Clinical trial NCT04377308
Conditions: COVID-19 Cytokine Storm
Keywords: IL-6 fluoxetine cytokine storm NF-KB COVID-19
Sponsor: University of Toledo Health Science Campus
Start date: 2020-05-01; Completion date: 2021-04-30
Enrollment: 16 participants; Allocation: NON_RANDOMIZED; Intervention model: PARALLEL

Brief summary:
This project will test the efficacy of fluoxetine to prevent serious consequences of COVID-19 infection, especially death. Becoming sick with COVID-19 virus or any other serious respiratory condition is not fun. However, the dramatic effects of the COVID-19 pandemic on human society stem from its significant mortality, not the number of individuals who become sick. This project aims to prevent serious outcomes such as hospitalization, respiratory failure and death during the time it takes to develop vaccinations and other strategies to prevent COVID-19 infectionPoor outcomes with COVID-19 infection such as hospitalization, respiratory failure, organ failure and death are associated with a dysfunctional exaggerated immune response, called a cytokine storm, that is triggered by Interleukin-6 expression (IL-6) and seems to occur around day 5 to 7 of symptoms. Fluoxetine has extraordinarily strong evidence in its action as a blocker of IL-6 and cytokine storms in both animal models of infection and in human illness such as rheumatoid arthritis and others. This action of fluoxetine is an entirely separate pathway than the serotonergic pathway that allows fluoxetine to act as an antidepressant. This pathway has been demonstrated in cell culture, in animal models, in human illness and by novel bioinformatics analyses of protein transcripts to be relatively unique for fluoxetine and appears to be a novel pathway. This project aims to inhibit the increase in IL-6 expression and thereby prevent the cytokine storm that causes poor outcomes. Patients who have tested positive or are presumptively positive for COVID-19 will be entered into the study and given the option to start the medication fluoxetine, which is demonstrated to prevent IL-6 surges in infectious and inflammatory conditions. Participants will be monitored daily for COVID-19 symptoms and weekly for side effects and tolerance of fluoxetine. A subset of patients will have blood drawn weekly and stored to monitor IL-6 and other cytokine levels at a later date.

This project aims to reduce the serious outcomes of COVID-19 infection by preventing or inhibiting the cytokine storm associated with organ failure, respiratory failure and death.

Detailed description:

Arms: arm 1: Participants may choose to not take fluoxetine and remain in the study arm 2: Participants will take fluoxetine 20 mg initially, increasing as tolerated to a maximum of 60 mg until symptoms abate, then will be tapered by 20 mg per week off the fluoxetine Participants will be on fluoxetine for 2 weeks to 2 months depending on symptom duration
Interventions: intervention 1: Fluoxetine
Intervention descriptions: intervention 1: Fluoxetine 20 mg to 60 mg daily given from 2 weeks to 2 months duration

Protocol documents:
Prot_SAP_002.pdf: 
1 
 
 
  
  
  
  
  
  
  
Fluoxetine to Reduce the Risk of Morbidity and Mortality with COVID-19 Infection 
Cheryl McCullumsmith, MD PhD, Principal Investigator 
NCT04377308 
May 2, 2020  
 
 


2 
 
Fluoxetine to reduce the risk of morbidity and mortality with COVID-19 infection 
Cheryl McCullumsmith, MD PhD, Principal Investigator 
 
