Orakl Oncology utilizes an AI-native predictive platform integrating patient-derived biology and real-world data to advance oncology drug development. This platform uncovers novel drug targets and accurately predicts patient responses to streamline discovery and clinical translation. The company partners with pharmaceutical firms to de-risk clinical trials, improve R&D efficiency, and accelerate the delivery of new therapeutic options to cancer patients.
Funding
$14.9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Drug development in oncology faces challenges in accurately predicting real-world drug responses due to the limitations of traditional preclinical models. These models often fail to capture the complexity of individual patient tumors and their response to therapies, leading to high failure rates in clinical trials and hindering the development of personalized cancer treatments.
Solution
Orakl Oncology offers a tech bio platform that leverages AI-driven tumor models to better replicate real-life drug responses by integrating multi-modal experimental data with deep patient data. The platform utilizes patient-derived tumor models, including organoids, and matches them with detailed clinical and omics data to provide a comprehensive understanding of tumor biology. AI-powered analysis mines this data to identify relevant therapeutic targets and accelerate the development of personalized cancer treatments. By combining best-in-class biology with high-quality clinical and molecular data at scale, Orakl Oncology aims to improve the accuracy and efficiency of drug discovery in oncology.
Target Audience
Orakl Oncology primarily serves oncology researchers and pharmaceutical companies seeking to accelerate drug discovery and develop personalized cancer treatments.
Features
- AI-driven tumor models that mimic real-life drug responses
- Integration of multi-modal experimental data with deep patient data
- Patient-derived organoid models for accurate representation of tumor biology
- AI-powered analysis to identify relevant therapeutic targets
- Capability to screen organoids to predict clinical responses