Quris
AI for clinical prediction
Quris' Bio-AI Clinical Prediction Platform better predicts which drug candidates will safely work in humans, avoiding tremendous costs of failed clinical trials. The company is led by a team of top scientists and strategic investors and is preparing for clinical testing with its first AI-based drug.
Achievements to date
Predicting which drug candidates will be safe and efficacious in humans is a formidable challenge. Traditional pre-clinical data from lab research, animal testing and genomics is easily accessible, but it is very poor at predicting clinical safety and efficacy, which is why 89% of drugs fail in clinical trials. Quris’s unique machine-learning approach using the Bio-AI Clinical Prediction Platform is radically different.
In 2021, PI Impact made its first investment in Quris.
In 2020, Quris was founded with a mission to tackle the most impactful AI challenge of our time: A novel hybrid approach to predict drug safety.
First paid proof of concept pilot and core IP (18 patents) pending and granted.
Successful collaboration with Merck KGaA in a pre-clinical study to assess Quris’s ability to predict drug toxicity in comparison to traditional approaches.
Machine learning algorithm
Quris uses a patented process involving an automated, high-throughput system with next-generation nano-sensors to test known safe and unsafe drugs on miniaturized Patients-on-a-Chip. The resulting data is then classified and used to continuously retrain the machine learning algorithm, resulting in an approach that is highly predictive of clinical safety and efficacy.
Quris’s Chip on Chip platform is uniquely scalable. Successfully tackling the complexities of clinical prediction requires machine learning models to routinely run thousands, and eventually millions, of biological Patients-on-a-Chip experiments for AI training.
