Repositioning of drugs for the treatment of major depressive disorder based on prediction of drug-induced gene expression changes
Ivanov S.M.1 , Lagunin A.A.1, Poroikov V.V.2
1. Institute of Biomedical Chemistry, Moscow, Russia; Pirogov Russian National Research Medical University, Moscow, Russia 2. Institute of Biomedical Chemistry, Moscow, Russia
Major depressive disorder (MDD) is one of the most common diseases affecting millions of people worldwide. The use of existing antidepressants in many cases does not allow achieving stable remission, probably due to insufficient understanding of pathological mechanisms. This indicates the need for the development of more effective drugs based on in-depth understanding of MDD's pathophysiology. Since the high costs and long duration of the development of new drugs, the drug repositions may be the promising alternative. In this study we have applied the recently developed DIGEP-Pred approach to identify drugs that induce changes in expression of genes associated with the etiopathogenesis of MDD, followed by identification of their potential MDD-related targets and molecular mechanisms of the antidepressive effects. The applied approach included the following steps. First, using structure-activity relationships (SARs) we predicted drug-induced gene expression changes for 3690 worldwide approved drugs. Disease enrichment analysis applied to the predicted genes allowed to identify drugs that significantly altered expression of known MDD-related genes. Second, potential drug targets, which are probable master regulators responsible for drug-induced gene expression changes, have been identified through the SAR-based prediction and network analysis. Only those drugs whose potential targets were clearly associated with MDD according to the published data, were selected for further analysis. Third, since potential new antidepressants must distribute into brain tissues, drugs with an oral route of administration were selected and their blood-brain barrier permeability was estimated using available experimental data and in silico predictions. As a result, we identified 19 drugs, which can be potentially repurposed for the MDD treatment. These drugs belong to various therapeutic categories, including adrenergic/dopaminergic agents, antiemetics, antihistamines, antitussives, and muscle relaxants. Many of these drugs have experimentally confirmed or predicted interactions with well-known MDD-related protein targets such as monoamine (serotonin, adrenaline, dopamine) and acetylcholine receptors and transporters as well as with less trivial targets including galanin receptor type 3 (GALR3), G-protein coupled estrogen receptor 1 (GPER1), tyrosine-protein kinase JAK3, serine/threonine-protein kinase ULK1. Importantly, that the most of 19 drugs act on two or more MDD-related targets, which may produce the stronger action on gene expression changes and achieve a potent therapeutic effect. Thus, the revealed 19 drugs may represent the promising candidates for the treatment of MDD.
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Keywords: drug repositioning, major depressive disorder, drug-induced gene expression, master regulators, signaling network, structure-activity relationships
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Ivanov S.M., Lagunin A.A., Poroikov V.V. (2024) Repositioning of drugs for the treatment of major depressive disorder based on prediction of drug-induced gene expression changes. Biomeditsinskaya Khimiya, 70(6), 403-412.
Ivanov S.M. et al. Repositioning of drugs for the treatment of major depressive disorder based on prediction of drug-induced gene expression changes // Biomeditsinskaya Khimiya. - 2024. - V. 70. -N 6. - P. 403-412.
Ivanov S.M. et al., "Repositioning of drugs for the treatment of major depressive disorder based on prediction of drug-induced gene expression changes." Biomeditsinskaya Khimiya 70.6 (2024): 403-412.
Ivanov, S. M., Lagunin, A. A., Poroikov, V. V. (2024). Repositioning of drugs for the treatment of major depressive disorder based on prediction of drug-induced gene expression changes. Biomeditsinskaya Khimiya, 70(6), 403-412.
