OBJECTIVES/GOALS: To develop an untargeted metabolomics assay that can holistically characterize the small molecule signatures of different inborn errors of metabolism (IEM) for biomarker discovery and identification of novel IEMs, with the goal of implementing the assay into the clinical laboratory to improve testing efficiency. METHODS/STUDY POPULATION: A hydrophilic interaction liquid chromatography (HILIC) column and reverse phase (RP) column were assembled in tandem on a SCIEX X500B quadrupole time-of-flight (QTOF) system to create a dual liquid chromatography (LC), tandem mass spectrometry method. The X500B was operated in data-independent acquisition mode with both positive and negative ionization. A mixture of 165 reference standards from eleven compound classes common to IEMs were used to evaluate the capability of the assay to resolve small molecules. Chromatographic resolution for each standard was determined qualitatively by comparison to a reference spectral database. External validation of the assay will be performed by analyzing a commercial library of reference metabolites. RESULTS/ANTICIPATED RESULTS: A total of 88% (146/165) of the standards were detected by the assay. The RP column alone resolved 71% (117/165) of the standards, the HILIC column resolved 33% (55/165), while 17% (29/165) of the standards were resolved by both columns. The HILIC column resolved standards that were more polar, while the RP column resolved more non-polar compounds. To evaluate matrix effects, the reference standard mixture was spiked into pooled plasma. In the presence of plasma 6/146 (4%) of the standards were suppressed to levels below the limit of detection. We expect external validation with the commercial metabolite library will corroborate these results, and that the high-quality spectral data attained from this reference library can be used to improve identification of unknown metabolites in patient samples. DISCUSSION/SIGNIFICANCE: We have shown our untargeted metabolomics assay can detect known biomarkers for IEMs. Clinical implementation of this method could streamline diagnosis of IEMs while simultaneously improving patient outcomes by leveraging the metabolome for biomarker discovery, and improved understanding of IEM mechanisms to inform novel treatment strategies.