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Pan SM, Yin XY, Dai DM, Zhang LW, Qi Q, Wang PJ, Hui L, Zhu ZH. Unraveling the potential of Morinda officinalis oligosaccharides as an adjuvant of escitalopram in depression treatment and exploring the underlying mechanisms. JOURNAL OF ETHNOPHARMACOLOGY 2024; 328:118124. [PMID: 38556138 DOI: 10.1016/j.jep.2024.118124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/07/2024] [Accepted: 03/27/2024] [Indexed: 04/02/2024]
Abstract
ETHNOPHAMACOLOGICAL RELEVANCE Morinda officinalis oligosaccharides (MOs) is a mixture of oligosaccharides extracted from the roots of Morinda officinalis (MO). It is approved by Chinese Food and Drug Administration (CFDA) for depression treatment. MOs could improve the antidepressant efficacy of escitalopram in clinic. AIM OF THE STUDY We aim to explore the antidepressant activity and potential mechanism of the combination usage of MOs and escitalopram on animal model of depression. MATERIALS AND METHODS Depressive animal model was induced by chronic mild stress (CMS). Behavioral tests were conducted to evaluate the antidepressant efficacy of MOs and escitalopram. Serum neurotransmitter levels were detected by High-performance liquid chromatography (HPLC). Quantitative real-time PCR and Western blotting were applied to assay the hippocampus neurotrophic factors' mRNA and protein levels. Peripheral cytokines levels were measured through Enzyme-Linked Immunosorbent Assay (ELISA). Micorglia polization phenotype was assayed by immunofluorescence and flow cytometry. RESULTS MOs and escitalopram obviously attenuated depression-like behaviors of CMS mice. Importantly, MOs plus escitalopram exhibited better antidepressant activity on CMS mice than monotherapy. At the same time, MOs combined escitalopram treatment significantly increased hippocampus neurotransmitters and neurotrophic factor levels, stimulated hippocampus neurogenesis and relieved central nervous system (CNS) microglia over-activation of CMS mice. The combination therapy had greater effect on neuroprotection and inflammation attenuation of CMS mice than monotherapy. CONCLUSION Our results indicates MOs combined escitalopram might produce antidepressant activity through protecting neuron activity, relieving inflammation and modulating microglia polarization process.
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Affiliation(s)
- Shu-Man Pan
- Department of Pharmacy, Suzhou Guangji Hospital, Medical College of Soochow University, Suzhou, 215137, Jiangsu, PR China
| | - Xu-Yuan Yin
- Research Center of Biological Psychiatry, Suzhou Guangji Hospital, Medical College of Soochow University, Suzhou, 215137, Jiangsu, PR China
| | - Dong-Mei Dai
- Medical College of Soochow University, Suzhou, 215123, PR China
| | - Li-Wan Zhang
- Medical College of Soochow University, Suzhou, 215123, PR China
| | - Qi Qi
- Research Center of Biological Psychiatry, Suzhou Guangji Hospital, Medical College of Soochow University, Suzhou, 215137, Jiangsu, PR China
| | - Pei-Jie Wang
- Research Center of Biological Psychiatry, Suzhou Guangji Hospital, Medical College of Soochow University, Suzhou, 215137, Jiangsu, PR China
| | - Li Hui
- Research Center of Biological Psychiatry, Suzhou Guangji Hospital, Medical College of Soochow University, Suzhou, 215137, Jiangsu, PR China.
| | - Zhen-Hua Zhu
- Research Center of Biological Psychiatry, Suzhou Guangji Hospital, Medical College of Soochow University, Suzhou, 215137, Jiangsu, PR China.
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Kuzin M, Haen E, Kuzo N, Endres K, Hiemke C, Paulzen M, Schoretsanitis G. Assessing Pharmacokinetic Correlates of Escitalopram-Related Adverse Drug Reactions. Ther Drug Monit 2024; 46:246-251. [PMID: 38377253 PMCID: PMC10930353 DOI: 10.1097/ftd.0000000000001183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/08/2023] [Indexed: 02/22/2024]
Abstract
BACKGROUND To assess the pharmacokinetic correlates of reported adverse drug reactions (ADRs) under antidepressant treatment with escitalopram (ESC) using a large therapeutic drug monitoring database. METHODS A large naturalistic sample of inpatients and outpatients prescribed ESC was analyzed. ADRs were classified using the Udvalg for Kliniske Undersogelser side effect rating scale. We compared ESC-treated patients with (n = 35) and without ADRs (n = 273) using ESC plasma concentrations as the primary outcome. We also compared ADR rates in the 2 groups based on 2 cut-off ESC levels reflecting the recommended upper thresholds of the therapeutic reference range of 80 ng/mL, suggested by the consensus therapeutic drug monitoring guidelines, and 40 ng/mL, based on recent meta-analysis data. The effects of age, sex, smoking, daily ESC dose, plasma concentrations, and concentrations corrected for daily dose were included in a binary logistic regression model to predict ADRs. RESULTS No differences in clinical, demographic, or pharmacokinetic parameters were observed between patients with and without ADRs ( P > 0.05). Patients with ESC-related ADRs were more frequently diagnosed with psychotic disorders than those without (25% vs. 7.1%, P = 0.004). None of the variables was associated with ADR risk. Overall, ADR rates were not significantly different in patients above versus below thresholds of ESC concentrations (ESC concentrations >40 [n = 59] vs. ≤40 ng/mL [n = 249] and >80 [n = 8] vs. ≤80 ng/mL [n = 300]; P = 0.56 and P = 1.0, respectively). CONCLUSIONS No distinct pharmacokinetic patterns underlying ESC-associated ADRs were observed. Further studies with more specific assessments of ADRs in larger cohorts are required to better identify potential underlying patterns.
