
The activity to satisfy all users’ needs requested a long discussion, which
involved not only partners of the consortium but also users outside. However,
for a QSAR model to be used, it is not sufficient that it addressed an endpoint
xii Forewords
useful for regulatory purposes: the model has to be recognised as valid, reliable
and reproducible. Thus, we also need to put very solid basis for the model, i.e.
the toxicity data have to be of high quality. It means that these data have to be
produced only according to official protocols. Indeed, in the real word, to assess
the toxicity of a pesticide, only experiments done according to official protocols
arevalid.Thispointreferstothecharacterizationoftheinputs,intermsofquality.
However, we added, as far a possible, further quality criteria, comparing
data between three high-quality databases, in the modelling phase, and we further
checked the quality of the used data with five other high-quality databases in the
validation phase. The use and comparison of so many high-quality data is unique
in the QSAR modelling.
The comparison of the experimental data allowed us also to have a much
better characterisation of the variability of the experimental data. This is another
unique feature of our project, because in practically all QSAR models, only a
single value for toxicity is used per chemical, without any knowledge on the
related variability of the toxicity data. But of course the accuracy of any model is
related to the variability of the input data.
Wealsomentionherethatweappliedrestrictiverules,forinstanceusingfor
our models only pesticides with a relatively small variability, to have even more
reliable data, eliminating pesticides whose toxicity values have been accepted for
regulatory purposes. In this, our model relies on data of higher quality than those
used for regulatory purposes, which do not define an acceptability level for the
variability of the experimental data.
The quality of the input data is not only related to the toxicity data. The
data on the chemical compounds have to be of high quality too, of course. This
issue is not often mentioned in QSAR studies, because it is assumed that the
chemical information is correct. Actually, there are possibilities of many mistakes
starting from the very simple chemical formula, chemical identification, chemical
structure,etc.Wecheckedalltheseelements(andwealsofoundsomemistakesin
the original used databases), and all chemical structures have been independently
checked by at least two researchers, using different sources and methods.
Forthechemicalstructures,wealsousedcrystallographicdataandabinitio
calculations, even to identify which tautomer use. This is a further procedure
to achieve high-quality data, even if not strictly necessary, because less valid
approaches can be used. In this way, we addressed the issue of the validity and
reproducibility of the model.
Allthesestepsinthequalityassessmentoftheinputtestifythegreatattention
given to put the best basements to our models. This took a long effort and time-
consuming activities that are very seldom done because expensive in terms of
human resources.
InthespecificcaseofQSARforpesticideecotoxicity,manydifferentmech-
anisms exist, producing the final toxicity. We wanted to model the heterogeneous
classesofpesticides,becausedevelopingsomesimplermodelsforspecificclasses,
Forewords xiii
such as triazines, would be surely much easier, but the utility much reduced, or
maybe null, because nobody would use them, for the low interest in developing
other triazines. In future development, more focused models can be added, but we
gave priority to the development of a general tool.
We verified that, in this heterogeneous complex situation, no simple model
can produce acceptable results for ecotoxicological properties. A recent document
fromtheDanishEPAreachedthesameconclusions(Hansen,2004).Theapproach
we used was to develop advanced models, taking advantage of different inno-
vative methodologies, both to describe the chemical compounds and to produce
sophisticated algorithms. A priori it is difficult to choose a suitable combination
of chemical descriptors and algorithms. Many attempts have been done, within a
good collaboration between partners with different skills.
Weareconvincedthatforaheterogeneousdataset,asinourcase,nounique
model exist. Several possibilities exist, which provide more or less valid models.
Our decision was to combine different models, into an integrated, hybrid system.
In this way, positive features of different methods can be added.
The reader may feel uncomfortable for the use of exoteric techniques.
Actually, all our modelling studies, all the mathematical algorithms, have been
used to identify a list of best models. These models have been combined, but
the final model is relatively simple, such as a linear equation with a series of
coefficientsandchemicalparameters.Whathasbeendifficultwastoachievethese
coefficients and to identify the most important chemical features. And for this
advanced methods were necessary, because simple tools are not sufficient, as also
reported by the Danish EPA.
Nowadays advanced information technology tools are becoming part of our
life.Forinstance,artificialneuralnetworks,whichhavebeentestedinourproject,
are continuously used by everybody for common electronic tools.
Actually we put some efforts in reducing the complexity of the models,
for instance preferring chemical descriptors which can be calculated by users
without buying many expensive packages or avoiding complex three-dimensional
descriptors which require time and experience.
