Multivariate QSBR Modeling of Biodehalogenation Half-Lives of Halogenated Aliphatic Hydrocarbons

Eriksson, Lennart C.; Jönsson, Jan‐Ingvar; Tysklind, Mats

doi:10.1897/1552-8618(1995)14[209:mqmobh]2.0.co;2

Cited by 4 publications

(6 citation statements)

References 16 publications

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“…No firm guidelines are available for the size of each cross validation subset (i.e., N ); however, Eriksson et al [46] recommend against the leave‐one‐out approach, in which each subset contains only one observation. In studies that did not use the leave‐one‐out approach, reported values for N range from one‐quarter to one‐ninth of the number of observations in the original training set [4,47]. For QSAR2, cross‐validation was performed using a 20% leave‐out (i.e., the training set was divided into 5 subsets, each subset containing 9 observations), as well as a leave‐one‐out approach.…”

Section: Resultsmentioning

confidence: 99%

“…), it is unreasonable to expect that a QSAR can predict the biotransformation rate constant of a compound with a high degree of accuracy. On the other hand, QSARs have been shown to be capable of producing an order‐of‐magnitude estimate of the biotransformation rate [3–5]. Additionally, QSARs provide valuable insight into the rate of biotransformation of a compound relative to other compounds with a similar structure [3,5].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of microbial reductive transformation rates for chlorinated benzenes and phenols using a quantitative structure–activity relationship approach

Tebes-Stevens

Jones

2004

Enviro Toxic and Chemistry

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A set of literature data was used to derive several quantitative structure-activity relationships (QSARs) to predict the rate constants for the microbial reductive dehalogenation of chlorinated aromatics. Dechlorination rate constants for 25 chloroaromatics were corrected for the effects of hydrophobic partitioning and adjusted for the observed distribution of product species. A number of physicochemical properties and molecular parameters were considered for inclusion in the QSARs. Multivariate statistical analyses were used to select the optimal set of descriptors to minimize multicollinearity between the descriptors, as well as to minimize the p-value of the regression coefficients. The final QSAR included four descriptors: The logarithm of the octanol-water partition coefficient (Kow), the summation of the Hammett sigma constants, and the sigma induction constants in the ortho and meta positions relative to the transformation reaction center. The predictive ability of this QSAR was evaluated using 24 site-specific rate constants that were measured in five separate studies and were not used to derive the expression. The peer-reviewed literature was screened carefully to ensure that all rate constant data were representative of environmentally relevant conditions.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of microbial reductive transformation rates for chlorinated benzenes and phenols using a quantitative structure–activity relationship approach

Tebes-Stevens

Jones

2004

Enviro Toxic and Chemistry

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“…The PLS methodology was applied to the original data set containing all biochemical effect values in matrix X and all population-level effects in matrix Y. The PLS simultaneously projects the X and Y variables onto the same subspace, in such a manner that a good relationship exists between the position of the observation in the X space and in the Y plane [10]. To that end, the model calculates the X score vector t, the Y score vector u, the X loading vector p, the X weight vector w, and the Y weight vector c. The vector t can be seen as the new variables that are used to predict the Y matrix, and the X and Y weights w and c indicate how the variables are combined to make t and u in order to express the quantitative relationship between X and Y.…”

Section: Pls Analysismentioning

confidence: 99%

“…Within (eco)toxicological applications PLS has been used to establish quantitative structureactivity relationships (e.g., for pheromones [7], for polychlorinated dibenzo-p-dioxins and dibenzofurans [8] and for unsaturated dialdehydes [9]). Eriksson et al [10] developed multivariate quantitative structure-biodegradability relationships (QSBRs) for halogenated aliphatic hydrocarbons. Andren et al [11] were able to create models for biological responses (toxicity tests with Microtox [Azure Environmental, Carlsbad, CA, USA] inhibition of nitrification, and algal growth inhibition with Selenastrum capricornutum) pinpointing the two major chemical groups (inorganic metals and organic pollutants grouped as adsorbable organic halogens, chemical oxygen demand, and total organic carbon) that caused toxicity.…”

Section: Introductionmentioning

confidence: 99%

A multivariate biomarker‐based model predicting population‐level responses of Daphnia magna

