Paola Gramatica - Profile on Academia.edu (original) (raw)

Uploads

Papers by Paola Gramatica

Predicting the joint algal toxicity of multi-component s-triazine mixtures at low-effect concentrations of individual toxicants

Aquatic Toxicology, Dec 1, 2001

Herbicidal s-triazines are widespread contaminants of surface waters. They are highly toxic to al... more Herbicidal s-triazines are widespread contaminants of surface waters. They are highly toxic to algae and other primary producers in aquatic systems. This results from their specific interference with photosynthetic electron transport. Risk assessment for aquatic biota has to consider situations of simultaneous exposure to various of these toxicants. In tests with freshwater algae we predicted and determined the toxicity of multiple mixtures of 18 different s-triazines. The toxicity parameter was the inhibition of reproduction of Scenedesmus vacuolatus. Concentration-response analyses were performed for single toxicants and for mixtures containing all 18 s-triazines in two different concentration ratios. Experiments were designed to allow a valid statistical description of the entire concentration-response relationships, including the low concentration range down to EC1. Observed effects and effect concentrations of mixtures were compared to predictions of mixture toxicity. Predictions were calculated from the concentration-response functions of individual s-triazines by applying the concepts of concentration addition and independent action (response addition) alternatively. Predictions based on independent action tend to underestimate the overall toxicity of s-triazine mixtures. In contrast, the concept of concentration addition provides highly accurate predictions of s-triazine mixture toxicity, irrespective of the effect level under consideration and the concentration ratio of the mixture components. This also holds true when the mixture components are present in concentrations below their individual NOEC values. Concentrations statistically estimated to elicit non-significant effects of only 1% still contribute to the overall toxicity. When present in a multi-component mixture they can co-operate to give a severe joint effect. Applicability of the findings obtained with s-triazines to mixtures of other contaminants in aquatic systems and consequences for risk assessment procedures are discussed.

3D-Modelling and Prediction by Whim Descriptors. Part 7. Physico-Chemical Properties of Haloaromatics: Comparison Between Whim and Topological Descriptors

Sar and Qsar in Environmental Research, Dec 1, 1997

Abstract Quantitative Structure-Activity Relationships (QSAR) studies are powerful tools to ratio... more Abstract Quantitative Structure-Activity Relationships (QSAR) studies are powerful tools to rationalize complex systems where physico-chemical properties and biological activities of compounds of environmental and toxicological interest are involved. In these fields, due to costs, time, and difficulties in obtaining experimental measures, one of the main objectives is to fill the need for general predictive models. The challenge is to describe and represent, on a quantitative basis, all the molecular structural features in order to use them as ...

Descrittori molecolari WHIM: aspetti teorici e applicazione allo studio di proprietà chimico-fisiche degli idrocarburi aromatici policondensati

Springer eBooks, 2000

The selection of compounds with a similar toxicological mode of action is a key problem in the st... more The selection of compounds with a similar toxicological mode of action is a key problem in the study of chemical mixtures. In this paper, an approach for the selection of chemicals with similar mode of action, based on the analysis of structural similarities by means of QSAR and chemometric methods, is described. As a ®rst step, a complete representation of chemical structures for examined chemicals (phenylureas and triazines) by dierent sets of molecular descriptors allows a preliminary exploration of similarity using multi-dimensional scaling (MDS). The use of genetic algorithm (GA) to select the most relevant molecular descriptors in modeling toxicity data makes it possible to develop predictive toxicity models. The ®nal step is a similarity analysis, based again on MDS, using selected molecular descriptors, really relevant in describing the toxicological eect.

Modelling of physico-chemical properties for organic pollutants

9th Annual Meeting of SETAC-Europe, Leipzig (Germany), 1999

Stima della persistenza ambientale di PAH studiata mediante spettrometria di massa e metodi QSAR

