Quantitative structure–activity relationships to predict in vitro teac and ec50 of synthetic anilines

Quantitative Structure-Activity Relationships (QSAR) are useful in understanding how chemical structure relates to the biological activity of natural or synthetic compounds and for designing newer and better compounds. In the present study, 22 N-arylmethyl substituted anilines were treated with ABTS...

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Autores:
Tafurt García, Geovanna
Martinez, Jairo René
Stashenko, Elena
Vargas, Leonor Y.
Tipo de recurso:
Article of journal
Fecha de publicación:
2010
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/30001
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/30001
http://bdigital.unal.edu.co/20075/
http://bdigital.unal.edu.co/20075/2/
Palabra clave:
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:Quantitative Structure-Activity Relationships (QSAR) are useful in understanding how chemical structure relates to the biological activity of natural or synthetic compounds and for designing newer and better compounds. In the present study, 22 N-arylmethyl substituted anilines were treated with ABTS (2,2’-azinobis- (3- ethylbenzothiazoline-6-sulfonic-acid)) and DPPH (2,2-diphenyl-1-picrylhydracyl) radicals in order to evaluate their TEAC (mmol trolox/mmol antioxidant, Trolox Equivalent Antioxidant Capacity) and EC50 (mmol antioxidant/mmol initial DPPH, Antioxidant Equivalent Concentration to decrease the initial DPPH concentration by 50 %) values, respectively. Different QSARs were developed based on these data, using theoretical descriptors derived from geometry-optimized molecular structures. A model with electronic energy (EE), total charge weighted partial positively charged surface area (PPSA-2), and exact polarizability (alfa - zz) as descriptors showed satisfactory predictive TEAC performance according to internal and external validation procedures. It can be useful in predicting data and setting a testing priority for those compounds not yet synthesized or for which experimental data are not available.