Current topics in medicinal chemistry最新文献

Objective: NLRP3 inflammasomes are considered to be key factors in the pathogenesis of Acute Liver Failure (ALF). Some Traditional Chinese Medicines (TCMs) have shown protective and therapeutic effects against ALF by inhibiting NLRP3 inflammasomes. However, the inhibitory effects of most TCMs on ALF remain to be further elucidated. This study aimed to screen potential herbs that can treat ALF based on the inhibition of NLRP3 inflammasomes.

Methods: Initially, we constructed the target set for 502 herbs. Subsequently, based on the target set and the gene set related to the NLRP3 inflammasome, using the ssGSEA algorithm, we evaluated herb scores and NLRP3 scores in the ALF expression matrix and performed a preliminary herb screening based on score correlations. Through bioinformatics approaches, we identified the key targets for candidate herbs and determined core herbs based on the herb-compound-target network. Furthermore, molecular docking and molecular biology methods validated the screening results of the herbs.

Results: A total of 18 crucial targets associated with the inhibition of the NLRP3 inflammasome were identified, which included ALDH2, HMOX1, and VEGFA. Subsequently, based on these key targets, a set of 10 primary herbs was chosen, notably Qinghao, Duzhong, and Gouteng. Moreover, the results were verified through molecular docking and molecular dynamic simulation.

Conclusion: Ten key herbs have been identified as potential inhibitors of the NLRP3 inflammasome, offering insights into ALF therapy for drug development.

Numerous studies suggest that common genetic and epigenetic factors such as p53, histone deacetylase (HDAC), brain-derived neurotrophic factor (BDNF), the (Ataxia Telangiectasia mutated) ATM gene, cyclin-dependent kinase 5 (CDK5), glycogen synthase kinase 3 (GSK3) and altered expression of microRNA (miRNA) play a crucial role in cancer and neurodegeneration. As there is growing evidence that epigenetic aberrations in cancer and neurological diseases lead to complex pathophysiological changes, the simultaneous targeting of epigenetic and other related pathways by dual-target inhibitors may contribute to the discovery of more effective and personalized therapeutic options. Computer-Aided Drug Design (CADD) provides comprehensive bioinformatic, chemoinformatic, and chemometric approaches for the design of novel chemotypes of epigenetic dual-target inhibitors, enabling efficient discovery of new drug candidates for innovative treatments of these multifactorial diseases. The detailed anticancer mechanisms by which the epigenetic dual-target inhibitors alter metastatic and tumorigenic properties, influence the tumor microenvironment, or regulate the immune response are also presented and discussed in the review. To improve our understanding of the pathogenesis of cancer and neurodegeneration, this review discusses novel therapeutic agents targeting different molecular mechanisms involved in these multifactorial diseases.

Filariasis is one of the oldest, most dangerous, debilitating, disfiguring diseases and often ignores tropical disorders. It presents with a range of clinical symptoms, a low death rate, and a high morbidity rate, which contributes to social discrimination. This condition has major effects on people's socioeconomic circumstances. This illness is carried by mosquitoes that have spread malaria. Lymphatic filariasis, caused by Wuchereria bancrofti, Brugia malayi, and Brugia timori, is a crippling illness with serious social and economic consequences. The infection persisted despite therapy with conventional antifilarial medications such as diethylcarbamazine (DEC), albendazole, and ivermectin, which are mostly microfilaricides. Current treatments (ivermectin, diethylcarbamazine, and albendazole) have limited effectiveness against adult parasites and produce side effects; therefore, innovative antifilarial medications are urgently required. Hence, macrofilaricides, embryostatic agents, and improved microfilaricides are required. The following article discusses the typical synthetic methodologies established for antifilarial activity as well as their marketed pharmaceuticals, which will help researchers, medicinal chemists, and pharmaceutical scientists to develop new and effective antifilarial therapies. This review can help to identify new lead compounds and optimize existing commercial medications to improve their therapeutic efficacy. The majority of the studies addressed in this review concern the forms of filariasis, parasite life cycle, symptoms, medications used to treat filariasis, synthetic schemes, SAR, and results from the reported research.