As we said, the final models are relatively simple, and no ab initio calcu-
lation, no complex mathematical knowledge is required to run the model. All the
theory and the techniques we used are presented in this book, but this knowledge
is not a requirement for the use of the model. It is presented for correctness and
for interested scientists and users.
We think it is important to clarify the criteria that have to be used in the
evaluation of the models we propose. These criteria have to be the same used for
the general assessment of pesticides, applied to the specific case. We said that the
model should be useful, reliable and reproducible. Furthermore, for the use of the
model, we have to provide information on some other points, to achieve a reliable
model: the validation procedure and the applicability domain.
xiv Forewords
For the validation, we applied a battery of tools, including internal and
external validation. The applicability domain refers to pesticides, of course, but
specific boundaries have been evaluated and characterized.
Finally, the issue of reproducibility. We already introduced some consider-
ations on the reproducibility of chemical structures. More important, we produced
models that will give the same result once applied by the different users. Some
models,especiallythoseinvolvingoptimizationofthethree-dimensionalstructure,
can provide different chemical descriptors, depending on the manual procedure
to optimize the three-dimensional structure. We avoided this risk. Parameters for
the final model are fixed. We also make available some of the general modelling
toolsforscientistswhowanttodeveloptheirownmodels,alsoforotherpurposes,
but these tools should not be confused with the models produced for QSAR for
regulatory purposes, which passed all the quality criteria we introduced.
Nowthewordisgiventotheusers.Thesemodelsrepresentthestate-of-the-
art in modelling properties of pesticides. They are not the ultimate models, and
improvements are possible. The main source of improvement is the extension of
the toxicity experimental data used to build up the model. We solicit industry to
make available experimental data of their toxicity studies for more chemicals, to
improve the knowledge basis of future models.
The present book explains in detail the activities done within DEMETRA.
We believe that this experience represents an useful example not only for the case
of pesticides but also for the prediction of ecotoxicity and toxicity in general, for
theinnovativeapproachandmethodologiesdeveloped.Forthisreason,thelessons
heregivenapplytoamuchbroaderfield,whenQSARwantstocontributesolving
real world problems.
Emilio Benfenati
Coordinator of DEMETRA
REFERENCE
Hansen O.C. (2004) Quantitative Structure-Activity Relationships (QSAR) and Pesticides. Danish
EnvironmentalProtectionAgency,PesticidesResearchNo.94.
xv
Preface
Emilio Benfenati, Mosè Casalegno
Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Far-
macologiche “Mario Negri”, Milano, Italy
THE PESTICIDES AND THEIR ECOTOXICOLOGICAL
PROPERTIES
Since before 500 BC, humans have used pesticides to prevent damage to their
crops.Thefirstknownuseofpesticidewassulphur.Bythefifteenthcentury,toxic
chemicals such as arsenic, mercury and lead were being applied to crops to kill
pests. In the seventeenth century, nicotine sulphate was extracted from tobacco
leaves for use as an insecticide. The nineteenth century saw the introduction of
two more natural pesticides, pyrethrum, which came from chrysanthemums, and
rotenone, from the roots of tropical legumes. In 1939, Paul Müller discovered that
DDT was a very effective insecticide. It quickly became the most widely used
pesticide in the world. However, in the 1960s, it was discovered that DDT was
preventing many fish-eating birds from reproducing, which was a huge threat to
biodiversity. DDT was also found to cause birth defects in animals and humans.
DDT is now banned in at least 86 countries but is still used in some developing
nations to prevent malaria and other tropical diseases by killing mosquitoes and
other disease-carrying insects. DDT represented the main precursor of modern
pesticides.
Nowadays, the term pesticide indicates different chemicals intended for
preventing, destroying, repelling or mitigating any pest. These include algicides,
antifouling agents, antimicrobials, biopesticides, biocides, disinfectants, fungi-
cides, fumigants, herbicides, insecticides and many others (Tomlin, 1997; Hurst
et al., 1991). Pesticides are used worldwide to reduce the damage to plants by
insects and other pests, to control overgrowth of undesirable plant species and to
protect public health from disease vectors such as mosquitoes, ticks, cockroaches,
rats and disease-causing organisms. Accepting all the benefits coming from the
useofpesticidesmeansalsoacceptingtherelatedrisks.Agriculturalanddomestic
use of pesticides inevitably leads to exposure of non-target organisms, including
humans. For this reason, pesticides’ toxic potential has to be carefully evaluated
before marketing and distribution, taking into account that each compound might
be harmful to humans, as well as to other animal species, and the environment.