ChemInform Abstract: SYNTHESIS OF ESTRAGOLE, A GENERAL ROUTE TO ALLYLPHENOLS FROM PROPENYLPHENOLS

Chemischer Informationsdienst, 1974

Journal of Computational Chemistry, 2021

The new software QSARINS-Chem standalone version is a multiplatform tool, freely downloadable, fo... more The new software QSARINS-Chem standalone version is a multiplatform tool, freely downloadable, for the in silico profiling of multiple properties and activities of organic chemicals. This software, which is based on the concept of the QSARINS-chem module embedded in the QSARINS software, has been fully redesigned and redeveloped in the Java™ language. In addition to a selection of models included in the old module, the new software predicts biotransformation rates and aquatic toxicities of pharmaceuticals and personal care products in multiple organisms, and offers a suite of tools for the analysis of predictions. Furthermore, a comprehensive and transparent database of molecular structures is provided. The new QSARINS-Chem standalone version is an informative and solid tool, which is useful to support the assessment of the potential hazard and risks related to organic chemicals and is dedicated to users which are interested in the application of QSARs to generate reliable predictions. K E Y W O R D S alternatives to animal testing, in silico predictions, QSAR, QSARINS, virtual screening 1 | INTRODUCTION Chemical pollution has great impact on human and environmental health and the development of strategies to guarantee a more sustainable use of chemicals is a main challenge for chemical regulations worldwide. 1-3 The need to properly address and manage chemical risks as well as to track and substitute potentially hazardous chemicals with less dangerous ones, has in the last decade pushed toward a faster development and integration of in vitro and in silico strategies within regulations. The effort spent in traditional and regulatory science to facilitate the application of in silico tools, making them more transparent, easy to apply, and efficient, is major. 3 In silico approaches, such as models based on Quantitative Structure Activity Relationships (QSAR), are used to predict many different properties and activities of regulatory interest and for different chemical categories. 4-17 These models are useful to fill data gaps, for virtual screenings, and/or for the identification of safer alternatives to unsafe pollutants. Furthermore, the availability of multiple models, which can be combined to generate consensus predictions, helps to reduce the uncertainty associated with the prediction of a single property/activity, they can cross-validate in silico predictions and experiments, and support decision making processes. 4-13 QSARINS-Chem 17 was proposed in 2014 as an additional module embedded in the software QSARINS 18 to provide a database to store models and a tool to facilitate their application. The QSARINS-Chem module included a database of chemical structures (with 3D

2.1.Date of QMRF: 30/01/2015 2.2.QMRF author(s) and contact details: [1]Paola Gramatica Insubria ... more 2.1.Date of QMRF: 30/01/2015 2.2.QMRF author(s) and contact details: [1]Paola Gramatica Insubria University, Department of Theoretical and Applied Sciences (DiSTA), via J.H. Dunant 3, 21100 Varese (Italy) +390332421573 paola.gramatica@uninsubria.it http://www.qsar.it/ [2]Stefano Cassani Insubria University, Department of Theoretical and Applied Sciences (DiSTA), via J.H. Dunant 3, 21100 Varese (Italy) +390332421439 stefano.cassani@uninsubria.it http://www.qsar.it/ 2.3.Date of QMRF update(s): 2.4.QMRF update(s): 2.5.Model developer(s) and contact details: [1]Stefano Cassani Insubria University, Department of Theoretical and Applied Sciences (DiSTA), via J.H. Dunant 3, 21100 Varese (Italy) +390332421439 stefano.cassani@uninsubria.it http://www.qsar.it/ [2]Paola Gramatica Insubria University, Department of Theoretical and Applied Sciences (DiSTA), via J.H. Dunant 3, 21100 Varese (Italy) +390332421573 paola.gramatica@uninsubria.it http://www.qsar.it/ 2.6.Date of model development and/or...

The Long Range Transport (LRT) potential of chemicals is due to the combination of their persiste... more The Long Range Transport (LRT) potential of chemicals is due to the combination of their persistence in the environment and their inherent tendency towards mobility, and is an undesirable property of POPs (Persistent Organic Pollutants). Finding the best combination of chemical properties to minimize LRT is a multicriteria problem that can be approached by MultiCriteria Decision-Making (MCDM) techniques. Utility functions have been applied to two proposed indexes, the “global persistence index” and the “mobility index”, allowing a ranking of the studied chemicals according to their LRT potential. The “global persistence index” was obtained by linear combination, by Principal Component Analysis of the half-life data in various environmental compartments. Half-life data are commonly used as persistence indicators, but the availability of such data is limited to only a few organic compounds, thus QSAR (Quantitative Structure-Activity Relationships) models were used to predict such data...

