Molecular Diversity最新文献

Photodynamic therapy (PDT) has received much attention in cancer treatment because of its low toxicity and side effects. In this study, we successfully synthesized 14 novel porphyrin-formononetin derivatives. In reactive oxygen species detection experiments, the target compounds 4a-6d caused a significant decrease in the fluorescence intensity of DPBF compared with the porphyrin parent and formononetin feedstock after illumination, and it was found that the target compound had a higher ROS quantum yield, among which the quantum yield of compound 6c was higher. In the in vitro anti-tumor activity assay, the target compounds 4a-6d exhibited a certain degree of growth inhibition against six cancer cells (A549, MDA-MB-231, HCT-116, HGC-27, DU145, and TCCSUP) under light conditions, whereas the cytotoxicity of the target compounds against the normal cells H9c2 was less. The results of the scratch assay showed that 6c could inhibit the growth of tumor cells by inhibiting the migration of DU145 cells. The experimental results indicate that the target compounds achieve the synergistic effect of PDT and chemotherapy.

The overexpression of the Aldehyde Dehydrogenases 1A subfamily (ALDH1As) in various diseases, particularly in cancer, has made it an important target for therapeutic applications. Interestingly, the 1A1 isoenzyme plays a role in tumor initiation and progression, being identified as a biomarker for cancer stem cells. However, although promising, current ALDH1A1 inhibitors suffer from a lack of isoform selectivity and off-target toxicity. This study aims to address these limitations by developing a new class of ALDH1A1-selective inhibitors. By leveraging structural analogies with Isatin-based ALDH1A1 inhibitors, we designed compounds containing a dihydrobenzo[4,5]imidazo[2,1-c][1,2,4]triazine-3,4-dione (BITD) core, that emerged from a repositioning approach. Using a microwave-assisted protocol, a small library of derivatives was synthesized, and enzymatic assays highlighted a promising isoform specificity for ALDH1A1 among ALDH1As, with the best-in-class compound 5, showing an inhibition of the enzyme activity of 86% for ALDH1A1 and no inhibition for 1A2 and 1A3 isoenzymes. In silico studies further elucidated the binding mode of 5, providing a rational basis for the observed selectivity. These findings represent a promising strategy for the development of more selective ALDH1A1 inhibitors, laying the foundation for further optimization processes.

Farnesoid X receptor (FXR) is a key regulator of bile acid, lipid, and glucose homeostasis, making it a promising target for treating metabolic diseases. FXR antagonists have shown therapeutic potential in cholestasis, metabolic disorders, and certain cancers, while clinically approved FXR antagonists remain unavailable and underrepresented in current treatment strategies. To address this, we developed deep learning models for predicting FXR antagonistic activity (ANTCL) and toxicity (TOXCL). Screening 217,345 compounds from the HMDB database identified eleven human metabolite candidates with significant FXR binding potential. Molecular dynamics simulations and binding free energy calculations revealed five more stable complexes compared to the reference compound Gly-MCA, with HMDB0253354 (Fulvestrant) and HMDB0242367 (ZM 189154) standing out for their binding free energies. Hydrophobic interactions, particularly involving residues MET328, PHE329, and ALA291, contributed to their stability. These results demonstrate the effectiveness of deep learning in FXR antagonist discovery and highlight the potential of HMDB0253354 and HMDB0242367 as promising candidates for metabolic disease treatment.

Organoboron compounds play a pivotal role in diverse scientific disciplines, including chemistry, materials science, energy research, and medicinal chemistry. In recent years, research efforts have predominantly focused on 1,2-metallate migrations of tetracoordinate boronate complexes, while remote migrations, particularly 1,n-metallate migrations (n > 2), remain challenging due to their inherent complexity. This comprehensive review systematically examines seminal contributions to the field of 1,n-metallate migration reactions (n > 2). Our critical analysis reveals that progress in this domain has been fundamentally driven by the strategic design and synthesis of novel tetracoordinate boron complexes, with a notable evolution from conventional O-B coordination motifs to more sophisticated C-B-bonded architectures. Recent methodological advancements have further expanded the structural diversity and mechanistic understanding of these transformations. Although the number of reported cases remains limited and the research landscape is somewhat fragmented, the existing systems underscore the significance of these migration reactions, drawing considerable attention to this area and inspiring further exploration.

