OBJECTIVES/GOALS: Patient adherence with antidepressant therapy is a critical aspect of clinical management. Drug molecules often interact with other off-target proteins resulting in adverse events. We utilized an interactome model for predicting the likelihood of adverse drug reactions in order to accurately prescribe antidepressant medications to a patient. METHODS/STUDY POPULATION: We utilized an interactome model to study physical interactions between proteins and biological functions disrupted by major depression to understand how changes in genes alter patient-specific drug efficacy and cause adverse drug-reactions. To study how drugs and diseases propagate through the proteins and biological functions, we harnessed diffusion profiles to represent nodes in the graphical model and used a biased random walks model to illustrate how signals propagate in a heterogeneous biological network. The edge weights were defined for the drug, disease, protein, and biological function node types. The interactions studied included drug-protein, disease-protein, protein-protein, protein-biological function, biological function-biological function. RESULTS/ANTICIPATED RESULTS: Based on previous studies, we anticipate that the genetic variants modulating antidepressant response include 5-HTT, STin2, HTR1A, HTR2A, TPH1 and BDNF. Additionally, genetic variants such as SLC6A4 as well as the HPA pathway may play an important role in antidepressant therapeutic response. With regards to medication drug response, the genes, SLC6A4 and HTR2A, have been known for encoding proteins that affect the synaptic cleft of serotonergic neurons. Also, the gene SLC6A4 has been shown to encode for the serotonin reuptake transporter, which is the main pharmacological target of SSRIs. In female patients with major depression, the polymorphism associated with MAOA gene may be involved in the pharmacological response. DISCUSSION/SIGNIFICANCE: The key to successful depression treatment is early adherence but nearly 60% of patients discontinue antidepressants within three months. By developing a strong understanding of the genes that alter treatment efficacy, we can provide patients with more awareness regarding the most effective treatment plan to minimize adverse events.