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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.E.Papa and P.Gramatica, J.Mol. Graph. Model.
2008, 27, 59.
New tools, based
on molecular structure and multivariate explorative methods,
heve been developed for the ranking, identification and
prioritization of PBTs. A new PBT index has been proposed.
E.Papa, and
P.Gramatica Green Chemistry, 2010, 12, 836-843; [Selected
as HOT Article]
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; E.Papa and P.Gramatica,
SAR QSAR Environ Res. 2008, 19, 655. P.P. Roy et al. J.Comput.Chem,
2011 in press.
-
*
Environmental partitioning of organic pollutants (pesticides
and other emerging).
* QSAR modeling of phys-chem properties: Koc, WSol,
VP, H, etc.
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.[JMGM,
2007:Most cited paper in Computer Science]
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, esters, flame retardants, perfluorinated compounds,
triazoles. The environmental distribution based on phys
chem. properties and multivariate analysis by PCA is studied
also for PFCs and (benzo) triazoles in the EU-FP7 Project
CADASTER. 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; Chemosphere, 2000, 41,
763; Chemosphere , 2002, 47, 947; Int. J. Environ. Anal.
Chem. 2004, 84, 65; Fresenius Environ. Bull. 2004, 13,
1258. J. Mol. Graph. Model., 2007, 25, 755. [Most cited
paper in Computer Sci]
E.Papa et al.QSAR& Comb. Sci., 2009, 28, 790; Molecular
Informatics, 2011, in press. B. Bhhatarai and P.Gramatica,
Environ. Sci. Technol., 2010 online first; Water Research,
2011, 45,1463; B. Bhhatarai et al. Molecular Informatics,
2011, in press.
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[Most downloaded
in 2007; 131 citations into 2010].
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
Book chapters (see below)
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.
In the EU-FP7
Project CADASTER toxicity of perfluorinated chemicals
(PFCs) is studied: oral and inhalation rat and mouse toxicity
have been modeled with the principal aim to select priority
PFCs for experimental tests.
B. Bhatarai
and P. Gramatica, Chem. Res. Toxicol., 2010, 23, 528;
Molecular Diversity, 2010 online first
Similarly, oral
acute toxicity and cytotoxic activity of fragrance materials
in rodents for fragrances have been modeled:
E. Papa et al.
SAR QSAR Environ Res., 2009, 20, 767.
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, but 8%
should be prioritized for testing.
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; Chemosphere,
2008, 70, 1889; Comb. Chem. & H. T S. (special issue on
"Machine learning for virtual screening") 2009, 12, 490.
Androgen receptor
(AR) binders and pleiotropic endocrine disruptors (with
double activity on AR and ER) have been also studied and
modeled by regression and classification QSAR models for
screening purposes.
J. Li and P.
Gramatica, Molecular Diversity, 2010, 14, 687; J. Chem.
Inf. Mod, 2010, 50, 861; SAR &QSAR in Environ Res, 2010,
21, 657.
In the EU-FP7
Project CADASTER the endocrine disruptor activity of flame
retardants is also studied.
E.Papa et al.
Chem Res. Toxicol, 2010, 23, 946. S. Kovarich et al. J.Hazard.
Mat., 2011 in press
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.,2007, 18, 169;E.
Papa et al. SAR QSAR Environ. Res., 2008 , 19,115.
P. Gramatica
Modelling Chemicals in the Environment, Chapter 17 in
Drug Design Synthesis: quantitative approaches, D. Livingstone
Ed. Royal Chemical Society, London, U.K. 2011. in press.
P. Gramatica
On the development and validation of QSAR models, in Computational
Toxicology, B. Reisfeld, A. N. Mayeno Eds., Humana Press,
Springer Science, New York, USA, 2011 in press.
P. Gramatica
Chemometric Methods and Theoretical Molecular Descriptors
in Predictive QSAR
Modeling of
the Environmental Behaviour of Organic Pollutants, Chapter
12 in Recent Advances in QSAR Studies , T. Puzyn - J.
Leszczynski - M.T.D. Cronin Eds., (Challenges and Advances
in Computational Chemistry and Physics), Springer-Verlag
New York Inc, 2009.
R. Todeschini,
V. Consonni, and P. Gramatica Chemometrics in QSAR. In:
Brown S, Tauler R, Walczak R (eds.) Comprehensive Chemometrics,
volume 4, pp. 129-172 Oxford: Elsevier. 2009
Studies on the
information related to particular descriptors (WHIM of
shape) have been performed.
P.Gramatica,
QSAR & Comb. Sci., 2006, 25, 327.
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., 2008, 86, 419
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. J. H. Kim,
P. Gramatica et al. SAR QSAR Environ. Res., 2007, 18,
729. J. Li et al. J. Comput. Chem., 2008, 29, 2636 ; J.
Comput. Chem. 2010, 31, 973. H. Zhu et al. J.Chem.Inf.Model,
2008, 48, 766. I. Sushko et al., J. Chem. Inf. Model.,
2010, 50, 2094. B. Lei et al., Atmos. Environ, 2010, 44,
2954. L.Xi et al. Chem. Engin. J., 2010, 163, 195. B.
Bhhatarai et al. Molecular Informatics, 2010, 29, 511.
P. Gramatica et al. J. Biol. Inorg Chem. 2010, 15, 1157
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