Diseño y síntesis química de un análogo de los inhibidores de la HMG-CoA reductasa mediante QSAR

Cardiovascular diseases, mostly related to hyperlipidemia, have a significant impact on the population; it is known that HMG-CoA reductase inhibitors are the first-line medications for their treatment, being the most prescribed worldwide. However, their prolonged use can lead to drug-induced myopath...

Full description

Autores:
Blanco Niño, Manuel Andres
Delgado Cruz, Diana Paola
Tipo de recurso:
https://purl.org/coar/resource_type/c_7a1f
Fecha de publicación:
2024
Institución:
Universidad El Bosque
Repositorio:
Repositorio U. El Bosque
Idioma:
spa
OAI Identifier:
oai:repositorio.unbosque.edu.co:20.500.12495/12092
Acceso en línea:
https://hdl.handle.net/20.500.12495/12092
Palabra clave:
Hipercolesterolemia
Inhibidores de la HMG CoA-Reductasa
QSAR
Drug Discovery
615.19
Hypercholesterolemia
HMG CoA Reductase Inhibitors
QSAR
Drug Discovery
Rights
closedAccess
License
Acceso cerrado
Description
Summary:Cardiovascular diseases, mostly related to hyperlipidemia, have a significant impact on the population; it is known that HMG-CoA reductase inhibitors are the first-line medications for their treatment, being the most prescribed worldwide. However, their prolonged use can lead to drug-induced myopathy, with the potential development of rhabdomyolysis. Hence arises the need for the design of safer statin analogs for patients, which is the aim of this research. The project was conducted in two sections, an in silico one corresponding to QSAR models and rational design, where parameters such as affinity values, biological activity (IC50 and CC50), log P, and toxicity were evaluated; resulting in the selection of analog 13a. The second section, corresponding to the experimental phase, involved the chemical synthesis of the analog and its characterization. Finally, it was concluded that analog 13a demonstrated better pharmacological and physicochemical attributes compared to atorvastatin, proving that rational analog design and the use of neural networks are potential tools for the development of new drugs.