Hazard Evaluation Support System (HESS) for predicting repeated dose toxicity using toxicological categories

Sakuratani, Yuki; Zhang, H. Q.; Nishikawa, Satoshi; Yamazaki, Kazuko; Yamada, Takashi; Yamada, Jun; Gerova, K.; Chankov, Georgi; Mekenyan, Ovanes G.; Hayashi, Makoto

doi:10.1080/1062936x.2013.773375

Cited by 64 publications

(63 citation statements)

References 15 publications

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“…Comparing our study results with previous published models for LOEL endpoints, our model has shown better predictive power than studies published by De Julian-Ortiz et al [29], Mazzatorta et al [30] and Gadaleta et al [24]. The Sakuratani et al [31] study had only categorized chemicals into 33 chemical categories, while in our study we formed new categories for each of the chemicals to facilitate better prediction of their LOELs. The study performed by Mumtaz et al [32] used 234 chemicals for construction of the QSAR model, but authors did not confirm the predictive power of this model using an external test set, thus it is not possible to compare our results with this model.…”

Section: Resultssupporting

confidence: 51%

Acute Toxicity-Supported Chronic Toxicity Prediction: A k-Nearest Neighbor Coupled Read-Across Strategy

Chavan

Friedman

Nicholls

2015

IJMS

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A k-nearest neighbor (k-NN) classification model was constructed for 118 RDT NEDO (Repeated Dose Toxicity New Energy and industrial technology Development Organization; currently known as the Hazard Evaluation Support System (HESS)) database chemicals, employing two acute toxicity (LD50)-based classes as a response and using a series of eight PaDEL software-derived fingerprints as predictor variables. A model developed using Estate type fingerprints correctly predicted the LD50 classes for 70 of 94 training set chemicals and 19 of 24 test set chemicals. An individual category was formed for each of the chemicals by extracting its corresponding k-analogs that were identified by k-NN classification. These categories were used to perform the read-across study for prediction of the chronic toxicity, i.e., Lowest Observed Effect Levels (LOEL). We have successfully predicted the LOELs of 54 of 70 training set chemicals (77%) and 14 of 19 test set chemicals (74%) to within an order of magnitude from their experimental LOEL values. Given the success thus far, we conclude that if the k-NN model predicts LD50 classes correctly for a certain chemical, then the k-analogs of such a chemical can be successfully used for data gap filling for the LOEL. This model should support the in silico prediction of repeated dose toxicity.

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Section: Resultssupporting

confidence: 51%

Acute Toxicity-Supported Chronic Toxicity Prediction: A k-Nearest Neighbor Coupled Read-Across Strategy

Chavan

Friedman

Nicholls

2015

IJMS

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“…[10][11][16][17][18] A number of in silico profilers have been developed for a variety of organ-level toxicities, such as skin sensitisation, respiratory sensitisation, genotoxicityand hepatotoxicity. [13][14][15][19][20][21] However, very few profilers have dealt with toxicity induced by mitochondrial dysfunction. 1,22,23 This is, in part, due to the number of mechanisms by which a chemical could induce mitochondrial dysfunction 24 .…”

Section: -12mentioning

confidence: 99%

Development of an in Silico Profiler for Mitochondrial Toxicity

Nelms

Mellor

Cronin

et al. 2015

Chem. Res. Toxicol.

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This study outlines the analysis of mitochondrial toxicity for a variety of pharmaceutical drugs extracted from Zhang et al. These chemicals were grouped into categories based upon structural similarity. Subsequently, mechanistic analysis was undertaken for each category to identify the Molecular Initiating Event driving mitochondrial toxicity. The mechanistic information elucidated during the analysis enabled mechanism-based structural alerts to be developed and combined together to form an in silico profiler. This profiler is envisaged to be used to develop chemical categories based upon similar mechanisms as part of the Adverse Outcome Pathway paradigm. Additionally, the profiler could be utilised in screening large dataset in order to identify chemicals with the potential to induce mitochondrial toxicity.

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“…Several related studies have been conducted, and recently a prediction system based on categorization for repeated dose toxicity, called the Hazard Evaluation Support System (HESS), was developed in Japan. [11] In a similar manner, the approach based on cell-based assays involves conducting tests using cells instead of animals, and assessing the presence and degree of toxicity of the target substance based on these tests. The Ames test (detection of mutagenicity using bacteria), which evaluates the mutagenicity of chemical substances, is well known, and several cell-test methods are described in the guidelines of the Organisation for Economic Co-operation and Development (OECD).…”

Section: Estimation Based On Structure-activity Relationships And/or mentioning

confidence: 99%

Development of a framework for risk tradeoff analysis of chemical substance substitution

Masashi

Takeshita

2016

Synthesiology English edition

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it is insufficient merely to demonstrate reduction in the risks associated with the original substance. It is necessary also to consider the potential risks associated with the replacement substance. It is necessary to determine whether the overall risk of the replacement substance is less than that of the original substance; and in addition, by comparing the risks before and after substitution, to assess whether the given substitution produces a risk reduction effect that exceeds the cost of countermeasures. Kishimoto[5] argued for the necessity of developing an assessment method for human health risks associated with chemical substance use, which would reflect social requirements, including the new social demand for "comparing risks of different types of chemical substances, and the assessment of cost-effectiveness of emission reduction measures;" and presented a case study involving toluene risk assessment. Here, he proposed a method for quantifying human health risks, using quality of life (QOL) as the risk index, by backcasting from the demand. The method enabled the comparison of the cost-effectiveness of risk reduction measures for toluene with that of risk reduction measures for other chemical substances, infectious diseases, accidents, disasters, etc.However, Kishimoto's proposal is, in practice, applicable only to substances such as toluene, for which abundant toxicity information is available. In risk tradeoff analysis associated with substance substitution, the substitution often involves replacing a substance for which there is relatively abundant information, with one for which there is insufficient information. For such risk tradeoff analysis to become a IntroductionWhile the use of chemical substances is necessary for achieving a prosperous and sustainable society, there is concern about related risks against humans and ecosystems. The production, use, and emission of chemical substances determined to pose significant risks are regulated by laws and through self-management. For example, such substances as polychlorinated biphenyls (PCBs), some chloride agrichemicals, brominated flame retardants, and heavy metals such as mercury, cadmium, and lead are strictly regulated, while the emissions of substances like air pollutants such as toluene are declining every year due to voluntary emission control.[1]Although the risk associated with a particular substance can be reduced by regulating the use and emission of that substance, related measures may be costly; and replacement by different, less hazardous substances is often made, while maintaining the functionality of the product in which the original substance was used. For example, the brominated flame retardant, decabromodiphenyl ether (decaBDE), has been replaced by substances such as bisphenol-A bis(diphenyl phosphate) (BDP); [2] lead solder alloys by 'lead-free solder alloys,' such as tin-silver-copper alloys, which do not contain lead; [3] and chlorinated solvents by carbohydrate or aqueous varieties, for use as industrial cleansers.[4]The occur...

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Hazard Evaluation Support System (HESS) for predicting repeated dose toxicity using toxicological categories

Cited by 64 publications

References 15 publications

Acute Toxicity-Supported Chronic Toxicity Prediction: A k-Nearest Neighbor Coupled Read-Across Strategy

Acute Toxicity-Supported Chronic Toxicity Prediction: A k-Nearest Neighbor Coupled Read-Across Strategy

Development of an in Silico Profiler for Mitochondrial Toxicity

Development of a framework for risk tradeoff analysis of chemical substance substitution

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