Glycogen synthase kinase-3 beta, GSK-3β, is one of the most common targets for cancer treatment. Inhibiting the biological activity of the enzyme can lead to the prevention of cancer development. Especially, estimating a new inhibitor for preventing GSK-3β by using natural compounds is of great interest. In this context, the marine compounds were investigated for their ligand-binding affinity to GSK-3β via atomistic simulations. The compounds, including xanalteric acid I, chaunolidone A, macrolactin V, and aspergiolide A, were suggested that can inhibit GSK-3β via molecular docking and steered-MD simulations. Moreover, the potency of these compounds was also confirmed via the perturbation simulations. Furthermore, the toxicity prediction also indicates that these compounds would adopt less toxicity. Therefore, it may be argued that four compounds can play as potential inhibitors preventing GSK-3β. In addition, the residues including Ile62, Val135, Pro136, Arg141, Lys183, Gln185, Asn186, and Asp200 play a crucial role in the GSK-3β binding process.

The process of various virus entry into host cells, including SARS-CoV-2, is mediated by clathrin-mediated endocytosis (CME). AP-2 plays a crucial role in this process by recognizing membrane receptors and binding with clathrin, facilitating the formation of clathrin-coated vesicles and promoting CME. AAK1 catalyzes the phosphorylation of AP2M1 subunit at Thr156. Therefore, suppressing AAK1 activity can hinder virus invasion by blocking CME. indicating that AAK1 could be a potential target for developing novel antiviral drugs against SARS-CoV-2. In this study, we present a series of novel AAK1 inhibitors based on previously reported AAK1 inhibitors. Drug design was carried out by fusing the 1H-indazole scaffold of SGC-AAK1-1 with pharmacophore groups of compound 6, and further optimized with the assistance of molecular docking. Among the 42 compounds novelly synthesized, compounds 9i, 9s, 11f and 11l exhibited comparable antiviral activity against SARS-CoV-2 infection compared to reference compound 6 at the concentration of 3 μM. Particularly, 11f showed almost no cytotoxicity at all tested concentrations. Additionally, 11f exhibited favorable predictive pharmacokinetic properties. These findings support the potential of 11f as a lead compound for developing antiviral drugs targeting SARS-CoV-2 infection, as well as potentially other viruses which are dependent on the CME process to enter host cells. In summary, we have expanded the structural types of AAK1 inhibitors and successfully obtained effective AAK1 inhibitors with antiviral capabilities.

The TIGIT-PVR signalling pathway is a key mechanism of tumour immune evasion, making it an attractive target for cancer immunotherapy. Despite the recent advances in anti-TIGIT antibodies, monoclonal antibody-based therapeutics present significant challenges because of their immunogenicity and immune-related side effects. This study presents a new path involving natural compounds as potential small molecule inhibitors of TIGIT, providing a possible alternative to antibodies in cancer immunotherapy. Through a comprehensive in silico workflow combining structure-based virtual screening, ADMET analysis, Molecular docking and molecular dynamics simulations, six promising candidates, mostly of bacterial origin, were identified: Neomycin K, 4'-Deoxybutirosin A, 5-Glucosyl-neamine, S-11-A, 12-carbamoylstreptothricin E acid, and Zwittermicin A. These candidates demonstrated favourable binding energies, stable interactions, and the capacity to block TIGIT-PVR signalling. The compounds can potentially compete with PVR to bind to TIGIT, limiting the formation of the TIGIT-PVR complex, which typically activates an inhibitory cascade in T cells and NK cells, reducing their anti-tumour activity. By disrupting this interaction, the identified compounds have the potential to stimulate T cell and NK cell responses against cancer cells. Such natural compounds potentially provide better tissue penetration and reduced immunogenicity compared to conventional antibody therapies. The discovery of bacterial-derived compounds as TIGIT inhibitors presents a new direction in the investigation of microbial metabolites for cancer immunotherapy. This strategy not only identifies a new class of TIGIT inhibitors but also provides a robust computational framework for discovering and characterizing small molecule immune checkpoint inhibitors, paving the way for subsequent experimental validation to explore their efficacy in restoring anti-tumour immune responses and improving clinical outcomes for cancer patients.