1. Background, Review of the Literature, Significance  
Much of the morbidity and mortality with COVID-19 infection is thought to be the result of 
multisystem organ failure after a cytokine storm (Huang et al. Lancet 2020). This cytokine storm is 
very similar if not identical to the Secondary Hemophagochytic Lymphohistiocytosis (SHLH)  seen in 
Severe Acute Respiratory Syndrom (SARS) also caused by a coronavirus, and includes increased 
levels of IL-2, IL-7, C-CSF, TNF-2, interferon-G inducible protein, MCP1 and MIP1a, all of which are 
driven by increased levels of IL-6. (Taraz 2013, Gobin, 2014) These findings have led to use of 
medications such as tocilizumab, an IL-6 receptor antagonist, to treat COVID-19 pneumonia.  
Fluoxetine and certain other selective serotonin reuptake inhibitors (SSRIs) have demonstrated in vitro 
and in vivo efficacy at reducing IL-6 and both preventing and treating hyperimmune responses 
associated with elevated levels of IL-6. Further, fluoxetine has a very safe therapeutic profile and could 
be used both prophylactically to prevent the cytokine storm with COVID-19, as well as an adjuvant 
treatment for critically ill patients.  
In vitro and in vivo data demonstrate that fluoxetine and some other selective serotonin 
reuptake inhibitors (SSRIs) effectively reduce IL-6 to prevent hyperimmune response. In addition to 
SSRI use in adjuvant treatment for critically ill patients, the established safety profiles of many SSRIs 
allow these drugs to be used prophylactically in order to prevent cytokine storm associated with 
COVID-19 infections. SSRI treatment before starting antiviral therapies reduces incidence of 
interferon-induced depression and carries minimal side effect profiles consisting of rare 
dizziness (Udina et al., 2014). The SSRI fluoxetine demonstrates a particularly pronounced ability to 
inhibit IL-6 activation (Durairaj, Steury, & Parameswaran, 2015; Hashioka et al., 2007). This effect is 
mediated by non-serotonergic pathways involving NF-kB suppression and inhibition of dendritic cell 
antigen presentation (Branco-de-Almeida et al., 2011; D. Liu et al., 2011; R. P. Liu et al., 2014; 
Vollmar, Haghikia, Dermietzel, & Faustmann, 2008; Young et al., 2014). Animal models demonstrate 
that SSRIs protect against hyperinflammatory conditions and diseases states. In fact, pretreatment with 
fluoxetine frequently outperforms pretreatment with corticosteroids  (Dong et al., 2016). Recently, 
clinical trials have demonstrated compelling relationships between SSRI treatment and cytokine 
levels (Amitai et al., 2016). A recent 2019 review and meta-analysis by Wang et al. echoes previous 
animal work and suggests that SSRI therapy indeed reduces IL-6 levels in human subjects (Wang et 
al., 2019). 


3 
 
These data are significant to COVID-19 patients at risk for pulmonary symptoms. Pretreatment 
with SSRIs results in decreased destructive immune reactions across a variety of animal models. In rat 
models of chronic obstructive pulmonary disease, fluoxetine decreases lung injury and inhibits 
cytokines such as TNF-a and IL-6 (Cai et al., 2017). Indeed, fluoxetine reduces inflammatory reaction 
in several models of human disease, providing marked decreases in reactive cytokines (Roumestan et 
al., 2007). In addition to decreasing expression of inflammatory cytokines, pretreatment with 
fluoxetine also decreases incidence of pulmonary arterial hypertension, pulmonary arterial 
muscularization, and extracellular matrix remodeling  (Li et al., 2011). Bronchial asthma models show 
therapeutic effects of fluoxetine pretreatments in rats (Sherkawy, Abo-Youssef, Salama, & Ismaiel, 
2018), and several studies suggest positive therapeutic efficacy may be concurrent with reduced levels 
of cytokines including IL-6 (Blatteau et al., 2012; Blatteau et al., 2015). 
Overall, there is significant preclinical data supporting the use of SSRI’s in the abrogation of harmful 
inflammatory side effects, such as those driving COVID19 morbidity and mortality. Furthermore, 
clinical data shows robust safety and efficacy profiles supporting the use of fluoxetine pretreatment 
interventions. At the University of Toledo Medical Campus, we are fortunate to have a leading expert 
on the relationship between antidepressant drugs and their anti-inflammatory therapeutic effects. Dr. 
Kevin Pan, who will be leading the laboratory work described in this proposal, has published 
extensively on the use of antidepressants to limit organ damage, decrease pro-inflammatory cytokine 
production, and inhibit intracellular migration of early-stage inflammatory response  (Lu, Wang, Chen, 
Jiang, & Pan, 2020). A recent Nature publication from Dr. Pan and his team explore connections 
between antidepressant drug administration and cytokine signaling, providing compelling support for 
the use of antidepressants in inflammatory disease states. Several studies from Pan et al. indicate 
antidepressant use inhibits pro-inflammatory cytokine gene expression both in vitro and in vivo. Using 
Dr. Pan’s methods, we will assay and investigate cytokine expression in our control and experimental 
patients while utilizing the expertise that Dr. Pan and his University of Toledo researcher team has 
developed on this topic. 
 
Further evidence for the efficacy of fluoxetine in decreasing the IL-6 response that stimulates 
the cytokine storm in COVID-19 infection comes from an analysis comparing the transcriptomes of 
cells lines treated with fluoxetine, dexamethasone (a steroid established to decrease IL-6), and 2 other 
antidepressants : bupropion and paroxetine to the transcriptome of the same cell line with a knock 
down in the IL-6 signal transduction molecule. This analysis was performed this week in the 
bioinformatics group in the Department of Neurosciences at University of Toledo and is illustrated 


4 
 
below. The concordance of the fluoxetine and IL-6ST knockdown cell lines is remarkably high, 
averaging over 0.7 . This concordance is greater than that seen with dexamethasone treatment, the 
standard anti-inflammatory steroid, and is higher than 2 other commonly used antidepressants, 
bupropion and paroxetine.  
 
