An In Silico Approach to Uncover Selective JAK1 Inhibitors for Breast Cancer from Life Chemicals Database.

Area of Science:

• Oncology
• Computational Chemistry
• Drug Discovery

Background:

• Janus kinase 1 (JAK1) is crucial in oncogenic pathways and its expression in immune cells correlates with better breast cancer prognosis.
• JAK1 signaling, particularly through IL-6 and ERBB2, promotes breast cancer metastasis and STAT3 activation.
• Targeting JAK1 presents a potential therapeutic strategy for breast cancer treatment.

Purpose of the Study:

• To identify selective JAK1 inhibitors using a comprehensive in silico approach.
• To screen chemical libraries for compounds with potential anticancer activity against JAK1.
• To evaluate the selectivity, stability, and binding affinity of identified JAK1 inhibitors.

Main Methods:

• In silico screening using molecular docking against JAK1.
• Cross-docking for selectivity assessment and Lipinski's Rule of Five (RO5) for drug-likeness.
• In silico ADMET prediction (Protox-II), biological activity prediction (PASS), and reactivity analysis (cDFT).
• Molecular dynamics (MD) simulations and MM/PBSA calculations for binding free energy determination.

Main Results:

• Identified three promising selective JAK1 inhibitors: F2638-0133, F3408-0020, and F5833-7435.
• These compounds demonstrated favorable binding interactions and stability profiles compared to the standard drug abrocitinib.
• In silico analyses confirmed the potential of these compounds for selective JAK1 targeting.

Conclusions:

• The identified compounds are promising lead candidates for selective JAK1 inhibition in breast cancer.
• Further experimental validation is warranted to confirm the therapeutic potential of these novel inhibitors.
• This study highlights the efficacy of in silico methods in accelerating drug discovery for targeted cancer therapies.