A series of novel 2-trifluoromethyl-4-aminoquinazoline derivatives were designed and synthesized, and their antitumor activities were evaluated. Among them, several target compounds exhibited nanomolar inhibitory activities against K562 and LNCaP. Meanwhile, the results of in vitro and in vivo activity evaluation showed that compound 9 had the significant selective anticancer activity and the lower toxicity. The target prediction and pathway analysis showed that the mechanism of compound 9 on the proliferation inhibitory activity of K562 and PC3 cells may be via inhibiting werner helicase (WRN) activity and affecting DNA damage repair. As expected, biological evaluation showed that compound 9 bind to WRN, significantly downregulated the expression of WRN, inhibited the MDM2/p53 pathway, to render the damaged DNA unrepaired, eventually causing mitotic arrest and cell death. Our findings provide a foundation for further research of trifluoromethyl-quinazoline-4-amines as WRN-dependent anticancer agents that targeting DNA damage repair pathway.

An ongoing escalation in malaria cases is a critical global health issue and also a leading source of increased mortality rates. Infected mosquitos spread the Plasmodium parasite-based disease,. Recent advancements in antimalarial drug discovery have focused on developing novel molecules with unique mechanisms of action to combat this growing resistance. This review focuses on the latest findings in synthesizing hybrid antimalarial compounds, which combine different pharmacophores to enhance therapeutic efficacy, resulting in compounds that exhibit potent action against both resistant/sensitive strains of chloroquine, quinine, and artemisinin. Notably, the incorporation of diverse scaffolds such as 1,2,4-trioxanes, halogenated quinolines, pyrazoline, and sulfonamides has led to promising candidates that not only demonstrate high potency but also favorable pharmacokinetic profiles for drug development, the exploration of novel targets. This review highlights the progress (2019-2024) made in identifying and synthesizing hybrid antimalarial molecules while addressing existing challenges in the field.

Antiviral peptides (AVPs) represent a novel and promising therapeutic alternative to conventional antiviral treatments, due to their broad-spectrum activity, high specificity, and low toxicity. The emergence of zoonotic viruses such as Zika, Ebola, and SARS-CoV-2 have accelerated AVP research, driven by advancements in data availability and artificial intelligence (AI). This review focuses on the development of AVP databases, their physicochemical properties, and predictive tools utilizing machine learning for AVP discovery. Machine learning plays a pivotal role in advancing and developing antiviral peptides and peptidomimetics, particularly through the development of specialized databases such as DRAVP, AVPdb, and DBAASP. These resources facilitate AVP characterization but face limitations, including small datasets, incomplete annotations, and inadequate integration with multi-omics data.The antiviral efficacy of AVPs is closely linked to their physicochemical properties, such as hydrophobicity and amphipathic α-helical structures, which enable viral membrane disruption and specific target interactions. Computational prediction tools employing machine learning and deep learning have significantly advanced AVP discovery. However, challenges like overfitting, limited experimental validation, and a lack of mechanistic insights hinder clinical translation.Future advancements should focus on improved validation frameworks, integration of in vivo data, and the development of interpretable models to elucidate AVP mechanisms. Expanding predictive models to address multi-target interactions and incorporating complex biological environments will be crucial for translating AVPs into effective clinical therapies.