Investigating an in-silico approach for prioritizing antidepressant drug prescription based on drug-induced expression profiles and predicted gene expression

In clinical practice, 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 determined the transcriptomic profile of citalopram remitters by performing a transcriptomic-wide association study on STAR*D 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 high positive correlation with the expression profiles of remitters and they 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.