Quris

About Quris

Quris offers a Bio-AI Clinical Prediction Platform that utilizes machine learning trained on miniaturized Patients-on-a-Chip to predict the safety of drug candidates in humans. This approach aims to reduce the high failure rate of clinical trials, which currently sees 89% of drugs not making it to market, thereby lowering development costs.

```xml <problem> The traditional drug development process is inefficient and costly, with a high failure rate in clinical trials due to unforeseen safety issues in humans. Animal testing, the current standard, often fails to accurately predict human responses, leading to late-stage failures and increased development expenses. </problem> <solution> Quris offers a Bio-AI Clinical Prediction Platform that leverages machine learning and miniaturized Patients-on-a-Chip to predict the safety and efficacy of drug candidates in humans. The platform uses stem cells to create miniaturized versions of human organs and tissues on a chip, which are then exposed to drug candidates. Nanosensors monitor the reactions, and the resulting data trains AI models to predict human responses, identify potential toxicity risks, and select the best drug candidates for specific patient subtypes. This approach aims to reduce reliance on animal testing, improve the accuracy of preclinical predictions, and accelerate the drug development timeline. </solution> <features> - Miniaturized Patients-on-a-Chip: Uses stem cells to create miniaturized human organs and tissues for drug testing. - Nanosensor Technology: Employs miniature sensors to monitor organ and tissue reactions to drug candidates. - Machine Learning Models: Trains AI algorithms on the data generated from the Patients-on-a-Chip to predict human responses. - Toxicity Prediction: Identifies potential liver toxicity and other safety risks associated with drug candidates. - Patient Subtype Selection: Predicts which patients are the best candidates for specific drugs based on genomic subtypes. - High-Throughput Testing: Enables testing of over 1,000 drugs on the same patient cohort simultaneously. - Partnership with NY Stem Cell Foundation: Special access to the organization's stem cell workflow. - Patent Portfolio: 18 patents filed to protect the proprietary AI platform. </features> <target_audience> The primary target audience includes pharmaceutical companies seeking to improve drug safety prediction, reduce clinical trial failures, and accelerate drug development, as well as researchers focused on personalized medicine and drug discovery. </target_audience> ```

What does Quris do?

Quris offers a Bio-AI Clinical Prediction Platform that utilizes machine learning trained on miniaturized Patients-on-a-Chip to predict the safety of drug candidates in humans. This approach aims to reduce the high failure rate of clinical trials, which currently sees 89% of drugs not making it to market, thereby lowering development costs.

Where is Quris located?

Quris is based in Tel Aviv, Israel.

How much funding has Quris raised?

Quris has raised 53440000.

Location
Tel Aviv, Israel
Funding
53440000
Employees
64 employees
Major Investors
SoftBank Vision Fund

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Quris

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Executive Summary

Quris offers a Bio-AI Clinical Prediction Platform that utilizes machine learning trained on miniaturized Patients-on-a-Chip to predict the safety of drug candidates in humans. This approach aims to reduce the high failure rate of clinical trials, which currently sees 89% of drugs not making it to market, thereby lowering development costs.

quris.ai3K+
cb
Crunchbase
Tel Aviv, Israel

Funding

$

Estimated Funding

$50M+

Major Investors

SoftBank Vision Fund

Team (50+)

No team information available.

Company Description

Problem

The traditional drug development process is inefficient and costly, with a high failure rate in clinical trials due to unforeseen safety issues in humans. Animal testing, the current standard, often fails to accurately predict human responses, leading to late-stage failures and increased development expenses.

Solution

Quris offers a Bio-AI Clinical Prediction Platform that leverages machine learning and miniaturized Patients-on-a-Chip to predict the safety and efficacy of drug candidates in humans. The platform uses stem cells to create miniaturized versions of human organs and tissues on a chip, which are then exposed to drug candidates. Nanosensors monitor the reactions, and the resulting data trains AI models to predict human responses, identify potential toxicity risks, and select the best drug candidates for specific patient subtypes. This approach aims to reduce reliance on animal testing, improve the accuracy of preclinical predictions, and accelerate the drug development timeline.

Features

Miniaturized Patients-on-a-Chip: Uses stem cells to create miniaturized human organs and tissues for drug testing.

Nanosensor Technology: Employs miniature sensors to monitor organ and tissue reactions to drug candidates.

Machine Learning Models: Trains AI algorithms on the data generated from the Patients-on-a-Chip to predict human responses.

Toxicity Prediction: Identifies potential liver toxicity and other safety risks associated with drug candidates.

Patient Subtype Selection: Predicts which patients are the best candidates for specific drugs based on genomic subtypes.

High-Throughput Testing: Enables testing of over 1,000 drugs on the same patient cohort simultaneously.

Partnership with NY Stem Cell Foundation: Special access to the organization's stem cell workflow.

Patent Portfolio: 18 patents filed to protect the proprietary AI platform.

Target Audience

The primary target audience includes pharmaceutical companies seeking to improve drug safety prediction, reduce clinical trial failures, and accelerate drug development, as well as researchers focused on personalized medicine and drug discovery.

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