Risk assessment procedures aimed at evaluating the impact of pesticides on the
environment are extremely demanding in terms of money and time.
xvi Preface
MOVING FORWARDS THE USE OF QSAR TO PREDICT
TOXICOLOGICAL PROPERTIES
The multitude of different compounds, non-target organisms (pets, birds, fishes
and mammalians) and adverse effects (eye and skin irritation, neurotoxicity, can-
cer and birth defects) to be tested require huge efforts in terms of testing animals
and money. To reduce direct costs, a number of possible alternatives have been
proposed and evaluated by regulatory authorities. Among them, the use of (Quan-
titative)Structure-ActivityRelationships[(Q)SAR]isstronglyencouragedbyboth
EU and USA regulators as a tool for supporting and optimizing risk assessment
strategies. In Chapter 1, we will present many cases were QSAR is used for
regulatory purposes in the world.
(Q)SARs are estimation methods developed and used to predict certain
propertiesofchemicalswhichareprimarilybasedonthestructureofthesubstance.
Useof(Q)SARtechniqueswillallowpotentialsavingsofmilliontestanimalsand
billion euros, boosting cost- and time-effectiveness of risk assessment procedure.
Within the EU risk assessment framework, the role of QSAR has been clearly
statedinthetechnicalguidancedocumentinsupporttotheEUdirective93/67/EEC
(EEC, 1996). In the document, a general framework in which (Q)SARs can be
used within the risk assessment process is presented. Use of (Q)SARs is proposed
for the following purposes:
1) Assisting data evaluation.
2) Contributing to the decision-making process on whether further testing is
necessary to clarify an endpoint of concern.
3) Establishing input parameters necessary to conduct exposure assessment.
4) Identifying effects which may be of potential concern on which test data
are not available.
All the four above listed purposes testify the importance of (Q)SAR in
supporting the risk assessment procedures.
More recently, the REACH legislation clearly mentioned QSAR as tool to
reduce the use of animals and resources for the purpose of assessing industrial
chemicals. This also suggests that (Q)SARs are ideal tools for addressing regu-
latory tasks, and indeed, they are widely used by USEPA for these purposes. In
Europe, their use will be strongly encouraged within current and forthcoming EU
chemical policies. At present, however, several concerns about the validity and
applicability of the (Q)SARs have not yet been solved.
The lack of standardised, reproducible, and reliable (Q)SAR protocols has
raised serious concerns about the reliability of current in silico predictions. To
Preface xvii
date, more than 20000 (Q)SARs have been developed and published, each adopt-
ing different combinations of human hand-feeding actions and computational
resources. Despite their effectiveness, none of them could individually face the
challenge posed by the current EU chemical legislation.
THE DEMETRA PROJECT
Tobeusedforoptimallyexploiteachmodel’spotentialandtargetregulatoryobjec-
tives, the most reliable solution would be to combine several models, integrating
them into a decision support system. The DEMETRA project has been developed
followingthisprinciple,withtheaimtoassistregulatorsintakingdecisionsduring
the risk assessment process. The project’s heart is the decision to refer in all steps
to the target, which means to refer to the intended use of the models according to
the latest EU regulatory directives. With its innovative applications and services,
DEMETRA places the current (Q)SAR dimension closer to the regulatory one.
The main objective of DEMETRA was to develop tools for pesticides
and related compounds (such as their metabolites) toxicity prediction against
fiveendpoints.Regulatorybodies,industries,non-governmentalorganizationsand
researchersaremajorpotentialusersthatmightbeinterestedinexploitingthesoft-
ware.Inaddition,regulatoryevaluatorsmightgreatlybenefitbyusingDEMETRA
in the data evaluation process for approval applications. The intrinsic complexity
of the project planning and development has made mandatory the splitting of the
main goal in several sub goals, as defined in the project and here reported:
•
Toselectatleastfiverelevanttoxicityendpointsformodellinginthisproject
and to compile the quality-controlled data sets required for the project.
•
To calculate chemical descriptors of the chemicals selected in the five data
sets as above defined.
•
To analyse, develop and propose algorithms for toxicity of pesticides.
•
Tointegratetheknowledgeacquiredthroughdifferentapproachesinahomo-
geneous manner, within a hybrid system, for each endpoint.
•
To validate the hybrid systems for the five selected endpoints.
•
Todevelopthewebsiteasuser-orientedportaltoaccessthehybridsystems,
the online documentation and user manuals.
•
Toexploitandtodisseminateresultsoftheproject,notablytowidernumber
of regulatory bodies and users and to organize a European workshop to
present results.
xviii Preface
The list above depicts a clear picture of all different aspects covered by
DEMETRA during its development. Each objective represents a single project
step to be addressed before passing to the next one.
Contractors and subcontractors involved in the project are listed below.