2. Objectives/Specific Aims  
 
Aim 1: Fluoxetine treatment begun early after COVID-19 infection will reduce hospitalizations, 
intubations, and death from COVID-19 and complications more than no medication 
Aim 2:  Fluoxetine will decrease intubations and mortality in patients hospitalized with COVID-19 
infection greater than no medication. 
Aim 3 : Fluoxetine treatment of COVID-19 infected patients will not cause significant side effects or 
increase morbidity and mortality compared to no medication 
Aim 4.  Collect blood samples from 40 subjects for future cytokine analysis: both subjects taking 
fluoxetine and not taking fluoxetine samples taken at baseline and weekly after study initiation. 
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Fluoxetine
Dexamethasone
Bupropion
Paroxetine
Transcriptome Analysis Concordance with IL-6 
Signal Transduction Knock Down in same Cell Line
A375
HEPG2
mean


5 
 
3. Methods 
 
a) Study Design : Pragmatic Clinical Trial : Experimental and Observational 
 
b) Method of treatment assignment  
Single arm patient centered clinical trial 
At study entry, participants will consent study participation via daily monitoring and will choose 
whether to participate in the activity monitoring, cytokine assessment and drug treatment portions 
of the trial. Participants who do not choose to take fluoxetine will serve as the control subjects. 
Additionally, patients will have the choice weekly to decide to start, continue, or discontinue the 
fluoxetine.  See figure below : 
 
c) Inclusion/exclusion criteria  
  
Inclusion :  
Patients aged 18 and above, able to give informed consent 


6 
 
COVID-19 test positive or presumptive positive awaiting COVID testin or  
results by following criteria: fever, cough and shortness of breath  
 
Overall Study Exclusion Criteria : 
Unable to give informed consent 
Prisoner/ institutionalized patient  
Under age 18 
Exclusion from Fluoxetine Arm:  
 
Active bleeding requiring blood products 
 
 
 
Bipolar disorder not on mood stabilizing medication* 
 
 
 
Known allergy or hypersensitivity to fluoxetine 
 
 
 
Currently taking the following medications : MAO I, pimozide, thioridine  
Currently taking hydroxychloroquine 
 
 
 
Pregnant or breastfeeding (pregnancy questionnaire attached) 
 
 
 
For hospitalized patients : QTc greater than 500 ms 
*Hospitalized patient may be on hydroxychloroquine if QTc<500 and the 
primary attending approves 
 
Exclusion from Blood Sample Provision: 
 
 
 
Pregnant 
 
 
 
Self-report of under 110 pounds 
d) Justification of the number of subjects  
All power calculations were done with the clincalc program and assume a dichotomous endpoint in 
a two independent sample study and are based on achieving the standards of a power of 0.80 and 
with an alpha of 0.05. Please note, that the assumptions of fluoxetine effect size are based on the 
absolute most conservative values in the published literature reviewed above. 
Aim 1 : Hospitalization rates  
Assumptions: 
1. 2/3 of patients who enter the trial will choose to take fluoxetine 
2. 10% of COVID-19 + patients will progress to a clinical condition requiring hospitalization 
3. Fluoxetine treatment decreases hospitalization rate by 50% 
Sample Size : 312 in the no medication group and 624 in the 2 fluoxetine groups, for a total sample 
size of 936. This sample size is very achievable given the current coronavirus pandemic. 
Prevention of Hospitalization 


7 
 
Hospitalization, no medication 
10% 
Hospitalization, Fluoxetine 
5% 
Alpha 
0.05 
Beta 
0.2 
Power 
0.8 
Sample Size 
No medication 
312 
Fluoxetine, low or high 
624 
Total 
936 
Aim 2 : Intubation in those hospitalized 
Assumptions 
1. 10% of patients requiring hospitalization for COVID-19 + will progress to intubation 
2. Fluoxetine treatment decreases intubation rate by 20% 
Aim 2:  Death in those hospitalized 
Assumptions 
1. 1% of patients requiring hospitalization for COVID-19 + will die during that hospitalization 
2. Fluoxetine treatment decreases mortality rate by 20% 
Sample Size : 3213 in the no medication group and 3213 in the fluoxetine groups, for a total sample 
size of 6426.These sample sizes will not be achievable in this pilot study. Data from Aims 3 and 4 will 
therefore be used qualitatively for the gathering of pilot data.  
Prevention of intubation, death 
Sample Size 
No medication 
3213 
Fluoxetine 
3213 
Total 
6426 
Prevention of Intubation  


