{"id":11639,"date":"2024-04-17T11:21:32","date_gmt":"2024-04-17T11:21:32","guid":{"rendered":"https:\/\/dailyai.com\/?p=11639"},"modified":"2024-04-17T12:42:25","modified_gmt":"2024-04-17T12:42:25","slug":"scientists-accelerate-search-for-parkinsons-treatments-using-ai","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2024\/04\/scientists-accelerate-search-for-parkinsons-treatments-using-ai\/","title":{"rendered":"Los cient\u00edficos aceleran la b\u00fasqueda de tratamientos para el Parkinson mediante IA"},"content":{"rendered":"

Investigadores de la Universidad de Cambridge han aprovechado la IA para acelerar dr\u00e1sticamente la b\u00fasqueda de nuevas terapias contra la enfermedad de Parkinson.\u00a0<\/b><\/p>\n

Utilizando t\u00e9cnicas de aprendizaje autom\u00e1tico, fueron capaces de examinar millones de posibles compuestos farmacol\u00f3gicos e identificar los candidatos m\u00e1s prometedores diez veces m\u00e1s r\u00e1pido y 1.000 veces m\u00e1s rentables que los m\u00e9todos convencionales.<\/span><\/p>\n

La enfermedad de Parkinson es una enfermedad neurodegenerativa compleja y progresiva que afecta aproximadamente a 6 millones de personas en todo el mundo. Se prev\u00e9 que esa cifra se triplique de aqu\u00ed a 2040. <\/span><\/p>\n

En la actualidad, ning\u00fan tratamiento puede ralentizar o detener de forma fiable la progresi\u00f3n de la enfermedad.\u00a0<\/span><\/p>\n

El proceso tradicional de cribado de vastas bibliotecas qu\u00edmicas para encontrar posibles f\u00e1rmacos candidatos es extremadamente lento, caro y, a menudo, infructuoso.<\/span><\/p>\n

\"Una v\u00eda para buscar tratamientos potenciales para el Parkinson requiere la identificaci\u00f3n de peque\u00f1as mol\u00e9culas que puedan inhibir la agregaci\u00f3n de la alfa-sinucle\u00edna, que es una prote\u00edna estrechamente asociada a la enfermedad\", explica el investigador principal, el catedr\u00e1tico Michele Vendruscolo. <\/span>a la Universidad de Cambridge.<\/span><\/a><\/p>\n

\"Pero se trata de un proceso extremadamente largo: s\u00f3lo identificar a un candidato principal para realizar m\u00e1s pruebas puede llevar meses o incluso a\u00f1os\".<\/span><\/p>\n

Para afrontar este reto, Vendruscolo y su equipo desarrollaron un m\u00e9todo de aprendizaje autom\u00e1tico en cinco pasos. El sitio <\/span>estudiar<\/span><\/a> se public\u00f3 en Naturaleza Biolog\u00eda Qu\u00edmica<\/em>.\u00a0<\/span><\/p>\n

    \n
  1. Empezar con un peque\u00f1o conjunto de compuestos, identificados mediante simulaciones, que muestren potencial para bloquear la aglutinaci\u00f3n de la prote\u00edna alfa-sinucle\u00edna, que es la causa principal del Parkinson. A continuaci\u00f3n, probar experimentalmente su eficacia.<\/span><\/li>\n
  2. Utilizar los resultados para entrenar un modelo de aprendizaje autom\u00e1tico que permita predecir qu\u00e9 estructuras y propiedades moleculares hacen que un compuesto sea eficaz para prevenir la agregaci\u00f3n de prote\u00ednas.<\/span><\/li>\n
  3. Implemente el modelo entrenado para examinar r\u00e1pidamente una biblioteca virtual con millones de compuestos y predecir los contendientes m\u00e1s potentes.<\/span><\/li>\n
  4. Validar experimentalmente en el laboratorio los mejores candidatos seleccionados por la IA. Vuelve a introducir estos resultados en el modelo para perfeccionar su capacidad de predicci\u00f3n.<\/span><\/li>\n
  5. Repite este ciclo de predicci\u00f3n computacional y pruebas experimentales, con el modelo de IA cada vez m\u00e1s inteligente, concentr\u00e1ndose en los compuestos m\u00e1s potentes.<\/span><\/li>\n<\/ol>\n
    \"AI
    Sistema iterativo de descubrimiento de f\u00e1rmacos contra el Parkinson de la Universidad de Cambridge. Fuente: Naturaleza Biolog\u00eda Qu\u00edmica<\/a> (acceso abierto)<\/figcaption><\/figure>\n

    A lo largo de m\u00faltiples iteraciones, la tasa de optimizaci\u00f3n -el porcentaje de compuestos probados que inhib\u00edan la aglutinaci\u00f3n de alfa-sinucle\u00edna asociada con el Parkinson- aument\u00f3 de 4% a m\u00e1s de 20%.\u00a0<\/span><\/p>\n

    Adem\u00e1s, los compuestos hallados por la IA eran, por t\u00e9rmino medio, mucho m\u00e1s potentes que cualquiera de los identificados anteriormente. Algunos mostraron una actividad prometedora a dosis ocho veces inferiores. Tambi\u00e9n eran qu\u00edmicamente m\u00e1s diversos, ya que el modelo descubri\u00f3 compuestos eficaces que difer\u00edan de las estructuras conocidas.<\/span><\/p>\n

