Elypta is developing a metabolism-based liquid biopsy platform that analyzes glycosaminoglycan profiles in blood samples to detect cancer signatures noninvasively. This technology enables early cancer detection and monitoring, potentially identifying individuals at high risk up to six years before diagnosis.
Funding
$29.8M 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.
TRFounders
Product
Problem
Current cancer detection methods often rely on invasive procedures or late-stage diagnosis, leading to reduced treatment options and poorer patient outcomes. There is a need for non-invasive, early-detection methods that can identify cancer signatures before conventional symptoms appear. Existing methods may also lack the sensitivity to detect recurrence effectively.
Solution
Elypta is developing a metabolism-based liquid biopsy platform, the MIRAM® Kit, designed for non-invasive cancer detection and monitoring. The platform analyzes glycosaminoglycan (GAG) profiles in blood or urine samples to identify cancer signatures linked to tumor metabolism. By measuring the GAGome, a system biomarker of tumor metabolism, and applying machine learning algorithms, the platform aims to detect cancer early and monitor recurrence. This approach has the potential to identify individuals at high risk of developing cancer up to six years before diagnosis and to improve the management of cancer recurrence through simplified urine-based testing.
Target Audience
The primary target audience includes clinical researchers, oncologists, and urologists focused on early cancer detection, recurrence monitoring, and risk assessment, as well as individuals with a high risk of developing cancer, particularly renal cell carcinoma and bladder cancer.
Features
- Non-invasive liquid biopsy using blood or urine samples
- Analysis of glycosaminoglycan (GAG) profiles to detect cancer signatures
- MIRAM® Kit for measuring the GAGome, a system biomarker of tumor metabolism
- Machine learning algorithms for early cancer detection and risk prediction
- Potential to identify individuals at high risk of developing cancer years before diagnosis
- Application in early detection of recurrence in renal cell carcinoma
- High-throughput glycosaminoglycan extraction and UHPLC-MS/MS quantification in human biofluids