Prediction of Noninteractive Mixture Toxicity of Organic Compounds Based on a Fuzzy Set Method

Mwense, Mulaisho; Wang, Xue Zhong; Buontempo, Frances V.; Horan, N. J.; Young, Anita; Osborn, Daniel

doi:10.1021/ci0499368

Cited by 48 publications

(13 citation statements)

References 72 publications

(129 reference statements)

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“…Multiple chemical sensitivity can cause asthma, chest pains, and hives among other symptoms. Each year, industry adds 10,000 newly synthesized chemicals to the over 1 million already in existence [1][2][3][4]. However, most of the commercially used chemicals do not have proper documentation for their toxicologically relevant properties [1].…”

Section: Introductionmentioning

confidence: 99%

QSAR with quantum topological molecular similarity indices: toxicity of aromatic aldehydes toTetrahymena pyriformis

Kar

Harding

Roy

et al. 2010

SAR and QSAR in Environmental Research

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Extensive production and utilization of aromatic aldehydes and their derivatives without proper certification is alarming with regard to environmental safety. This concern motivated our construction of predictive quantitative structure-activity relationship (QSAR) models for the toxicity of aldehydes to the ecologically important species Tetrahymena pyriformis. Quantum topological molecular similarity (QTMS) descriptors, along with the lipid-water partition coefficient (log K(o/w)), were used as predictor variables. The QTMS descriptors were calculated at different levels of theory including AM1, HF/3-21G(d), HF/6-31G(d), B3LYP/6-31 + G(d,p), B3LYP/6-311 + G(2d,p) and MP2/6-311+G(2d,p). The data set of 77 aromatic aldehydes was divided into a training set (n = 58) and a test (n = 19) set, and 58 models were developed using partial least squares (PLS) and genetic partial least squares (G/PLS). We evaluated the overall predictive capacity of the models based on leave-one-out predictions for the training set compounds and model derived predictions for the test set compounds. For both PLS and G/PLS, the models built at the HF/6-31G(d) level show better predictivity (based on overall prediction) than the models developed at any of the other five levels. Further validation was also performed utilizing (process and model) randomization tests. We show that improved predictive QSAR models for aldehydic toxicity to Tetrahymena pyriformis can be generated using QTMS descriptors along with log K(o/w).

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Section: Introductionmentioning

confidence: 99%

QSAR with quantum topological molecular similarity indices: toxicity of aromatic aldehydes toTetrahymena pyriformis

Kar

Harding

Roy

et al. 2010

SAR and QSAR in Environmental Research

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“…Hence, researchers are devoted to developing alternative methods to conquer these limitations of traditional experimental methods. QSAR (Roy et al, 2015a , b ), Read-across (RA) (Jeliazkova et al, 2010 ), Molecular docking (Yao et al, 2013 ) and Expert systems (Roy and Kar, 2016 ), are deemed faster, cheaper and can manifest more information (Mwense et al, 2004 ). In these developing alternative methods, the QSAR model is one of the most widely used methods in silico .…”

Section: Methods Of Concentration Addition and Independence Actionmentioning

confidence: 99%

Mathematical Modeling Approaches for Assessing the Joint Toxicity of Chemical Mixtures Based on Luminescent Bacteria: A Systematic Review

Wang

Bai

et al. 2020

Front. Microbiol.

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Developments in industrial applications inevitably accelerate the discharge of enormous substances into the environment, whereas multi-component mixtures commonly cause joint toxicity which is distinct from the simple sum of independent effect. Thus, ecotoxicological assessment, by luminescent bioassays has recently brought increasing attention to overcome the environmental risks. Based on the above viewpoint, this review included a brief introduction to the occurrence and characteristics of toxic bioassay based on the luminescent bacteria. In order to assess the environmental risk of mixtures, a series of models for the prediction of the joint effect of multi-component mixtures have been summarized and discussed in-depth. Among them, Quantitative Structure-Activity Relationship (QSAR) method which was widely applied in silico has been described in detail. Furthermore, the reported potential mechanisms of joint toxicity on the luminescent bacteria were also overviewed, including the Trojan-horse type mechanism, funnel hypothesis, and fishing hypothesis. The future perspectives toward the development and application of toxicity assessment based on luminescent bacteria were proposed.

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“…Mwense et al [36] proposed a methodology named the integrated fuzzy concentration addition-independent action modeling (INFCIM) approach which employs fuzzy set theory and molecular descriptors to illustrate the degree of likeness and differences of mixture components, and integrates the independent action and concentration addition models (Figure 6). Authors tested their approach in two case studies employing four mixtures’ datasets and most importantly, the obtained results were compared with those of both independent action and concentration addition models.…”

Section: Successful Application Of Computational Modeling For Predmentioning

confidence: 99%

Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures

Kar

Leszczyński

2019

Toxics

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Industrial advances have led to generation of multi-component chemicals, materials and pharmaceuticals which are directly or indirectly affecting the environment. Although toxicity data are available for individual chemicals, generally there is no toxicity data of chemical mixtures. Most importantly, the nature of toxicity of these studied mixtures is completely different to the single components, which makes the toxicity evaluation of mixtures more critical and challenging. Interactions of individual chemicals in a mixture can result in multifaceted and considerable deviations in the apparent properties of its ingredients. It results in synergistic or antagonistic effects as opposed to the ideal case of additive behavior i.e., concentration addition (CA) and independent action (IA). The CA and IA are leading models for the assessment of joint activity supported by pharmacology literature. Animal models for toxicity testing are time- and money-consuming as well as unethical. Thus, computational approaches are already proven efficient alternatives for assessing the toxicity of chemicals by regulatory authorities followed by industries. In silico methods are capable of predicting toxicity, prioritizing chemicals, identifying risk and assessing, followed by managing, the risk. In many cases, the mechanism behind the toxicity from species to species can be understood by in silico methods. Until today most of the computational approaches have been employed for single chemical’s toxicity. Thus, only a handful of works in the literature and methods are available for a mixture’s toxicity prediction employing computational or in silico approaches. Therefore, the present review explains the importance of evaluation of a mixture’s toxicity, the role of computational approaches to assess the toxicity, followed by types of in silico methods. Additionally, successful application of in silico tools in a mixture’s toxicity predictions is explained in detail. Finally, future avenues towards the role and application of computational approaches in a mixture’s toxicity are discussed.

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Prediction of Noninteractive Mixture Toxicity of Organic Compounds Based on a Fuzzy Set Method

Cited by 48 publications

References 72 publications

QSAR with quantum topological molecular similarity indices: toxicity of aromatic aldehydes toTetrahymena pyriformis

QSAR with quantum topological molecular similarity indices: toxicity of aromatic aldehydes toTetrahymena pyriformis

Mathematical Modeling Approaches for Assessing the Joint Toxicity of Chemical Mixtures Based on Luminescent Bacteria: A Systematic Review

Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures

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