Eigen develops combination therapies that modulate target expression in cancer cells to enhance the efficacy of existing targeted treatments while minimizing toxicity. Their platform utilizes machine learning and high-throughput screening to create co-therapies that improve patient eligibility and reduce relapse rates across diverse cancer types.
Funding
$7M 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.
ZVFounders
Product
Problem
Existing targeted cancer therapies, such as CAR T-cell therapies and antibody-drug conjugates, are highly effective but often limited by heterogeneous target expression within tumors. This variability leads to some cancer cells escaping treatment, reducing patient eligibility and increasing the risk of relapse.
Solution
Eigen develops combination therapies that modulate target expression in cancer cells, enhancing the efficacy of existing targeted treatments while minimizing toxicity. Their platform utilizes high-throughput, high-speed combinatorial screening to determine the therapeutic window of drug combinations. Sophisticated machine learning models and a heterogeneity atlas guide co-therapy discovery, marking cancer cells and unmarking healthy cells by modulating the expression of their targets. This approach aims to improve outcomes for a wider range of patients by better controlling target levels, reducing side effects, and improving the likelihood that targeted therapies will hit their mark.
Target Audience
Eigen's primary customers are cancer patients who are not eligible for existing targeted therapies, as well as oncologists and pharmaceutical companies seeking to improve the efficacy and reduce the toxicity of cancer treatments.
Features
- High-throughput, high-speed combinatorial screening to determine drug therapeutic windows
- Complex phenotypic screening workflow to create realistic *in vitro* models
- Automated laboratory to reduce the time it takes to identify promising therapies
- Robotic workcells that execute millions of repetitive laboratory actions with consistency
- Custom-developed lab operations software to perform complex biology experiments
- Machine learning models and a heterogeneity atlas to guide co-therapy discovery