Unraveling Cordia myxa’s anti-malarial potential: integrative insights from network pharmacology, molecular modeling, and machine learning

Autor: Yufei Miao, Wenkang Liu, Sarah Mohammed Saeed Alsallameh, Norah A. Albekairi, Ziyad Tariq Muhseen, Christopher J. Butch
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: BMC Infectious Diseases, Vol 24, Iss 1, Pp 1-20 (2024)
Druh dokumentu: article
ISSN: 1471-2334
DOI: 10.1186/s12879-024-10078-9
Popis: Abstract Malaria is a potentially fatal infective illness caused due to parasites that belong to the Plasmodium genus, which are transferred to humans with the help of the stings of affected female Anopheles mosquitoes, and it persists as a serious public wellness problem worldwide. Cordia myxa is a medicinal plant that possesses various medicinal characteristics like antimicrobial, anti-inflammation, antioxidant, and antidiabetic activities, which makes it an important natural resource for the therapy of different maladies in traditional medicine. In this investigation, a certain network pharmacology method has been utilized to identify the potent active components, possible targets as well as signaling pathways present in C. myxa in relation to malaria therapy. The active compounds were submitted to molecular docking approaches to validate their successful activity against the potential targets. The study concluded that three constituents named cosmosiin, stigmastanol, robinetin, and quercetin were highly active and could regulate the expression of Interleukin 6 (IL6) and Cysteine-aspartic acid protease 3 (CASP3), which may act as a potential therapeutic target for malaria treatment. These analyses are validated by molecular dynamics simulation which reflects on the overall structural stability of the intermolecular conformation and interactions. These results can also be witnessed in simulation-based trajectories binding free energies, which concluded the significant role of electrostatic and van der Waals energies in total intermolecular interactions. Finally, we utilized machine learning to predict the anti-malarial activity of C. myxa compounds, comparing them with approved drugs. Using the Chemprop model and MAIP predictions, we assessed ten compounds, revealing their potential as lead anti-malarial agents. This study establishes a groundwork for comprehending the function of the anti-malaria action of C. myxa.
Databáze: Directory of Open Access Journals
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