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MAIN TOPICS OF
RESEARCHES PERFORMED AT INSUBRIA QSAR RESEARCH UNIT IN
ENVIRONMENTAL CHEMISTRY AND ECOTOXICOLOGY (SINCE 1998)
POPs
have been studied for their intrinsic tendency to global
persistence and mobility (Long Range Transport, LRT) by
applying multivariate methods (Principal Component
Analysis and Multicriteria Descision making) and QSAR
modelling, both by classification and regression models.
A Global Half-life Index (GHLI) useful for the
preventive screening and ranking of possible POPs, even
before the synthesis, has been recently defined (ES&T,
2007).
P.Gramatica
et al., Chemosphere, 2001, 43, 655; SAR and QSAR in Env.
Res., 2002, 13, 205; Fresenius Environ. Bull. 2004, 13,
1204. P.Gramatica and E.Papa, Environ. Sci. Technol.,
2007, 41, 2833.
Development
of new tools, based on molecular structure, for the
ranking, identification and prioritization of PBTs.
Proposal of a new PBT index. P.Gramatica and E.Papa, in
progress.
(VOCs)
The kinetic rate constants for the degradation reactions
by tropospheric oxidants (hydroxyl and nitrate radicals
, ozone) of heterogeneous Volatile Organic Compounds (VOCs)
have been modelled by regression models (Multiple Linear
Regression, MLR) using different kind of theoretical
molecular descriptors, selected by Genetic Algorithms.
Ranking of VOCs for their combined atmospheric
degradability has been modelled by QSAR, in order to
propose useful tools for the highlighting (also before
the synthesis) of chemicals potentially persistent in
troposphere.
P.Gramatica
et al., Chemosphere, 1999, 38, 1371; SAR QSAR Env Res.,
2002, 13, 743; Atm. Environ., 2003, 37, 3115; QSAR &Comb.
Sci., 2003, 22, 364; J. Chem. Inf. Comp. Sci.. 2004, 44,
1794; Atm. Environ. 2004, 38, 6167.
The
partitioning of pesticides into different environmental
compartments and the dangerous leaching in ground-water
depends mainly on the physico-chemical properties. The
combination of two multivariate approaches (Principal
Component Analysis and Hierarchical Cluster Analysis)
and CART (Classification and Regression Tree) model was
used to rank and classify pesticides of various chemical
categories according to their distribution tendency in
various media. A Leaching Index (LIN) and a Volatility
Index (VIN) have been also proposed and modeled by MLR.
The soil sorption coefficient, expressed as the ratio
between chemical concentration in soil and in water,
normalized to organic carbon (Koc), an important
parameter when studying soil mobility and environmental
distribution of chemicals, has been also modeled by
externally validated highly predictive regression models,
based on few simple molecular descriptors.
P.Gramatica
et al., Chemosphere, 2000, 41, 763; Chemosphere , 2002,
47, 947; Int. J. Environ. Anal. Chem. 84, 65-74, 2004 ;
J. Mol. Graph. Model., 2007, 25, 755.
A
great attention is always devoted to the validation of
the QSAR models developed by the group. Paola Gramatica,
who is member of the OECD group of QSAR Experts and
participated to the proposal and definition of the OECD
Principles (since the Setubal principles, 2002) has been
involved in validation works for EU-JRC and OECD and has
published papers and reports on validation (external,
applicability domain, etc.)
A.Tropsha,
P.Gramatica, V. K. Gombar QSAR &Comb. Sci., 2003,
22, 69-77. L. Eriksson et al. Environ. Health
Perspectives 2003, 111, 1361. T. I. Netzeva et al. ATLA
2005, 33, 155. Vracko M. et al. SAR QSAR Environ. Res.,
2006, 17, 265. P. Gramatica QSAR Comb.Sci. 2007, 26,
694. Reports for EU and OECD: P. Gramatica, 2004
http://appli1.oecd.org/olis/2004doc.nsf/linkto/env-jm-mono(2004)24
http://ecb.jrc.it/QSAR/ under QSARs/documents/public
access
The
Bioconcentration Factor (BCF) is an important parameter
in environmental assessment as it is an estimate of the
tendency of a chemical to concentrate and, consequently,
to accumulate in an organism. The most common QSAR
method, and the oldest, for estimating chemical
bioconcentration is the establishment of correlations
between BCF and chemical hydrophobicity using Kow, i.e.
the n-octanol/water partition coefficient, but LogKow is
an highly variable descriptors. Different models on BCF
using theoretical molecular descriptors have been
developed by the author group, with particular attention,
as usual, to the external predictivity and the chemical
applicability domain, according to the OECD principles.
