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BC-TESIS-5727.Pdf (9.449Mb) UNIVERSIDAD NACIONAL PEDRO RUIZ GALLO FACULTAD DE CIENCIAS BIOLÓGICAS DEPARTAMENTO ACADÉMICO DE BIOLOGÍA DETECCIÓN IN SILICO DE REACCIONES CRUZADAS DE FÁRMACOS TESIS PARA OPTAR EL TÍTULO PROFESIONAL DE LICENCIADO EN BIOLOGÍA PRESENTADO POR: Br. Juan Enrique Faya Castillo 1 DETECCIÓN IN SILICO DE REACCIONES CRUZADAS DE FÁRMACOS TESIS PARA OPTAR EL TÍTULO PROFESIONAL DE LICENCIADO EN BIOLOGÍA PRESENTADO POR: Br. Juan Enrique Faya Castillo APROBADO POR: Dra. MARTHA A. VERGARA ESPINOZA PRESIDENTE MSc. CONSUELO ROJAS IDROGO SECRETARIO MSc. JULIO CÉSAR SILVA ESTELA VOCAL PhD. PEDRO JORGE CHIMOY EFFIO PATROCINADOR 2 “El Biólogo en esencia, soluciona problemas” (Pedro Jorge Chimoy Effio) 3 DEDICATORIA A Margarita y Enrique mis padres, por su apoyo incondicional y por su esfuerzo diario para hacerme salir adelante. A mis familiares por ser ejemplo para mí. A Aracelly Gomez por ser mi apoyo moral en todo momento. A Pedro Chimoy, por ser mi amigo, guía, y maestro. A Erick Suclupe por siempre saber decir las palabras correctas para continuar. A todas aquellas personas que utilizan la ciencia para difundir los nuevos conocimientos. 4 AGRADECIMIENTOS Mis sinceros agradecimientos: Al PhD. Pedro Jorge Chimoy Effio, docente principal del departamento de Biología, laboratorio de Bioquímica, por su apoyo incondicional, además por su amistad y todos los consejos brindados que han permitido seguir en el camino de la investigación. A M. Sc Erick Suclupe Farro co-orientador por apoyarme en el transcurso de la tesis además por esa amistad ganada en el trascurso del tiempo. Al PhD. Alessandro Silva Nascimento, Universidad de Física de Sao Carlos – USP (Brasil); del grupo de biotecnología molecular, por permitirme realizar parte de la investigación en su laboratorio y por orientarme a esta rama de la ciencia. Al PhD. Richard Charles Garratt, Universidad de Física de Sao Carlos – USP (Brasil); por su amistad, apoyo incondicional y por brindarme los medios necesarios para conocer una nueva visión de ciencia. 5 A la M. Sc. Consuelo Rojas Idrogo, docente principal del departamento de Botánica-Cultivo de tejidos vegetales, Facultad de Ciencias Biológicas, Universidad Nacional Pedro Ruiz Gallo por ser amiga y maestra a lo largo de mi carrera profesional. A Gilbert, gran amigo y por estar al tanto de mi investigación y por los deseos de buena fe en el transcurso de la tesis. A Diego y Daniel por hacerme sentir en casa cuando estábamos lejos de ella, además por esas largas conversaciones sobre ciencia. A Alessandra, Ada, María, Olivia, Diana, Arly, Debir, Jhonson, Christian, Richard, Aldemir, Juan Carlos, por ser grandes amigos en el trascurso de los años de mi carrera universitaria en el área de Biología. A todo el Código 2010-I por ser el mejor de todos. A los integrantes del Centro de Estudios Genómicos del Perú – CEGENP Además, a todas aquellas personas que de alguna u otra manera contribuyeron en la realización del presente trabajo. 6 CONTENIDO I. INTRODUCCIÓN…………………...……………………………….. 11 II. ANTECEDENTES BIBLIOGRÁFICOS……………………………. 17 III. MATERIAL Y MÉTODO…………….………………….……..….…. 22 3.1. Material………………..………………………….…….....….… 22 3.1.1. Equipos………………………….………………....….…. 22 3.1.2. Softwares..……………………...……………………..… 22 3.1.3. Base de Datos.………………...………………………... 22 3.1.4. Outputs e Imputs..…………..…….…………………….. 22 3.1.5. Sistemas Operativos…………………………………….. 23 3.1.6. Paquetes de Datos………………………………………. 23 3.2. Métodos…………………………...….………………………….. 23 3.2.1. Campo de estudio………………………………………... 23 3.2.2. Procedimientos.……………………………….………….. 23 3.2.2.1 Obtener Coordenadas atómicas de los fármacos y compararlos entre ellos....………………………………….. 24 3.2.2.1.1. MolShacs…………..….…………………..….. 24 3.2.2.2 Análisis de Clusters con Radatools 3.2 y Visualización de Comunidades con Pajek…………………………………… 27 3.2.2.2.1 Radatools 3.2……………………….…………. 27 3.2.2.2.2 Pajek…………………….…………………….... 33 3.2.2.3 Análisis fármaco-dinámica de comunidades cercanas ………………...…………..…………………………………... 35 7 3.2.2.3.1 CHIMERA 1.11.2..…………………………….. 35 3.2.2.3.2 AutoDocks Tools ..…………………………….. 36 3.2.2.3.3 AutoDocks Vina..………………………………. 37 IV. RESULTADOS y DISCUSIONES………………………………..…… 38 4.1. Clusters obtenidos con Radatools y visualizados en Pajek … 40 4.1.1. Comunidades en Radatools 3.2………………………...….. 40 4.1.2. Visualización de las Comunidades en Pajek….…………... 42 4.1.3. Fármaco-Dinámica de fármacos identificados como similares por su cercanía.……………………………………………………… 43 Comunidad 4………………………………………………………. 45 Comunidad 5………………………………………………………. 46 Docking 1…………………………………………………………... 47 Comunidad 3…………………………………………………….… 51 Comunidad 12……………………………………………………... 52 Docking 2………………………………………………………….. 54 Docking 3………………………………………………………….. 57 Comunidad 17…………………………………………………….. 60 Comunidad 19…………………………………………………….. 