Date Posted: Jan 7, 2019
Advances in biology and immunology continue to refine our understanding of cancer pathogenesis, elucidating potential mechanisms of tumor-cell growth, survival, angiogenesis and the systematic suppression of cancer immunity progressing toward precision medicine. Implicit to the concept of precision medicine is heterogeneity of treatment benefit among patients and patient sub-populations. Yet, precision medicine presents challenges to traditional paradigms of clinical translational for which estimates of population-averaged effects are used as the basis for selecting dose-scheduling strategies as well as demonstrating comparative benefit with randomized study.
Aspects of the traditional clinical research paradigm may not ideally suit the development of non-cytotoxics that challenge its foundational assumptions pertaining to dose-response and inter-patient exchangeability. Tumor biology and/or host immunity may better delineate target treatment populations than histology. Several emerging molecularly targeted and immunotherapeutic agents have produced durable responses in first-in-human trials. The U.S. regulatory landscape has also changed, with a growing number of accelerated approvals on the basis of single-arm trials. Collectively, these phenomena have prompted innovations with drug development strategies devised to consolidate phases in the traditional paradigm and rapidly expand accrual with “seamless” trial designs.
This postdoctoral fellowship is devised to develop analytical and computational technology for optimal designs of trials and drug development strategies for non-cytotoxic oncology treatments that span multiple indications. With the mentorship of Dr. Brian Hobbs (Cleveland Clinic) the trainee will research techniques for effectively identifying sources of subpopulation heterogeneity from real-world databases as well as optimal designs of master protocols and seamless trials that enroll potentially non-exchangeable patients.
The fellow will interact regularly with researchers of Amgen’s Statistical Innovation Team. Additionally, it is expected that the fellow will publish their findings in peer-reviewed academic journals as well as disseminate the ideas to both clinical and data science academic communities at conferences.
Interested candidates should send a cover letter, CV and 2-3 reference letters to Dr Brian Hobbs at Hobbsb@ccf.org.