Advances in pharmacology最新文献

Strategies for recognizing predictive cavities and binding site identification are decisive for drug discovery, molecular docking, and tracing protein-ligand interactions. The two major approaches experimental and computational strive for prognosticating and distinguishing protein's binding sites. Profuse diminutive molecules are associated with the binding sites and influence normal biological functioning. The various structure-based strategies such as molecular dynamics, docking simulations, algorithms for pocket identification, and homology modeling are covered under computational techniques, where they propound the exhaustive comprehension of possible binding pockets hinge on the structure of protein and its physiochemical properties. The various sequence-based approaches rely on the homogeneousness of the sequence and machine learning replicas edified on already known protein and ligand composites to anticipate the interactive sites of novel proteins. The high-resolution structural identification and foot printing of protein to map the confirmational changes attributable to ligand and binding sites can be identified through diverse experimental methods such as NMR spectroscopy, mass spectrometry, and x-ray crystallography. These techniques are pivotal for drug discovery and designing, as the efficiency and specificity of ligands can be amplified through virtual screening and structural-based drug designing. Moreover, the ongoing developments in this domain promise to drive the revolution and efficiency in drug discovery and research in molecular biology.

Molecular Modelling in Drug Designing or Computer Aided Drug Designing (CADD) plays a significant role in new drug identification in the current world. However, it has sensitivity challenges and limitation because theoretical models involve assumption and approximations Computational models are not very accurate, some of the major challenges that face these models include the following. These include, for instance, molecular-docking or molecular-dynamics-simulation models which may not represent an accurate biological system and thus the predictions will be wrong. CADD depends on the availability of accurate, high-quality structural information for target proteins and ligand. Unfortunately, there are instances when experimental structures are not available, and homology models are employed, which can be imprecise. The computational cost is another drawback; only high accuracy simulations call for huge amounts of computational power and time well-suited for screening a multitude of agents. Moreover, they have weaknesses in determining pharmacokinetic and toxicity patterns of compounds that influence drug performance and effectiveness. In other words, even though CADD greatly helps drug discovery, it is still constrained by experimental validation to solve its drawbacks and optimize its foretelling.

The study of interoganellar contactology represents a substantial advance in conceiving cells and their organelles. This presents a great challenge in terms of understanding their function and response to aging and in the development of different pathologies. This chapter will address changes in mitochondria-associated membranes (MAMs) in aging-related heart diseases, such as acute myocardial infarction and heart failure, emphasizing the role of endoplasmic reticulum stress (ERS). We also discuss the role of MAMs as possible markers of cardiovascular disease progression in geriatrician clinics, with a view to personalized therapy. Finally, we will contemplate the use of naturally occurring drugs that have been used in the experimental setting for the regulation of mitochondrial-ER communication. (119 words).

Cellular senescence (CS) is characterized by stable cell cycle arrest and is resistant to growth-promoting stimuli allied with aging. Cardiac senescent cells (SCs) are highly heterogeneous cells that can regulate the pathophysiology of cardiovascular diseases (CVDs). SCs accumulate in the cardiovascular system, leading to typical age-related cardiovascular conditions. Such conditions advance in cardiovascular pathologies, including heart failure, coronary artery disease, cardiac fibrosis, etc., by evocating the production of proinflammatory mediators and profibrotic senescence-associated secretory phenotype (SASP). SCs release different factors depending on the cell type that became senescent. Many factors are responsible for CS with the aging process. The primary senescence causes are oxidative stress, metabolic dysfunction, telomere shortening, and epigenetic deregulation. However, it isn't easy to understand the molecular mechanisms that lead to CS and the consequences of CS in developing new strategies and therapeutic approaches to treat CVDs. Among all, senotherapies are an emerging approach for intervening against CS mechanisms in CVDs to potentially prevent and treat CVDS. Senotherapies allow targeting the underlying causes of aging rather than treating disorders and could reduce polypharmacy. Essentially, senotherapeutics represent an emerging anti-SC treatment and comprise three therapeutic approaches such as molecules to selectively kill SCs that are defined senolytics, compounds able to reduce evocated SC SASP, acting hence as SASP suppressors, called senomorphics, and inhibition of increase of the number of SCs in the cardiovascular tissues. Senotherapies might delay or prevent the CVDs in the elderly. Therefore, senotherapeutics represent the potential clinical application in CVDs, stressing benefits and signifying potential solutions for applying them as soon as effective anti-CVD treatments.

The pathophysiology of neurodevelopmental disorders is associated with multiple genetic and environmental risk factors. Epigenetics, owing to its potential to recover global gene expression changes associated with disease conditions, is a crucial target to address neurodevelopmental disorders influenced by genetic and environmental factors. Here, we discuss the discovery of selective inhibitors of lysine-specific demethylase 1 (LSD1) enzyme activity and their therapeutic potential for neurodevelopmental disorders through epigenetic regulation in the brain. Conventional LSD1 inhibitors not only inhibit LSD1 enzymatic activity but also interfere with LSD1-cofactor complex formation, thus leading to hematological side effects. Notably, investigations on the structure-activity relationship have revealed (aminocyclopropyl)benzamide and (aminocyclopropyl)thiophene carboxamide derivatives as novel series of LSD1 inhibitors with fewer hematological side effects. Subsequently, we discovered T-448 and TAK-418 (clinical candidate) that selectively and potently inhibit LSD1 enzymatic activity without disrupting the LSD1-cofactor complex, resulting in potent epigenetic modulation without significant hematological toxicity risks in rodents. T-448 and TAK-418, at doses that achieved almost complete LSD1 occupancy in the brain, improved behavioral abnormalities in multiple rodent models of neurodevelopmental disorders. Furthermore, comprehensive RNA expression analyses revealed that, although gene expression abnormalities exhibited limited commonality across disease models, TAK-418 normalized each aberrant gene expression pattern in these rodent models. A positron emission tomography tracer was discovered to potentially measure the occupancy of TAK-418 at the LSD1 active site in the brain to improve the translatability of its preclinical efficacy to therapeutic effects in humans. TAK-418-type LSD1 inhibitors may offer novel treatment options for neurodevelopmental disorders.

