Abstract
In clinical practice, an antidepressant prescription is a trial and error approach, which is time consuming and discomforting for patients. This study investigated an in silico approach for ranking antidepressants based on their hypothetical likelihood of efficacy. We predicted the transcriptomic profile of citalopram remitters by performing an in silico transcriptomic-wide association study on STAR*D GWAS data (N = 1163). The transcriptional profile of remitters was compared with 21 antidepressant-induced gene expression profiles in five human cell lines available in the connectivity-map database. Spearman correlation, Pearson correlation, and the Kolmogorov-Smirnov test were used to determine the similarity between antidepressant-induced profiles and remitter profiles, subsequently calculating the average rank of antidepressants across the three methods and a p value for each rank by using a permutation procedure. The drugs with the top ranks were those having a high positive correlation with the expression profiles of remitters and that may have higher chances of efficacy in the tested patients. In MCF7 (breast cancer cell line), escitalopram had the highest average rank, with an average rank higher than expected by chance (p = 0.0014). In A375 (human melanoma) and PC3 (prostate cancer) cell lines, escitalopram and citalopram emerged as the second-highest ranked antidepressants, respectively (p = 0.0310 and 0.0276, respectively). In HA1E (kidney) and HT29 (colon cancer) cell types, citalopram and escitalopram did not fall among top antidepressants. The correlation between citalopram remitters' and (es)citalopram-induced expression profiles in three cell lines suggests that our approach may be useful and with future improvements, it can be applicable at the individual level to tailor treatment prescription.
Highlights
Major Depressive Disorder (MDD) is a primary health issue and the third leading cause of disability in adolescents and young adults, while the second leading cause of disability in middle aged adults on a global scale (1)
We developed and tested this approach by computing gene expression profile associated with remission to citalopram in the Sequence treatment alternative to relieve depression (STAR*D) study and comparing this profile with citalopram and other ADs induced transcriptional responses available from the Connectivity Map (CMap) database
The average rank across tests for ADs showed that escitalopram (S-enantiomer of citalopram) was the AD with the highest average rank followed by amitriptyline in MCF7
Summary
Major Depressive Disorder (MDD) is a primary health issue and the third leading cause of disability in adolescents and young adults, while the second leading cause of disability in middle aged adults on a global scale (1). According to the World Health Organization, more than 264 million people are living with depression worldwide. This heavy disease burden is partly due to the complex pathogenic mechanisms of MDD, the inter-individual heterogeneity of antidepressant response and the lack of reliable response predictors (2). Antidepressant (AD) choice in MDD is based on prescription guidelines and prior clinical experience, but the lack of reproducible predictors of AD response makes it a ‘trial and error’ approach which can take up to several weeks or months and a number of treatment changes before symptom remission is achieved. Prior studies suggest that AD response and remission are heritable traits (4), offering the opportunity to use genetic markers to develop predictors applicable in clinical practice to guide drug prescription. The combination of clinical presentation, genomic information and metabolic characteristics was suggested as a possible strategy for the development of precision psychiatry (5)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