8 
 
Intubation, death in no medication 
10% 
Intubation, death in fluoxetine 
8% 
Alpha 
0.05 
Beta 
0.2 
Power 
0.8 
 
e) Study setting  
Telephone, outpatient offices, emergency department, Hospital 
f) Primary and secondary outcome measures  
Primary Outcome Measures: 
Aim 1 : Rates of Hospitalization 
Aim 2 : Rates of Intubation and Death 
Aim 3 : Side effect profile 
Secondary Outcome Measures: 
Aim 1 and 2 : Depression, Anxiety, Suicidal thinking,  Days of Illness, Severity of Illness 
 
g) Variables  
Demographic information (age, gender, medical history, co-morbidities, etc.)   
Medications 
Symptom profile 
• Number of days of symptoms prior to testing 
• Epidemiologic information from CDC/Health Department surveillance interview 
• Hospital Course 
Days in Hospital, Intubation, Complications 
Outcome: Never hospitalized, Hospitalized, Intubation, Death 
 
h) Procedures, interventions, schedule  


9 
 
 
Week/Day 
Patient Intervention 
Setting/ 
Responsible 
Individual 
Standard 
of Care 
Research 
Purposes Only  
1/1 
Informed Consent 
Phone/ research team 
------ 
Yes 
1/1 
Baseline Study Data  
Phone/ research team 
 
Yes 
1 
Blood Sample  
Laboratory Services 
Inpatient 
outpatient 
1 
Physician Interview/ 
Medication Prescription 
Phone/Study 
physician 
---- 
Yes 
Daily 
Symptom/ Side effect 
Check In 
Phone/ research team 
 
Yes 
Weekly 
Physician Interview/ 
Medication Prescription 
Phone/Study 
physician 
---- 
Yes 
Weekly or 
Change in 
Clinical 
Status* 
Blood Sample  
Laboratory Services 
Inpatient 
outpatient 
 
*Change in Clinical Status includes outpatient to inpatient, transfer to ICU, and intubation 
Procedures: 
 
Instrument 
Duration 
Assessment Time 
Points 
Frequency 
 
 
 
 
Baseline Intake Form 
Chart Review Form  
         04/1/19-present 
Baseline Demographics and past 
medical history  
30 min 
Initial study entry 
Once 
PHQ-9 
5 min 
Initial Study Entry 
Weekly 
GAD-7 
5 min 
Initial Study Entry 
Weekly 
Columbia Screener-recent 
3 min 
Initial study Entry 
------- 
Symptom Checklist 
5 min 
(included in baseline) 
Daily assessments 
Columbia Screener – since last 
visit 
3 min 
------- 
Daily assessments 
 
 
 
 
Physician Interview/Medication 
Prescription 
40 min 
Baseline 
weekly 


10 
 
Drug Distribution 
 
Once, at entry 
monthly 
Blood Draw for future 
assessment of cytokine levels 
60 min 
Baseline 
Weekly, as possible 
Chart Review Followup 
 
At study exit, one 
month, 3 months, 6 
months and one year 
after study exit 
See notes to left 
5 data points 
From 4/1/19- 4/1/23 
Physician Interview/Medication Prescription 
Interview includes : 
1) Assessment for pregnancy, contraindicated medications and review of baseline intake 
2) Explanation of risks and benefits of medication : see patient handout information sheet 
Blood Draw for Future Assessment of Cytokine Levels : 
• Not to exceed 2 blood draws per week or a total of 50 ml total over 8 weeks time period 
• Outpatient blood draws will occur only if study participants have regular clinical care blood 
draws until adequate personal protection equipment (PPE) is available for use in research 
settings. Study outpatient blood draw will occur by appointment at UTMC Emergency 
Department in a negative pressure room. Outpatient blood draws will not be done until there is 
adequate PPE available for research study purposes.  
o Study participant and phlebotomist will be equipped in full PPE.  
• Inpatient blood draws will occur during normal clinical care blood draws.  
• Blood samples will be stored at -80 degrees F in the hospital laboratory freezers for future 
cytokine analysis  
 


11 
 
4) Planned data analysis  
Aim 1, 2, 3 : Descriptive statistics, 
Compare rates of primary and secondary outcome measures in subjects receiving fluoxetine with 
those not receiving fluoxetine by Student t-test and Chi-square test as appropriate. 
 