P.Gramatica
and E.Papa, QSAR &Comb. Sci., 2003, 22, 374; QSAR
&Comb. Sci. 2005, 24, 953. E. Papa et al.
Chemosphere, 2007, 67, 351.
An
innovative strategy for the selection of compounds with
a similar toxicological mode of action was proposed in
the study of chemical mixtures (PREDICT EU-Research
Project). A representation of chemical structures for
phenylureas and triazines by different molecular
descriptors allowed a preliminary exploration of
similarity based on Principal Components Analysis (PCA),
MultiDimensional Scaling (MDS) and Hierarchical Cluster.
The use of Genetic Algorithm to select the most relevant
molecular descriptors in modeling toxicity data makes it
possible to both develop good predictive QSAR toxicity
models, and select the most similar phenylureas and
triazines. P.Gramatica et al. Chemosphere, 2001, 42,
873.
A
proposal of new water quality objectives (WQO) for EEC
priority List 1 (76/464/EEC) was realized by QSAR models
and chemometric analysis of toxicity data. M.Vighi,
P.Gramatica, et al. Ecotox and Environ Safety, 2001, 49,
206.
The
Duluth data set of toxicity data on Pimephales promelas
was recently studied by the author group: new
statistically validated MLR models were developed to
predict the aquatic toxicity of chemicals classified
according to their Mode Of Action (MOA). Also, a unique
general model for Direct Toxicity Prediction (DTP model)
was developed to propose a predictive tool with a wide
applicability domain, applicable independently of a
priori knowledge of the MOA of chemicals. Similarly, new
QSAR models for esters' aquatic toxicity and a ranking
for cumulative end-points were proposed.
E.Papa
et al., J. Chem. Inf. Model 2005, 45,1256; Chemosphere
2005, Vol 58, 559.
Because
of the large number of endocrine-disrupting chemicals (EDCs)
in the environment, there is a great need for an
effective tool of rapidly assessing ED activity in the
toxicology assessment process and in the context of the
new European REACH policy. Classification and regression
QSAR models were developed to predict the estrogen
receptor binding affinity based on a large data set of
heterogeneous chemicals and theoretical molecular
descriptors from DRAGON. The built OLS regression model,
was validated comprehensively (internal and external
validation, Y-randomization test) and all the
validations indicate that the proposed QSAR model is
robust and satisfactory. For the classification models,
three nonlinear classification methodologies: Least
Square Support Vector Machine (LS-SVM), Counter
Propagation Artificial Neural Network (CP-ANN), and
k-nearest Neighbor (kNN) were applied, by using four
molecular structural descriptors as inputs. The models
were also applied to about 58 000 discrete organic
chemicals; about 77% were predicted not to bind to an
estrogen receptor. QSAR Studies on Selective Ligands for
the Thyroid Hormone Receptor beta has been also
performed.
H.Liu
et al. Chem. Res. Toxicol., 2006, 19, 1540; J.Mol. Graph.
Model. 2007, 26, 135; Bioorg. Med. Chem., 2007, 15,
5251.
The
potential for mutagenicity of chemicals of environmental
concern, like aromatic amines and PAHs, is of high
relevance. With regard to this important topic, our
group has published interesting MLR models, always
verified for their external predictivity on new
chemicals, for the Ames test results on amines and
Nitro-PAHs. Externally validated classification models,
by k-NN and CART, were also developed for the
mutagenicity of benzo-cyclopentaphenanthrenes and
chrysenes, determined by the Ames test, and PAH
mutagenicity, determined on human B-lymphoblastoid.
