Major Depressive DisorderOPADENCT06550037
Optimize and Predict Antidepressant Efficacy for Patient With MDD Using Multi-omics Analysis and AI-predictive Tool
An observational study of Major Depressive Disorder, run by Alessio Fasano at 1 site in Italy.
Trial summary
OPADE is a non-profit, observational, multicenter, open-label study aimed at defining personalized treatment for Major Depressive Disorder (MDD). In particular, we will combine genetics, epigenetics, microbiome, immune response data together with anamnesis, questionnaires, electroencephalography (EEG) collected from… Read more about this study
- Who is it for?
- Ages 14 to 50 years
- Conditions the study is for
- Major Depressive Disorder
- How many people
- 350 participants
- Where
- 1 siteItaly
- Study dates
- 7 Aug 2023to 31 May 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 14 to 50 years
- All sexes
- No healthy volunteers
- Child, Adult
Study population: Patients with Major Depressive Disorders
Who may be able to join
- Patients diagnosed with Major Depressive Disorder as certified by a SCID 5 (Structured Clinical Interview for DSM-5) for DSM-S for adults and K-SADS-PL-DSM 5 (Kiddie Schedule for Affective Disorders and Schizophrenia - Present and Lifetime for DSM 5) for adolescents.
- Currently experiencing a major depressive episode with a HAM-D (Hamilton Depression) score of 18 or greater, or alternatively, a MADRS (Montgomery-Asberg Depression Rating Scale) score of 18 or greater.
- About to start a new antidepressant.
- Not concurrently starting a new psychotropic medication.
- Age 14-50 years.
- Able to use mobile devices (smart phone, tablet).
- Willingness to provide written informed consent to participate.
Who cannot join
- Intellectual disability.
- Neurological disease (multiple sclerosis, severe neurocognitive disorder, epilepsy).
- Current psychotic disorder or mood disorder with psychotic features.
- Primary diagnosis of alcohol or substance use disorder (DSM-5).
- Patients who started concomitant psychotropic medications less than one week ago.
- Active, ongoing inflammatory diseases (such as rheumatoid arthritis and rheumatic polymyalgia). or severe and unstable physical illness (such as recent myocardial infarction).
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- A history of hepatitis B or C, human immunodeficiency virus, or evidence of active tuberculosis infection or any active systemic infection within 2 weeks prior to the start of the study.
- Use of antibiotics or other medications that may have affected the composition of the microbiota during the 30 days prior to baseline.
- Pregnancy and lactation.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Optimize and Predict Antidepressant Efficacy for Patient With Major Depressive Disorders Using Multi-omics Analysis and AI-predictive Tool
OPADE is a non-profit, observational, multicenter, open-label study aimed at defining personalized treatment for Major Depressive Disorder (MDD). In particular, we will combine genetics, epigenetics, microbiome, immune response data together with anamnesis, questionnaires, electroencephalography (EEG) collected from subjects suffering MDD. Eventually, an Artificial Intelligence (AI)/Machine Learning (ML) predictive tool will be created to guide clinicians in improving MDD treatment and patient's stratification.
Read the detailed description
Three hundred and fifty patients diagnosed with MDD will be enrolled for 24 months and divided into 4 groups according to age: 14-17 years (70 pediatric patients), 18-30 years (100 adult patients), 31-39 years (90 adult patients), 40-50 years (90 adult patients).
The study protocol includes 6 follow-up visits: T0 (enrollment), T1, T2, T3, T4, and T5. At each medical visit, psychometric questionnaires will be administered to the patients and contextual biological samples including blood, stool and saliva will be collected. The study will use a multi-omics approach including: metagenomic sequencing to characterize the microbiome composition; metabolomics to detect circulating metabolites; transcriptomics to quantify microRNAs; epigenomics to assess methylation variability between and within groups and immune assays to analyze the antibody immune response and inflammatory profiles (cytokines, interleukins and growth factors). Cortisol and lipoproteins will also be quantified. In parallel, cognitive assessment and emotional status will be recorded remotely by each patient via chatbot and wearable EEG devices, respectively. Specifically, the chatbot will collect patient's conversations and monitoring her/his feelings; the chat conversation will be than transformed in a machine-readable data. The EEG device is a mobile app that will also allows to associate brainwaves with patients' feelings.
