Exploration of Antituberculosis Potential of Ni(II) and Zn(II) Serine-Tyrosine Dithiocarbamate Complexes: Synthesis, Characterization, In Silico Profile, and In Vitro Evaluation
Abstract
Tuberculosis (TB) remains a global health challenge, with millions of new cases and deaths reported each year. The emergence of drug resistance and treatment side effects has driven the development of new therapeutic agents, including the use of dithiocarbamate metal complex compounds. This study aims to synthesize and characterize Ni(II) and Zn(II) complexes with serine-tyrosine dithiocarbamate ligands and evaluate their antituberculosis bioactivity. The method in this study involves the synthesis and characterization of Ni(II) and Zn(II) complexes with serine-tyrosine dithiocarbamate, including melting point determination, conductivity measurement, and spectroscopic analyses (UV-Vis, FT-IR, XRD, and SEM-EDS). Furthermore, the bioactivity of the synthesized metal complexes was evaluated using both in silico approaches (Lipinski's rule of 5, ADMET profiling, and molecular docking) and in vitro testing using the Lowenstein-Jensen method. The synthesis and characterization results show that the successfully synthesized metal complexes are solid, non-electrolyte, stable, and have typical characteristics of dithiocarbamate complexes. In silico analysis shows compliance with Lipinski’s rule and most ADMET parameters, indicating potential as oral drug candidates. Molecular docking revealed strong interactions, particularly for the Ni(II)Ser-Tyr Dtc complex with the highest binding score ( 95.0923 kJ/mol), exceeding INH ( 65.8232 kJ/mol). In vitro results confirmed antibacterial…
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.