References
WHO website. Retrieved October 9, 2024, from: https://www.who.int/ru/news-room/fact-sheets/detail/depression. Scholar google search
Caldiroli A., Capuzzi E., Tagliabue I., Capellazzi M., Marcatili M., Mucci F., Colmegna F., Clerici M., Buoli M., Dakanalis A. (2021) Augmentative pharmacological strategies in treatment-resistant major depression: A comprehensive review. Int. J. Mol. Sci., 22(23), 13070. CrossRef Scholar google search
di Masi J.A., Grabowski H.G., Hansen R.W. (2016) Innovation in the pharmaceutical industry: New estimates of R&D costs. J. Health Econ., 47, 20–33. CrossRef Scholar google search
Mullard A. (2012) Drug repurposing programmes get lift off. Nat. Rev. Drug Discov., 11(7), 505–506. CrossRef Scholar google search
Mullard A. (2014) Bank tests drug development waters. Nat. Rev. Drug Discov., 13(9), 643–644. CrossRef Scholar google search
Roessler H.I., Knoers N.V.A.M., van Haelst M.M., van Haaften G. (2021) Drug repurposing for rare diseases. Trends Pharmacol. Sci., 42(4), 255–267. CrossRef Scholar google search
Bortolasci C.C., Jaehne E.J., Hernández D., Spolding B., Connor T., Panizzutti B., Dean O.M., Crowley T.M., Yung A.R., Gray L., Kim J.H., van den Buuse M., Berk M., Walder K. (2023) Metergoline shares properties with atypical antipsychotic drugs identified by gene expression signature screen. Neurotox. Res., 41(6), 502–513. CrossRef Scholar google search
Lamb J., Crawford E.D., Peck D., Modell J.W., Blat I.C., Wrobel M.J., Lerner J., Brunet J.P., Subramanian A., Ross K.N., Reich M., Hieronymus H., Wei G., Armstrong S.A., Haggarty S.J., Clemons P.A., Wei R., Carr S.A., Lander E.S., Golub T.R. (2006) The Connectivity Map: Using gene-expression signatures to connect small molecules, genes, and disease. Science, 313(5795), 1929–1935. CrossRef Scholar google search
Musa A., Ghoraie L.S., Zhang S.D., Glazko G., Yli-Harja O., Dehmer M., Haibe-Kains B., Emmert-Streib F. (2018) A review of connectivity map and computational approaches in pharmacogenomics. Brief. Bioinform., 19(3), 506–523. CrossRef Scholar google search
Shoaib M., Giacopuzzi E., Pain O., Fabbri C., Magri C., Minelli A., Lewis C.M., Gennarelli M. (2021) Investigating an in silico approach for prioritizing antidepressant drug prescription based on drug-induced expression profiles and predicted gene expression. Pharmacogenomics J., 21(1), 85–93. CrossRef Scholar google search
Lagunin A., Ivanov S., Rudik A., Filimonov D., Poroikov V. (2013) DIGEP-Pred: Web service for in silico prediction of drug-induced gene expression profiles based on structural formula. Bioinformatics, 29(16), 2062–2063. CrossRef Scholar google search
Davis A.P., Wiegers T.C., Johnson R.J., Sciaky D., Wiegers J., Mattingly C.J. (2023) Comparative Toxicogenomics Database (CTD): Update 2023. Nucleic Acids Res., 51(D1), D1257–D1262. CrossRef Scholar google search
Filimonov D.A., Lagunin A.A., Gloriozova T.A., Rudik A.V., Druzhilovskii D.S., Pogodin P.V., Poroikov V.V. (2014) Prediction of the biological activity spectra of organic compounds using the Pass Online web resource. Chem. Heterocycl. Comp., 50(3), 444–457. CrossRef Scholar google search
Lagunin A., Stepanchikova A., Filimonov D., Poroikov V. (2000) PASS: Prediction of activity spectra for biologically active substances. Bioinformatics, 16(8), 747–748. CrossRef Scholar google search
Poroikov V.V., Filimonov D.A., Gloriozova T.A., Lagunin A.A., Druzhilovskiy D.S., Rudik A.V., Stolbov L.A., Dmitriev A.V., Tarasova O.A., Ivanov S.M., Pogodin P.V. (2019) Computer-aided prediction of biological activity spectra for organic compounds: the possibilities and limitations. Russ. Chem. Bull., 68, 2143–2154 CrossRef Scholar google search