Participant Participant Participant Team Town,
Name Short Name Leader Country
P1 (CO) “Mario Negri” IRFMN Emilio Milano,
Institute Benfe- Italy
nati
P2 BioChemics BCX Marco Orléans,
Consulting SAS Pintore France
P3 Central Science CSL Qasim York, UK
Laboratory Chaudhry
P4 University UGAL Viorel Galati,
“Dunarea de Minzu Romania
Jos” of Galati
P5 Politecnico di POLIMI Giuseppina Milano,
Milano Gini Italy
P6 University of UNIPATRAS Nikolaos Patras,
Patras Avouris Greece
P7 Syngenta Croop SYNGENTA Bruno Basel,
Protection AG Lefevbre Switzerland
Participant Participant Participant Team Town,
Name Short Name Leader Country
S1 The Pesticide PSD Mark York, UK
Safety Clook
Directorate
S2 BASF BASF Peter Limburgerhof,
Agricultural Dolmen Germany
Centre
Preface xix
Participant Participant Participant Team Town,
Name Short Name Leader Country
S3 KnowledgeMiner KNOWLEDGE Frank Panketal,
Software Frank MINER Lemke Germany
Lemke
S4 Technology for TfG Nick York, UK
Growth Price
S5 Bradford UBRAD Daniel Bradford,
University Neagu UK
THE BOOK CHAPTERS
Thefollowingchapterswillpresentthemaintheoreticalissues,themethodologies
and the results. Chapter 1 gives details of the legislative requirements according
to the EU regulation. On the basis of these requirements, the main used endpoints
for pesticides are presented, and criteria established to identify the most useful
QSAR models, to reduce the number of animals, the cost of the experiment, the
frequency of the tests, their severity, etc. QSAR requirements are also discussed
on the basis of opinions from users.
Chapter 2 describes the sources of the toxicity data we choose. Only high-
quality data have been considered. The reproducibility of the data is presented.
We also compared the values in the different databases. In order to achieve a
standardized format for data representation, a module was produced for the XML
format.
Chapter 2 also describes how data have been selected, from the source
identified above. It is common that for the same pesticide more then one toxicity
value is reported, because of the variability and uncertainty of the experimental
procedure.Wedefinedaprotocoltoselectthemostusefulandreliablevalues.We
report the five datasets for the five endpoints that have been finally considered
– two aquatic endpoints: trout and daphnia; quail (dietary and oral exposure);
finally bee.
Chapter3explainshowtodefineandprocesschemicalstructures,andwhat
kind of chemical descriptors to calculate. Two- and three-dimensional descriptors
are introduced. Another possibility is the use of chemical fragments. Several
thousands of chemical parameters can be obtained.
xx Preface
Chapter 4 describes the algorithms for in silico modelling. We discuss the
methods to select chemical descriptors, which is useful considering the presence
of a very high number of parameters. Methods to obtain continuous or categorical
toxicity values are discussed.
Chapter 5 explains the possible ways to integrate different models into a
combined,hybridmodel.Takingadvantageofthepositivefeaturesoftheseparate
individual models within an intelligent strategy improves the overall final results.
Chapter 6 deals with the validation of QSAR models. Internal and external
(with a separate test set) validation methods are presented. Validation in case of
regression methods and classifiers are described. Besides mathematical methods,
we discuss the specific features requested by the intended application of the
models, describing the false-negative issue.
Chapter 7 presents the results of the DEMETRA models. Thousands of
models have been obtained, and here, we report the most successful. Results
are discussed relatively to classification methods regression models and hybrid
systems. Five separate hybrid models have been obtained, one for each selected
toxicity endpoint.
Chapter 8 discusses the innovative aspects of the DEMETRA models. The
OECD guidelines for validation of QSAR are introduced and DEMETRA models
commented in relation to them. Future perspectives are also presented.
Chapter 9 describes the public use of the obtained models. The five final
models are public available, for non-commercial use, through the Internet. The
final models have been optimized for the five endpoints, as described in the book.
In addition, a general, flexible toolbox is available, to develop further models, for
other purposes.
ACKNOWLEDGEMENT
The editor gratefully acknowledges financial support from the Commission of
the European Communities, under the European Union’s Fifth Framework for
Research and Technological Development Programme, for the project “Develop-
ment of Environmental Modules for Evaluation of Toxicity of pesticide Residues
in Agriculture” QLK5-CT-2002-00691.
DISCLAIMER
This publication does not necessarily reflect the European Commission’s views
and in no way anticipates the Commission’s future policy in this area. Its content
is the sole responsibility of the authors.