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A quantitative structure-activity relationship (QSAR) study of dermal absorption using theoretical molecular
S C Basak1, D Mills, M M Mumtaz
1University of Minnesota Duluth, Natural Resources Research Institute, 5013 Miller Trunk Hwy, Duluth, MN 55811, USA. sbasak@nrri.umn.edu
Quantitative structure-activity relationship (QSAR) models predict dermal absorption for chemicals. These computational models aid in prioritizing chemicals for toxicological evaluation and risk assessment.
Area of Science:
- Computational chemistry
- Toxicology
- Pharmacokinetics
Background:
- Dermal absorption is a critical factor in chemical risk assessment.
- Predictive models are needed to evaluate large numbers of chemicals efficiently.
Purpose of the Study:
- To develop Quantitative Structure-Activity Relationship (QSAR) models for predicting dermal absorption.
- To assess the performance of different regression methods for QSAR modeling.
Main Methods:
- Calculated molecular descriptors (topostructural, topochemical, 3D, quantum chemical).
- Developed predictive models using ridge regression (RR), principal components regression (PCR), and partial least squares regression (PLS).
- Validated models using cross-validation on a diverse set of 101 chemicals (79 cyclic, 22 acyclic).
Main Results:
- Ridge regression (RR) yielded superior predictive performance compared to PLS and PCR.
- Cross-validated correlation coefficients ranged from 0.67 to 0.87 for the full set and subsets.
- QSAR models demonstrated good predictive accuracy for dermal absorption.
Conclusions:
- QSAR modeling is a valuable tool for predicting dermal absorption.
- These models can augment experimental data, aiding in prioritizing chemicals for toxicological assessment and risk management.
- The developed QSAR models offer a cost-effective approach to chemical safety evaluation.
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