Transient receptor potential vanilloid (TRPV) 4 is involved in signaling pathways specifically mediating pain and inflammation, making it a promising target for the treatment of various painful and inflammatory conditions. However, only one drug candidate targeting TRPV4 has entered the clinical trials. To identify potential TRPV4 inhibitors for drug development, we screened a library of ion channel-modulating compounds using both structure- and ligand-based virtual screening approaches. Since a high-resolution experimental structure of the human TRPV4 (hTRPV4) was not available during this study, we used a comparative model of hTRPV4 for the structure-based screening by molecular docking. The ligand-based virtual screening was performed using the pharmacophoric features of two known TRPV4 antagonists. Five potential hits were selected based on either the binding stability or the pharmacophore match, and their effect on hTRPV4 was tested using a FLIPR assay. All tested compounds inhibited hTRPV4 at 30 µM, with compound Z1213735368 showing an IC of 8 µM at a concentration of 10 µM. Furthermore, natural stilbenoids, known to modulate other TRP channels, were evaluated for their hTRPV4 binding and inhibitory potential. The findings provide insight into the structural determinants of hTRPV4 modulation and may facilitate further efforts in developing therapeutic hTRPV4 ligands.
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http://dx.doi.org/10.3390/molecules30010100 | DOI Listing |
Molecules
December 2024
Structural Bioinformatics Laboratory, Faculty of Science and Engineering, Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland.
Transient receptor potential vanilloid (TRPV) 4 is involved in signaling pathways specifically mediating pain and inflammation, making it a promising target for the treatment of various painful and inflammatory conditions. However, only one drug candidate targeting TRPV4 has entered the clinical trials. To identify potential TRPV4 inhibitors for drug development, we screened a library of ion channel-modulating compounds using both structure- and ligand-based virtual screening approaches.
View Article and Find Full Text PDFEur J Med Chem
January 2025
Department of Pharmacy, University of Naples Federico II, 80131 Naples, Italy. Electronic address:
Breast cancer, a leading cause of cancer-related mortality in women, is characterized by genomic instability and aberrant gene expression, often influenced by noncanonical nucleic acid structures such as G-quadruplexes (G4s). These structures, commonly found in the promoter regions and 5'-untranslated RNA sequences of several oncogenes, play crucial roles in regulating transcription and translation. Stabilizing these G4 structures offers a promising therapeutic strategy for targeting key oncogenic pathways.
View Article and Find Full Text PDFCurr Pharm Des
January 2025
Department of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, P.O Box 13140, Amman 11942, Jordan.
Introduction: The emergence of SARS-CoV-2 and the COVID-19 pandemic highlighted the urgent need for novel antiviral therapies. The main protease (Mpro) of SARS-CoV-2 is a key enzyme in viral replication and a promising therapeutic target.
Methods: This study employed virtual screening approaches to identify potential Mpro inhibitors, leveraging both structure- and ligand-based methods.
In Vivo
December 2024
Doctoral School of Biomedical Sciences, University of Oradea, Oradea, Romania.
Background/aim: Alzheimer's disease is a complex, incurable to date, multifactorial disease, which suggests the need for continued development of pharmacotherapy.
Materials And Methods: A comprehensive literature search was conducted to identify known ligands with anticholinesterase activity, resulting in the discovery of over 100 alkaloids that are also available in the PubChem database. Subsequently, the ligands underwent molecular docking to evaluate their affinity for the target enzyme.
Brief Bioinform
November 2024
Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing 100020, China.
Drug resistance in Mycobacterium tuberculosis (Mtb) is a significant challenge in the control and treatment of tuberculosis, making efforts to combat the spread of this global health burden more difficult. To accelerate anti-tuberculosis drug discovery, repurposing clinically approved or investigational drugs for the treatment of tuberculosis by computational methods has become an attractive strategy. In this study, we developed a virtual screening workflow that combines multiple machine learning and deep learning models, and 11 576 compounds extracted from the DrugBank database were screened against Mtb.
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