At Immune 2.0, we're using machine learning to predict effictiveness of immunotherapy treatments.
One of humanity's greatest challenges has been cancer. Lately, new steps in immunotherapies such as CAR T-cell therapy have opened the door to new possibilities. Here at Immune 2.0, our analysis platform can help assess a patient's immune system and develop new immunotherapies and neoantigen vaccines to help the fight against cancer.
With our analysis platform for microbiome data, we will be able to identify and quantify the living microorganisms in someone's gastrointestinal tract. Microbiome makeup has been strongly linked to immunity and using data found in the human gut, we can make inferences about a host's reaction.
Our immune system is the main line of defense against infectious agents. Being able to predict and monitor the immune response to these infections can be very useful in determining next steps for treatment of infections. By combining our immune and microbiome sequencing platforms we can make more informed decisions about a patient’s present and future situation.
With new developments over the past few years on gene-engineered T-cell receptors, vaccines, and other immunotherapies, there is an increasing need to optimize our system. A large part of this is drug discovery is to increase the effectiveness of this process. Our prediction model at Immune 2.0 is able to identify which molecules bind best to each other in our immune system allowing faster generation of therapies.