    \"El aprendizaje autom\u00e1tico est\u00e1 teniendo un impacto real en el descubrimiento de f\u00e1rmacos: est\u00e1 acelerando todo el proceso de identificaci\u00f3n de los candidatos m\u00e1s prometedores\", afirma Vendruscolo. <\/span><\/p>\n

    \"Al utilizar los conocimientos que obtuvimos del cribado inicial con nuestro modelo de aprendizaje autom\u00e1tico, pudimos entrenar el modelo para identificar las regiones espec\u00edficas en estas peque\u00f1as mol\u00e9culas responsables de la uni\u00f3n, entonces podemos volver a cribado y encontrar mol\u00e9culas m\u00e1s potentes.\"<\/span><\/p>\n

    \"Para nosotros, esto significa que podemos empezar a trabajar en varios programas de descubrimiento de f\u00e1rmacos, en lugar de en uno solo. Es posible hacer muchas cosas gracias a la enorme reducci\u00f3n de tiempo y costes: es un momento apasionante\".<\/span><\/p>\n

    Los investigadores subrayan que esto es s\u00f3lo el principio de lo que los enfoques basados en la IA podr\u00edan permitir en el descubrimiento de f\u00e1rmacos para el Parkinson y otras enfermedades caracterizadas por el mal plegamiento y la agregaci\u00f3n de prote\u00ednas.\u00a0<\/span><\/p>\n

    Con un mayor desarrollo y conjuntos de datos de entrenamiento m\u00e1s amplios, el poder predictivo de estos modelos no har\u00e1 sino mejorar.<\/span><\/p>\n

    Aunque todav\u00eda queda un largo camino por recorrer para convertir estos candidatos identificados por la IA en tratamientos aprobados, este estudio demuestra c\u00f3mo el aprendizaje autom\u00e1tico, combinado inteligentemente con la biolog\u00eda experimental, puede acelerar enormemente las primeras fases del descubrimiento de f\u00e1rmacos.<\/span><\/p>\n

    Esto se basa en una serie de investigaciones que abordan el reto de localizar tratamientos farmacol\u00f3gicos nuevos y novedosos, entre ellos <\/span>del MIT y Tufts<\/span><\/a>que acaba de crear un modelo capaz de examinar unos 100 millones de compuestos al d\u00eda. <\/span><\/p>\n

    Varios <\/span>modelos de descubrimiento de antibi\u00f3ticos<\/span><\/a> han producido compuestos experimentales, algunos de los cuales son rumbo a los ensayos cl\u00ednicos<\/a>.<\/span><\/p>\n

    Otro <\/span>proyecto de gran envergadura<\/span><\/a> en colaboraci\u00f3n con el Moorfields Eye Hospital del Reino Unido, utiliz\u00f3 el a\u00f1o pasado esc\u00e1neres oculares para detectar los primeros signos del Parkinson, un m\u00e9todo novedoso que permite la IA.\u00a0<\/span><\/p>\n

    Con este nuevo estudio que pretende descubrir tratamientos eficaces contra el Parkinson, los m\u00e9todos de IA se muestran inmensamente prometedores para redefinir la medicina y la asistencia sanitaria.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Investigadores de la Universidad de Cambridge han utilizado la IA para acelerar dr\u00e1sticamente la b\u00fasqueda de nuevas terapias contra la enfermedad de Parkinson. Mediante el uso de t\u00e9cnicas de aprendizaje autom\u00e1tico, fueron capaces de examinar millones de posibles compuestos farmacol\u00f3gicos e identificar los candidatos m\u00e1s prometedores diez veces m\u00e1s r\u00e1pido y 1000 veces m\u00e1s rentables que los m\u00e9todos convencionales. El Parkinson es una enfermedad neurodegenerativa compleja y progresiva que afecta a unos 6 millones de personas en todo el mundo. Se prev\u00e9 que esa cifra se triplique de aqu\u00ed a 2040. En la actualidad, ning\u00fan tratamiento puede ralentizar o detener de forma fiable la progresi\u00f3n de la enfermedad. El proceso tradicional de cribado de vastas bibliotecas qu\u00edmicas para encontrar posibles candidatos a f\u00e1rmacos es extremadamente<\/p>","protected":false},"author":2,"featured_media":11641,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[204,178],"class_list":["post-11639","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-healthcare","tag-medicine"],"yoast_head":"\nScientists accelerate the search for Parkinson's treatments using AI | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/es\/2024\/04\/scientists-accelerate-search-for-parkinsons-treatments-using-ai\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Scientists accelerate the search for Parkinson's treatments using AI | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers at the University of Cambridge have harnessed AI to dramatically accelerate the search for new Parkinson’s disease therapies.\u00a0 By using machine learning techniques, they were able to screen millions of potential drug compounds and identify the most promising candidates ten times faster and 1000 times more cost-effectively than conventional methods. Parkinson’s disease is a complex, progressive neurodegenerative disease that afflicts approximately 6 million people worldwide. That figure is expected to triple by 2040. 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