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Published on: November 9, 2014
First multi-target QSAR model for predicting the cytotoxicity of acrylic acid-based dental monomers
Amit Kumar Halder1, António H S Delgado2, M Natália D S Cordeiro3
1LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal; Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Dr. Meghnad Saha Sarani, Bidhannagar, Durgapur 713212, West Bengal, India.
Objective:
Acrylic acid derivatives are frequently used as dental monomers and their cytotoxicity towards various cell lines is well documented. This study aims to probe the structural and physicochemical attributes responsible for higher toxicity of dental monomers, using quantitative structure-activity relationships (QSAR) modeling approaches.
Methods:
A regression-based linear single-target QSAR (st-QSAR) model was developed with a comparatively small dataset containing 39 compounds, the cytotoxicity of which has been assessed over the Hela S3 cell line. By contrast, a classification-based multi-target QSAR model was developed with 138 compounds, the cytotoxicity of which has been reported against 18 different cell lines. Both models were set up following rigorous validation protocols confirming their statistical significance and robustness.
Results:
The performance of the linear mt-QSAR model, developed with various feature selection and post-selection similarity searching-based schemes, superseded that of all non-linear models produced with six machine learning methods by hyperparameter optimization. The final derived st-QSAR and mt-QSAR linear models are shown to be highly predictive, as well as revealing the crucial structural and physicochemical factors responsible for higher cytotoxicity of the dental monomers.
Significance:
This study is the first attempt on unveiling the cytotoxicity of dental monomers over several cell lines by means of a single multi-target QSAR model. Further, such a model is ready to get widespread applicability in the screening of new monomers, judging from its almost accurate predictions over diverse experimental assay conditions.