Data Safety Monitoring Board 
Interim data analyses will be performed every 3 months and will be presented to a 4 member data 
safety monitoring board who will prepare a report on the ongoing safety of the clinical trial and 
consideration of  early termination of the study for either benefit or harm per the guidelines of 
Tyson et al. 2016.  
 
 
 


12 
 
Reference List 
1. Aksu, U., Guner, I., Yaman, O. M., Erman, H., Uzun, D., Sengezer-Inceli, M., . . . Sahin, G. 
(2014). Fluoxetine ameliorates imbalance of redox homeostasis and inflammation in an acute 
kidney injury model. J Physiol Biochem, 70(4), 925-934. doi: 10.1007/s13105-014-0361-0 
2. Amitai, M., Taler, M., Carmel, M., Michaelovsky, E., Eilat, T., Yablonski, M., . . . Fennig, S. 
(2016). The Relationship Between Plasma Cytokine Levels and Response to Selective Serotonin 
Reuptake Inhibitor Treatment in Children and Adolescents with Depression and/or Anxiety 
Disorders. J Child Adolesc Psychopharmacol, 26(8), 727-732. doi: 10.1089/cap.2015.0147 
3. Bah, T. M., Benderdour, M., Kaloustian, S., Karam, R., Rousseau, G., & Godbout, R. (2011). 
Escitalopram reduces circulating pro-inflammatory cytokines and improves depressive behavior 
without affecting sleep in a rat model of post-cardiac infarct depression. Behav Brain Res, 225(1), 
243-251. doi: 10.1016/j.bbr.2011.07.039 
4. Baharav, E., Bar, M., Taler, M., Gil-Ad, I., Karp, L., Weinberger, A., & Weizman, A. (2012). 
Immunomodulatory effect of sertraline in a rat model of rheumatoid arthritis. 
Neuroimmunomodulation, 19(5), 309-318. doi: 10.1159/000339109 
5. Basterzi, A. D., Aydemir, C., Kisa, C., Aksaray, S., Tuzer, V., Yazici, K., & Göka, E. (2005). IL-6 
levels decrease with SSRI treatment in patients with major depression. Hum Psychopharmacol, 
20(7), 473-476. doi: 10.1002/hup.717 
6. Bertoni, S., Arcaro, V., Vivo, V., Rapalli, A., Tognolini, M., Cantoni, A. M., . . . Barocelli, E. 
(2014). Suppression of inflammatory events associated to intestinal ischemia-reperfusion by 5-
HT1A blockade in mice. Pharmacol Res, 81, 17-25. doi: 10.1016/j.phrs.2014.02.002 
7. Bhat, R., Mahapatra, S., Axtell, R. C., & Steinman, L. (2017). Amelioration of ongoing 
experimental autoimmune encephalomyelitis with fluoxetine. J Neuroimmunol, 313, 77-81. doi: 
10.1016/j.jneuroim.2017.10.012 
8. Blatteau, J. E., Barre, S., Pascual, A., Castagna, O., Abraini, J. H., Risso, J. J., & Vallee, N. (2012). 
Protective effects of fluoxetine on decompression sickness in mice. PLoS One, 7(11), e49069. doi: 
10.1371/journal.pone.0049069 
9. Blatteau, J. E., de Maistre, S., Lambrechts, K., Abraini, J., Risso, J. J., & Vallée, N. (2015). 
Fluoxetine stimulates anti-inflammatory IL-10 cytokine production and attenuates sensory deficits 
in a rat model of decompression sickness. J Appl Physiol (1985), 119(12), 1393-1399. doi: 
10.1152/japplphysiol.00602.2015 
10. 
Branco-de-Almeida, L. S., Kajiya, M., Cardoso, C. R., Silva, M. J., Ohta, K., Rosalen, P. L., . . 
. Kawai, T. (2011). Selective serotonin reuptake inhibitors attenuate the antigen presentation from 
dendritic cells to effector T lymphocytes. FEMS Immunol Med Microbiol, 62(3), 283-294. doi: 
10.1111/j.1574-695X.2011.00816.x 
11. 
Cai, Z., Liu, J., Bian, H., Cai, J., Jin, Q., & Han, J. (2017). Fluoxetine, an Antidepressant Drug, 
Inhibited Cigarette Smoke-Induced Pulmonary Inflammation and Apoptosis in Rats. Inflammation, 
40(4), 1375-1381. doi: 10.1007/s10753-017-0580-y 
12. 
Chen, C. Y., Yeh, Y. W., Kuo, S. C., Liang, C. S., Ho, P. S., Huang, C. C., . . . Huang, S. Y. 
(2018). Differences in immunomodulatory properties between venlafaxine and paroxetine in 
patients with major depressive disorder. Psychoneuroendocrinology, 87, 108-118. doi: 
10.1016/j.psyneuen.2017.10.009 
13. 
Curzytek, K., Kubera, M., Majewska-Szczepanik, M., Szczepanik, M., Marcińska, K., Ptak, 
W., . . . Maes, M. (2013). Inhibition of 2,4-dinitrofluorobenzene-induced contact hypersensitivity 
reaction by antidepressant drugs. Pharmacol Rep, 65(5), 1237-1246. doi: 10.1016/s1734-
1140(13)71481-6 
14. 
Dong, C., Zhang, J. C., Yao, W., Ren, Q., Yang, C., Ma, M., . . . Hashimoto, K. (2016). Effects 
of escitalopram, R-citalopram, and reboxetine on serum levels of tumor necrosis factor-α, 


