434 In silico ADMET optimization and preliminary biologic activity of novel spermine oxidase inhibitors as neuroprotective agents

OBJECTIVES/GOALS: The goal of this project was to conduct a preliminary assessment of in vivo feasibility early on in the drug-discovery process in an effort to expedite the translation of novel drug scaffolds to potential clinical candidates. The data gathered in this study will be used to direct analog synthesis of our current lead compounds through rational drug design. METHODS/STUDY POPULATION: Based on virtual and physical high-throughput screening efforts and subsequent similarity searching, we identified a set of potent and selective spermine oxidase (SMOX) inhibitors adhering to a common structural scaffold. In order to address potential barriers to in vivo use, we then conducted a robust optimization analysis in an effort to identify analogs with improved drug-like characteristics. Docking simulations to predict binding were performed and visualized using molecular modeling software (MOE and PyMol). ADMET properties were calculated using a variety of software resources including SwissADME and CDD Vault. RESULTS/ANTICIPATED RESULTS: Through these optimization efforts, we were able to successfully identify analogs with improved drug-like characteristics, including increases in predicted CNS penetration, isosteric replacement of metabolically labile functional groups, increased lipophilicity, and elimination of structural attributes suggestive of off-target activity. Analogs were ranked according to predicted binding and properties of in vivo feasibility. Compounds achieving the highest scores were then selected as scaffolds to guide analog synthesis. DISCUSSION/SIGNIFICANCE: Despite evidence implicating induction of SMOX as a mechanism contributing to neuronal pathology, the lack of potent and selective inhibitors with profiles conducive for in vivo use has significantly impeded clinical investigation of this target. In this presentation, rational drug design focusing on translational optimization will be discussed.