Improving mental and neurological conditions, one neuron at a time.

PI

• Revital Amiaz, MD (Psychiatry Department Sheba Medical Center)

‍DESCRIPTION

The study involves generating neuronal cells from MDD patients to further understand the biological underpinnings of the disorder and establish Genetika+’s MDD disease model.

The study’s overarching aim is to investigate the potential of Genetika+’s blood test to predict treatment outcomes in patients with clinical depression. The study explores the possibility of identifying subgroups of patients with similar biological profiles and utilizing Genetika+’s biological markers to monitor the effectiveness of treatment and predict relapse.‍

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PARTICIPANTS

60 subjects with MDD, 20 antidepressant treatment responsive, 20 antidepressant non-responders, and 20 antidepressant differential responders, enrolled in the study.

PI

• Tom Fruchtman-Steinbok (PhD) (Geha Mental Health Center)

‍DESCRIPTION

The study involves generating neuronal cells from MDD patients to further understand the biological underpinnings of the disorder and establish Genetika+’s MDD disease model.

The study’s primary objective is to investigate the possibility of using Genetika+’s  blood test to predict how well patients with major depressive disorder (MDD) will respond to drug therapy. Furthermore, the study aims to identify specific biological markers associated with treatment responsiveness and resistance in MDD patients;  and explore potential methods for characterizing subgroups of patients based on their biological profiles and identify biological markers that can be used to monitor treatment effectiveness and predict relapse risk.

PI

• Karin Borgmann-Winter, MD
• Chang-Gyu Hahn MD, PhD (Department of Psychiatry and Human Behavior)

‍DESCRIPTION

The purpose of this study is to identify biomarkers that are associated with treatment responsiveness/resistance to anti-depressants. To that end, we aim to obtain neuronal cells derived from individuals who have well characterized history of responsiveness to treatment. The primary objectives of this study are to identify biomarkers associated with treatment responsiveness and treatment resistance in patients with major depressive disorder (MDD). In doing so, we hope to uncover biomarkers that may help predict appropriate therapy for patients with MDD.

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PARTICIPANTS

60 subjects with MDD, 20 antidepressant treatment responsive, 20 antidepressant non-responders, and 20 antidepressant differential responders, enrolled in the study.

PI

• Prof. Alon Shamir, MD, Head, Psychobiology Research Lab - Mazor Mental Health Medical Center
• and Muhammad Zoabi, MD

‍DESCRIPTION

This study aims to explore the morphological, molecular, and functional differences between microglia-like cells and neurons derived from schizophrenic patients and healthy individuals. It also seeks to evaluate the impact of antipsychotic treatment on the secretion of pro-inflammatory markers and neuronal activation in these cells.

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PARTICIPANTS

The study involves 120 individuals with schizophrenia and 60 healthy control subjects. The 120 individuals with schizophrenia are divided into two groups: those who are experiencing their first episode of schizophrenia or their first hospitalization for acute schizophrenia, and those who are in the non-acute phase of schizophrenia and are receiving antipsychotic treatment.

PI

• Dr. Tal Patalon, MD, LLB, MBA (Head of KSM Maccabi Research and Innovation Center)

‍DESCRIPTION

Genetika+ is developing a data-driven decision support system to help physicians personalize treatment choices for patients with major depressive disorder (MDD). This retrospective study aims to develop and validate a machine learning model for this system using the Maccabi dataset, which contains real-world data from a large and diverse patient population. The primary objective is to create a classification model that predicts treatment response to antidepressants based on individual patient characteristics. Secondary objectives include analyzing care pathways, prescription patterns, and healthcare utilization to identify potential predictors of treatment response.

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PARTICIPANTS

The study will involve anonymized electronic medical records of 90,000 patients with depression and 10,000 controls.

PI

• PI - Prof. Jonathan Rabinowitz, PhD (Elie Wiesel Professor, Bar-Ilan University)
• Sarah F. Feldman, MD, MPH, MSc

‍DESCRIPTION

Developing a Comprehensive Prediction Model for Antidepressant Response in MDD

This study aims to develop a machine learning-based classification model to predict antidepressant treatment response in patients with major depressive disorder (MDD). The model will incorporate a wide range of patient data, including demographics, clinical assessments, medical conditions, clinical rating scales, questionnaires, medical care information, behavioral measures, neurocognitive testing, and genomic, genetic, and proteomic profiles. The performance of the model will be evaluated using different response definitions, including the MADRS and QIDS scores. Additionally, the study will investigate potential biomarkers associated with treatment response, treatment resistance, and optimal dosage. Finally, correlations between clinical and molecular features will be explored to gain further insights into the underlying mechanisms of MDD and treatment response.

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PARTICIPANTS

CAN BIND 1st phase Cohort - 309 patients (206 MDD and 103 control).