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Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

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Reference dose prediction by using CDK molecular descriptors: A non-experimental method.

Shu-Hui Men¹, Jia-Yun Xu², Quan Zhou²

¹College of Water Sciences, Beijing Normal University, Beijing 100875, PR China; State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, PR China.

|June 25, 2022

Summary

This summary is machine-generated.

This study introduces a novel, non-experimental method to estimate reference doses (RfDs) for chemicals. The approach utilizes molecular descriptors and machine learning, offering improved efficiency for toxicological risk assessment.

Keywords:

Molecular descriptor Molecular similarity Multiple liner stepwise regression Reference dose

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Area of Science:

Toxicology
Computational Chemistry
Environmental Health

Background:

Traditional methods for estimating reference doses (RfDs) like NOAEL/LOAEL and benchmark dose (BMD) have limitations.
Accurate RfD estimation is crucial for assessing chronic exposure risks without adverse health effects.

Purpose of the Study:

To develop and validate a novel, non-experimental method for estimating chemical reference doses (RfDs).
To improve the efficiency and accessibility of RfD predictions using readily available chemical information.

Main Methods:

Utilized chemical data from the Integrated Risk Information System (IRIS) of the US Environmental Protection Agency (USEPA).
Calculated molecular descriptors for organic chemicals using an integrated platform.
Classified chemicals into categories based on structural similarity (e.g., benzene rings, heteroaromatics, halogen substituents).
Developed a predictive model for RfD estimation using multiple linear stepwise regression (MLR).

Main Results:

The predictive model achieved high accuracy, with approximately 95% of predicted values within a 10-fold difference and 82% within a 5-fold difference of true values.
The non-experimental approach demonstrated significant improvements in estimation efficiency compared to traditional methods.
Successful classification of chemicals into distinct structural categories facilitated model development.

Conclusions:

The novel non-experimental method provides a valuable and efficient approach for estimating reference doses (RfDs).
This method offers a reliable alternative for toxicological risk assessment, particularly when experimental data is limited.
The study highlights the potential of computational methods in chemical safety and risk evaluation.