Precision Targeting of Non-Small Cell Lung Cancer: Identifying Optimal Drug Targets and FDA-Approved Combinations for Enhanced Therapeutic Efficacy

Pranabesh Bhattacharjee, Aditya Lahiri, Norman Peter Reeves, Aniruddha Datta

Abstract: This study proposes a Boolean network model to identify optimal drug targets and select the most effective FDA-approved drug combinations for Non-Small Cell Lung Cancer (NSCLC). The Boolean network models the signaling pathways in NSCLC to capture the intricate molecular interactions driving tumor progression. We evaluate the model by employing the size difference (SD) score, which reflects the degree of cell dysregulation due to gene mutations and allows us to identify optimal drug targets in NSCLC cells to address this dysregulation. Specifically, leveraging the FDA-approved drug database, we identified the robust drug or drug combination for 1, 2, and 3 mutations that maximize tumor cell death and minimize cell proliferation for NSCLC-associated gene mutations. Our findings provide a strong foundation for personalized therapeutic strategies and hold promise for advancing precision oncology to effectively combat NSCLC.

External IDs:dblp:conf/bibe/BhattacharjeeLR23