Molecular Diversity最新文献

Hsp90, or heat shock protein 90, a well-preserved molecular chaperone that is essential for the coordination of numerous biological pathways and cellular processes. Hsp90 is a molecular chaperone, which promises a target for cancer treatment. Hsp90 inhibitors are a class of drugs that have been extensively studied in preclinical models and demonstrated promise in treating a variety of illnesses, particularly cancer. Hsp90 inhibitors, however, have been encountered a number of challenges during the clinical development process, such as low efficacy, toxicity, and drug resistance. This literature survey emphasizes the importance of HSP90 inhibitors incorporating diverse heterocyclic rings, such as pyrazole, indole, pyrimidine, triazole, and thioquinazoline, which have exhibited promising anticancer activity. This review covers several parameters, including kinetic investigation, binding interactions, IC₅₀ value, structure-activity relationship, and molecular docking studies of the most potent compound. There are several heterocyclic small molecules under investigation in clinical studies, such as AUY922, SNX-5422, STA-9090, and others. This review also contained a patent of HSP90 inhibitors, which showed greater effectiveness. Therefore, the main objective of this paper is to summarize all recent developments in the creation of anticancer medications that target HSP90 inhibitors in order to treat anticancer disease.

The global impact of emerging and re-emerging viral agents during epidemics and pandemics leads to serious health and economic burdens. Among the major emerging or re-emerging viruses include SARS-CoV-2, Ebola virus (EBOV), Monkeypox virus (Mpox), Hepatitis viruses, Zika virus, Avian flu, Influenza virus, Chikungunya virus (CHIKV), Dengue fever virus (DENV), West Nile virus, Rhabdovirus, Sandfly fever virus, Crimean-Congo hemorrhagic fever (CCHF) virus, and Rift Valley fever virus (RVFV). A comprehensive literature search was performed to identify existing studies, clinical trials, and reviews that discuss drug repositioning strategies for the treatment of emerging and re-emerging viral infections using databases, such as PubMed, Scholar Google, Scopus, and Web of Science. By utilizing drug repositioning, pharmaceutical companies can take advantage of a cost-effective, accelerated, and effective strategy, which in turn leads to the discovery of innovative treatment options for patients. In light of antiviral drug resistance and the high costs of developing novel antivirals, drug repositioning holds great promise for more rapid substitution of approved drugs. Main repositioned drugs have included chloroquine, ivermectin, dexamethasone, Baricitinib, tocilizumab, Mab114 (Ebanga™), ZMapp (pharming), Artesunate, imiquimod, saquinavir, capmatinib, naldemedine, Trametinib, statins, celecoxib, naproxen, metformin, ruxolitinib, nitazoxanide, gemcitabine, Dorzolamide, Midodrine, Diltiazem, zinc acetate, suramin, 5-fluorouracil, quinine, minocycline, trifluoperazine, paracetamol, berbamine, Nifedipine, and chlorpromazine. This succinct review will delve into the topic of repositioned drugs that have been utilized to combat emerging and re-emerging viral pathogens.

The potential downsides for the present treatment for psoriasis are drug resistance, reduced efficacy, risk of mental episodes, and drug interactions. Hence, this study aims to discover a new drug for psoriasis by considering global research efforts and exploring underrepresented chemical space regions. The objective was to identify novel PDE4D inhibitors from the dark chemical matter (DCM) database for treating psoriasis. To address this we have coupled molecular docking and pharmacophore screening with molecular dynamics (MD) to identify hit molecules. Additionally, pharmacokinetics optimization was performed using machine learning and artificial intelligence which are key parts of drug discovery and development processes. The 139,353 DCM molecules were evaluated for their binding mode and interaction with critical residues such as GLN369, ILE336, PHE340, and PHE372 of the phosphodiesterase-4D (PDE4D) enzyme. Here, 15 hits were obtained through successive virtual screening procedures and all the 15 molecules were subjected to MD simulations for hit identification. In the MD studies, a stable root mean square deviation (RMSD) and ligand-protein interactions were found with four molecules, namely 027230, 060628, 060576, and 085881. The ligand 085881 was found promising because it inhibits LPS-induced IL-6 and TNF-alpha secretion from THP-1 cells with IC₅₀ of 18.41 μM and 34.43 μM, respectively. In vivo erythema grading showed that 085881 possesses mild to moderate anti-psoriatic action. This study demonstrates the effective use of computational techniques to discover novel PDE4D inhibitors and provides insight into their therapeutic potential for treating inflammatory diseases such as psoriasis.

In previous studies, we discovered YZD-7082B, a selective estrogen receptor degrader (SERD) with excellent comprehensive properties. Here, we reported the development of an efficient multigram-scale synthetic process for YZD-7082 in 13 steps. The route featured a chiral resolution of a thiochroman intermediate with a unique cis-1,2-diaryl motif using a chiral amine and a mild reduction of amide using ZnEt₂/LiCl/(EtO)₃SiH system. This approach also overcomes the issues of high loadings of palladium catalysts and long reaction time. The developed process provided YZD-7082B with an HPLC purity of > 99.8% and ee of > 99%.

Biomedical knowledge graphs have emerged as powerful tools for drug discovery, but existing platforms often suffer from outdated information, limited accessibility, and insufficient integration of complex data. This study presents MedKG, a comprehensive and continuously updated knowledge graph designed to address these challenges in precision medicine and drug discovery. MedKG integrates data from 35 authoritative sources, encompassing 34 node types and 79 relationships. A Continuous Integration/Continuous Update pipeline ensures MedKG remains current, addressing a critical limitation of static knowledge bases. The integration of molecular embeddings enhances semantic analysis capabilities, bridging the gap between chemical structures and biological entities. To demonstrate MedKG's utility, a novel hybrid Relational Graph Convolutional Network for disease-drug link prediction, MedLINK was developed and used in case studies on clinical trial data for disease drug link prediction. Furthermore, a web-based application with user-friendly APIs and visualization tools was built, making MedKG accessible to both technical and non-technical users, which is freely available at http://pitools.niper.ac.in/medkg/.