Ivanov S.M., Rudik A.V., Lagunin A.A., Filimonov D.A., Poroikov V.V. (2024) DIGEP-Pred 2.0: A web application for predicting drug-induced cell signaling and gene expression changes. Mol. Inform., e202400032. CrossRef Scholar google search
Savosina P., Druzhilovskiy D., Filimonov D., Poroikov V. (2024) WWAD: The most comprehensive small molecule World Wide Approved Drug database of therapeutics. Front. Pharmacol., 15, 1473279. CrossRef Scholar google search
Piñero J., Ramírez-Anguita J.M., Saüch-Pitarch J., Ronzano F., Centeno E., Sanz F., Furlong L.I. (2020) The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res., 48(D1), D845–D855. CrossRef Scholar google search
Knox C., Wilson M., Klinger C.M., Franklin M., Oler E., Wilson A., Pon A., Cox J., Chin N.E.L., Strawbridge S.A., Garcia-Patino M., Kruger R., Sivakumaran A., Sanford S., Doshi R., Khetarpal N., Fatokun O., Doucet D., Zubkowski A., Rayat D.Y., Jackson H., Harford K., Anjum A., Zakir M., Wang F., Tian S., Lee B., Liigand J., Peters H., Wang R.Q.R., Nguyen T., So D., Sharp M., da Silva R., Gabriel C., Scantlebury J., Jasinski M., Ackerman D., Jewison T., Sajed T., Gautam V., Wishart D.S. (2024) DrugBank 6.0: The DrugBank knowledgebase for 2024. Nucleic Acids Res., 52(D1), D1265–D1275. CrossRef Scholar google search
Avram S., Wilson T.B., Curpan R., Halip L., Borota A., Bora A., Bologa C.G., Holmes J., Knockel J., Yang J.J., Oprea T.I. (2023) DrugCentral 2023 extends human clinical data and integrates veterinary drugs. Nucleic Acids Res., 51(D1), D1276–D1287. CrossRef Scholar google search
Lagunin A.A., Rudik A.V., Pogodin P.V., Savosina P.I., Tarasova O.A., Dmitriev A.V., Ivanov S.M., Biziukova N.Y., Druzhilovskiy D.S., Filimonov D.A., Poroikov V.V. (2023) CLC-Pred 2.0: A freely available web application for in silico prediction of human cell line cytotoxicity and molecular mechanisms of action for druglike compounds. Int. J. Mol. Sci., 24(2), 1689. CrossRef Scholar google search
Müller-Dott S., Tsirvouli E., Vazquez M., Ramirez Flores R.O., Badia-i-Mompel P., Fallegger R., Türei D., Lægreid A., Saez-Rodriguez J. (2023) Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities. Nucleic Acids Res., 51(20), 10934–10949. CrossRef Scholar google search
Türei D., Korcsmáros T., Saez-Rodriguez J. (2016) OmniPath: guidelines and gateway for literature-curated signaling pathway resources. Nat. Methods, 13(12), 966–967. CrossRef Scholar google search
Lee T., Yoon Y. (2018) Drug repositioning using drug-disease vectors based on an integrated network. BMC Bioinformatics, 19(1), 446. CrossRef Scholar google search
Yu H., Choo S., Park J., Jung J., Kang Y., Lee D. (2016) Prediction of drugs having opposite effects on disease genes in a directed network. BMC Syst. Biol., 10(Suppl 1), 2. CrossRef Scholar google search
Meng F., Xi Y., Huang J., Ayers P.W. (2021) A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors. Sci. Data, 8(1), 289. CrossRef Scholar google search
Fu L., Shi S., Yi J., Wang N., He Y., Wu Z., Peng J., Deng Y., Wang W., Wu C., Lyu A., Zeng X., Zhao W., Hou T., Cao D. (2024) ADMETlab 3.0: An updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support. Nucleic Acids Res., 52(W1), W422–W431. CrossRef Scholar google search
Pires D.E., Blundell T.L., Ascher D.B. (2015) pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J. Med. Chem., 58(9), 4066–4072. CrossRef Scholar google search
Demsie D.G., Altaye B.M., Weldekidan E., Gebremedhin H., Alema N.M., Tefera M.M., Bantie A.T. (2020) Galanin receptors as drug target for novel antidepressants: Review. Biologics, 14, 37–45. CrossRef Scholar google search
Freimann K., Kurrikoff K., Langel Ü. (2015) Galanin receptors as a potential target for neurological disease. Expert Opin. Ther. Targets, 19(12), 1665–1676. CrossRef Scholar google search
Lu C.L., Herndon C. (2017) New roles for neuronal estrogen receptors. Neurogastroenterol. Motil., 29(7), e13121. CrossRef Scholar google search
