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


Journal

The pharmacogenomics journal
ISSN: 1473-1150
Titre abrégé: Pharmacogenomics J
Pays: United States
ID NLM: 101083949

Informations de publication

Date de publication:
02 2021
Historique:
received: 21 04 2020
accepted: 08 09 2020
revised: 13 08 2020
pubmed: 19 9 2020
medline: 23 11 2021
entrez: 18 9 2020
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 32943772
doi: 10.1038/s41397-020-00186-5
pii: 10.1038/s41397-020-00186-5
doi:

Substances chimiques

Antidepressive Agents 0
Serotonin Uptake Inhibitors 0
Citalopram 0DHU5B8D6V

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

85-93

Subventions

Organisme : Department of Health
Pays : United Kingdom

Références

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Auteurs

Muhammad Shoaib (M)

Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.

Edoardo Giacopuzzi (E)

National Institute for Health Research (NIHR), Oxford Biomedical Research Centre, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.

Oliver Pain (O)

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

Chiara Fabbri (C)

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

Chiara Magri (C)

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

Alessandra Minelli (A)

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

Cathryn M Lewis (CM)

Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK. cathryn.lewis@kcl.ac.uk.
Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, London, UK. cathryn.lewis@kcl.ac.uk.

Massimo Gennarelli (M)

Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.
IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.

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