Bio-AI Clinical Prediction Platform

The Bio-AI Clinical Prediction Platform by Quris-AI is a radically new approach to predicting drug safety and efficacy in humans. Designed for pharmaceutical companies seeking to overcome the high failure rates of clinical drug development, the platform combines patented Patients-on-a-Chip biotechnology with advanced machine-learning algorithms to generate highly predictive, proprietary datasets that go far beyond traditional pre-clinical methods.

Traditional pre-clinical data — derived from lab experiments, animal models, or genomics — is widely accessible but notoriously poor at predicting clinical outcomes. With 89% of drugs failing in clinical trials despite promising pre-clinical results, Quris-AI addresses this critical gap by training its machine-learning model on data generated directly from miniaturized human patient models, creating a continuously improving, highly predictive system for clinical safety and efficacy.

Core Technology: Patients-on-a-Chip

• Utilises a patented AI Chip-on-Chip platform with 18 granted and pending patents to automate the testing of thousands of drugs on miniaturized Patients-on-a-Chip.
• Next-generation nano-sensors enable continuous monitoring of responses from each miniaturized organ to drug candidates.
• Known safe and unsafe drugs are tested through an automated, high-throughput system, generating data that is classified and used to continuously re-train the machine-learning algorithm.
• The classification algorithms are specifically designed to improve prediction of which drug candidates will safely and efficaciously work in humans.

Leveraging Genomic Diversity Through Stem Cells

• Training the machine-learning model on a single Patient-on-a-Chip is inherently limited; the platform overcomes this by training across hundreds of stem cell-derived Patients-on-a-Chip.
• These stem cell-derived models reflect an extremely broad genomic diversity, significantly improving the predictive power of the AI.
• This capability is made possible through an exclusive collaboration with the New York Stem Cell Foundation, recognised as the world leader in stem-cell automation.

Scalability and AI Integration

• The AI Chip-on-Chip platform is uniquely scalable, designed to routinely run thousands — and eventually millions — of biological experiments for AI training.
• Older organ-chip devices are unable to support experiments at this scale; Quris-AI's patented platform is purpose-built to overcome these limitations.
• The biological platform is tightly integrated with the AI system, enabling massive experimental throughput at a fraction of the cost of conventional approaches.
• Scalability is central to tackling the full complexity of clinical prediction, allowing the machine-learning model to grow more accurate over time as more data is generated.

Founding Vision and Scientific Approach

• Founded and led by Isaac Bentwich, MD, a genomics-AI pioneer who previously applied disruptive bio-AI methods to analyse the human genome and discover hundreds of novel microRNA genes — more than all universities in the world combined.
• The same disruptive approach is now applied at Quris-AI to revolutionise drug development through the Bio-AI Clinical Prediction Platform.
• The platform generates a massive proprietary dataset that is automated, continuously updated, and specifically optimised for clinical prediction.

The Bio-AI Clinical Prediction Platform is built for pharmaceutical partners seeking a fundamentally more reliable path through drug development. With its combination of patented chip technology, stem-cell genomic diversity, nano-sensor monitoring, and scalable AI integration, Quris-AI offers a comprehensive solution to one of the life sciences industry's most persistent and costly challenges.