13 
 
interleukin-10, and depression-like behavior in mice after lipopolysaccharide administration. 
Pharmacol Biochem Behav, 144, 7-12. doi: 10.1016/j.pbb.2016.02.005 
15. 
Durairaj, H., Steury, M. D., & Parameswaran, N. (2015). Paroxetine differentially modulates 
LPS-induced TNFα and IL-6 production in mouse macrophages. Int Immunopharmacol, 25(2), 
485-492. doi: 10.1016/j.intimp.2015.02.029 
16. 
Ehret, M., & Sobieraj, D. M. (2014). Prevention of interferon-alpha-associated depression with 
antidepressant medications in patients with hepatitis C virus: a systematic review and meta-
analysis. Int J Clin Pract, 68(2), 255-261. doi: 10.1111/ijcp.12268 
17. 
Gałecki, P., Mossakowska-Wójcik, J., & Talarowska, M. (2018). The anti-inflammatory 
mechanism of antidepressants - SSRIs, SNRIs. Prog Neuropsychopharmacol Biol Psychiatry, 
80(Pt C), 291-294. doi: 10.1016/j.pnpbp.2017.03.016 
18. 
Gobin, V., Van Steendam, K., Denys, D., & Deforce, D. (2014). Selective serotonin reuptake 
inhibitors as a novel class of immunosuppressants. Int Immunopharmacol, 20(1), 148-156. doi: 
10.1016/j.intimp.2014.02.030 
19. 
Hashioka, S., Klegeris, A., Monji, A., Kato, T., Sawada, M., McGeer, P. L., & Kanba, S. 
(2007). Antidepressants inhibit interferon-gamma-induced microglial production of IL-6 and nitric 
oxide. Exp Neurol, 206(1), 33-42. doi: 10.1016/j.expneurol.2007.03.022 
20. 
Horikawa, H., Kato, T. A., Mizoguchi, Y., Monji, A., Seki, Y., Ohkuri, T., . . . Kanba, S. 
(2010). Inhibitory effects of SSRIs on IFN-γ induced microglial activation through the regulation 
of intracellular calcium. Prog Neuropsychopharmacol Biol Psychiatry, 34(7), 1306-1316. doi: 
10.1016/j.pnpbp.2010.07.015 
21. 
Hu, T. M., Subeq, Y. M., Yang, F. L., Hsu, B. G., Lin, N. T., & Lee, R. P. (2013). The use of a 
selective serotonin reuptake inhibitor decreases heavy alcohol exposure-induced inflammatory 
response and tissue damage in rats. J Psychopharmacol, 27(10), 940-946. doi: 
10.1177/0269881113494938 
22. 
Koh, S. J., Kim, J. M., Kim, I. K., Kim, N., Jung, H. C., Song, I. S., & Kim, J. S. (2011). 
Fluoxetine inhibits NF-κB signaling in intestinal epithelial cells and ameliorates experimental 
colitis and colitis-associated colon cancer in mice. Am J Physiol Gastrointest Liver Physiol, 301(1), 
G9-19. doi: 10.1152/ajpgi.00267.2010 
23. 
Koh, S. J., Kim, J. W., Kim, B. G., Lee, K. L., Im, J. P., & Kim, J. S. (2015). Fluoxetine 
inhibits hyperresponsive lamina propria mononuclear cells and bone marrow-derived dendritic 
cells, and ameliorates chronic colitis in IL-10-deficient mice. Dig Dis Sci, 60(1), 101-108. doi: 
10.1007/s10620-014-3326-9 
24. 
Kubera, M., Kenis, G., Bosmans, E., Kajta, M., Basta-Kaim, A., Scharpe, S., . . . Maes, M. 
(2004). Stimulatory effect of antidepressants on the production of IL-6. Int Immunopharmacol, 
4(2), 185-192. doi: 10.1016/j.intimp.2003.11.006 
25. 
Li, X. Q., Wang, H. M., Yang, C. G., Zhang, X. H., Han, D. D., & Wang, H. L. (2011). 
Fluoxetine inhibited extracellular matrix of pulmonary artery and inflammation of lungs in 
monocrotaline-treated rats. Acta Pharmacol Sin, 32(2), 217-222. doi: 10.1038/aps.2010.187 
26. 
Liechti, F. D., Grandgirard, D., & Leib, S. L. (2015). The antidepressant fluoxetine protects the 
hippocampus from brain damage in experimental pneumococcal meningitis. Neuroscience, 297, 
89-94. doi: 10.1016/j.neuroscience.2015.03.056 
27. 
Liu, D., Wang, Z., Liu, S., Wang, F., Zhao, S., & Hao, A. (2011). Anti-inflammatory effects of 
fluoxetine in lipopolysaccharide(LPS)-stimulated microglial cells. Neuropharmacology, 61(4), 
592-599. doi: 10.1016/j.neuropharm.2011.04.033 
28. 
Liu, F. Y., Cai, J., Wang, C., Ruan, W., Guan, G. P., Pan, H. Z., . . . Chen, G. (2018). 
Fluoxetine attenuates neuroinflammation in early brain injury after subarachnoid hemorrhage: a 
possible role for the regulation of TLR4/MyD88/NF-κB signaling pathway. J Neuroinflammation, 
15(1), 347. doi: 10.1186/s12974-018-1388-x 


