TY - JOUR
T1 - Molecular Modeling of Vasodilatory Activity
T2 - Unveiling Novel Candidates Through Density Functional Theory, QSAR, and Molecular Dynamics
AU - Bernal, Anthony
AU - Márquez, Edgar A.
AU - Flores-Sumoza, Máryury
AU - Cuesta, Sebastián A.
AU - Mora, José Ramón
AU - Paz, José L.
AU - Mendoza-Mendoza, Adel
AU - Rodríguez-Macías, Juan
AU - Salazar, Franklin
AU - Insuasty, Daniel
AU - Marrero-Ponce, Yovani
AU - Agüero-Chapin, Guillermin
AU - Flores-Morales, Virginia
AU - Carrascal-Hernández, Domingo César
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/12
Y1 - 2024/12
N2 - Cardiovascular diseases (CVD) pose a significant global health challenge, requiring innovative therapeutic strategies. Vasodilators, which are central to vasodilation and blood pressure reduction, play a crucial role in cardiovascular treatment. This study integrates quantitative structure– (QSAR) modeling and molecular dynamics (MD) simulations to predict the biological activity and interactions of vasodilatory compounds with the aim to repurpose drugs already known and estimateing their potential use as vasodilators. By exploring molecular descriptors, such as electronegativity, softness, and highest occupied molecular orbital (HOMO) energy, this study identifies key structural features influencing vasodilatory effects, as it seems molecules with the same mechanism of actions present similar frontier orbitals pattern. The QSAR model was built using fifty-four Food Drugs Administration-approved (FDA-approved) compounds used in cardiovascular treatment and their activities in rat thoracic aortic rings; several molecular descriptors, such as electronic, thermodynamics, and topographic were used. The best QSAR model was validated through robust training and test dataset split, demonstrating high predictive accuracy in drug design. The validated model was applied on the FDA dataset and molecules in the application domain with high predicted activity were retrieved and filtered. Thirty molecules with the best-predicted pKI50 were further analyzed employing molecular orbital frontiers and classified as angiotensin-I or β1-adrenergic inhibitors; then, the best scoring values obtained from molecular docking were used to perform a molecular dynamics simulation, providing insight into the dynamic interactions between vasodilatory compounds and their targets, elucidating the strength and stability of these interactions over time. According to the binding energies results, this study identifies novel vasodilatory candidates where Dasabuvir and Sertindole seem to have potent and selective activity, offering promising avenues for the development of next-generation cardiovascular therapies. Finally, this research bridges computational modelling with experimental validation, providing valuable insight for the design of optimized vasodilatory agents to address critical unmet needs in cardiovascular medicine.
AB - Cardiovascular diseases (CVD) pose a significant global health challenge, requiring innovative therapeutic strategies. Vasodilators, which are central to vasodilation and blood pressure reduction, play a crucial role in cardiovascular treatment. This study integrates quantitative structure– (QSAR) modeling and molecular dynamics (MD) simulations to predict the biological activity and interactions of vasodilatory compounds with the aim to repurpose drugs already known and estimateing their potential use as vasodilators. By exploring molecular descriptors, such as electronegativity, softness, and highest occupied molecular orbital (HOMO) energy, this study identifies key structural features influencing vasodilatory effects, as it seems molecules with the same mechanism of actions present similar frontier orbitals pattern. The QSAR model was built using fifty-four Food Drugs Administration-approved (FDA-approved) compounds used in cardiovascular treatment and their activities in rat thoracic aortic rings; several molecular descriptors, such as electronic, thermodynamics, and topographic were used. The best QSAR model was validated through robust training and test dataset split, demonstrating high predictive accuracy in drug design. The validated model was applied on the FDA dataset and molecules in the application domain with high predicted activity were retrieved and filtered. Thirty molecules with the best-predicted pKI50 were further analyzed employing molecular orbital frontiers and classified as angiotensin-I or β1-adrenergic inhibitors; then, the best scoring values obtained from molecular docking were used to perform a molecular dynamics simulation, providing insight into the dynamic interactions between vasodilatory compounds and their targets, elucidating the strength and stability of these interactions over time. According to the binding energies results, this study identifies novel vasodilatory candidates where Dasabuvir and Sertindole seem to have potent and selective activity, offering promising avenues for the development of next-generation cardiovascular therapies. Finally, this research bridges computational modelling with experimental validation, providing valuable insight for the design of optimized vasodilatory agents to address critical unmet needs in cardiovascular medicine.
KW - QSAR modelling
KW - cardiovascular diseases
KW - drug repurposing
KW - molecular dynamics simulations
KW - vasodilators
UR - http://www.scopus.com/inward/record.url?scp=85212669151&partnerID=8YFLogxK
U2 - 10.3390/ijms252312649
DO - 10.3390/ijms252312649
M3 - Artículo
C2 - 39684360
AN - SCOPUS:85212669151
SN - 1661-6596
VL - 25
JO - International Journal of Molecular Sciences
JF - International Journal of Molecular Sciences
IS - 23
M1 - 12649
ER -