- Conditions
- Major Depressive Disorder
- Keywords
- Major Depressive Disorders, Microbiome, Metabolomic, Transcriptomics, Inflammation, Immune response profiling, Genetics, Artificial intelligence, Machine learning, Electroencephalography, Chatbot, Biomarkers, Personalized medicine
- Medical subject headings
- Depressive Disorder, Major, Inflammation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Pediatric patients affected by MDD | 14-17 years (70 pediatric patients) | |
| Group 1 of adult patients affected by MDD | 18-30 years (100 adult patients) | |
| Group 2 of adult patients affected by MDD | 31-39 years (90 adult patients) | |
| Group 3 of adult patients affected by MDD | 40-50 years (90 adult patients) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Identify neuroinflammatory indices
Several inflammatory markers such as G-CSF, GM-CSF, IFN-γ IL-10, IL-12p40, IL-15, IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8/CXCL8, MCP-1/CCL2, TNF-α, TNFβ will be analysed.
Measured: 2 years
Microbiome analysis
Identification of bacterial and fungal components.
Measured: 2 years
Metabolomic analysis
The metabolomic analysis will involve three different groups of metabolites: 1) Intermediate of tryptophan metabolism (tryptophan, serotonin, 5-HIAA, quinurenin, quinurenic acid and other hormones and derivatives involved in the pathway) and others related to purines (paraxanthin/xanthin ratio); 2) L-acylcarnitines (including short chain, medium long-lasting acylcarnitine), with particular emphasis on laurylcarnitine and acetylcarnitine; 3) Phenolic (and related), such as phenolic acid, mandelic acid or methoxy-hydroxyphenyl glycol.
Measured: 2 years
Analysis of lipoprotein profile
Different forms of lipoproteins will be evaluated: Apolipoproteins A1 and A2, HDL-apolipoproteins A1 and A2,free cholesterol HDL3, HDL3-apolipoprotein A1, HDL2-apolipoprotein A2, apolipoprotein A2, IDL, HDL-apolipoprotein A2, VLDL and its subtypes, VLDL2-triglycerides, VLDL3-triglyceridestriglycerides, VLDL2- cholesterol, VLDL3 cholesterol, VLDL4 cholesterol free of VLDL4, phospholipids VLDL2, Phospholipids VLDL3, Cholesterol LDL5, Cholesterol free LDL5, Phospholipids LDL5, LDL5-apolipoprotein B, HDL3 cholesterol, HDL4 cholesterol HDL4, HDL3 cholesterol free, free cholesterol HDL4, HDL3-phospholipids, HDL4-phospholipids, HDL3-apolipoprotein A1, HDL4-apolipoprotein A1, HDL3-apolipoprotein A2 and HDL4-apolipoprotein A2.
Measured: 2 years
Identify immune-profile linked and epigenomic signatures
Methylome analysis on genomic DNA will be performed.
Measured: 2 years
AI-powered diagnostics predictive tool (companion diagnostic-like)
Deploy an AI-powered predictive tool (companion diagnostic-like) in clinical practice for the prescription of anti-depressants. OPADE AI-powered predictive tool will be a class C medical device under the In vitro diagnostic classification.
Measured: 2 years
Mood assessment through brain biomarker
Validate a patient tracking tool for mood assessment using brain biomarker.
Measured: 2 years
Patient engagement digital tool
Validate a patient engagement digital tool that can be deployed in any patient community to enhance clinical study outcomes.
Measured: 2 years
Discovery of a new set of biomarkers
Propose new set of biomarkers that can guide the development of new antidepressants
Measured: 2 years
Investigation of the gut-brain-axis and of the biomarkers of interest in the context of mental diseases starting with MDD
Identify indices in MDD to improve diagnostic accuracy for primary prevention and patients' stratification.
