Predictive toxicity modelling of benzodiazepine drugs using multiple in silico approaches: descriptor-based QSTR, group-based QSTR and 3D-toxicophore mapping

Abstract

Benzodiazepines have been widely used therapeutically for their ability to act as tranquilizers, sedative-hypnotics, antiepileptics and frequently prescribed to women during pregnancy for managing preeclampsia or eclampsia. The present report deals with quantitative structure–toxicity relationship (QSTR) modelling of a series of benzodiazepines, in the context of the 3R concept, to provide an insight into the main structural fragments that impart toxicity to these molecules. Three different in silico techniques, namely descriptor-based QSTR, group-based QSTR and 3D-toxicophore mapping, were employed to obtain statistically significant models. Multiple in silico models made it possible to reach a unified conclusion regarding the structural fragments and features responsible for the toxicity and provide consensus predictions which can be effectively utilised to design and predict less toxic new benzodiazepines.

Keywords::

• benzodiazepines
• in silico
• QSAR
• QSTR
• toxicophore

Acknowledgements

SK thanks the Department of Science and Technology, Government of India for awarding him a Research fellowship under the INSPIRE scheme. KR thanks the Council of Scientific and Industrial Research (CSIR), New Delhi, for awarding a major research project.

Notes

^1. Present Address: Manchester Institute of Biotechnology, Manchester M1 7DN, UK; Email: [email protected].