The c-Met receptor, a pivotal player in oncogenesis and tumor progression, has become a compelling target for anticancer drug development. This review explores the intricate landscape of Structure-Activity Relationship [SAR] studies and molecular binding analyses performed on c-Met inhibitors. Through a comprehensive examination of various chemical scaffolds and modifications, SAR investigations have elucidated critical molecular features essential for the potent inhibition of c-Met activity. Additionally, molecular docking studies have provided invaluable insights into how c-Met inhibitors interact with their target receptor, facilitating the rational design of novel compounds with enhanced efficacy and selectivity. This review highlights key findings from recent SAR and docking studies, particularly focusing on the structural determinants that govern inhibition potency and selectivity. Furthermore, the integration of computational methodologies with experimental approaches has accelerated the discovery and optimization of c-Met inhibitors, fostering the advancement of promising candidates for clinical applications. Overall, this review underscores the pivotal role of SAR and molecular docking studies in advancing our understanding of c-Met inhibition and guiding the rational design of next-generation anticancer agents targeting this pathway.

Diabetes mellitus, characterized as a chronic metabolic disorder or a polygenic syndrome; is increasing at a very fast pace among every group of the population worldwide. It arises due to the inability of the body to produce enough insulin (the hormone responsible for controlling blood sugar levels) or inability to utilize the insulin, leading to hyperglycaemic condition, which, if left uncontrolled gives rise to chronic microvascular and macrovascular complications like retinopathy, neuropathy, nephropathy, coronary artery disease, cognitive impairment, etc. Several therapeutic approaches are available for the treatment of diabetes; among which dipeptidyl peptidase (DPP-IV) inhibitors (gliptins) hold a significant place. DPP-IV is a multifunctional enzyme or a serine exopeptidase that plays an imperative role in cleaving bioactive molecules. DPP-IV causes the breakdown of incretin hormone (GLP-1: Glucagon-like peptide 1 and GIP: Glucose-dependent insulinotropic peptide) that is essential for controlling glycaemic levels in the body. Inhibition of DPP-IV enzyme (DPP-IV inhibitors: Sitagliptin, Saxagliptin, Linagliptin, Alogliptin) prevents this breakdown, thereby controlling blood glucose levels and saving the patients from deleterious effects of prolonged hyperglycaemic conditions. Triazole-based DPP-IV inhibitors are a significant class of drugs used to treat Type 2 diabetes mellitus in a dose-dependent manner. Clinical trials have demonstrated their efficacy as monotherapy or in combination with other antidiabetic agents. This review highlights the molecular docking studies and structure-activity relationship of potential synthetic derivatives that may act as lead molecules for future drug discovery and yield drug molecules with enhanced efficacy, potency and reduced toxicity profile.

Introduction: Cancer is a leading cause of death worldwide, driving the urgent need for new and effective treatments. Benzimidazole and pyrazole derivatives have gained attention for their potential as anticancer agents due to their diverse biological activities. The development of resistance in cancer cells, toxicity concerns, and inconsistent efficacy across different types of cancer are a few of the challenges. To overcome these challenges, optimisation of these nuclei using the structure-activity relationships is necessary.

Objective: This review aimed to examine various benzimidazole, pyrazole, and their hybrid derivatives by focusing on their structure-activity relationships (SAR) as anticancer agents. Results of the most potent and least potent benzimidazole, pyrazole compounds, and their hybrid derivatives published by researchers were compiled.

Method: The findings of different researchers working on benzimidazole and pyrazole nuclei were reviewed and analysed for different targets and cell lines. Moreover, substitutions on different positions of pyrazole, benzimidazole, and their hybrid were summarised to derive an optimised pharmacophore.

Result: Based on our analysis of existing studies, we anticipate that this review will guide researchers in creating potent pyrazole, benzimidazole, and hybrid derivatives crucial for combating cancer effectively.

Conclusion: Structure-Activity Relationship (SAR) studies can help in developing pyrazolebenzimidazole hybrids that are more powerful and selective in targeting specific aspects of cancer.

