Immune checkpoint therapy has become a multi-billion dollars industry based on its potential to cure advanced cancer patients; but it can simultaneously cause toxicities that are costly and potentially fatal!
Over a decade, in hundreds of clinical trials, experts at academic centers have learned and developed best practice to better manage patients on these therapies, leading to lower toxicities, and improved outcome.
The clinical best practice involves not just monitoring of PRO to detect early symptoms; it depends on appropriate treatment by the clinical team to minimize toxicities so patients can stay on immune checkpoint therapy.
Oncologists in real-world practice do not have the resource nor the experienced teams to deliver the needed care, leading to more toxicities, higher cost, incomplete treatment courses, and poorer outcome for patients.
Lack of experience and resource results in reluctance to prescribe, premature termination of treatment, or delayed recognition of drug-related AE and consequent increased cost of care / reduced effectiveness / poorer outcome.
Deploying data and AI technologies, we will capture the collective expertise and the latest clinical evidences in clinical decision support algorithms to help practicing oncologists better manage their patients in the real world.
Leveraging this, we will build a novel R&D platform that is powered by prospective patient-level deep data to drive a system approach in discovery and development of better and safer drugs for patients.