Graduate Departmental Seminar Series - Dr. Roger Howell

Speaker: Roger W Howell, PhD, FSNMMI Distinguished Professor, Rutgers New Jersey Medical School, Newark NJ  USA
DR. ROGER W. HOWELL is a Distinguished Professor at Rutgers University with appointments in the Departments of Radiology and Radiation Oncology, and he is a Fellow of the Society of Nuclear Medicine and Molecular Imaging (SNMMI). Dr. Howell’s laboratory conducts research on dosimetry and radiobiology of internal radionuclides, with emphasis on the microscopic dose distributions encountered in diagnostic and therapeutic nuclear medicine. He also studies radiation-induced bystander effects and their contribution to tumor response in radiopharmaceutical therapy, and the capacity of vitamins and other natural agents to protect reproductive organs, bone marrow, and the gastrointestinal tract against damage caused by low- and high-LET ionizing radiation. Dr. Howell has taught physics to radiology residents and lectured at the Rutgers Edward J Bloustein School of Planning and Public Policy. Dr. Howell is a member of the Medical Internal Radiation Dose (MIRD) Committee of the SNMMI and a Commissioner of the International Commission on Radiation Units and Measurements (ICRU). He is the Editor of the Journal of the ICRU. Howell earned his bachelor and doctoral degrees in Physics from the University of Massachusetts, Amherst.  Peer-reviewed publications can be found at: https://pubmed.ncbi.nlm.nih.gov/?term=howell+rw&sort=date.

AI Tools to Formulate Optimized Radiopharmaceutical Cocktails for Therapy

Radiopharmaceutical cocktails have been developed over the years to treat cancer. Cocktails of agents are attractive because one radiopharmaceutical is unlikely to have the desired therapeutic effect due to nonuniform uptake by the targeted cells. Therefore, multiple radiopharmaceuticals targeting different receptors on a cell and different degrees of diffusion into micrometastases is warranted. However, past implementations of radiopharmaceutical cocktails in vivo have not met with convincing results due to the absence of optimization strategies. We have developed artificial intelligence (AI) tools, within our recently released MIRDcell V4 application, that can optimize cocktails of radiopharmaceuticals. The AI tool determines the molar activities for each radiopharmaceutical in the cocktail that minimize the total disintegrations of the therapeutic radionuclide that are required to achieve the desired tumor cell kill. Tools are provided for populations of cells that represent circulating or disseminated tumor cells, and for micrometastases. The capabilities of the AI tool will be demonstrated using both model and experimental data to show that this approach could be used to analyze a sample of cells obtained from cell culture, animal, or patient to predict the best combination of radiopharmaceuticals for maximum therapeutic effect with the least total disintegrations.