Estrategia computacional para la identificación de compuestos multitarget: aplicaciones en la enfermedad de Alzheimer.
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
Universidad de Concepción
Abstract
El Alzheimer es una enfermedad neurodegenerativa que afecta la memoria y las funciones cognitivas, asociada con la acumulación de placas de beta amiloide y la disminución de la actividad colinérgica. Dado que los tratamientos actuales son limitados, los enfoques multitarget, que buscan diseñar moléculas para actuar sobre varios blancos terapéuticos simultáneamente, se presentan como una estrategia prometedora. En este estudio, se priorizaron 6 blancos terapéuticos relevantes, y se aplicaron métodos combinados de Cribado Virtual Basado en Ligandos (LBVS) y Basado en Estructura (SBVS) para encontrar potenciales compuestos multitarget. Esto permitió reducir una base de datos inicial de 14 millones de compuestos a 21 moléculas candidatas para validación experimental. Se realizaron ensayos biológicos, incluyendo el método de Ellman para evaluar el IC50 frente a AChE y ensayos ABTS y DPPH para medir el atrapamiento de radicales libres. Los compuestos E3 y M3 fueron las moléculas destacadas, con IC50 de 8.84 ± 0.04 µM y 6.67 ± 0.03 µM, respectivamente. Adicionalmente, M3 mostró un 76% de atrapamiento de radicales libres. Este estudio demuestra cómo los métodos computacionales para cribado virtual permiten identificar eficientemente moléculas que pueden llegar a convertirse en multitarget. E3 y M3 destacan por su actividad biológica, abriendo nuevas posibilidades para tratamientos efectivos en Alzheimer.
Alzheimer's disease is a neurodegenerative condition that affects memory and cognitive functions, associated with the accumulation of beta-amyloid plaques and a decrease in cholinergic activity. Given the limitations of current treatments, multitarget approaches, which aim to design molecules capable of acting on multiple therapeutic targets simultaneously, emerge as a promising strategy. In this study, six relevant therapeutic targets were prioritized, and combined methods of Ligand-Based Virtual Screening (LBVS) and Structure-Based Virtual Screening (SBVS) were applied to identify potential multitarget compounds. This approach reduced an initial database of 14 million compounds to 21 candidate molecules for experimental validation. Biological assays were performed, including the Ellman method to evaluate IC50 against AChE and ABTS and DPPH assays to measure free radical scavenging activity. Compounds E3 and M3 stood out, with IC50 values of 8.84 ± 0.04 µM and 6.67 ± 0.03 µM, respectively. Additionally, M3 showed 76% free radical scavenging activity. This study demonstrates how computational virtual screening methods efficiently identify molecules with potential multitarget capabilities. E3 and M3 stand out for their biological activity, opening new possibilities for effective Alzheimer’s treatments.
Alzheimer's disease is a neurodegenerative condition that affects memory and cognitive functions, associated with the accumulation of beta-amyloid plaques and a decrease in cholinergic activity. Given the limitations of current treatments, multitarget approaches, which aim to design molecules capable of acting on multiple therapeutic targets simultaneously, emerge as a promising strategy. In this study, six relevant therapeutic targets were prioritized, and combined methods of Ligand-Based Virtual Screening (LBVS) and Structure-Based Virtual Screening (SBVS) were applied to identify potential multitarget compounds. This approach reduced an initial database of 14 million compounds to 21 candidate molecules for experimental validation. Biological assays were performed, including the Ellman method to evaluate IC50 against AChE and ABTS and DPPH assays to measure free radical scavenging activity. Compounds E3 and M3 stood out, with IC50 values of 8.84 ± 0.04 µM and 6.67 ± 0.03 µM, respectively. Additionally, M3 showed 76% free radical scavenging activity. This study demonstrates how computational virtual screening methods efficiently identify molecules with potential multitarget capabilities. E3 and M3 stand out for their biological activity, opening new possibilities for effective Alzheimer’s treatments.
Description
Tesis presentada para optar al grado de Magister en Bioquímica y Bioinformática
Keywords
Biomoléculas, Simulación por computadores, Enfermedad de Alzheimer