Advancing Personalized Antidepressant Treatment Using PET/MRI
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|ClinicalTrials.gov Identifier: NCT02623205|
Recruitment Status : Recruiting
First Posted : December 7, 2015
Last Update Posted : November 8, 2019
|Condition or disease||Intervention/treatment||Phase|
|Depression||Drug: Escitalopram Drug: Placebo||Phase 4|
Aim 1: Determine a Pretreatment Marker of SSRI Effectiveness Using PET. With the goal of reducing MDD burden, many studies have assessed the utility of FDG-PET in antidepressant treatment prediction. However, due to the limitations listed above, there is no consensus on which brain regions are predictive of treatment efficacy. In addition to serving as a biomarker of SSRI effectiveness, only conclusive determination of these regions will provide insight into depression pathophysiology, helping uncover SSRI mechanism of action, and aiding in the search of novel therapeutics. Based on the investigators' preliminary data and other, similar studies, the investigators hypothesize that SSRI-induced change in the Hamilton Depression Rating Scale (deltaHDRS) will be correlated with pretreatment metabolic rate of glucose (MRGlu, quantified using arterial blood analysis) in three potential regions: (1) midbrain, (2) right anterior insula, and/or (3) left ventral prefrontal cortex.
Aim 2: Isolate the Neurobiological Basis of the "Loss" Research Domain Criteria (RDoc) and the Change Associated with Treatment. Using a factor analysis of the HDRS, the investigators have previously demonstrated that the "loss" RDoC criteria is significantly correlated to MRGlu in frontal cortical areas. The investigators therefore hypothesize that change in MRGlu (pre to post treatment) in these regions will be correlated with symptom improvement specifically in "loss" symptoms. As an exploratory extension, the investigators will determine whether these changes are treatment-specific (i.e. to SSRI or placebo). A validation of the hypothesis suggests a targeted mechanism of action, and provides a significant step forward for precision treatment. If regional changes in MRGlu are not correlated to improvement in this RDoC category, it suggests that SSRI (or placebo) induced changes may be a downstream effect that should be examined further.
Aim 3: Validate NonInvasive Full Quantification of MRGlu Using Simultaneous Estimation. Full quantification of brain MRGlu with FDG (as performed in this study) requires measuring FDG in arterial plasma (input function) from arterial catheter insertion and blood analysis. This costly and invasive procedure creates a barrier to widespread PET use. The investigators have developed an innovative method for Simultaneous Estimation (SimE) of input information and PET outcome measures (e.g. MRGlu). SimE fully quantifies brain MRGlu without requiring an arterial catheter. In the case of FDG, the investigators' data suggests that SimE used with a single venous sample can provide accurate results. The investigators further hypothesize that the venous sample may be entirely replaced by study data (e.g., injected dose) and biometrics (e.g., body surface area, lean body mass index). Using two different approaches (statistical imputation and physiological parametric modeling) and previously collected data, the investigators will train the SimE for accurate quantification in the absence of blood data. The rich data collected in this study will then provide a robust benchmark for validation of the SimE approach.
Aim 4: Validate Noninvasive Estimates of Plasma Radioactivity from a Novel miniPET Scanner. In parallel to SimE (algorithm/software) development, the investigators will test a noninvasive method of plasma analysis using hardware. FDG concentration will be measured at the wrist, arm, ankle or leg with a novel synchronized PET scanner developed by co-Investigator, Dr. Paul Vaska.
|Study Type :||Interventional (Clinical Trial)|
|Estimated Enrollment :||150 participants|
|Intervention Model:||Parallel Assignment|
|Masking:||Double (Participant, Investigator)|
|Official Title:||Advancing Personalized Antidepressant Treatment Using PET/MRI|
|Actual Study Start Date :||May 2015|
|Estimated Primary Completion Date :||January 1, 2020|
|Estimated Study Completion Date :||January 1, 2020|
Active Comparator: Escitalopram
Active Comparator: Escitalopram (Lexapro) 10mg Week 1, 20mg Weeks 2 and 3, and 30mg for Weeks 4 to 8 (or until 12 for patients close to remission)
Participants randomized to the escitalopram group will be given the medication for 8 weeks, after which they will be given the option to continue for 4 more weeks if they are close to remission.
Other Name: Lexapro
Placebo Comparator: Placebo
Lactose pill manufactured to mimic Escitalopram pill
To reduce the burden of the patients on placebo, the placebo trial will have a target of 8 weeks. To provide an opportunity for placebo non-responders to receive active drug and to aid in recruitment, we will provide 8-12 weeks of SSRI treatment to placebo non-responders.
Other Name: Lactose pill
- Change from Baseline in Hamilton Depression Rating Scale at 8 weeks [ Time Frame: 8 weeks ]
- Change from Baseline in MRGlu (quantified using arterial blood analysis) at 8 weeks [ Time Frame: 8 weeks ]
- Quantification of brain MRGlu without an arterial catheter by training SimE [ Time Frame: 8 weeks ]
- FDG concentration measured at the wrist, arm, ankle, or leg with a novel synchronized PET scanner [ Time Frame: 8 weeks ]
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT02623205
|Contact: Nichole Hoehn, MAemail@example.com|
|Contact: Study Coordinatorfirstname.lastname@example.org|
|United States, New York|
|Stony Brook University Hospital||Recruiting|
|Stony Brook, New York, United States, 11794|
|Contact: Nichole Hoehn, MA 631-638-2053 Nichole.Hoehn@stonybrookmedicine.edu|
|Contact: Juhayer Alam 631-638-2053 email@example.com|
|Principal Investigator: Christine DeLorenzo, PhD|
|Principal Investigator:||Christine DeLorenzo, PhD||Stony Brook University|