Circular Genomics utilizes circular RNA biomarkers to enhance the precision of diagnosing and treating psychiatric and neurological disorders. Their MindLight test predicts patient responsiveness to SSRI antidepressants, reducing the reliance on trial-and-error methods in mental health treatment.
Funding
$12.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Diagnosing and treating psychiatric and neurological disorders often relies on trial-and-error methods, leading to delays in effective treatment and prolonged suffering for patients. Current methods lack precision in predicting individual patient responses to medications, particularly in the treatment of Major Depressive Disorder (MDD) with SSRI antidepressants.
Solution
Circular Genomics leverages circular RNA biomarkers to improve the precision of diagnosing and treating psychiatric and neurological disorders. Their MindLight Antidepressant Response Test is designed to predict a patient's responsiveness to selective serotonin reuptake inhibitor (SSRI) antidepressant treatment. By analyzing brain-derived biomarkers, MindLight aims to streamline the treatment selection process, reducing the reliance on lengthy trial-and-error approaches. This approach facilitates more personalized and effective treatment plans for individuals with MDD.
Target Audience
The primary target audience includes psychiatrists, neurologists, and other healthcare professionals involved in the diagnosis and treatment of psychiatric and neurological disorders, specifically those treating patients with Major Depressive Disorder (MDD).
Features
- Circular RNA-based biomarker analysis for predicting SSRI antidepressant response
- Identification of brain-derived biomarkers to improve understanding of mental health conditions
- Streamlined treatment selection process to reduce trial-and-error
- Personalized treatment plans based on individual patient responses