In silico Prediction of Malvaviscus arboreus Metabolites and Green Synthesis of Silver Nanoparticles – Opportunities for Safer Anti-Bacterial and Anti-Cancer Precision Medicine

Afrah E Mohammed,1,* Sahar S Alghamdi,2,3 Ashwag Shami,1,* Rasha Saad Suliman,4 Kawther Aabed,1 Modhi O Alotaibi,1 Ishrat Rahman5 1Department of Biology, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia; 2Department of Pharmaceutical Sciences, College of Pharmacy, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; 3King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia; 4Department of Pharmacy, Fatima College of Health Sciences, Abu Dhabi, 3798, United Arab Emirates; 5Department of Basic Dental Sciences, College of Dentistry, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia*These authors contributed equally to this workCorrespondence: Ishrat Rahman, Department of Basic Dental Sciences, College of Dentistry, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia, Email imrahman@pnu.edu.saIntroduction: Biogenic silver nanoparticles (AgNPs) may be a feasible therapeutic option in the research and development towards selectively targeting specific cancers and microbial infections, lending a role in precision medicine. In-silico methods are a viable strategy to aid in drug discovery by identifying lead plant bioactive molecules for further wet lab and animal experiments.Methods: Green synthesis of M-AgNPs was performed using the aqueous extract from the Malvaviscus arboreus leaves, characterized using UV spectroscopy, FTIR, TEM, DLS, and EDS. In addition, Ampicillin conjugated M-AgNPs were also synthesized. The cytotoxic potential of the M-AgNPs was evaluated using the MTT assay on MDA-MB 231, MCF10A, and HCT116 cancer cell lines. The antimicrobial effects were determined using the agar well diffusion assay on methicillin-resistant S. aureus (MRSA) and S. mutans, E. coli, and Klebsiella pneumoniae. Additionally, LC-MS was used to identify the phytometabolites, and in silico techniques were app