OSLO, Norway--(BUSINESS WIRE)--OncoImmunity has become the first company to obtain a CE-IVD mark for the clinical use of a machine-learning based neoantigen prediction technology. This quality assurance certification represents an important step in raising the standards for the use of neoantigen prediction technologies for the design of personalized cancer vaccines and cell therapies.
The certification means that personalized cancer vaccine and cell therapy companies can now meet their own quality assurance requirements for their clinical development by leveraging OncoImmunity’s proven quality assurance methodology. OncoImmunity’s neoantigen prediction technology, known as the Immuneoprofiler™, allows cancer immunotherapy companies to gain access to a quality assured prediction technology that also has superior performance in predicting the presentation of neoantigens to the tumor cell surface.
Richard Stratford CEO at OncoImmunity said:
“At OncoImmunity we understand intimately the complexity of neoantigen prediction and the need to select neoantigens for personalized cancer immunotherapy to a high clinical standard. We have designed machine-learning solutions to fill some of the gaps in the neoantigen prediction challenge and this certification of the Immuneoprofiler™ will facilitate both cost-saving and time-efficient filing for the accreditation of our partners cancer immunotherapy platforms”
About CE-IVD
The CE-mark (CE-IVD) indicates that an IVD device complies with the European In Vitro Diagnostics Directive (98/79/EC), and that the device may be legally commercialized and distributed in the EU.
About OncoImmunity
OncoImmunity is a machine-learning company offering a proprietary technology to address the key knowledge gaps in the prediction of bona fide immunogenic neoantigens for personalized cancer immunotherapy. OncoImmunity's software facilitates effective patient selection for cancer immunotherapy and identifies neoantigen targets for truly personalized cancer vaccines & cell therapies in a clinically actionable time-frame.