P.Gramatica
et al., SAR QSAR Env Res., 2003, 14, 237; Ecotox.
Environ. Safety, 2007, 66, 353; SAR QSAR Env Res., 18 ,
169 2007.
In
addition to Koc modelling, other common physico-chemical
properties have been successfully modeled by combining
different kinds of theoretical molecular descriptors:
the basic physico-chemical properties of organic
solvents, PCBs and esters. A general classification of
152 organic solvents has been also proposed by applying
k-nearest neighbor method and counterpropagation
artificial neural network (CP-ANN), in particular
Kohonen Maps.
P.Gramatica
et al. Chemom. Intell. Lab. Syst., 1998, 40, 53-63;
Trends Anal. Chem., 1999, 18, 461-471; Fresenius Environ.
Bull. 2004, 13, 1258-1262.
Some
applications to QSAR modeling of new molecular
descriptors and studies on the information related to
particular descriptors are performed.
P.Gramatica,
QSAR Comb. Sci., 2006, 25, 327. V.Consonni et al. J.
Chem. Inf. Comput. Sci.; 2002, 42, 693.
The
group has also collaborated with other research groups
for the interpretation of environmental data (metal
accumulation by mosses as bioindicators, environmental
sustainability parameters, leaching of pesticides in
Uzbekistan rivers, identification of structurally
representative and biodegradable antibiotics) by
applying different chemometric explorative methods of
multivariate analysis.
P.Gramatica
et al. Environ. Sci. Pollut. Res, 2006, 13, 28 ;
Fresenius Environ. Bull., 2006, 15, 731. E.Papa et al.
Water Res. 2004, 38 , 3485 ; Environ. Sci. Technol.,
2007, 41, 1653. S. Bastianoni , et al. J. Environ.
Manag., 2007, doi:10.1016/j.jenvman.2006.04.018.
P.Gramatica
has participated to EU-Projects on mixture toxicity (PREDICT
and BEAM), as scientific responsible of Insubria Unit,
and collaborated with Milano-Bicocca University in the
UNARO project on atmospheric pollution. In the three
projects, QSAR models and chemometric tools have been
developed and applied.
Mixture
Toxicity: P. Gramatica et al. Chemosphere, 2001, 42,
873; M. Vighi, P. Gramatica, et al. Ecotox and Environ
Safety, 2001, 49, 206. M.Faust et al. Aquatic Toxicology,
2001, 56, 13; Aquatic Toxicology 2003, 63, 43. H. Walter
et al. Ecotoxicology, 2002, 11, 299; M.Vighi, et al.
Ecotox. Environ. Safety, 54, 2003, 139-150. Backhaus, T.
et al. Continental Shelf Research 2003, 23, 1757;
Environ. Toxicol. Chem. 2004, 23, 258. Atmospheric
Pollution: P. Gramatica et al. Fresenius Environ. Bull.
2002,11, 757; Analysis in air, toxicology and QSAR
modeling with nitrophenols. In: Air Pollution X, WIT
Press, A. Brebbia, J. F. Martin-Duque eds, Southampton,
U.K., pp 731-740. 2002.
Different
works, which are not classifiable in the above topics,
are also performed. The majority of them are
collaborations with other research groups for chemical
design.
P.
Gramatica, Chemistry Today 2001, 18-24. T.Benincori, et
al. Chem. Materials, 2001, 13, 1665. E. Bolzacchini,
Bioconjugate Chemistry, 1999, 10, 332. P.Gramatica et
al. Annali di Chimica, 2005, 95, 199. M. Pavan et al. In
"Partial Order in Chemistry and Environmental
Sciences" (R. Brüggemann and L. Carlsen Ed.), Chap.
3, 181, Springer-Verlag, 2006. L. Nizzetto et al.
Environ. Sci. Technol., 2006, 40, 6580. S. Banfi et al.
J. Med. Chem. 2006, 49, 3293. E. Papa et al. Environ.
Sci. Technol., 2007, 41, 1653. |