Toumba M., Kythreotis A., Panayiotou K., Skordis N. (2024) Estrogen receptor signaling and targets: Bones, breasts and brain (review). Mol. Med. Rep., 30(2), 144. CrossRef Scholar google search
Zhang Y., Tan X., Tang C. (2024) Estrogen-immunoneuromodulation disorders in menopausal depression. J. Neuroinflammation, 21(1), 159. CrossRef Scholar google search
Gałecka M., Szemraj J., Su K.P., Halaris A., Maes M., Skiba A., Gałecki P., Bliźniewska-Kowalska K. (2022) Is the JAK-STAT signaling pathway involved in the pathogenesis of depression? J. Clin. Med., 11(7), 2056. CrossRef Scholar google search
Gulbins A., Grassme H., Hoehn R., Kohnen M., Edwards M.J., Kornhuber J., Gulbins E. (2016) Role of janus-kinases in major depressive disorder. Neurosignals, 24(1), 71–80. CrossRef Scholar google search
Huang X., Wu H., Jiang R., Sun G., Shen J., Ma M., Ma C., Zhang S., Huang Z., Wu Q., Chen G., Tao W. (2018) The antidepressant effects of α-tocopherol are related to activation of autophagy via the AMPK/mTOR pathway. Eur. J. Pharmacol., 833, 1–7. CrossRef Scholar google search
Zhang X., Bu H., Jiang Y., Sun G., Jiang R., Huang X., Duan H., Huang Z., Wu Q. (2019) The antidepressant effects of apigenin are associated with the promotion of autophagy via the mTOR/AMPK/ULK1 pathway. Mol. Med. Rep., 20(3), 2867–2874. CrossRef Scholar google search
Garay R.P., Bourin M., de Paillette E., Samalin L., Hameg A., Llorca P.M. (2016) Potential serotonergic agents for the treatment of schizophrenia. Expert Opin. Investig. Drugs, 25(2), 159–170. CrossRef Scholar google search
Haus U., Varga B., Stratz T., Späth M., Müller W. (2000) Oral treatment of fibromyalgia with tropisetron given over 28 days: Influence on functional and vegetative symptoms, psychometric parameters and pain. Scand. J. Rheumatol., 29(sup113), 55–58. CrossRef Scholar google search
Lecrubier Y., Puech A.J., Azcona A., Bailey P.E., Lataste X. (1993) A randomized double-blind placebo-controlled study of tropisetron in the treatment of outpatients with generalized anxiety disorder. Psychopharmacology (Berlin), 112(1), 129–133. CrossRef Scholar google search
Lin J., Liu W., Guan J., Cui J., Shi R., Wang L., Chen D., Liu Y. (2023) Latest updates on the serotonergic system in depression and anxiety. Front. Synaptic. Neurosci., 15, 1124112. CrossRef Scholar google search
Greenway S.E., Pack A.T., Greenway F.L. (1995) Treatment of depression with cyproheptadine. Pharmacotherapy, 15(3), 357–360. CrossRef Scholar google search
Khurana K., Bansal N. (2019) Lacidipine attenuates reserpine-induced depression-like behavior and oxido-nitrosative stress in mice. Naunyn Schmiedebergs Arch. Pharmacol., 392(10), 1265–1275. CrossRef Scholar google search
Khan I., Kahwaji C.I. (2023) Cyclobenzaprine. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024. Scholar google search
Ratajczak P., Kus K., Zielińska-Przyjemska M., Skórczewska B., Zaprutko T., Kopciuch D., Paczkowska A., Nowakowska E. (2020) Antistress and antidepressant properties of dapoxetine and vortioxetine. Acta Neurobiol. Exp. (Warsaw), 80(3), 217–224. CrossRef Scholar google search
Garcia-Recio S., Gascón P. (2015) Biological and pharmacological aspects of the NK1-receptor. Biomed. Res. Int., 2015, 495704. CrossRef Scholar google search
Muñoz M., Coveñas R. (2014) Involvement of substance P and the NK-1 receptor in human pathology. Amino Acids, 46(7), 1727–1750. CrossRef Scholar google search
Sadri A. (2023) Is target-based drug discovery efficient? Discovery and “off-target” mechanisms of all drugs. J. Med. Chem., 66(18), 12651–12677. CrossRef Scholar google search
Hopkins A.L. (2008) Network pharmacology: The next paradigm in drug discovery. Nat. Chem. Biol., 4(11), 682–690. CrossRef Scholar google search
