Integrating network pharmacology and computational biology to propose Yiqi Sanjie formula's mechanisms in treating NSCLC: molecular docking, ADMET, and molecular dynamics simulation.

Abstract

Background: Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related deaths globally. Current treatments often do not fully meet efficacy and quality of life expectations. Traditional Chinese medicine (TCM), particularly the Yiqi Sanjie formula, shows promise but lacks clear mechanistic understanding. This study addresses this gap by investigating the therapeutic effects and underlying mechanisms of Yiqi Sanjie formula in NSCLC.

Methods: We utilized network pharmacology to identify potential NSCLC drug targets of the Yiqi Sanjie formula via the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Compounds with favorable oral bioavailability and drug-likeness scores were selected. Molecular docking was conducted using AutoDock Vina with structural data from the Protein Data Bank and PubChem. Molecular dynamics (MD) simulations were performed with Desmond Molecular Dynamics System, analyzing interactions up to 500 nanoseconds using the OPLS4 force field. ADMET predictions were executed using SwissADME and ADMETlab 2.0, assessing pharmacokinetic properties.

Results: Using network pharmacology tools, we performed Search Tool for the Retrieval of Interaction Genes/Proteins (STRING) analysis for protein-protein interaction, Kyoto Encyclopedia of Genes and Genomes (KEGG) for pathway enrichment, and gene ontology (GO) for functional enrichment, identifying crucial signaling pathways and biological processes influenced by the hit compounds bifendate, xambioona, and hederagenin. STRING analysis indicated substantial connectivity among the targets, suggesting significant interactions within the cell cycle regulation and growth factor signaling pathways as outlined in our KEGG results. The GO analysis highlighted their involvement in critical biological processes such as cell cycle control, apoptosis, and drug response. Molecular docking simulations quantified the binding efficiencies of the identified compounds with their targets-CCND1, CDK4, and EGFR-selected based on high docking scores that suggest strong potential interactions crucial for NSCLC inhibition. Subsequent MD simulations validated the stability of these complexes, supporting their potential as therapeutic interventions. Additionally, the novel identification of ADH1B as a target underscores its prospective significance in NSCLC therapy, further expanded by our comprehensive bioinformatics approach.

Conclusions: Our research demonstrates the potential of integrating network pharmacology and computational biology to elucidate the mechanisms of the Yiqi Sanjie formula in NSCLC treatment. The identified compounds could lead to novel targeted therapies, especially for patients with overexpressed targets. The discovery of ADH1B as a therapeutic target adds a new dimension to NSCLC treatment strategies. Further studies, both in vitro and in vivo, are needed to confirm these computational findings and advance these compounds towards clinical trials.