Alternatives to Laboratory Animals, 2005

This is the 52nd report of a series of workshops organised by the European Centre for the Validat... more This is the 52nd report of a series of workshops organised by the European Centre for the Validation of Alternative Methods (ECVAM). The main objective of ECVAM, as defined in 1993 by its Scientific Advisory Committee, is to promote the scientific and regulatory acceptance of alternative methods which are of importance to the biosciences, and that reduce, refine or replace the use of laboratory animals. The ECVAM workshop on the quantitative structure-activity relationship applicability domain was held at ECVAM on 29 September-1 October 2004, under the chairmanship of Andrew Worth. The workshop was attended by experts from academia, industry, international organisations and regulatory authorities. The aim of the workshop was to review the state of the art of methods for identifying the domain of applicability of structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs), collectively referred to as (Q)SARs. The report is intended to provide a source of input to the development of an OECD Guidance Document on (Q)SAR Validation. The report also makes recommendations for further research needed to understand and apply the concept of the (Q)SAR applicability domain (AD).

Handbook of Computational Chemistry, 2017

Several different chemical properties/activities must be contemporaneously taken into account to ... more Several different chemical properties/activities must be contemporaneously taken into account to prioritize compounds for their hazardous behavior. Examples of application of chemoinformatic methods, such as principal component analysis for obtaining ranking indexes and hierarchical cluster analysis for grouping chemicals with similar properties, are summarized for various classes of compounds of environmental concern. These cumulative endpoints are then modeled

Green Chemistry, 2016

New externally validated QSAR models for aquatic toxicity of PCPs are proposed and applicable in ... more New externally validated QSAR models for aquatic toxicity of PCPs are proposed and applicable in QSARINS for thea priorichemical design of environmentally safer PCPs.

Environmental Toxicology and Chemistry, 2015

In the present study, quantitative structure activity relationships were developed for predicting... more In the present study, quantitative structure activity relationships were developed for predicting ready biodegradability of approximately 200 heterogeneous fragrance materials. Two classification methods, classification and regression tree (CART) and knearest neighbors (kNN), were applied to perform the modeling. The models were validated with multiple external prediction sets, and the structural applicability domain was verified by the leverage approach. The best models had good sensitivity (internal !80%; external !68%), specificity (internal !80%; external 73%), and overall accuracy (!75%). Results from the comparison with BIOWIN global models, based on group contribution method, show that specific models developed in the present study perform better in prediction than BIOWIN6, in particular for the correct classification of not readily biodegradable fragrance materials.

Chemometrics in QSAR

Comprehensive Chemometrics, 2009

Todeschini, R., Consonni, V., & Gramatica, P. (2009). Chemometrics in QSAR. In S. Brown, R. T... more Todeschini, R., Consonni, V., & Gramatica, P. (2009). Chemometrics in QSAR. In S. Brown, R. Tauler, & R. Walczak (a cura di), Comprehensive Chemometrics (pp. 129-172). Oxford : Elsevier. ... There are no files associated with this item. ... Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

On the Use of Local and Global QSPRs for the Prediction of Physico-chemical Properties of Polybrominated Diphenyl Ethers

Molecular Informatics, 2011

Polybrominated diphenyl ethers (PBDEs) are persistent chemicals that have been among the most mar... more Polybrominated diphenyl ethers (PBDEs) are persistent chemicals that have been among the most marketed flame retardants used all over the world in the last decades. PBDEs have been detected in all environmental compartments, as well as in humans and wildlife, where they are able to accumulate and exert their toxic effects. At present only a limited amount of experimental data is available to characterize the physico-chemical and toxicological behavior of PBDEs and similar brominated flame retardants. QSA(P)R approaches are very useful tools to predict missing data starting from the chemical structure of compounds. In this study several local QSPR models, developed specifically for the prediction of logKoa, logKow and melting point of PBDEs, were compared with predictions by global QSPR models, such as KoaWIN, KowWIN and MPBPWIN from the EPI Suite package, and AlogP and MlogP from DRAGON software, which were trained on heterogeneous and large datasets. The analysis addressed in the paper supported the identification of points of strength and weaknesses of both local models, and global models. The results are relevant to support decisions made by general QSAR users and regulators, when they have to select and apply one of the analyzed models to predict properties for PBDEs.