14 
 
29. 
Liu, R. P., Zou, M., Wang, J. Y., Zhu, J. J., Lai, J. M., Zhou, L. L., . . . Zhu, J. H. (2014). 
Paroxetine ameliorates lipopolysaccharide-induced microglia activation via differential regulation 
of MAPK signaling. J Neuroinflammation, 11, 47. doi: 10.1186/1742-2094-11-47 
30. 
Lu, Y., Xu, X., Jiang, T., Jin, L., Zhao, X. D., Cheng, J. H., . . . Piao, L. X. (2019). Sertraline 
ameliorates inflammation in CUMS mice and inhibits TNF-α-induced inflammation in microglia 
cells. Int Immunopharmacol, 67, 119-128. doi: 10.1016/j.intimp.2018.12.011 
31. 
Pizzi, C., Mancini, S., Angeloni, L., Fontana, F., Manzoli, L., & Costa, G. M. (2009). Effects of 
selective serotonin reuptake inhibitor therapy on endothelial function and inflammatory markers in 
patients with coronary heart disease. Clin Pharmacol Ther, 86(5), 527-532. doi: 
10.1038/clpt.2009.121 
32. 
Roumestan, C., Michel, A., Bichon, F., Portet, K., Detoc, M., Henriquet, C., . . . Mathieu, M. 
(2007). Anti-inflammatory properties of desipramine and fluoxetine. Respir Res, 8, 35. doi: 
10.1186/1465-9921-8-35 
33. 
Sacre, S., Medghalchi, M., Gregory, B., Brennan, F., & Williams, R. (2010). Fluoxetine and 
citalopram exhibit potent antiinflammatory activity in human and murine models of rheumatoid 
arthritis and inhibit toll-like receptors. Arthritis Rheum, 62(3), 683-693. doi: 10.1002/art.27304 
34. 
Sherkawy, M. M., Abo-Youssef, A. M., Salama, A. A. A., & Ismaiel, I. E. (2018). Fluoxetine 
protects against OVA induced bronchial asthma and depression in rats. Eur J Pharmacol, 837, 25-
32. doi: 10.1016/j.ejphar.2018.08.026 
35. 
Taler, M., Gil-Ad, I., Korob, I., & Weizman, A. (2011). The immunomodulatory effect of the 
antidepressant sertraline in an experimental autoimmune encephalomyelitis mouse model of 
multiple sclerosis. Neuroimmunomodulation, 18(2), 117-122. doi: 10.1159/000321634 
36. 
Taraz, M., Khatami, M. R., Dashti-Khavidaki, S., Akhonzadeh, S., Noorbala, A. A., Ghaeli, P., 
& Taraz, S. (2013). Sertraline decreases serum level of interleukin-6 (IL-6) in hemodialysis 
patients with depression: results of a randomized double-blind, placebo-controlled clinical trial. Int 
Immunopharmacol, 17(3), 917-923. doi: 10.1016/j.intimp.2013.09.020 
37. 
Tousoulis, D., Drolias, A., Antoniades, C., Vasiliadou, C., Marinou, K., Latsios, G., . . . 
Stefanadis, C. (2009). Antidepressive treatment as a modulator of inflammatory process in patients 
with heart failure: effects on proinflammatory cytokines and acute phase protein levels. Int J 
Cardiol, 134(2), 238-243. doi: 10.1016/j.ijcard.2008.02.013 
38. 
Tsai, J. H., Kuo, C. H., Yang, P., Cheng, K. H., Wang, P. W., Chen, C. C., & Hung, C. H. 
(2014). Effects of antidepressants on IP-10 production in LPS-activated THP-1 human monocytes. 
Int J Mol Sci, 15(8), 13223-13235. doi: 10.3390/ijms150813223 
39. 
Tynan, R. J., Weidenhofer, J., Hinwood, M., Cairns, M. J., Day, T. A., & Walker, F. R. (2012). 
A comparative examination of the anti-inflammatory effects of SSRI and SNRI antidepressants on 
LPS stimulated microglia. Brain Behav Immun, 26(3), 469-479. doi: 10.1016/j.bbi.2011.12.011 
40. 
Udina, M., Hidalgo, D., Navinés, R., Forns, X., Solà, R., Farré, M., . . . Martín-Santos, R. 
(2014). Prophylactic antidepressant treatment of interferon-induced depression in chronic hepatitis 
C: a systematic review and meta-analysis. J Clin Psychiatry, 75(10), e1113-1121. doi: 
10.4088/JCP.13r08800 
41. 
Uher, R., Perroud, N., Ng, M. Y., Hauser, J., Henigsberg, N., Maier, W., . . . McGuffin, P. 
(2010). Genome-wide pharmacogenetics of antidepressant response in the GENDEP project. Am J 
Psychiatry, 167(5), 555-564. doi: 10.1176/appi.ajp.2009.09070932 
42. 
van Noort, V., Schölch, S., Iskar, M., Zeller, G., Ostertag, K., Schweitzer, C., . . . Bork, P. 
(2014). Novel drug candidates for the treatment of metastatic colorectal cancer through global 
inverse gene-expression profiling. Cancer Res, 74(20), 5690-5699. doi: 10.1158/0008-5472.CAN-
13-3540 
43. 
Vollmar, P., Haghikia, A., Dermietzel, R., & Faustmann, P. M. (2008). Venlafaxine exhibits an 
anti-inflammatory effect in an inflammatory co-culture model. Int J Neuropsychopharmacol, 11(1), 
111-117. doi: 10.1017/S1461145707007729 


