Chlorosilane Acute Inhalation Toxicity and Development of an LC50 Prediction Model

Jean, Paul A.; Gallavan, Robert H.; Kolesar, Gary B.; Siddiqui, Waheed H.; Oxley, Jon A; Meeks, Robert G.

doi:10.1080/08958370600686093

Cited by 12 publications

(4 citation statements)

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“…Currently, many quantitative structure–property relationship (QSPR) models have been developed to predict acute rodent toxicity of organic chemicals. In these studies, there are various mathematical methods applied to construct regression models (RMs) and classification models (CMs), such as multiple linear regression (MLR), − linear regression, , neural network (NN), − k nearest neighbors, , random forest (RF), , hierarchical clustering, support vector machine (SVM), , relevance vector machine (RVM), and local lazy learning (LLL) . In terms of RMs, Lu et al constructed prediction models using the LLL method, which yielded a maximized linear correlation coefficient ( R 2 ) for large test sets.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction

Pei

Lai

2017

J. Chem. Inf. Model.

200

138

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Median lethal death, LD, is a general indicator of compound acute oral toxicity (AOT). Various in silico methods were developed for AOT prediction to reduce costs and time. In this study, we developed an improved molecular graph encoding convolutional neural networks (MGE-CNN) architecture to construct three types of high-quality AOT models: regression model (deepAOT-R), multiclassification model (deepAOT-C), and multitask model (deepAOT-CR). These predictive models highly outperformed previously reported models. For the two external data sets containing 1673 (test set I) and 375 (test set II) compounds, the R and mean absolute errors (MAEs) of deepAOT-R on the test set I were 0.864 and 0.195, and the prediction accuracies of deepAOT-C were 95.5% and 96.3% on test sets I and II, respectively. The two external prediction accuracies of deepAOT-CR are 95.0% and 94.1%, while the R and MAE are 0.861 and 0.204 for test set I, respectively. We then performed forward and backward exploration of deepAOT models for deep fingerprints, which could support shallow machine learning methods more efficiently than traditional fingerprints or descriptors. We further performed automatic feature learning, a key essence of deep learning, to map the corresponding activation values into fragment space and derive AOT-related chemical substructures by reverse mining of the features. Our deep learning architecture for AOT is generally applicable in predicting and exploring other toxicity or property end points of chemical compounds. The two deepAOT models are freely available at http://repharma.pku.edu.cn/DLAOT/DLAOThome.php or http://www.pkumdl.cn/DLAOT/DLAOThome.php .

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Section: Introductionmentioning

confidence: 99%

Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction

Pei

Lai

2017

J. Chem. Inf. Model.

200

138

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“…Physiological effects of chlorosilane exposure have been reported in some animal studies. 1 However, human exposure has rarely been reported and clinical effect on humans are barely known. 2 We report three cases of human exposure to an accidental trichlorosilane spill.…”

Section: Introductionmentioning

confidence: 99%

“…This material is widely used in the modern industries like semiconductor manufacture. Physiological effects of chlorosilane exposure have been reported in some animal studies [ 1 ]. However, human exposure has rarely been reported and clinical effect on humans are barely known [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Exposure to an accidental trichlorosilane spill: three case reports

et al. 2022

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Chlorosilane is a hazardous chemical compound which is used as a raw material in the production of silicone. Despite strict restrictions, accidental spillage of chlorosilane is often reported. However, human exposure was rarely reported in the past decades and the effect on humans is barely known. We report cases of human exposure to an accidental trichlorosilane spill. Three middle aged male industrial workers visited our emergency department after exposure to trichlorosilane. They presented with shortness of breath and burns on multiple sites. Chest radiograph and laboratory studies were performed. None of the reports showed serious results and were discharged after conservative management.

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“…R-alkoxysilanes are then used in applications such as sol-gel synthesis, silicones, and silsesquioxanes (Abe and Gunji 2004). R-alkoxysilanes are more desirable than their R-chlorosilane precursors because of their increased stability to hydrolysis, decreased toxicity, and decreased corrosive properties (Yanagisawa et al 1989;Arkles et al 1992;Jean et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Green routes to silicon-based materials and their environmental implications

Furgal

Lenora

2019

Physical Sciences Reviews

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Abstract The “greening” of silicon chemistry is fundamentally important for the future of the field. Traditional methods used to make silicon-based materials rely on carbon rich processes that are highly energy intensive, cause pollution, and are unsustainable. Researchers have taken up the challenge of developing new chemistries to circumvent the difficulties associated with traditional silicon material synthesis. Most of this work has been in the conversion of the “green” carbon neutral biogenic silica source rice hull ash (RHA, ~85 % silica) into useful silicon building blocks such as silica’s, silicon, and alkoxysilanes by using the inherently higher surface area and reactivity of RHA to sidestep the low reactivity of mined silica sources. This is a review of the work that has been done in the area of developing more environmentally benign methods for the synthesis and use of silicon containing materials to eliminate the negative impact on the environment.

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Chlorosilane Acute Inhalation Toxicity and Development of an LC50 Prediction Model

Cited by 12 publications

References 0 publications

Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction

Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction

Exposure to an accidental trichlorosilane spill: three case reports

Green routes to silicon-based materials and their environmental implications

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