Hydroxamic acids (HAs) are chemical compounds characterized by the general structure RCONR'OH, where R and R' can denote hydrogen, aryl, or alkyl groups. Recognized for their exceptional chelating capabilities, HAs can form mono or bidentate complexes through oxygen and nitrogen atoms, rendering them remarkably versatile. These distinctive structural attributes have paved the way for a broad spectrum of medicinal applications for HAs, among which their pivotal role as inhibitors of essential Ni(II) and Zn(II)-containing metalloenzymes. In 1962, a significant breakthrough occurred when Kobashi and colleagues identified hydroxamic acids (HAs) as potent urease inhibitors. Subsequent research has increasingly underscored their capability in combatting infections induced by ureolytic microorganisms, including Helicobacter pylori and Proteus mirabilis. However, comprehensive reviews exploring their potential applications in treating infections caused by ureolytic microorganisms remain scarce in the scientific literature. Thus, this minireview aims to bridge this gap by offering a systematic exploration of the subject. Furthermore, it seeks to explore the significant advancements in obtaining hydroxamic acid derivatives through environmentally sustainable methodologies.

Amperometric biosensors have emerged as a cutting-edge technology in clinical diagnostics, thanks to their high level of sensitivity, rapid analytical results, compact size, and ability to monitor health parameters non-invasively and continuously using flexible and wearable sensors. This review explores the latest developments in the field of amperometric biosensing for medical applications. It discusses the materials used to construct these sensors and pays particular attention to biosensors designed to measure glucose, lactate, cholesterol, urea, and uric acid levels. The review also addresses the technological limitations and drawbacks of these devices. Furthermore, it presents the current status and identifies future trends in the development of flexible, wearable biosensors capable of providing continuous monitoring of a patient's health status.

Rapid growth in nanotechnology, also known as 21st-century technology, is occurring in response to the increasing diversity of diseases. The development of safe and effective drug delivery methods to enhance bioavailability is of paramount importance. Researchers have focused on creating safe, cost-effective, and environmentally friendly nanoparticle construction processes. Natural polysaccharides, a type of multifaceted polymer with a wide range of applications and advantages, are particularly well suited for nanoparticle formulations, as they can mitigate the adverse consequences of synthetic nanoparticle formulations and promote sustainability. This review summarizes various sources of natural-based polysaccharides and polyacrylamide-based nanomaterials in nanoparticle preparation. Additionally, it discusses the use of natural polysaccharides in formulations beyond nanotechnology, highlighting their importance in green synthesis and different preparation methods.

Background: Epilepsy remains the most common and chronic disorder demanding longterm management. The impact of epilepsy disease is a cause of great concern and has resulted in efforts to develop treatment for epilepsy. It occurs due to an increase in neuronal excitability produced by changes affecting the voltage-dependent properties of Voltage-gated Sodium Channels (VGSCs).

Materials and methods: Weka, a popular suite for machine learning techniques, was used on a dataset comprising 1781 chemical compounds, showing inhibition activity for sodium channel protein IX alpha subunit. After the analysis of the dataset obtained from ChEMBL, molecular fingerprints were computed for the molecules by the ChemDes server. Different classifiers available in the Weka software were explored to find out the algorithm that could be more suitable for the dataset or produce the highest accuracy for the classification of molecules as active or inactive.

Results: In this work, a comprehensive comparison of different classifiers in the Weka suite for the prediction of active, inactive, and intermediate classes of molecules showing inhibition against human NaV1.7 protein was made. The prediction accuracy of these classifiers was assessed based on performance measures, including accuracy, Root Mean Squared Error (RMSE), Receiver Operating Characteristic (ROC), precision, Mathews Correlation Coefficient (MCC), recall, and Fmeasure. The comparison of results for model performance demonstrated that the OneR classifier performed best over others when validated using percentage split, cross-validation, and supplied test methods. J48 and Bagging also performed equally well in the prediction of different classes with an MCC value of 1, ROC area equal to 1, and RMSE close to 0.

Conclusion: Machine Learning (ML) tools provide a fast, reliable, and cost-effective approach required to identify or predict inhibitory molecules for the treatment of a disease. This study shows that the ML methods, particularly OneR, J48, and Bagging have the ability to identify active and inactive classes of compounds for the human NaV1.7 protein target. Such predictive models may provide a reliable and time-saving approach that can aid in the design of potential inhibitors for the treatment of epilepsy disease.