Saverio Niccolini
CEO
NO
NEC OncoImmunity AS
NEC OncoImmunity AS utilizes proprietary machine learning algorithms to predict neoantigen presentation and immunogenicity, enabling the development of personalized immunotherapies for cancer patients. The company's technology enhances the selection of patient-specific targets in immuno-oncology clinical trials, improving treatment outcomes.
- Artificial Intelligence
- Biotechnology
- Healthcare Technology
Updated 23 months ago
Funding
Raised to date
$4.8MRaised 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.
Funding rounds are not available yet.
Founders
Product
Problem
Developing personalized cancer immunotherapies requires identifying which neoantigens, unique to a patient's tumor, will elicit a strong immune response, a process that is traditionally complex and inefficient. Selecting the most promising neoantigens for clinical trials is challenging, hindering the effectiveness of personalized cancer treatments.
Solution
NEC OncoImmunity AS offers machine-learning-driven software solutions that predict neoantigen presentation and immunogenicity, thereby streamlining the development of personalized cancer immunotherapies. Their technology employs proprietary algorithms and data analysis to enhance the selection of patient-specific targets in immuno-oncology clinical trials. By accurately predicting which neoantigens are most likely to trigger an immune response, the platform enables researchers and clinicians to focus on the most promising candidates, potentially improving treatment outcomes and accelerating the development of novel cancer therapies. The AI-powered approach aims to empower neoantigen clinical trial success through truly personalized cancer medicine.
Target Audience
The primary audience includes researchers and clinicians involved in immuno-oncology clinical trials, as well as pharmaceutical companies developing personalized cancer immunotherapies.
Features
- Proprietary machine learning algorithms for predicting antigen presentation.
- Prediction of neoantigen immunogenicity to identify targets that will elicit a strong immune response.
- Software solutions to guide the discovery of neoantigen-based personalized immunotherapies.
- Identification of biomarkers to monitor treatment response and patient outcomes.