Using Patient iPSC-Derived Neurons to Uncover Mechanisms Associated with Response to Antidepressant Drugs: A Framework for Precision Medicine in Depression

Introduction Depression is a leading cause of disability worldwide affecting over 300 million individuals globally. Despite the abundance of approved antidepressants, the majority of depressed patients do not respond to their first prescribed antidepressant, with over 30% not responding to subsequent drugs. This is largely attributed to individual differences in the underlying pathophysiology of the disease, which make some medications efficacious for some patients while ineffective for others. Multiple studies have been conducted with the aim of identifying patient subgroups associated with drug response, with a large focus on variations in genetic and gene expression levels that may underlie response to specific drugs. However, these studies yielded mostly inconsistent results, with robust genetic effects largely related to drug pharmacokinetics and gene expression level associations poorly reproducible. In this study, we assessed gene expression levels in iPSC-derived neurons from depressed patients with a known response profile to various medications, alongside genetic variations. iPSC-derived neurons have the potential to unravel mechanisms that are neuronal specific and thus not observed in patient whole blood molecular analysis. Methods Patient-derived lymphoblastoid cell lines from the Sequenced Treatment Alternatives to Relieve Depression (STARD) study with known response to Citalopram or Bupropion were reprogrammed and then differentiated to cortical neurons. Analysis of genetic variants and differential gene expression was performed on the derived neurons to identify variants and gene expression levels that are associated with drug response. Results Significant differential gene expression was shown between Bupropion responders and non-responders as well as between Citalopram responders and non-responders. Functional enrichment analysis revealed biologically relevant pathways that differ between responders and non-responders in Bupropion and in Citalopram. In addition, we found an interplay between genetics and neuronal gene expression, that is associated with patient drug response. This was specifically observed in genes implicated in drug mechanism of action, including COMT and BDNF. Conclusions Patient-derived neurons have utility in mechanistic disease and drug modeling, and can elucidate mechanisms that cannot be read out from systemic whole blood analysis. In addition, combining genetics and target organ gene expression levels has the potential to be used as biomarkers for drug response. Together, these findings support a novel framework for precision medicine in depression. Funding Genetika+