Soluble epoxide hydrolase (sEH), encoded by the EPHX2 gene, is a critical enzyme involved in the metabolism of polyunsaturated fatty acids, specifically anti-inflammatory epoxy fatty acids (EpFAs). By converting EpFAs into less active forms, sEH promotes inflammation. Preclinical data using knock-out and overexpression of the Ephx2 gene have demonstrated its key role in the development and progression of symptoms in various disease models. Inhibition of sEH increases EpFAs, thereby enhancing their anti-inflammatory effects and reducing the levels of pro-inflammatory mediators. Numerous preclinical studies suggest that sEH inhibitors show promise in reducing inflammation and its related symptoms across various diseases, highlighting their therapeutic potential. This chapter reviews the role of sEH in the development and progression of various disorders including psychiatric disorders (depression, schizophrenia, autism spectrum disorder), neurological disorders (Alzheimer's disease, Parkinson's disease, brain injury), and pain.

In the current scenario of pandemics, drug discovery and design have undergone a significant transformation due to the integration of advanced computational methodologies. These methodologies utilize sophisticated algorithms, machine learning, artificial intelligence, and high-performance computing to expedite the drug development process, enhances accuracy, and reduces costs. Machine learning and AI have revolutionized predictive modeling, virtual screening, and de novo drug design, allowing for the identification and optimization of novel compounds with desirable properties. Molecular dynamics simulations provide a detailed insight into protein-ligand interactions and conformational changes, facilitating an understanding of drug efficacy at the atomic level. Quantum mechanics/molecular mechanics methods offer precise predictions of binding energies and reaction mechanisms, while structure-based drug design employs docking studies and fragment-based design to improve drug-receptor binding affinities. Network pharmacology and systems biology approaches analyze polypharmacology and biological networks to identify novel drug targets and understand complex interactions. Cheminformatics explores vast chemical spaces and employs data mining to find patterns in large datasets. Computational toxicology predicts adverse effects early in development, reducing reliance on animal testing. Bioinformatics integrates genomic, proteomic, and metabolomics data to discover biomarkers and understand genetic variations affecting drug response. Lastly, cloud computing and big data technologies facilitate high-throughput screening and comprehensive data analysis. Collectively, these computational innovations are driving a paradigm shift in drug discovery and design, making it more efficient, accurate, and cost-effective.

Structure-based drug design (SBDD) and molecular docking have revolutionized drug discovery by providing effective strategies for identifying and optimizing therapeutic agents. This review highlights the principles and methodologies of SBDD, which uses high-resolution structural data of biological targets to design drugs with enhanced selectivity and efficacy. Techniques like nuclear magnetic resonance (NMR) spectroscopy, cryo-electron microscopy (cryo-EM), and X-ray crystallography are key in providing the structural information necessary for SBDD. Molecular docking, a crucial component of modern drug discovery, simulates interactions between drug candidates and biological targets. By predicting how a ligand fits into a receptor's binding site, researchers can assess the strength and nature of these interactions, guiding compound selection. Advances in molecular docking have incorporated machine learning to improve scoring functions and prediction accuracy. Docking studies include search algorithms, scoring functions, and binding site identification to predict the optimal orientation of a ligand when bound to a protein. Despite its widespread use, molecular docking has limitations, such as challenges in achieving high prediction accuracy, modeling protein flexibility, and accounting for solvation effects. Improvements in computational power and the integration of machine learning techniques are addressing these issues. This review emphasizes the importance of ongoing innovation and interdisciplinary collaboration in enhancing molecular docking and its role in discovering novel therapies.

NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome, a pivotal regulator of the innate immune system, orchestrates inflammatory responses implicated in neurodegenerative and inflammatory diseases. Over the past 20 years, the exploration of NLRP3 activation pathways has advanced significantly. Upon NLRP3 activation, it initiates the formation of a cytosolic multiprotein complex known as the inflammasome. This complex activates caspase-1, which then processes proinflammatory cytokines IL-1β and IL-18 and leads to gasdermin-mediated cell death, pyroptosis. Structural insights into NLRP3 inflammasome assembly and caspase-1 activation have spurred development of novel small molecule inhibitors targeting this pathway, aiming to mitigate excessive inflammation without compromising immune surveillance. The initial NLRP3 inhibitor reported was glyburide, an FDA-approved antidiabetic drug of the sulfonylurea class, which was found to inhibit the release of IL-1β induced by stimuli in human monocytes and murine macrophages. Subsequently, MCC950 (also known as CRID3), a direct NLRP3 inhibitor, was discovered. While showing promising results in preclinical and clinical trials for treating diseases, higher doses of MCC950 led to elevated transaminase levels and hepatotoxicity concerns. Recent studies using MCC950 as a research tool have prompted the development of safer and more effective NLRP3 inhibitors, including a series of compounds currently undergoing clinical trials, highlighting the potential of NLRP3 inhibitors in attenuating disease progression and improving therapeutic outcomes. In this chapter, we delve into the latest progress in understanding the mechanism of NLRP3 inflammasome activation and its roles in the pathophysiology of neurological diseases. We also summarize recent development of small molecule NLRP3 inhibitors along with the associated obstacles and concerns.