15 
 
44. 
Wang, L., Wang, R., Liu, L., Qiao, D., Baldwin, D. S., & Hou, R. (2019). Effects of SSRIs on 
peripheral inflammatory markers in patients with major depressive disorder: A systematic review 
and meta-analysis. Brain Behav Immun, 79, 24-38. doi: 10.1016/j.bbi.2019.02.021 
45. 
Wang, Y., Gu, Y. H., Liu, M., Bai, Y., & Wang, H. L. (2017). Fluoxetine protects against 
methamphetamine-induced lung inflammation by suppressing oxidative stress through the 
SERT/p38 MAPK/Nrf2 pathway in rats. Mol Med Rep, 15(2), 673-680. doi: 
10.3892/mmr.2016.6072 
46. 
Więdłocha, M., Marcinowicz, P., Krupa, R., Janoska-Jaździk, M., Janus, M., Dębowska, W., . . 
. Szulc, A. (2018). Effect of antidepressant treatment on peripheral inflammation markers - A meta-
analysis. Prog Neuropsychopharmacol Biol Psychiatry, 80(Pt C), 217-226. doi: 
10.1016/j.pnpbp.2017.04.026 
47. 
Young, K. C., Bai, C. H., Su, H. C., Tsai, P. J., Pu, C. Y., Liao, C. S., . . . Tsao, C. W. (2014). 
Fluoxetine a novel anti-hepatitis C virus agent via ROS-, JNK-, and PPARβ/γ-dependent pathways. 
Antiviral Res, 110, 158-167. doi: 10.1016/j.antiviral.2014.08.002 
48. 
Yuan, X. Q., Qiu, G., Liu, X. J., Liu, S., Wu, Y., Wang, X., & Lu, T. (2012). Fluoxetine 
promotes remission in acute experimental autoimmune encephalomyelitis in rats. 
Neuroimmunomodulation, 19(4), 201-208. doi: 10.1159/000334095