Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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

The Pharmacogenomics Journal volume 21, pages 85–93 (2021)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Illustration of antidepressants ranking method using data from STAR*D and Connectivity-Map (CMap).

Similar content being viewed by others

References

  1. James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1789–858.

    Article  Google Scholar 

  2. Fabbri C, Tansey KE, Perlis RH, Hauser J, Henigsberg N, Maier W, et al. New insights into the pharmacogenomics of antidepressant response from the GENDEP and STAR∗D studies: rare variant analysis and high-density imputation. Pharmacogenomics J. 2018;18:413–21.

    Article  CAS  Google Scholar 

  3. Leuchter AF, Cook IA, Hamilton SP, Narr KL, Toga A, Hunter AM, et al. Biomarkers to predict antidepressant response. Curr Psychiatry Rep. 2010;12:553–62.

    Article  Google Scholar 

  4. Tansey KE, Guipponi M, Hu X, Domenici E, Lewis G, Malafosse A, et al. Contribution of common genetic variants to antidepressant response. Biol Psychiatry. 2013;73:679–82.

    Article  CAS  Google Scholar 

  5. Gandal MJ, Leppa V, Won H, Parikshak NN, Geschwind DH. The road to precision psychiatry: translating genetics into disease mechanisms. Nat Neurosci. 2016;19:1397–407.

    Article  CAS  Google Scholar 

  6. Uher R, Tansey KE, Rietschel M, Henigsberg N, Maier W, Mors O, et al. Common genetic variation and antidepressant efficacy in major depressive disorder: a meta-analysis of three genome-wide pharmacogenetic studies. Am J Psychiatry. 2013;170:207–17.

    Article  Google Scholar 

  7. Wigmore EM, Hafferty JD, Hall LS, Howard DM, Clarke TK, Fabbri C, et al. Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP. Pharmacogenomics J. 2020;20:329–41.

    Article  CAS  Google Scholar 

  8. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313:1929–35.

    Article  CAS  Google Scholar 

  9. Tsuchimine S, Ochi S, Tajiri M, Suzuki Y, Sugawara N, Inoue Y, et al. Effects of cytochrome P450 (CYP) 2C19 genotypes on steady-state plasma concentrations of escitalopram and its desmethyl metabolite in Japanese patients with depression. Ther Drug Monit. 2018;40:356–61.

    Article  CAS  Google Scholar 

  10. Fava M, Rush AJ, Trivedi MH, Nierenberg AA, Thase ME, Sackeim HA, et al. Background and rationale for the sequenced treatment alternatives to relieve depression (STAR*D) study. Psychiatr Clin N Am. 2003;26:457–94.

    Article  Google Scholar 

  11. Garriock HA, Kraft JB, Shyn SI, Peters EJ, Yokoyama JS, Jenkins GD, et al. A genomewide association study of citalopram response in major depressive disorder. Biol Psychiatry. 2010;67:133–8.

    Article  CAS  Google Scholar 

  12. Novick D, Hong J, Montgomery W, Dueñas H, Gado M, Haro JM. Predictors of remission in the treatment of major depressive disorder: Real-world evidence from a 6-month prospective observational study. Neuropsychiatr Dis Treat. 2015;11:197–205.

    PubMed  PubMed Central  Google Scholar 

  13. Rush AJ, Trivedi MH, Ibrahim HM, Carmody TJ, Arnow B, Klein DN, et al. The 16-item quick inventory of depressive symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry. 2003;54:573–83.

    Article  Google Scholar 

  14. Gaynes BN, Warden D, Trivedi MH, Wisniewski SR, Fava M, Rush AJ. What did STAR* D teach us? Results from a large-scale, practical, clinical trial for patients with depression. Psychiatric services. 2009;60:1439–45.

    Article  Google Scholar 

  15. Lam M, Awasthi S, Watson HJ, Goldstein J, Panagiotaropoulou G, Trubetskoy V, et al. RICOPILI: Rapid Imputation for COnsortias PIpeLIne. Bioinformatics. 2020;36:930–3.

    CAS  PubMed  Google Scholar 

  16. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48:245–52.

    Article  CAS  Google Scholar 

  17. Gusev A, Mancuso N, Won H, Kousi M, Finucane HK, Reshef Y, et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet. 2018;50:538–48.

    Article  CAS  Google Scholar 

  18. Mancuso N, Gayther S, Gusev A, Zheng W, Penney KL, Kote-Jarai Z, et al. Large-scale transcriptome-wide association study identifies new prostate cancer risk regions. Nat Commun. 2018;9:1–11.

    Article  CAS  Google Scholar 

  19. Stegle O, Parts L, Durbin R, Winn J. A bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLoS Comput Biol. 2010;6:1–11.

    Article  Google Scholar 

  20. The GTEx Consortium. Genetic effects on gene expression across human tissues. Nature. 2017;7675:204–13.

    Article  Google Scholar 

  21. Pain O, Pocklington AJ, Holmans PA, Bray NJ, O’Brien HE, Hall LS, et al. Novel insight into the etiology of autism spectrum disorder gained by integrating expression data with genome-wide association statistics. Biol Psychiatry. 2019;86:265–73.