Coen

Janssen

2003

Enviro Toxic and Chemistry

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A multivariate model is proposed relating short-term biomarker measurements in Daphnia magna to chronic effects (21-d exposure) occurring at the population level (time to death, mean brood size, mean total young per female, intrinsic rate of natural increase, net reproductive rate, and growth). The results of the short-term exposure (48 h-96 h) to eight model toxicants (cadmium, chromium, mercury, tributyl tin, linear alkylsulfonic acid, sodium pentachlorophenolate, lindane, and 2,4-dichlorophenoxyacetic acid) on the following biomarkers were used for the multivariate model: digestive enzymes (amylase, cellulase, beta-galactosidase, trypsin, and esterase), enzymes of the intermediary metabolism (glycogen phosphorylase, glucose-6-phosphate dehydrogenase, pyruvate kinase, lactate dehydrogenase, and isocitrate dehydrogenase), cellular energy allocation (CEA) (protein, carbohydrate, and lipid content and electron transport activity), and DNA damage and antioxidative stress activity. Using partial least squares to latent structures (PLS), a two-component model was obtained with R2 of 0.68 and a Q2 value of 0.60 based on the combined analysis of a limited number of the 48- and 96-h biomarker responses. For the individual population-level responses, the R2 values varied from 0.66 to 0.77 and the Q2 values from 0.52 to 0.69. Energy-related biomarkers (cellular energy allocation, lipid contents, anaerobic metabolic activity--pyruvate kinase, and lactate dehydrogenase), combined with parameters related to oxidative stress (catalase) and DNA damage measured after 48 and 96 h of exposure, were able to predict long-term effects at higher levels of biological organization.

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“…To this end, increasing use is made of semiempirical mathematical models, notably quantitative structure-activity relationships (QSARs), that are able to model and predict which compounds are likely to degrade and which will withstand degradation. 3 - 8 Peijnenburg et al ' recently reported rate constants of reductive transformation (degradation) of a series of 17 halogenated aliphatic hydrocarbons in anoxic sediment samples. These 17 compounds were previously selected to be representative of a larger series (numbering 58) of halogenated aliphatic hydrocarbons.…”

Section: Introductionmentioning

confidence: 99%

Multivariate QSAR modelling of the rate of reductive dehalogenation of haloalkanes

1996

Self Cite

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The pseudo first-order rate constants for reductive dehalogenation under anoxic conditions have recently been reported for a series of halogenated aliphatic hydrocarbons. in this paper it is shown that multivariate quantitative stnicture-activity relationship (QSAR) modelling of these data i s posible. Based on a training set of nine compounds and using hformation from 36 chemical and biological model systems, a QSAR was developed explaining 82% (R') and predicting 53% (e:,) of the variation in reductive dehalogenation. The QSAR was calculated using the partial least squares (PLS) method. The derived QSAR was validated using an e x t e d vaiidation set of six compounds for which expenmentally determined rate constants were available. The extemal validation showed that the QSAR could predict 62% ( Q2J of the response variation in the validation set. In addition, the validity of the QSAR was explored using a combination of cross-validation and permutation. Fifty repetitive randomizations of the real response were made and for each resulting reordered response the full PLS analysis was conducted. This yielded 50 pairs of Qi, and Qtt which were compared with those values of the 'real' model. Thus it was demonstrated that the QSAR produced Qkdues significantly exceedmg those of the random models and consequently the model was concluded to be valid. The interpretation of the QSAR suggested that the mechanism of reductive dehalogenation involves elements of hydrophobicity and electron transfer processes. O 1996 by John Wiley & Sons, Ltd.

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Multivariate QSBR Modeling of Biodehalogenation Half-Lives of Halogenated Aliphatic Hydrocarbons

Cited by 4 publications

References 16 publications

Estimation of microbial reductive transformation rates for chlorinated benzenes and phenols using a quantitative structure–activity relationship approach

Estimation of microbial reductive transformation rates for chlorinated benzenes and phenols using a quantitative structure–activity relationship approach

A multivariate biomarker‐based model predicting population‐level responses of Daphnia magna

Multivariate QSAR modelling of the rate of reductive dehalogenation of haloalkanes

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