This study addresses the urgent need for new drugs to combat multi-drug-resistant tuberculosis (MDR-TB). Focusing on MmpL3, a protein essential for mycobacterial cell wall synthesis, we designed and synthesised 50 new pyrazole-based amide derivatives. These compounds were then tested for their ability to inhibit the growth of various Mycobacterium tuberculosis (Mtb) strains, including both drug-susceptible and drug-resistant strains (resistant to isoniazid, rifampicin, or both). Two compounds, 15 and 35, emerged as potent inhibitors. They showed strong activity against both drug-susceptible and drug-resistant Mtb strains, with low minimum inhibitory concentration (MIC) values of 2 µg/mL and 2-4 µg/mL, respectively. Importantly, these compounds also demonstrated a high selectivity index, meaning they were significantly more toxic to Mtb cells than to human liver cells (HepG2). Compound 15 further proved to be bactericidal, effectively killing Mtb within six days. Interestingly, compounds 15 and 35 were inactive against lab-generated Mtb strains resistant to SQ109, a known MmpL3 inhibitor. This finding, supported by molecular docking, molecular dynamics simulations, and genetic analysis of the mmpl3 gene in the SQ109-resistant strains, strongly suggests that these novel compounds also target MmpL3. This research highlights the potential of pyrazole-based amides as a promising new class of anti-TB drugs. By targeting MmpL3, these compounds offer a novel mechanism of action to combat drug-resistant TB, potentially leading to improved treatment outcomes.

Neuraminidase (NA) is an essential enzyme located at the outer layer of the influenza virus and plays a key role in the release of virions from infected cells. The rising incidence of global epidemics has made the urgent need for effective antiviral medications an urgent public health priority. Furthermore, the emergence of resistance caused by specific mutations in the influenza viral genome exacerbates the challenges of antiviral therapy. In view of this, this study aims to identify and analyse possible inhibitors of NA from different subtypes of influenza viruses. Initially, a thorough search was conducted in the Protein Data Bank (PDB) to gather structures of NA proteins that were attached with oseltamivir, a widely recognized inhibitor of NA. Here, 36 PDB entries were found with NA-oseltamivir complexes which were studied to evaluate the diversity and mutations present in various subtypes. Finally, N1(H1N1) protein was selected that demonstrated low IC50 value of oseltamivir with mutation H275Y. In addition, the study utilized BiMODAL generative model to generate 1000 novel molecules with comparable structures to oseltamivir. A QSAR model, based on machine learning (ML), was built utilizing the ChEMBL database to improve the selection process of candidate inhibitors. These inhibitors were subsequently analysed by molecular docking and further the best hits compounds (compound_375, compound_106 and compound_597) were appended to make a bigger molecule (compound_106-375, compound_106-597, and compound_375-597) to fit into the binding pocket of protein. Further, triplicate molecular dynamics simulations lasting 100 ns to assess their effectiveness and binding stability showed that compound_106-375 had the most stable binding with the protein. Key residues, including Asn146, Ala138, and Tyr155, form critical interactions with the ligand, contributing to its stability. The investigation was enhanced by employing principal component analysis (PCA), free energy landscape (FEL), and binding free energy calculations. The total binding free energy (G_TOTAL) of - 169.62 kcal/mol suggests that the contact between compound_106-375 and the mutant N1 (H1N1) protein is thermodynamically favourable. This approach allowed for a thorough comprehension of the binding interactions and possible effectiveness of the discovered inhibitors. Overall, these findings demonstrate that compound_106-375 exhibits favourable binding characteristics and stability. Further experimental validation is required to confirm its efficacy against the H275Y mutant neuraminidase protein and its potential to overcome influenza drug resistance. However, compound_106-375 is suggested as a promising candidate for further development as a therapeutic agent against the mutant N1 (H1N1) protein. This finding will assist in drug development and to overcome the challenges associated with drug resistance in influenza strains.

Telomerase, a reverse transcriptase implicated in replicative immortality of cancers, remains a challenging target for therapeutic intervention due to its structural complexity and the absence of clinically approved small-molecule inhibitors. In this study, we explored drug repurposing as a pragmatic approach to address this gap, leveraging FDA-approved drugs to accelerate the identification of potential telomerase inhibitors. Using a structure-based drug discovery framework, we screened the DrugBank database through a previously validated pharmacophore model for the FVYL pocket in the hTERT thumb domain, the established binding site of BIBR1532. This was followed by molecular docking, pharmacokinetic filtering, and molecular dynamics (MD) simulations to evaluate the stability of protein-ligand complexes. Binding free energy calculations (MM-PBSA and MM-GBSA) were employed for cross-validation, identifying five promising candidates. Experimental validation using the Telomerase Repeat Amplification Protocol (TRAP) assay confirmed the inhibitory potential of Raltitrexed, showing significant inhibition with IC₅₀ 8.899 µM in comparison to control. Decomposition analysis and Structure-Activity Relationship (SAR) studies further offered insights into the binding mechanism, reinforcing the utility of the FVYL pocket as a druggable site. Raltitrexed's dual mechanism of action, targeting both telomerase and thymidylate synthase, underscores its potential as a versatile anticancer agent, suitable for combination therapies or standalone treatment. As the top lead, Raltitrexed demonstrates the potential of repurposed drugs in telomerase-targeted therapies, offering a time and cost-effective strategy for advancing its clinical development. The study also provides a robust framework for future drug development, addressing challenges in targeting telomerase for anticancer therapy.