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Affiliation(s)
- Maxim Kuzin
- Clienia Schloessli, Private Psychiatric Hospital and Academic Teaching Hospital of the University of Zurich, Oetwil am See/Zurich, Switzerland;
- Department of Basic and Clinical Sciences at the Medical School, University of Nicosia, Nicosia, Cyprus
| | - Ekkehard Haen
- Clinical Pharmacology, Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany;
- Clinical Pharmacology, Department of Pharmacology and Toxicology, University of Regensburg, Regensburg, Germany;
- Clinical Pharmacology, Institute AGATE gGmbH, Pentling, Germany;
| | - Nazar Kuzo
- Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zurich, Zurich, Switzerland;
| | - Katharina Endres
- Clinical Pharmacology, Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany;
- Clinical Pharmacology, Department of Pharmacology and Toxicology, University of Regensburg, Regensburg, Germany;
- Clinical Pharmacology, Institute AGATE gGmbH, Pentling, Germany;
| | - Christoph Hiemke
- Department of Psychiatry and Psychotherapy, University Medical Center of Mainz, Mainz, Germany;
- Institute of Clinical Chemistry and Laboratory Medicine, University Medical Center of Mainz, Mainz, Germany;
| | - Michael Paulzen
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany;
- JARA—Translational Brain Medicine, Jülich, Germany;
- Alexianer Hospital Aachen, Aachen, Germany;
| | - Georgios Schoretsanitis
- Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zurich, Zurich, Switzerland;
- Department of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, New York; and
- Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, New York
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Puac-Polanco V, Ziobrowski HN, Ross EL, Liu H, Turner B, Cui R, Leung LB, Bossarte RM, Bryant C, Joormann J, Nierenberg AA, Oslin DW, Pigeon WR, Post EP, Zainal NH, Zaslavsky AM, Zubizarreta JR, Luedtke A, Kennedy CJ, Cipriani A, Furukawa TA, Kessler RC. Development of a model to predict antidepressant treatment response for depression among Veterans. Psychol Med 2023; 53:5001-5011. [PMID: 37650342 PMCID: PMC10519376 DOI: 10.1017/s0033291722001982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). METHODS A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. RESULTS In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. CONCLUSIONS Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.
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Affiliation(s)
| | | | - Eric L. Ross
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Howard Liu
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
| | - Brett Turner
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Ruifeng Cui
- VISN 4 Mental Illness Research, Education and Clinical Center, VA Pittsburgh Health Care System, Department of Veterans Affairs, Pittsburgh, PA, USA
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Lucinda B. Leung
- Center for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
- Division of General Internal Medicine and Health Services Research, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Robert M. Bossarte
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
- Department of Behavioral Medicine and Psychiatry, West Virginia University, Morgantown, WV, USA
| | - Corey Bryant
- Center for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA
| | - Jutta Joormann
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Andrew A. Nierenberg
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Dauten Family Center for Bipolar Treatment Innovation, Massachusetts General Hospital, Boston, MA, USA
| | - David W. Oslin
- VISN 4 Mental Illness Research, Education, and Clinical Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Wilfred R. Pigeon
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA
| | - Edward P. Post
- Center for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA
- Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Nur Hani Zainal
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Alan M. Zaslavsky
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Jose R. Zubizarreta
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Department of Statistics, Harvard University, Cambridge, MA, USA
- Department of Biostatistics, Harvard University, Cambridge, MA, USA
| | - Alex Luedtke
- Department of Statistics, University of Washington, Seattle, WA, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris J. Kennedy
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | | | - Toshiaki A. Furukawa
- Department of Health Promotion and Human Behavior, School of Public Health, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
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