Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis

Cronin, Mark T.D.; Aptula, Aynur O.; Duffy, Judith; Netzeva, Tatiana I.; Rowe, Philip H.; Valkova, Iva; Schultz, T.W.

doi:10.1016/s0045-6535(02)00508-8

Cited by 164 publications

(156 citation statements)

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“…A search in Scirus (www.scirus.com) gave close to 7700 articles that include the term QSAR. Many QSAR papers relate descriptors, for example, to activity [14][15][16], to bioconcentration and bioaccumulation [17,18], and to toxicity [19][20][21][22][23][24][25][26][27][28]. Also, QSAR is used as a successful tool in drug design [29][30][31][32][33] and the use of QSARs have recently been suggested to facilitate risk assessment [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

QSBMR—Quantitative structure biomagnification relationships: physicochemical and structural descriptors important for the biomagnification of organochlorines and brominated flame retardants

Lundstedt‐Enkel

Lek²,

Lundstedt

et al. 2006

Journal of Chemometrics

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The aim of this project is to establish models to predict the biomagnification of contaminants present in Baltic Sea biota. In this paper a quantitative model that we term QSBMR-Quantitative Structure Biomagnification Relationships is presented. This model describes the relationship between the biomagnification factors (BMFs) for several organochlorines (OCs) and brominated flame retardants (BFRs), for example, polychlorinated biphenyls (PCBs), polybrominated diphenylethers (PBDEs) and hexabromocyclododecane (HBCD), and their descriptors, for example, physico-chemical properties and structural descriptors.The concentrations of contaminants in herring (Clupea harengus) muscle and guillemot (Uria aalge) egg from the Baltic Sea were used. The BMFs were calculated with the randomly sampled ratios (RSR) method that denotes the BMFs with a measure of the variation. In order to describe the physico-chemical properties and chemical structures, approximately 100 descriptors for the contaminants were generated: (a), by using the software (TSAR); (b) finding log K ow values from the literature, and (c) creating binary fingerprint variables that described the position of the chlorine and bromine for the respective PCB and PBDE molecules. Partial least squares (PLS) regression was used to model the relationship between the contaminants' BMF and the descriptors and the resulting QSBMR revealed that more than 20 descriptors in combination were important for the biomagnification of OCs and BFRs between herring and guillemot.The model including all contaminants (R For validation, a training set of seven contaminants was selected by multivariate design (MVD) and a model was established. This model was then used to predict the BMFs of the test set (seven contaminants not included in the model). The resulting R 2 for the regression Observed BMF versus Predicted BMF was high (0.65). The good models showed that descriptors important for the biomagnification of OCs and BFRs had been used. These types of models will be useful for in silico predictions of the biomagnification of new, not yet investigated, compounds as an aid in risk assessments.

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Section: Introductionmentioning

confidence: 99%

QSBMR—Quantitative structure biomagnification relationships: physicochemical and structural descriptors important for the biomagnification of organochlorines and brominated flame retardants

Lundstedt‐Enkel

Lek²,

Lundstedt

et al. 2006

Journal of Chemometrics

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“…However, due to the non-sufficient large data sets of experimentally derived parameters, QSARs have been developed based on descriptors derived from quantum mechanical computation because they are not restricted to closely related compounds and can be easily obtained. Also, they can explain the clearly mechanistic meaning of toxicology in QSAR studies by Sixt et al (1995), Schmitt et al (2000, Cronin et al (2002), Hatch and Colvin (1997).…”

Section: Quantitative Structure-activity Relationship (Qsar)mentioning

confidence: 99%

“…shows that some compounds were outliers due to phenol substituted in the 2-or 4-position by an amino or a nitro group; (2) Phenols substituted with three or more chlorines; (3) hydroquinones (Cronin et al, 2002). Such compounds are associated with the weak respiratory uncoupling mechanism of toxic action (Terada, 1990 ( The inter-correlations between the variables in eq.…”

Section: Chloro-phenolsmentioning

confidence: 99%

“…Reactivity between an electrophile and a nucleophile increases when i) the E HOMO is increased or ii) the E LUMO is decreased (Fleming, 1976). Given that the toxicological receptor is constant for a series of chemicals to be modeled by a QSAR, the relative reactivity and thus toxicity of a series of chemicals may be modeled by looking at their The most commonly used correlative method is regression analysis due to its simplicity.Three techniques, namely: 1) Multiple Linear Regression (MLR), 2) Partial Least Squares (PLS), and 3) Principle Component Regression (PCR), are used in these aspects.To assess quality, it is important that different modeling techniques are compared so that their strengths and weaknesses may be evaluated (Cronin et al, 2002).In the next step, the reliability or quality of the developed QSAR model can be the Validation of (Q)SAR Models," which provided detailed criteria in five categories: i) a defined endpoint, ii) an unambiguous algorithm, iii) a defined domain of applicability, iv)appropriate measures of good-of-fit, robustness, and predictivity, and v) a mechanistic, with the aim of providing guidance on how specific QSAR models can be evaluated with respect to the OECD principles. 10…”

mentioning

confidence: 99%

“…To assess quality, it is important that different modeling techniques are compared so that their strengths and weaknesses may be evaluated (Cronin et al, 2002).…”

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Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

Wang¹

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Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (E LUMO ) via QSAR modelling and analysis; 2) to validate the models by using internal and external cross-validation techniques; 3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as E LUMO is using QSAR analysis. In this study, number of chlorine (N Cl ) and number of carbon (N C ) as well as energy of the highest occupied molecular orbital (E HOMO ) are used as molecular descriptors.vii There are typically three approaches used in QSAR model development: 1) Linear or Multi-linear Regression (MLR); 2) Partial Least Squares (PLS); and 3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro-or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature.The results show that: 1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; 2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; 3) E LUMO are shown to correlate highly with the N Cl for several classes of DBPs; and 4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from N Cl for all DBP classes. ix IntroductionDuring water treatment processes, the disinfection is commonly used to destroy pathogenic organisms and prevent the outbreak of waterborne infectious diseases.Although the benefits of water disinfection are well documented, there is an undesirable side effect of producing various Disinfection By-Products (DBPs) when disinfectants such as chlorine react with natural inorganic and organic matters in the water. Quantitative Structure-Activity Relationship (QSAR)QSAR analysis is a promising tool based on the assumption that t...

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2009

Molecular Descriptors for Chemoinformatics

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Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis

Cited by 164 publications

References 29 publications

QSBMR—Quantitative structure biomagnification relationships: physicochemical and structural descriptors important for the biomagnification of organochlorines and brominated flame retardants

QSBMR—Quantitative structure biomagnification relationships: physicochemical and structural descriptors important for the biomagnification of organochlorines and brominated flame retardants

Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

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