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Aug 2024
- Study start7 Aug 2023
- Primary completionApr 2027 (estimated)
- Study completion31 May 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Major Depressive DisorderStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 350 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Alessio Fasano
- Sponsor type
- University, hospital or other
- Collaborators
- University of Siena, Fundació Eurecat, Perseus Biomics, ARTIFICIAL INTELLIGENCE EXPERT SRL, Mama Health Technologies GmbH, Protobios OU, Cephalgo, Biokeralty Research Institute, Sanitas University, Accare, Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta, Istanbul Medipol University Hospital, CEINGE, Fondazione di ricerca biomedica EBRIS
- Responsible party
- Sponsor-investigator, Alessio Fasano, Massachusetts General Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06550037
- Sponsor's protocol ID
- Pro23479
- Acronym
- OPADE
- Status last verified
- May 2026
- First submitted
- 24 Jul 2024
- Last updated
- 15 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 15 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Optimize and Predict Antidepressant Efficacy for Patient With Major Depressive Disorders Using Multi-omics Analysis and AI-predictive Tool
- ClinicalTrials.gov ID
- NCT06550037
- Acronym / sponsor ID
- OPADE / Pro23479
- Conditions
- Major Depressive Disorder
- Keywords
- Major Depressive Disorders, Microbiome, Metabolomic, Transcriptomics, Inflammation, Immune response profiling, Genetics, Artificial intelligence
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 350 participants (estimated)
- Lead sponsor
- Alessio FasanoUniversity, hospital or other
- Collaborators
- University of Siena, Fundació Eurecat, Perseus Biomics, ARTIFICIAL INTELLIGENCE EXPERT SRL, Mama Health Technologies GmbH, Protobios OU, Cephalgo, Biokeralty Research Institute, Sanitas University, Accare, Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta, Istanbul Medipol University Hospital, CEINGE, Fondazione di ricerca biomedica EBRIS
- Study start
- 7 Aug 2023 (actual)
- Primary completion
- Apr 2027 (estimated)
- Study completion
- 31 May 2027 (estimated)
- First posted
- 12 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
OPADE is a non-profit, observational, multicenter, open-label study aimed at defining personalized treatment for Major Depressive Disorder (MDD). In particular, we will combine genetics, epigenetics, microbiome, immune response data together with anamnesis, questionnaires, electroencephalography (EEG) collected from subjects suffering MDD. Eventually, an Artificial Intelligence (AI)/Machine Learning (ML) predictive tool will be created to guide clinicians in improving MDD treatment and patient's stratification.
Detailed description
Three hundred and fifty patients diagnosed with MDD will be enrolled for 24 months and divided into 4 groups according to age: 14-17 years (70 pediatric patients), 18-30 years (100 adult patients), 31-39 years (90 adult patients), 40-50 years (90 adult patients).
The study protocol includes 6 follow-up visits: T0 (enrollment), T1, T2, T3, T4, and T5. At each medical visit, psychometric questionnaires will be administered to the patients and contextual biological samples including blood, stool and saliva will be collected. The study will use a multi-omics approach including: metagenomic sequencing to characterize the microbiome composition; metabolomics to detect circulating metabolites; transcriptomics to quantify microRNAs; epigenomics to assess methylation variability between and within groups and immune assays to analyze the antibody immune response and inflammatory profiles (cytokines, interleukins and growth factors). Cortisol and lipoproteins will also be quantified. In parallel, cognitive assessment and emotional status will be recorded remotely by each patient via chatbot and wearable EEG devices, respectively. Specifically, the chatbot will collect patient's conversations and monitoring her/his feelings; the chat conversation will be than transformed in a machine-readable data. The EEG device is a mobile app that will also allows to associate brainwaves with patients' feelings.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Pediatric patients affected by MDD | – | –14-17 years (70 pediatric patients) |
| Group 1 of adult patients affected by MDD | – | –18-30 years (100 adult patients) |
| Group 2 of adult patients affected by MDD | – | –31-39 years (90 adult patients) |
| Group 3 of adult patients affected by MDD | – | –40-50 years (90 adult patients) |
Who may qualify
- Ages
- 14 to 50 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult
- Study population
- Patients with Major Depressive Disorders
Key inclusion criteria (7 listed)
- Patients diagnosed with Major Depressive Disorder as certified by a SCID 5 (Structured Clinical Interview for DSM-5) for DSM-S for adults and K-SADS-PL-DSM 5 (Kiddie Schedule for Affective Disorders and Schizophrenia - Present and Lifetime for DSM 5) for adolescents.
