Identificación Computacional De Nuevos Compuestos Líderes Con Actividad Analgésica

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Identificación Computacional De Nuevos Compuestos Líderes Con Actividad Analgésica IDENTIFICACIÓN COMPUTACIONAL DE NUEVOS COMPUESTOS LÍDERES CON ACTIVIDAD ANALGÉSICA Autor: Earmin Shangoo. Tutores: Dr. Yovani Marrero Ponce MSc. Arelys López Sacerio Lic. Gerardo Maikel Casañola Martín “Año 49 de la Revolución Cubana” Curso 2006-2007 PENSAMIENTO “For all flesh is as grass, and all the glory of man as the flower of grass. The grass withereth, and the flower thereof falleth away: But the word of the Lord endureth for ever.” 1 Peter1:24,25. “Todo carne es como hierba y toda la gloria del hombre como flor de la hierba; la hierba se seca y la flor se cae, mas la palabra del Señor permanece para siempre.” 1 Pedro 1:24,25. DEDICATORIA For all my friends and family, especially my daughter, Naarah Melani Nerissa Athanase and my husband, Jeremiah Wayn Athanase, with love and admiration. “I have fought a good fight, I have finished my course…” 2Tim 4:7. Con mucho cariño y admiración, a todas mis amigos y mi familia en particular a mi hija, Naarah Melani Nerissa Athanase y mi esposo, Jeremiah Wayn Athanase. “He peleada la buena batalla, he acabado la carrera…” 2Ti 4:7 AGRADECIMIENTOS For me it is an enormous pleasure to be able to express my sincere gratefulness to all those people that in one way or another has helped me to successfully culminate my studies and this work. I would especially like to thank God because without him I am nothing; to my family for their faith, support, love and patience; to my tutors especially Arelys for their attention, dedication and orientation during this work; to all the professors of the career that made possible with their patient, constant and opportune help my formation as a pharmaceutical professional; to my friends, in particular to Solange and Harun; to all thank you. Para mi es un enorme placer poder expresar mis sinceras agradecimientos a todas aquellas personas que de una manera u otra me han ayudado a culminar exitosamente mis estudios y este trabajo. Quisiera agradecer en especial a Dios porque sin él nada soy; a mi familia por su fe, apoyo, amor y paciencia; a mis tutores especialmente a Arelys por su atención, dedicación y orientación durante este trabajo; a todos los profesores de la carrera que con su ayuda paciente, constante y oportuna hicieron posible mi formación como profesional farmacéutica; a mis amigos, en particular a Solange y Harun; a todas muchas gracias. ABSTRACT In this work the fundamental objective was to identify new leader compounds with analgesic activity by means of in silico tests. To this end, firstly a database was compiled using compounds reported in the literature with analgesic activity to obtain a reliable analysis and modulation of the data. Using TOMOCOMD-CARDD descriptors, quantitative structure- activity relationships (QSAR) models was obtained that allowed virtual screening processes to be carried out, using a database of 1190 compounds and the lineal stochastic and non- stochastic atomic indices as molecular descriptors. Through the multivariate selection method descriptors were identified that allow the separation of the data into two classes (analgesic and non-analgesic compounds). The results of the analyses indicate that the total and local TOMOCOMD-CARDD descriptors, provides an excellent separation of the data (> 88%) in the training and prediction series. Finally, the models obtained were applied to the virtual screening of chemical compounds which allowed the in-silico estimation of the activity of compounds with other pharmacological uses as well as new molecular entities. Several drugs current used in therapy and new series heads was identified as possible analgesics although the activity of the compounds selected as analgesic has to be corroborated experimentally. Generally we can conclude that the TOMOCOMD-CARDD method is promising in the development of QSAR models with a view to the biosilic discovery of new drugs with analgesic activity. RESUMEN En este trabajo el objetivo fundamental fue identificar nuevas compuestos líderes de actividad analgésicos mediante ensayos in silico. Con este fin, primeramente se recolecta una gran base de datos de la literatura de compuestos reportados con actividad analgésica para acceder al análisis y la modelación confiable de la data. Utilizando descriptores TOMOCOMD-CARDD se obtienen modelos de relaciones cuantitativas estructura-actividad (QSAR), que permitan realizar procesos de cribado virtual, empleando una base de datos de 1190 compuestos, y los índices lineales de átomos: estocásticos y no-estocásticos como descriptores moleculares. A través del método de selección de variables se identificaron los descriptores que permiten la separación de la data en dos clases (compuestos analgésicos y no-analgésicos). El análisis de pasos hacia delante permitió el desarrollo de los modelos usando el Análisis Discriminante Lineal. Los resultados de los