Modelación computacional de dipéptidos inhibidores de la enzima convertidora de angiotensina como fármacos antihipertensivos
Fecha
2021
Autores
Cruz Fernández, Félix Manuel
Título de la revista
ISSN de la revista
Título del volumen
Editor
Universidad Central ¨Marta Abreu¨ de Las Villas. Facultad de Química y Farmacia. Departamento de Farmacia
Resumen
La hipertensión es una enfermedad crónica, considerada por la Organización Mundial de la Salud un problema de salud pública mundial. Esta patología contribuye a la carga de cardiopatías, accidentes cerebrovasculares e insuficiencia renal, y a la mortalidad y discapacidad prematuras, así como complicaciones del cuadro clínico de las enfermedades infecciosas como la COVID-19. La mayoría de los medicamentos para el tratamiento de la presión arterial son fáciles de tomar, pero todos los tienen efectos secundarios. Por esta razón, entender los mecanismos por los cuales actúan es un proceso importante para el diseño y desarrollo de nuevos candidatos a fármacos. En esta investigación se usó la metodología QSAR para encontrar una correlación entre la estructura química de un grupo de dipéptidos inhibidores de la enzima convertidora de angiotensina y la actividad antihipertensiva. Para desarrollar el modelo QSAR-RLM fue usado el software QSARINS y los descriptores moleculares del DRAGON obteniéndose un ajuste superior al 80% con solo cinco variables predictivas. Además, fue desarrollada una validación exhaustiva de los principios regulatorios establecidos por la OECD, demostrando que el modelo es robusto (R2=0,83), estable (Q2LOO=0,78 y Q2LMO=0,77), con un buen poder predictivo (R2ext=0,81) y no es producto de una correlación casual (R2Y-scr=0,.12 y Q2Y-scr=-0,19). También fue definido el dominio de aplicación para el cual las predicciones del modelo son fiables. De acuerdo a los resultados obtenidos la herramienta propuesta puede ser de gran utilidad para la identificación futura de nuevos compuestos con acción hipotensora.
Hypertension is a chronic disease, considered by the World Health Organization a global public health problem. This pathology contributes to the burden of heart disease, stroke and kidney failure, and premature mortality and disability, as well as complications of the clinical picture of infectious diseases such as COVID-19. Most blood pressure medications are easy to take, but all have side effects. For this reason, understanding the mechanisms by which they act is an important process for the design and development of new drug candidates. In this investigation, the QSAR methodology was used to find a correlation between the chemical structure of a group of angiotensin converting enzyme inhibitor dipeptides and antihypertensive activity. To develop the QSAR-RLM model, the QSARINS software and the DRAGON molecular descriptors were used, obtaining an adjustment greater than 80% with only five predictive variables. In addition, an exhaustive validation of the regulatory principles established by the OECD was developed, showing that the model is robust (R2=0,83), stable (Q2LOO=0,78 y Q2LMO=0,77), with good predictive power (R2ext=0,81) and is not the product of a casual correlation (R2Y-scr=0,.12 y Q2Y-scr=-0,19). The application domain for which the model predictions are reliable was also defined. According to the results obtained, the proposed tool can be very useful for the future identification of new compounds with hypotensive action.
Hypertension is a chronic disease, considered by the World Health Organization a global public health problem. This pathology contributes to the burden of heart disease, stroke and kidney failure, and premature mortality and disability, as well as complications of the clinical picture of infectious diseases such as COVID-19. Most blood pressure medications are easy to take, but all have side effects. For this reason, understanding the mechanisms by which they act is an important process for the design and development of new drug candidates. In this investigation, the QSAR methodology was used to find a correlation between the chemical structure of a group of angiotensin converting enzyme inhibitor dipeptides and antihypertensive activity. To develop the QSAR-RLM model, the QSARINS software and the DRAGON molecular descriptors were used, obtaining an adjustment greater than 80% with only five predictive variables. In addition, an exhaustive validation of the regulatory principles established by the OECD was developed, showing that the model is robust (R2=0,83), stable (Q2LOO=0,78 y Q2LMO=0,77), with good predictive power (R2ext=0,81) and is not the product of a casual correlation (R2Y-scr=0,.12 y Q2Y-scr=-0,19). The application domain for which the model predictions are reliable was also defined. According to the results obtained, the proposed tool can be very useful for the future identification of new compounds with hypotensive action.
Descripción
Palabras clave
Hipertensión, Terapéutica, Modelos QSAR, Descriptores Moleculares