Breast cancer remains the most prevalent malignancy among women worldwide and a leading cause of cancer-related mortality. Despite advances in therapeutic strategies, treatment challenges such as resistance, metastasis, and limited subtype-specific options persist especially in aggressive subtypes like triple-negative breast cancer (TNBC). Janus kinase 1 (JAK1), a critical component of the JAK/STAT signaling pathway, plays a central role in IL-6-mediated tumor progression, immune evasion, and metastatic spread. Given its involvement in key oncogenic processes, JAK1 represents a promising therapeutic target in breast cancer. 

In this study, we employed a structure-based virtual screening approach using the Enamine compound database to identify selective small-molecule inhibitors of JAK1. The protein structure was prepared and active site residues were targeted for molecular docking and cross-docking using Auto Dock tools. ADMET profiling, conceptual DFT (cDFT) calculations, and in silico toxicity prediction were performed to evaluate pharmacokinetics, reactivity, and safety. The bioactivity potential was further assessed using the PASS server. 

Molecular dynamics simulations were conducted to confirm the binding stability of lead compounds, and MM/PBSA calculations were used to estimate binding free energies. Among the screened candidates, compound T6316633 showed strong binding affinity, favorable drug-like properties, and stable interaction within the JAK1 active site. In vitro validation using MTT assay, hemolysis assay, CAM assay, scratch assay, RTPCR, immunofluorescence and western blot further supported its anticancer potential. Based on the integrated in silico and experimental findings, T6316633 is proposed as a promising JAK1 inhibitor for further in vivo evaluation in breast cancer models.

Sruthy Sathish is currently pursuing her Ph.D. in the Department of Genetic Engineering at SRM Institute of Science and Technology, India. She is a research scholar working under the guidance of Dr. Thirumurthy M in the Computational Biology Lab. She has authored 8 publications, which have received over 85 citations, with an h-index of 3 and a Research Interest Score of 69.9. Her research focuses on computational drug discovery, and she continues to contribute to the scientific community through her active involvement in publishing and academic collaborations.