Quris

Our machine-learning bio-platform better predicts which drug candidates will safely work in humans, avoiding tremendous costs of failed clinical trials. Quris’s unique machine-learning approach automatically generates classified data by testing drugs on miniaturized ‘patients-on-a-chip’ and the machine-learning then trains itself on this automatically-tagged data Quris’s unique machine-learning approach using the Bio-AI Clinical Prediction Platform is radically different: we automatically generate a completely novel biological dataset, which is highly predictive of clinical safety and efficacy. We do this by testing known safe and unsafe drugs on miniaturized Patients-on-a-Chip, in a patented process in which this data is automatically classified and continuously re-trains the machine learning. Quris’s AI Chip-on-Chip platform (18 granted and pending patents) allows automated testing of thousands of drugs on miniaturized ‘patients on a chip’, while next-generation nano-sensors continuously monitor the responses of each miniaturized organ to these drugs. This massive, highly predictive, data generated – is automatically classified and analyzed by the machine learning and trains it. The ability to train the machine learning model on a single ‘patient on a chip’ is limited. Quris’s platform leverages powerful AI to train itself on hundreds of stem cell-derived ‘patients on a chip’, which reflect an extremely broad genomic diversity. We are preparing for clinical testing of our first drug developed with our Bio-AI Clinical Prediction Platform, targeting Fragile-X Syndrome, and thus demonstrates the tremendous potential of our platform to de-risk and drastically cut drug development time

Artificial Intelligence, Machine Learning

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