62 Docking 4………………………………………………………….. 63 V. CONCLUSIONES….………………………………………………..… 72 VI. RECOMENDACIONES.………………………………………..……... 74 VII. RESUMEN………………………………….………………………...… 75 VIII.ABSTRACT….………………………………………………..……...… 77 8 IX. REFERENCIAS BIBLIOGRÁFICAS.………..…………..........…...…. 79 ANEXOS…..…………………...................................……………..….. 88 9 INDICE DE FIGURAS Figura 1. Transformación de una extensión .dat a una extensión .net…………………………………………………................……………….. 41 Figura 2. Transformación de una extensión .lol a una extensión .clu…………………………..………………………………………………….. 41 Figura 3. Visualización de las 21 comunidades en Pajek….…………….. ……………………………………..……………………………………..…….. 42 Figura 4: Fármacos utilizados para Docking Molecular………………….. 44 Figura 5: cAMP-specific 3',5'-cyclic phosphodiesterase, Pentoxifilline y Benzoctamine….……………..…………………………………..………….. 50 Figura 6: Transportador de dopamina dependiente de sodio, Phentermine y Venlafaxine..….………………………………………………...………….. 56 Figura 7: Monoamina Oxidasa, Phentermine .…. …………………………………………………………...………...………….. 59 Figura 8: Receptor de la dopamina, Levodopa y Cholpropamide .….. ……………………………………………………...……………………….…. 69 Figura 9: Receptor de la dopamina 3, Levodopa y Cholpropamide..…. ……………..……………………………………………………...………….. 71 INDICE DE ANEXOS Anexo 1. Base de Datos Anexo 2. 921 fármacos divididos en 21 comunidades Anexo 3. Listado de 21 comunidades 10 I.INTRODUCCIÓN Un fármaco es una sustancia química de estructura establecida distinta de un nutriente o un componente alimentario esencial, que produce un efecto biológico cuando se administra a un ser vivo. Los fármacos actúan sobre un receptor o molécula diana, el cual mantiene reciprocidad con él, cada tipo de fármacos se une a determinadas moléculas y determinadas moléculas diana reconocen determinados tipos de fármacos, pero en muchos casos cuando se aumenta la dosis del fármaco o se usan de manera prolongada, la inespecificidad del fármaco-diana se produce, dando lugar a efectos secundarios como las reacciones cruzadas de fármacos (San Román, 2013). Las reacciones cruzadas de fármacos, también llamados ADR o RAM´s son efectos no deseados de un fármaco, que se dan por la no especificidad de este hacia su diana, es decir que la estructura química no se correlaciona. Desde los primeros estudios de fármacos, las reacciones cruzadas han sido de gran importancia para los procesos farmacológicos, puesto que el procedimiento para desarrollar un fármaco es muy largo y costoso, y arriesgarse a que el producto final sea sacado del mercado por presentar reacciones adversas para los consumidores ocasionaría pérdidas de centenas de millones de dólares, además de muchos procesos legales y lo más importante evitar afectar la salud. 11 Esta gran pérdida se evita, ahora, con los estudio de fármacos In Silico o en Computadoras, uno de los principales usos son los estudios relacionados con la estructura-actividad (la relación de la estructura química, los efectos farmacodinámicos y farmacocinéticos, en los sistemas biológicos) los primeros estudios cuantitativos comenzaron con Hansch & Fujita (Hansch & Fujita, 1964) a mediados del siglo pasado naciendo así los “Quantitative Structure-Activity Relationship” o QSARs, seguido luego en los 80 y 90 por gráficos computacionales y modelamientos moleculares. (S Ekins, Mestres, & Testa, 2007b) Los QSAR´s consisten en la construcción de modelos matemáticos que relacionan una estructura molecular para una propiedad química o biológica. Esto no es sencillo ya que existe una alta posibilidad de que diferentes moléculas actúan para diferentes mecanismos o interactúan con el receptor en diferentes enlaces. (S Ekins, Mestres, & Testa, 2007a) La especificidad de un fármaco a su receptor se determina por las interacciones moleculares entre una molécula y su diana, estás interacciones están basadas por la estructura de la molécula y por sus propiedades físico-químicas. Idealmente cada fármaco debe ejercer su efecto sobre un único alvo o receptor biológico; en quimiorreceptores individuales o 'balas mágicas' de Ehrlich (Kaufmann, 2008), concepto basado en el paradigma "un gen, una droga, una enfermedad", el cual 12 surge de una congruencia entre el reduccionismo genético y las nuevas tecnologías de biología molecular que permitieron el aislamiento y caracterización de enfermedades causadas por genes (Sams-Dodd, 2005). Este paradigma ha conllevado a la suposición de que el objetivo del descubrimiento de fármacos es diseñar ligandos exquisitamente selectivos que tienen que actuar sobre una única diana en la enfermedad o que tiene que unirse a sólo un receptor (Especificidad fármaco- receptor). Sin embargo esto no sucede, ya que efectos colaterales resultan de muchas combinaciones fármaco-alvo, un ejemplo fue Rofecobix un fármaco
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