Myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) are two common forms of myeloid malignancies that primarily affect older adults and have a dismal outcome. Most MDS/AML patients treated with combination therapy develop multidrug resistance (MDR) as the treatments proceed, resulting in treatment failure and patient death. Our previous studies demonstrated that NOL1/NOP2/Sun domain family proteins, NSUN1 (NOP2, NOL1, P120) and NSUN2, play a key role in determining MDR in MDS and AML through formation of distinct transcription and translation structures (ATTS) (Cheng, J.X. et al. Nat Commun 9, 2018; Wood, S. et al. Curr Cancer Drug Targets 21, 2021). By leveraging the Argonne artificial intelligence (AI) supercomputer, we identified several druggable ligand-binding modules/surfaces in NSUN1 and NSUN2 proteins and designed small-molecule compound libraries to target these computationally predicted ligand-binding modules. We identified both pan-lineage (non-lineage-specific) and lineage-specific hits/inhibitors by screening the small-molecule compound libraries using drug-resistant leukemia and carcinoma cell lines. We selected a couple of lead inhibitors that can disrupt NSUN1/2-ATTS and effectively kill drug-resistant leukemia cells in vitro and in vivo. Pre-clinical validation and optimization our lead inhibitors for an Investigational New Drug (IND) application are our current focus. Our goal is to develop novel NSUN1/2-targeted therapeutics to overcome MDR for effective treatment of MDS and AML.

Aim 1: In vitro validation and optimization of the lead inhibitors.

Aim 2: In vivo characterization of the lead inhibitors and pharmacokinetic studies.

Aim 3: IND-enabling studies and commercialization.

A wide range of methods have been used. These methods include biological characterization of NSUN1/2, AI structural modeling and drug design, high-throughput screening, phenotypic screening, in vitro and in vivo Pharmacokinetics and CRISPR-based target verification.