Targets Involved in Skin Aging and Photoaging and their Possible Inhibitors: A Mini-review.

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.

In Silico Studies on Natural Products and Derivatives Against Different Types of Cancer.

According to the World Health Organization (WHO), cancer is the second cause of death worldwide, responsible for almost 10 million deaths and accounting for one in every six deaths. It is a disease that can affect any organ or tissue with rapid progression to the final stage, which is metastasis, in which the disease spreads to different regions of the body. Many studies have been carried out to find a cure for cancer. Early diagnosis contributes to the individual achieving the cure; however, deaths are increasing considerably due to late diagnosis. Thus, this bibliographical review discussed several scientific research works pointing to in silico analyses in the proposition of new antineoplastic agents for glioblastoma, breast, colon, prostate, and lung cancer, as well as some of their respective molecular receptors involved in molecular docking simulations and molecular dynamics. This review involved articles describing the contribution of computational techniques for the development of new drugs or already existing drugs with biological activity; thus, important data were highlighted in each study, such as the techniques used, results obtained in each study, and the conclusion. Furthermore, 3D chemical structures of the molecules with the best computational response and significant interactions between the tested molecules and the PDB receptors were also presented. With this, it is expected to help new research in the fight against cancer, the creation of new antitumor drugs, and the advancement of the pharmaceutical industry and scientific knowledge about studied tumors.

Challenges and Discoveries in Polypharmacology of Neurodegenerative Diseases.

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.