Journal
CHEMOSPHERE
Volume 305, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chemosphere.2022.135460
Keywords
Reference dose; Molecular similarity; Molecular descriptor; Multiple liner stepwise regression
Categories
Funding
- National Natural Science Foundation of China [41991310]
- National Key Research and Development Program of China [2021YFC3201005]
Ask authors/readers for more resources
In this study, a new non-experimental method for estimating reference dose was proposed. It involves classifying chemicals based on molecular similarity and using multiple linear stepwise regression to construct a predictive model. The method shows a high level of consistency between predicted values and true values.
Reference dose (RfD) is an estimate of a daily dose that individual can be exposed chronically without obvious deleterious effects during a lifetime. In the area of toxicology, researchers always use the traditional approach by employing NOAEL/LOAEL or the benchmark dose (BMD) and other dose-response approaches to estimate RfD. These methods have, despite their typicalness, certain limitations. In this study, we present a novel method of the estimation of reference dose without experiments. The information of the organic chemicals is available from the Integrated Risk Information System (IRIS) of USEPA. Molecular descriptors for each molecular structure were calculated by an integrated platform, and the chemicals were classified into four categories based on molecular similarity: 128 contained benzene rings, 47 were heteroaromatics, 104 contained halogen substituents and 44 were halogenated aliphatic hydrocarbons. The predictive model of RfD was constructed by the multiple linear stepwise regression (MLR) method. Approximately 95% and 82% of the data points differ by less than 10-fold and 5-fold between the predicted values and the true values respectively. The non-experimental method improves the estimation efficiency and has a certain reference value to predict.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available