análisis indican que los descriptores TOMOCOMD-CARDD totales y locales, proporcionan una excelente separación de la data (> 88%) en la serie de entrenamiento y en la serie de predicción. Finalmente, se aplicaron los modelos obtenidos al “screening” virtual de compuestos químicos permitieron estimar in-silico la actividad de compuestos con otros usos farmacológicos así como nuevas entidades moleculares. Se identificaron varios fármacos utilizados en la terapéutica actual y nuevos cabezas de serie como posibles analgésicos aunque la actividad de los compuestos seleccionados como analgésicos tiene que ser corroborada experimentalmente. De forma general podemos concluir que el método TOMOCOMD-CARDD resulta promisorio en el desarrollo de modelos QSAR con vistas al descubrimiento biosílico de nuevos fármacos con actividad analgésica. Índice Pág. RESUMEN GLOSARIO INTRODUCCIÓN 1 Objetivos 2 1 REVISIÓN BIBLIOGRÁFICA 4 1.1 Dolor 4 1.1.1 La apreciación global de vías de dolor 4 1.1.2 Tipos de dolor 5 1.2 Receptores opioides u opiáceos 7 1.3 Fármacos Analgésicos 11 1.3.1 Se clasifican en dos grupos 11 1.3.1.1 Analgésicos opiáceos 11 1.3.1.1.1 Clasificación de los fármacos opiáceos 12 1.3.1.1.2 Relaciones estructura-actividad 13 1.3.1.1.3 Mecanismo de acción de los fármacos analgésico 20 opiáceo 1.3.1.1.4 Efectos Adversos 21 1.3.1.2 Analgésicos no opiáceos 21 1.3.1.2.1 Antiinflamatorios no esteroides (AINE) 21 1.3.1.2.1.1 Clasificación de los AINE 22 1.3.1.2.1.2 Relación estructura-actividad (REA) 22 1.3.1.2.1.3 Mecanismo de acción de los fármacos 22 AINE 1.3.1.2.1.4 Efectos adversos 23 1.3.1.2.2 Paraaminofenoles 23 1.3.1.2.2.1 Mecanismo de acción del paracetamol 24 1.3.1.2.2.2 Relación estructura-actividad (REA) 24 1.3.1.2.2.3 Efectos adversos 24 2 MÉTODO COMPUTACIONAL Y ANÁLISIS QUIMIOMÉTRICO DE 25 LOS DATOS 2.1. Método Computacional 27 2.1.1 Descriptores TOMOCOMD-CARDD Basados en Relaciones entre 27 Núcleos Atómicos 2.1.1.1 Información Química y Vector Molecular Basado en Átomos 27 2.1.1.2 “Background” Sobre las Matrices de Densidad Electrónica 28 Grafo-Teóricas No Estocásticas y Estocásticas 2.1.1.3 Definición de los Índices Lineales basados en Relaciones de 30 Átomos 2.2 Análisis Quimiométrico de los datos 32 2.2.1 Análisis Discriminante Lineal (ADL) para la Predicción de la 32 Actividad 2.2.2 Estimación de los Coeficientes 33 2.2.3 Matriz de Clasificación 33 2.2.4 Significación de la Función Discriminante (FD) 33 2.2.5 Criterios de Selección de Variables 34 Índice 2.2.5.1 Selección del Número Óptimo de Predictores. Principio de la 35 Parsimonia 2.2.5.2 Tolerancia 35 2.2.6 Multicolinealidad Entre Variables, Redundancia de la Información y 35 Correlaciones Casuales 2.2.7 Validación Estadística de los Modelos QSAR 36 2.3 Conclusiones Parciales del Capítulo 37 3 MATERIALES Y MÉTODOS 38 3.1. Base de datos de los compuestos usados en la obtención de los modelos 38 ADL-QSAR (Análisis Discriminante Lineal - Quantitative Structure Activity Relationship) 3.2 Cálculo de los nuevos descriptores moleculares. TOMOCOMD-CARDD 41 Software 3.3 Análisis Estadístico de los Datos: Desarrollo de las Funciones 43 Discriminantes (FDs) usando ADL. 4 RESULTADOS Y DISCUSIÓN 45 4.1 División de la Data Original en Serie de Entrenamiento y Serie de 45 Predicción. 4.2 Desarrollo de Modelos QSAR-ADL 48 4.3 Identificación de Nuevas Entidades Moleculares como Analgésicos 53 utilizando los Modelos in silico: El ‘Screening’ Virtual como una Promisoria Alternativa para la Selección y el Diseño Racional de Fármacos Analgésicos. 5 CONCLUSIONES 55 6 RECOMENDACIONES 56 7 REFERENCIAS BIBLIOGRÁFICAS 57 ANEXOS 61 GLOSARIO λ Lambda de Wilks ΔP% Porcentaje de Clasificación AINEs Antiinflamatorios no esteroides ADL Análisis Discrimintante Lineal fk(x) k-ésimo Índice lineal basado en relaciones de núcleos atómicos CARDD Computed-Aided ‘Rational’ Drug Design D2 Distancia de Mahalanobis DMs Descriptores Moleculares Ec. Ecuación F Fisher FC Función de Clasificación FDs Función Discriminante Fig. Figura IV Intravenosa k-MCA Análisis de cluster de k-medias PG Prostaglandina QSAR Quantitative Structure Activity Relationship SE Serie de Entrenamiento SP Serie de Predicción SNC Sistema Nerviosa Central TOMOCOMD TOpological MOlecular COMputer Design x Vector Molecular basado en Átomos INTRODUCCIÓN La obtención de nuevos compuestos cabezas de serie (o también conocidos como compuestos líderes) útiles en el tratamiento del dolor, constituye una tarea de primer orden para la industria farmacéutica actual. Un estudio reciente llevado a cabo por el Boston Consulting Group (BCG), que involucró 50 compañías e instituciones académicas, mostró que el costo de desarrollo de un nuevo medicamento desde la identificación de su diana farmacológica, el descubrimiento y optimización de uno de los nuevos compuestos líderes, el desarrollo de los ensayos clínicos requeridos y su uso autorizado en terapéutica es como promedio de 880 millones de dólares (USD) y se necesita también como promedio un periodo de 15 años de investigación.
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