QSAR Modeling is not “Push a Button and Find a Correlation”: A Case Study of Toxicity of (Benzo-)triazoles on Algae

Molecular Informatics, 2012

A case study of toxicity of (benzo)triazoles ((B)TAZs) to the algae Pseudokirchneriella subcapita... more A case study of toxicity of (benzo)triazoles ((B)TAZs) to the algae Pseudokirchneriella subcapitata is used to discuss some problems and solutions in QSAR modeling, particularly in the environmental context. The relevance of data curation (not only of experimental data, but also of chemical structures and input formats for the calculation of molecular descriptors), the crucial points of QSAR model validation and the potential application for new chemicals (internal robustness, exclusion of chance correlation, external predictivity, applicability domain) are described, while developing MLR-OLS models based on molecular descriptors, calculated by various QSAR software tools (commercial DRAGON, free PaDEL-Descriptor and QSPR-THESAURUS). Additionally, the utility of consensus models is highlighted. This work summarizes a methodology for a rigorous statistical approach to obtain reliable QSAR predictions, also for a large number of (B)TAZs in the ECHA preregistration list of REACH (even if starting from limited experimental data availability), and has evidenced some ambiguities and discrepancies related to SMILES notations from different databases; furthermore it highlighted some general problems related to QSAR model generation and was useful in the implementation of the PaDEL-Descriptor software.

Predicting the joint algal toxicity of multi-component s-triazine mixtures at low-effect concentrations of individual toxicants

Aquatic Toxicology, Dec 1, 2001

3D-Modelling and Prediction by Whim Descriptors. Part 7. Physico-Chemical Properties of Haloaromatics: Comparison Between Whim and Topological Descriptors

Sar and Qsar in Environmental Research, Dec 1, 1997

Descrittori molecolari WHIM: aspetti teorici e applicazione allo studio di proprietà chimico-fisiche degli idrocarburi aromatici policondensati

Springer eBooks, 2000

Modelling of physico-chemical properties for organic pollutants

9th Annual Meeting of SETAC-Europe, Leipzig (Germany), 1999

Stima della persistenza ambientale di PAH studiata mediante spettrometria di massa e metodi QSAR

ChemInform Abstract: SYNTHESIS OF ESTRAGOLE, A GENERAL ROUTE TO ALLYLPHENOLS FROM PROPENYLPHENOLS

Chemischer Informationsdienst, 1974

Journal of Computational Chemistry, 2021

Alternatives to Laboratory Animals, 2005

Handbook of Computational Chemistry, 2017

Green Chemistry, 2016

Environmental Toxicology and Chemistry, 2015

Chemometrics in QSAR

Comprehensive Chemometrics, 2009

On the Use of Local and Global QSPRs for the Prediction of Physico-chemical Properties of Polybrominated Diphenyl Ethers

Molecular Informatics, 2011

QSAR Modeling is not “Push a Button and Find a Correlation”: A Case Study of Toxicity of (Benzo-)triazoles on Algae

Molecular Informatics, 2012

Classification of environmental pollutants for global mobility potential

The environmental behaviour of global organic contaminants is known to be controlled by the physi... more The environmental behaviour of global organic contaminants is known to be controlled by the physico-chemical properties of the compounds themselves. The principal component analysis of some physico-chemical properties, particularly relevant in determining mobility potential (vapour pressure, Henry's law constant, water solubility, K(OW), K(OA) and melting point) allows a multivariate approach to a ranking of organic pollutants according to their intrinsic tendency towards mobility, and the definition of four a priori mobility classes for screening purposes. Quantitative structure-property relationships (QSPRs) were used to predict missing values for octanol/air partition coefficients. Finally, a classification method employing theoretical molecular descriptors was used to assign studied chemicals to four mobility classes. The proposed approach assesses, directly and simply, a pollutant's inherent tendency towards mobility using only knowledge of the pollutant's molecular structure; the approach is particularly useful for a preliminary screening and the prioritisation of organic pollutants of emerging environmental concern.

Screening of pesticides for environmental partitioning tendency

The partitioning tendency of chemicals, in this study pesticides in particular, into different en... more The partitioning tendency of chemicals, in this study pesticides in particular, into different environmental compartments depends mainly on the concurrent relevance of the physico-chemical properties of the chemical itself. To rank the pesticides according to their distribution tendencies in the different environmental compartments we propose a multivariate approach: the combination, by principal component analysis, of those physico-chemical properties like organic carbon partition coefficient (Koc), n-octanol/water partition coefficient (Kow), water solubility (Sw), vapour pressure and Henry's law constant (H) that are more relevant to the determination of environmental partitioning. The resultant macrovariables, the PC1 and PC2 scores here named leaching index (LIN) and volatality index (VIN), are proposed as preliminary environmental partitioning indexes in different media. These two indexes are modeled by theoretical molecular descriptors with satisfactory predictive power. Such an approach allows a rapid pre-determination and screening of the environmental distribution of pesticides starting only from the molecular structure of the pesticide, without any a priori knowledge of the physico-chemical properties.