    Article  CAS  Google Scholar 

  22. Subramanian A, Narayan R, Corsello SM, Peck DD, Natoli TE, Lu X, et al. A next generation connectivity map: l1000 platform and the first 1,000,000 profiles. Cell. 2017;171:1437–52.e17.

    Article  CAS  Google Scholar 

  23. So HC, Chau CKL, Chiu WT, Ho KS, Lo CP, Yim SHY, et al. Analysis of genome-wide association data highlights candidates for drug repositioning in psychiatry. Nat Neurosci. 2017;20:1342–9.

    Article  CAS  Google Scholar 

  24. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545–50.

    Article  CAS  Google Scholar 

  25. Wise LH, Lanchbury JS, Lewis CM. Meta-analysis of genome searches. Ann Hum Genet. 1999;63:263–72.

    Article  CAS  Google Scholar 

  26. Hicks JK, Sangkuhl K, Swen JJ, Ellingrod VL, Müller DJ, Shimoda K, et al. Clinical pharmacogenetics implementation consortium guideline (CPIC) for CYP2D6 and CYP2C19 genotypes and dosing of tricyclic antidepressants: 2016 update. Clin Pharmacol Ther. 2017;102:37–44.

    Article  CAS  Google Scholar 

  27. Fabbri C, Tansey KE, Perlis RH, Hauser J, Henigsberg N, Maier W, et al. Effect of cytochrome CYP2C19 metabolizing activity on antidepressant response and side effects: Meta-analysis of data from genome-wide association studies. Eur Neuropsychopharmacol. 2018;28:945–54.

    Article  CAS  Google Scholar 

  28. Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, et al. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci Transl Med. 2011;3:96ra77.

    Article  CAS  Google Scholar 

  29. Dudley JT, Sirota M, Shenoy M, Pai RK, Roedder S, Chiang AP, et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci Transl Med. 2011;3:96ra76.

    Article  CAS  Google Scholar 

  30. Jiménez-Rojo L, Granchi Z, Graf D, Mitsiadis TA. Stem cell fate determination during development and regeneration of ectodermal organs. Front Physiol. 2012;3:1–11.

    Article  Google Scholar 

  31. Sakka L, Delétage N, Chalus M, Aissouni Y, Sylvain-Vidal V, Gobron S, et al. Assessment of citalopram and escitalopram on neuroblastoma cell lines. Cell toxicity and gene modulation. Oncotarget. 2017;8:42789–807.

    Article  Google Scholar 

  32. Jacobsen JPR, Plenge P, Sachs BD, Pehrson AL, Cajina M, Du Y, et al. The interaction of escitalopram and R-citalopram at the human serotonin transporter investigated in the mouse. Psychopharmacology. 2014;231:4527–40.

    Article  CAS  Google Scholar 

  33. Musa A, Tripathi S, Kandhavelu M, Dehmer M, Emmert-streib F. Harnessing the biological complexity of Big Data from LINCS gene expression signatures. PLoS ONE. 2018;13:1–16.

    CAS  Google Scholar 

Download references

Acknowledgements

We thank the NIMH for providing the opportunity of analyzing their data on the STAR*D sample. We would like to thank the CMap team for making their data available for community research use. This paper represents independent research part-funded by the National Institute for Health Research (NIHR) Biomedical Research Center at Oxford, South London, Maudsley NHS Foundation Trust, and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. We acknowledge the use of research computing facility at King’s College London, Rosalind (https://rosalind.kcl.ac.uk), which is delivered in partnership with the National Institute for Health Research (NIHR) Biomedical Research Center at South London & Maudsley and Guy’s & St. Thomas’ NHS Foundation Trusts, and part-funded by capital equipment grants from the Maudsley Charity (award 980) and Guy’s & St. Thomas’ Charity (TR130505).

Author information

Author notes

  1. These authors contributed equally: Edoardo Giacopuzzi, Oliver Pain, Chiara Fabbri

  2. These authors jointly supervised this work: Cathryn M. Lewis, Massimo Gennarelli

Authors and Affiliations

  1. Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy

    Muhammad Shoaib, Chiara Magri, Alessandra Minelli & Massimo Gennarelli

  2. Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK

    Muhammad Shoaib, Oliver Pain, Chiara Fabbri & Cathryn M. Lewis

  3. National Institute for Health Research (NIHR), Oxford Biomedical Research Centre, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK

    Edoardo Giacopuzzi

  4. IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy

    Edoardo Giacopuzzi & Massimo Gennarelli

  5. Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King’s College London, London, UK

    Cathryn M. Lewis

Authors
  1. Muhammad Shoaib
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Edoardo Giacopuzzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oliver Pain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chiara Fabbri
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chiara Magri
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alessandra Minelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Cathryn M. Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Massimo Gennarelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cathryn M. Lewis.

Ethics declarations

Conflict of interest

CML is a member of the Scientific Advisory Board of Myriad Neurosciences. The other authors declare no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

41397_2020_186_MOESM1_ESM.doc

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shoaib, M., Giacopuzzi, E., Pain, O. et al. Investigating an in silico approach for prioritizing antidepressant drug prescription based on drug-induced expression profiles and predicted gene expression. Pharmacogenomics J 21, 85–93 (2021). https://doi.org/10.1038/s41397-020-00186-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41397-020-00186-5

Search

Advanced search

Quick links