Canadian neurotech start-up Zentrela today announced the completion of an $850,000 seed round led by Jornic Ventures.
The investment will be used to further commercialize Zentrela’s proprietary EEG test for cannabis impairment and create what the firm described as the “world’s largest scientific database of cannabis product effects.”
In 2019, the company raised $1.2 million in funding from the Ontario Brain Institute and various government agencies to fund prototype development and clinical trials.
The neuroscience of cannabis, it said, is advancing quickly at pace with the speed of deregulation, as government regulators, license producers, drug testing experts, law enforcement officials and recreational cannabis users seek to better understand cannabis and its effects on the brain.
“Our research has already identified several proprietary signatures related to how cannabis impacts the brain,” said company CEO and founder Israel Gasperin.
“We are now cataloguing the entire universe of effects for Cannabis 2.0 products. This goldmine of data will help unlock new insights and opportunities for licensed producers and enable cannabis consumers to make more informed purchase decisions to ensure responsible and enjoyable cannabis use.”
Richard Sanson, president of Jornic Ventures, said the company expects “innovative cannabis 2.0 products will trigger the next wave of growth in the cannabis sector, especially as U.S. markets continue to deregulate and the prospect of national U.S. legalization becomes more real.
“Zentrela’s unique and scalable neuroscience-based methodology solves a major challenge for the industry and promises to provide a rapidly growing universe of cannabis users with the information they demand and need for safe and enjoyable cannabis use.”
Zentrela said its approach combines proprietary artificial intelligence algorithms with deep neuroscience expertise to decode electrical brain activity and convert it into reliable and meaningful information.
“The potential for neuroscience has exploded as artificial intelligence and machine learning algorithms have matured. We can now isolate neural ‘signatures’ of product effects and use that information rapidly to accelerate the process of mapping specific cannabis product effects for consumers,” said Gasperin.
