Computational QSAR and structure-based identification of plerixafor-derived PIM-1 kinase inhibitors in diffuse large B-Cell lymphoma.

INTRODUCTION

Diffuse large B-cell lymphoma (DLBCL) is the most common and aggressive subtype of non-Hodgkin lymphoma, with a substantial proportion of patients developing resistance to standard chemotherapy. Chromosomal translocations resulting in overexpression of the serine/threonine kinase Proviral Integration site for Moloney murine leukemia virus (PIM-1) contribute to disease progression, therapeutic resistance, and poor clinical outcomes, establishing PIM-1 as a promising molecular target. Unlike many kinases, the ATP-binding site of PIM-1 lacks a backbone hydrogen bond donor within the hinge region due to the presence of Pro123, conferring unique structural features relevant for selective inhibitor design. Although R-CHOP is the first-line treatment for DLBCL, approximately 30%-40% of patients develop refractory disease, highlighting the need for novel targeted therapies.

METHODS

In this study, an in-silico drug repurposing strategy was employed to investigate plerixafor analogues as potential ATP-competitive PIM-1 inhibitors. Quantitative Structure Activity Relationship (QSAR) modeling demonstrated strong predictive performance (R2 = 0.82), and molecular docking revealed favorable interactions within the ATP-binding pocket. Molecular dynamics simulations over 200 ns confirmed the dynamic stability of the compound 1-PIM-1 complex, as evidenced by stable Root-Mean Square Deviation (RMSD) values, reduced Root-Mean Square Fluctuation (RMSF) in the active site, and favorable Radius of Gyration (Rg) and Solvent Accessible Surface Area (SASA) profiles.

RESULTS

Additional MM/GBSA, Principal Component Analysis (PCA), Dynamic Cross-Correlation Matrix (DCCM), and free energy landscape (FEL) analyses further supported complex stability and restricted conformational dynamics.

DISCUSSION: Collectively, these findings identify plerixafor-derived compound 1 as a promising PIM-1 inhibitor and provide a robust computational framework for the development of targeted therapeutics against aggressive DLBCL, warranting further experimental validation.