Review of Estimation Models for Biodegradation

Pavan, Manuela; Worth, Andrew

doi:10.1002/qsar.200710117

Cited by 68 publications

(42 citation statements)

References 43 publications

(33 reference statements)

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“…1,10,14,20,44 These models provided reliable predictions within narrow AD, but they could not be applied widely due to the narrow AD. 45 We constructed the combinatorial classification models based on the largest heterogeneous data set of 1604 unique compounds so far, including 817 unpublished molecules with the numerical BOD value in Supporting Information Table S1 and Figure 1B. The range of the overall predictive accuracy was 74.1−100.0% for the external validation set using the seven best combinational global models and consensus model ( Table 5).…”

Section: Comparison Of Different Modeling and Featurementioning

confidence: 99%

“…Biodegradation is a complex chemical and biological process consisting of many steps, which depend not only on the amount and structure of the chemical, but also on environmental conditions into which the chemical is released. 45 It is very difficult to explain the biodegradation mechanism using individual or several simple chemical descriptors. Visualization Analysis of Substructure Alerts for Chemical Biodegradation.…”

Section: Comparison Of Different Modeling and Featurementioning

confidence: 99%

See 1 more Smart Citation

In Silico Assessment of Chemical Biodegradability

Cheng

Ikenaga

Zhou

et al. 2012

J. Chem. Inf. Model.

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Biodegradation is the principal environmental dissipation process. Due to a lack of comprehensive experimental data, high study cost and time-consuming, in silico approaches for assessing the biodegradable profiles of chemicals are encouraged and is an active current research topic. Here we developed in silico methods to estimate chemical biodegradability in the environment. At first 1440 diverse compounds tested under the Japanese Ministry of International Trade and Industry (MITI) protocol were used. Four different methods, namely support vector machine, k-nearest neighbor, naïve Bayes, and C4.5 decision tree, were used to build the combinatorial classification probability models of ready versus not ready biodegradability using physicochemical descriptors and fingerprints separately. The overall predictive accuracies of the best models were more than 80% for the external test set of 164 diverse compounds. Some privileged substructures were further identified for ready or not ready biodegradable chemicals by combining information gain and substructure fragment analysis. Moreover, 27 new predicted chemicals were selected for experimental assay through the Japanese MITI test protocols, which validated that all 27 compounds were predicted correctly. The predictive accuracies of our models outperform the commonly used software of the EPI Suite. Our study provided critical tools for early assessment of biodegradability of new organic chemicals in environmental hazard assessment.

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Section: Comparison Of Different Modeling and Featurementioning

confidence: 99%

Section: Comparison Of Different Modeling and Featurementioning

confidence: 99%

In Silico Assessment of Chemical Biodegradability

Cheng

Ikenaga

Zhou

et al. 2012

J. Chem. Inf. Model.

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“…However, there were no HAPs in the common category of log K ow ≥ 4 and BCF ≥ 500, which were the criteria of chronic exposures and bioconcentration potentials when trivial HAPs were exposed for a long period of time, indicating that potentials of the bioconcentration are considered to be low. [31][32][33] We also analyzed the estimated soil organic carbon-water partition coefficient (K oc ) possessing quantitative relationships of K ow .…”

Section: -30mentioning

confidence: 99%

Ecotoxicity Estimation of Hazardous Air Pollutants Emitted from Semiconductor Manufacturing Processes Utilizing QSAR

Park

Yeo²

2013

Bulletin of the Korean Chemical Society

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This study aims to assess the ecological risk of the hazardous air pollutants (HAPs) emitted in the semiconductor manufacturing processes in Korea by using Quantitative Structure Activity Relationship (QSAR, EPA, US, EPI Suite TM 4.1). Owing to the absence of environmental standards of hazardous air pollutants in the semiconductor manufacturing processes in Korea, 18 HAPs in the semiconductor field included in both the US EPA NESHAPs and the hazardous air pollutant list of Ministry of Environment in Korea were selected. As a results 8 chemicals (44.4%) of the selected 18 HAPs were VOCs. Cyanides (cyanides) and ethylene oxides (epoxy resins), and tetrachloro-ethylene (aliphatic compounds, halides) showed long half-lives. Cyanide HAPs especially had the highest half-life with the estimated value of 356.533 days. Nickel compounds (heavy metal compounds) possessed the highest water solubility followed by acetaldehyde (aldehyde compounds), ethylene oxides, and 1,4-dioxanes. The halides, including tetrachloro-ethylenes, carbon tetra-chlorides, benzene (aromatic compounds), and lead (heavy metals), are estimated to take the longest time for biodegradation. Tetrachloroethylene, with the acute toxicity end point of 3.685-7.033 mg/L, was assessed to be the most highly toxic substance among the 18 HAPs. However, considering the absence of the HAPs in the common category of log K ow ≥ 4 and BCF ≥ 500, which indicates the standard of bioconcentration potentials, potentials of the bioconcentration are considered to be low.

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“…The persistence was addressed using BIOWIN v4.10 model to predict the biodegradation and the Level III fugacity model to provide predictions of environmental partitioning (percentage in each media) of chemicals between air, water, soil and sediment. These models are widely used and accepted by regulatory agencies and authorities for the identification of chemicals with PBT potential [7,12,13]. …”

Section: (Q)sars Models and Toolsmentioning

confidence: 99%

Comparative PBT screening using (Q)SAR tools within REACH legislation

Zachary¹,

Greenway²

2009

SAR and QSAR in Environmental Research

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Small to medium sized enterprises (SMEs) in the EU are facing challenges due to the introduction of new legislation designed to protect consumers and the environment, REACH (Registration, Evaluation, Authorisation and Restriction of CHemicals). There can be high costs associated with implementing REACH because data on mammalian toxicity, environmental toxicity and environmental fate properties is required and if this data is obtained experimentally the cost is significant. These costs can be reduced if reliable quantitative structure-activity relationships ((Q)SAR) models are instead used to obtain the required information. In this paper we investigate how easily freely available (Q)SAR models can be applied for persistent, bioaccumulative and toxic (PBT) screening of 17 chemicals of interest to SMEs. In this study the PBT predictions obtained from the more user-friendly PBT Profiler and the Danish(Q)SAR database for the chemicals were compared with the results taken directly from the EPI Suite software. It was found that these widely used (Q)SAR databases might have some errors and examples are provided. It was concluded that extra care must be taken when considering the use of these databases for PBT screening. In addition, to increase the likelihood of a correct prediction, data estimates from various (Q)SAR models relevant to the PBT endpoints must be compared.

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Review of Estimation Models for Biodegradation

Cited by 68 publications

References 43 publications

In Silico Assessment of Chemical Biodegradability

In Silico Assessment of Chemical Biodegradability

Ecotoxicity Estimation of Hazardous Air Pollutants Emitted from Semiconductor Manufacturing Processes Utilizing QSAR

Comparative PBT screening using (Q)SAR tools within REACH legislation

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