Abstract
Model-based drug development in oncology is still lagging despite a good momentum in the clinical pharmacology and pharmacometry community in the past few years. The failure rate of late-stage oncology studies is one of the highest across therapeutic areas. The modeling of the relationship between longitudinal tumor size and overall survival has been proposed to enhance learning in early clinical studies, to predict overall survival, and to simulate clinical trials. This approach has the potential to support proof of concept, early clinical decisions, and design of late-stage trials, but it is not yet widely integrated into the oncology drug development process. In this article, we review the state of these modeling efforts and discuss several key applications of these models. We conclude by suggesting a few paths forward.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
