RESUMO
BACKGROUND: Skin aging is a natural process resulting from intrinsic (hormonal and genetic) and extrinsic (environmental) factors. Photoaging occurs due to prolonged exposure of the skin to ultraviolet radiation, accounting for 80% of facial aging. INTRODUCTION: Characteristics of aging skin include reduced elasticity, the appearance of fine wrinkles, uneven tone, and dryness. Clinical signs of photoaging involve the presence of deeper wrinkles, rough texture, dyschromia and a greater loss of elasticity compared to chronological aging. METHODS: This work reported several scientific articles that used computational techniques, such as molecular docking, molecular dynamics and quantitative structure-activity relationship (QSAR) to identify natural products and their derivatives against skin aging and photoaging. RESULTS: The in silico analyses carried out by the researchers predicted the binding affinity and interactions of the natural products with the targets matrix metalloproteinase-1, matrix metalloproteinase- 3, matrix metalloproteinase-9 and tyrosinase. Furthermore, some studies have reported the stability of the protein-ligand complex and the physicochemical properties of the studied compounds. Finally, this research proposes promising molecules against the targets. CONCLUSION: Thus, studies like this one are relevant to guide new research related to skin aging and photoaging.
Assuntos
Envelhecimento da Pele , Humanos , Raios Ultravioleta/efeitos adversos , Simulação de Acoplamento Molecular , Pele/metabolismo , EnvelhecimentoRESUMO
BACKGROUND: Neurological disorders are composed of several diseases that affect the central and peripheral nervous system; among these are neurodegenerative diseases, which lead to neuronal death. Many of these diseases have treatment for the disease and symptoms, leading patients to use several drugs that cause side effects. INTRODUCTION: The search for new treatments has led to the investigation of multi-target drugs. METHODS: This review aimed to investigate in the literature the multi-target effect in neurological disorders through an in silico approach. Studies were reviewed on the diseases such as epilepsy, Alzheimer's disease, Amyotrophic Lateral Sclerosis (ALS), Huntington's disease, cerebral ischemia, and Parkinson's disease. RESULTS: As a result, the study emphasize the relevance of research by computational techniques such as quantitative structure-activity relationship (QSAR) prediction models, pharmacokinetic prediction models, molecular docking, and molecular dynamics, besides presenting possible drug candidates with multi-target activity. CONCLUSION: It was possible to identify several targets with pharmacological activities. Some of these targets had diseases in common such as carbonic anhydrase, acetylcholinesterase, NMDA, and MAO being relevant for possible multi-target approaches.