- Currently experiencing a major depressive episode with a HAM-D (Hamilton Depression) score of 18 or greater, or alternatively, a MADRS (Montgomery-Asberg Depression Rating Scale) score of 18 or greater.
- About to start a new antidepressant.
- Not concurrently starting a new psychotropic medication.
- Age 14-50 years.
Key exclusion criteria (9 listed)
- Intellectual disability.
- Neurological disease (multiple sclerosis, severe neurocognitive disorder, epilepsy).
- Current psychotic disorder or mood disorder with psychotic features.
- Primary diagnosis of alcohol or substance use disorder (DSM-5).
- Patients who started concomitant psychotropic medications less than one week ago.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Identify neuroinflammatory indices
Time frame: 2 years
Several inflammatory markers such as G-CSF, GM-CSF, IFN-γ IL-10, IL-12p40, IL-15, IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8/CXCL8, MCP-1/CCL2, TNF-α, TNFβ will be analysed.
Microbiome analysis
Time frame: 2 years
Identification of bacterial and fungal components.
Metabolomic analysis
Time frame: 2 years
The metabolomic analysis will involve three different groups of metabolites: 1) Intermediate of tryptophan metabolism (tryptophan, serotonin, 5-HIAA, quinurenin, quinurenic acid and other hormones and derivatives involved in the pathway) and others related to purines (paraxanthin/xanthin ratio); 2) L-acylcarnitines (including short chain, medium long-lasting acylcarnitine), with particular emphasis on laurylcarnitine and acetylcarnitine; 3) Phenolic (and related), such as phenolic acid, mandelic acid or methoxy-hydroxyphenyl glycol.
Analysis of lipoprotein profile
Time frame: 2 years
Different forms of lipoproteins will be evaluated: Apolipoproteins A1 and A2, HDL-apolipoproteins A1 and A2,free cholesterol HDL3, HDL3-apolipoprotein A1, HDL2-apolipoprotein A2, apolipoprotein A2, IDL, HDL-apolipoprotein A2, VLDL and its subtypes, VLDL2-triglycerides, VLDL3-triglyceridestriglycerides, VLDL2- cholesterol, VLDL3 cholesterol, VLDL4 cholesterol free of VLDL4, phospholipids VLDL2, Phospholipids VLDL3, Cholesterol LDL5, Cholesterol free LDL5, Phospholipids LDL5, LDL5-apolipoprotein B, HDL3 cholesterol, HDL4 cholesterol HDL4, HDL3 cholesterol free, free cholesterol HDL4, HDL3-phospholipids, HDL4-phospholipids, HDL3-apolipoprotein A1, HDL4-apolipoprotein A1, HDL3-apolipoprotein A2 and HDL4-apolipoprotein A2.
Identify immune-profile linked and epigenomic signatures
Time frame: 2 years
Methylome analysis on genomic DNA will be performed.
AI-powered diagnostics predictive tool (companion diagnostic-like)
Time frame: 2 years
Deploy an AI-powered predictive tool (companion diagnostic-like) in clinical practice for the prescription of anti-depressants. OPADE AI-powered predictive tool will be a class C medical device under the In vitro diagnostic classification.
Mood assessment through brain biomarker
Time frame: 2 years
Validate a patient tracking tool for mood assessment using brain biomarker.
Patient engagement digital tool
Time frame: 2 years
Validate a patient engagement digital tool that can be deployed in any patient community to enhance clinical study outcomes.
Discovery of a new set of biomarkers
Time frame: 2 years
Propose new set of biomarkers that can guide the development of new antidepressants
Investigation of the gut-brain-axis and of the biomarkers of interest in the context of mental diseases starting with MDD
Time frame: 2 years
Identify indices in MDD to improve diagnostic accuracy for primary prevention and patients' stratification.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 May 2026.
- OPADE Official WebsiteLink provided by the sponsor.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Major Depressive Disorder, run by Alessio Fasano at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 9 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 15 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Identify neuroinflammatory indices (time frame: 2 years). The study lists 10 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 May 2026.