Known Medicine develops an organoid platform that utilizes machine learning-based sensitivity assays and omics data to analyze cancer cells from patient fluid samples. This technology enables oncologists to identify predictive biomarkers for drug response, facilitating personalized treatment strategies for cancer patients.
Funding
$9.6M 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.
CCKFounders
Product
Problem
Selecting effective cancer treatments is often hindered by the lack of predictive biomarkers that accurately reflect individual patient responses. Traditional methods struggle to replicate the complexity of a tumor's microenvironment and predict drug efficacy, leading to suboptimal treatment choices and increased healthcare costs.
Solution
Known Medicine offers a platform that utilizes 3D cell culture, machine learning, and multi-omic data to predict cancer drug response. The platform creates organoids from patient-derived tumor cells, then exposes these organoids to various drug treatments. High-content imaging and omics analysis are used to quantify drug sensitivity and identify predictive biomarkers. Machine learning models correlate drug response with genomic, proteomic, and transcriptomic data to predict treatment efficacy for individual patients. The resulting insights enable oncologists to personalize treatment strategies, improve patient outcomes, and reduce the reliance on ineffective therapies.
Target Audience
The primary target audience includes oncologists, pharmaceutical companies, and researchers seeking to improve cancer treatment selection and drug development through personalized medicine approaches.
Features
- Patient-derived organoid creation to mimic the tumor microenvironment
- High-throughput drug screening on 3D cell cultures
- Multi-omic analysis (genomics, proteomics, transcriptomics) to characterize drug response
- Advanced image analysis using machine learning to quantify cellular changes
- Predictive modeling to identify personalized treatment strategies
- Biomarker discovery to understand mechanisms of drug resistance and sensitivity
- Proprietary algorithms trained on a large, purpose-built 3D cell culture dataset