Analysis of the Registry of Toxic Effects of Chemical Substances (RTECS) Files and Conversion of the Data in These Files for Input to the Environmental Chemicals Data and Information Network (ECDIN)

Ole, Norager; Town, William G.; Petrie, J. Howard

doi:10.1021/ci60015a005

Cited by 8 publications

(2 citation statements)

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“…Second, we have used our models to predict acute toxicity compounds in the RTECS (Norager et al 1978) data set (data were kindly provided by Todd Martin from the U.S. EPA), which contains approximately 7,000 compounds with rat LD 50 data. We removed compounds that we found within the ZEBET data set, as well as inorganic compounds and mixtures.…”

Section: Resultsmentioning

confidence: 99%

“…There were 254 and 235 compounds that had rat or mouse LD 50 (millimole/kilogram-body weight/day) values, respectively. Only LD 50 values published in the Registry of Toxic Effects of Chemical Substances (RTECS) (Norager et al 1978; Ruden and Hansson 2003) were used. The distributions of log(1/LD 50 ) values of ZEBET compounds, with the exception of a single outlier, were from −2.61 to 2.30 for the rat and from −2.50 to 2.19 for the mouse.…”

Section: Methodsmentioning

confidence: 99%

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A Novel Two-Step Hierarchical Quantitative Structure–Activity Relationship Modeling Work Flow for Predicting Acute Toxicity of Chemicals in Rodents

Zhu

Richard

et al. 2009

Environ Health Perspect

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Background Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public–private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. Objective A wealth of available biological data requires new computational approaches to link chemical structure, in vitro data, and potential adverse health effects. Methods and results A database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC 50 ) and in vivo rodent median lethal dose (LD 50 ) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments). The application of conventional quantitative structure–activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD 50 values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC 50 and LD 50 . However, a linear IC 50 versus LD 50 correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC 50 and LD 50 values: One group comprises compounds with linear IC 50 versus LD 50 relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models to predict the group affiliation based on chemical descriptors only. Third, we developed k -nearest neighbor continuous QSAR models for each subclass to predict LD 50 values from chemical descriptors. All models were extensively validated using special protocols. Conclusions The novelty of this modeling approach is that it uses the relationships between in vivo and in vitro data only to inform the initial construction of the hierarchical two-step QSAR models. Models resulting from this approach employ chemical descriptors only for external prediction of acute rodent toxicity.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Novel Two-Step Hierarchical Quantitative Structure–Activity Relationship Modeling Work Flow for Predicting Acute Toxicity of Chemicals in Rodents

Zhu

Richard

et al. 2009

Environ Health Perspect

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Development a national minimum data set for poisoning registry in Iran

Yazdipour

Sarbaz

Dadpour

et al. 2020

Health Planning & Management

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Objectives: In the developing countries with high mortality rates, poisoning is considered to be one of the most common causes of admission to emergency wards. Given the importance of registering data on poisoned patients, it is very important to have a complete poisoning Minimum Data Set (MDS). Therefore, the purpose of this study was to determine an MDS for poisoning registry in Iran. Methods: This applied and cross-sectional study was conducted through of Delphi technique in the poisoning ward of Imam Reza Hospital (northeastern Iran) in 2019. Literature reviews were initially carried out on such databases as PubMed, Web of Sciences, Scopus, and Embase. Then, Google search was done to retrieve poisoning forms and poisoning registry websites. Also, we considered International Classification of Diseases, 10th Revision coding guidelines of poisoning. Then, a questionnaire containing data elements of poisoning was developed. Results: In total, 558 data elements were developed during two rounds of Delphi technique. The MDS was divided into 10 categories including patient and communication data, encounter data, diagnostic data and medical history, exposure data, clinical data, treatment data, complications, paraclinical tests, biobank, and discharge data. Conclusions: Establishing an MDS as the first and most important step towards implementing poisoning registry can be the standard basis for collecting poisoned patient data. The data registered in the poisoning registry can be

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Integration and Use of the List of Organic Micropollutants in the Aquatic Environment in the ECDIN Data Bank

Penning

Aina

Helm

1986

Organic Micropollutants in the Aquatic Environment

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Analysis of the Registry of Toxic Effects of Chemical Substances (RTECS) Files and Conversion of the Data in These Files for Input to the Environmental Chemicals Data and Information Network (ECDIN)

Cited by 8 publications

References 0 publications

A Novel Two-Step Hierarchical Quantitative Structure–Activity Relationship Modeling Work Flow for Predicting Acute Toxicity of Chemicals in Rodents

A Novel Two-Step Hierarchical Quantitative Structure–Activity Relationship Modeling Work Flow for Predicting Acute Toxicity of Chemicals in Rodents

Development a national minimum data set for poisoning registry in Iran

Integration and Use of the List of Organic Micropollutants in the Aquatic Environment in the ECDIN Data Bank

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