Identificación computacional y corroboración experimental de nuevos compuestos con actividad analgésica
Fecha
2018
Autores
González González, Naylén
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Editor
Universidad Central ‘‘Marta Abreu’’ de Las Villas. Facultad de Química Farmacia. Departamento de Farmacia
Resumen
En la actualidad el dolor está estrechamente vinculado a patologías de alta incidencia a nivel mundial. Los métodos “in silico” engloban todas las técnicas asistidas por ordenadores usadas en el diseño/descubrimiento de compuestos con propiedades deseadas, evitando los altos costos para las actuales tareas de síntesis y bioensayos. En este sentido, el objetivo fundamental del presente trabajo es la identificación de nuevos candidatos analgésicos a través del cribado virtual “in silico” empleando árboles de clasificación. Con este propósito primeramente se recolecta una base de datos de la literatura a los que se le ha reportado experimentalmente actividad analgésica. A través del software DRAGON se calcularon una serie de descriptores moleculares y posteriormente se realizó un Análisis de Conglomerados Jerárquicos (CAs) en el software STATISTICA, permitiendo la separación de la base de datos inicial en serie de entrenamiento y serie de predicción. Luego se procedió a la obtención y validación del modelo utilizado (Tree J48) a través del software WEKA. Finalmente se cribaron ocho compuestos aislados de Boldoa purpurascens y 12 productos de la hidrólisis de estos, para un total de 20 compuestos. De ellos tres compuestos fueron evaluados experimentalmente “in vivo” con excelentes resultados como fármacos analgésicos. De forma general podemos concluir que el empleo de estas herramientas computacionales genera un gran ahorro de recursos con respecto a los métodos tradicionales de análisis y además permite realizar una identificación rápida de compuestos con una alta probabilidad de que sean potenciales analgésicos.
Currently, pain is closely linked to pathologies of high incidence worldwide. The "in silico" methods encompass all computer-aided techniques used in the design of compounds with desired properties, avoiding the high costs for the current tasks of synthesis and bioassays. In this sense, the fundamental objective of the present work is the identification of new analgesic candidates through virtual "in silico" screening using classification trees. For this purpose, a database of the literature is initially collected and analgesic activity has been reported experimentally. Through the DRAGON software, a series of molecular descriptors were calculated and a Hierarchical Conglomerate Analysis (CAs) was performed in the STATISTICA software, allowing the separation of the initial database in training series and prediction series. Then we proceeded to obtain and validate the model used (Tree J48) through the WEKA software. Finally, eight isolated compounds of Boldoa purpurascens and 12 products of the hydrolysis of these were screened for a total of 20 compounds. Of these three compounds were evaluated experimentally "in vivo" with excellent results as analgesic drugs. In general, we can conclude that the use of these computational tools generates a great saving of resources with respect to traditional methods of analysis and also allows a rapid identification of compounds with a high probability that they are potential analgesics.
Currently, pain is closely linked to pathologies of high incidence worldwide. The "in silico" methods encompass all computer-aided techniques used in the design of compounds with desired properties, avoiding the high costs for the current tasks of synthesis and bioassays. In this sense, the fundamental objective of the present work is the identification of new analgesic candidates through virtual "in silico" screening using classification trees. For this purpose, a database of the literature is initially collected and analgesic activity has been reported experimentally. Through the DRAGON software, a series of molecular descriptors were calculated and a Hierarchical Conglomerate Analysis (CAs) was performed in the STATISTICA software, allowing the separation of the initial database in training series and prediction series. Then we proceeded to obtain and validate the model used (Tree J48) through the WEKA software. Finally, eight isolated compounds of Boldoa purpurascens and 12 products of the hydrolysis of these were screened for a total of 20 compounds. Of these three compounds were evaluated experimentally "in vivo" with excellent results as analgesic drugs. In general, we can conclude that the use of these computational tools generates a great saving of resources with respect to traditional methods of analysis and also allows a rapid identification of compounds with a high probability that they are potential analgesics.
Descripción
Palabras clave
Fármacos Analgésicos, Boldoa Purpurascens, Propiedades Farmacológicas, Descriptores Moleculares