From prediction to decision: Digital twins and predictive modeling in clinical development

Jacqueline Law leads the team responsible for scientific engagement and applied research at Flatiron Health, where they focus on advancing the use of AI and predictive modeling for cancer research. Prior to joining Flatiron Health in 2021, Jacqueline led data sciences and biostatistics teams at pharmaceutical and diagnostic companies. Jacqueline graduated from UCLA with a Ph.D. in Biostatistics.

Nicole Gormley is the founder of Gormley Advisory LLC, an independent regulatory consulting practice focused on oncology drug development. She brings nearly two decades of federal service to her advisory work, including most recently serving as Division Director for the Division of Hematologic Malignancies II at the U.S. Food and Drug Administration and as Associate Director for Oncology Endpoint Development in the Oncology Center of Excellence. During her nearly 15 years at the FDA, Dr. Gormley oversaw drug development for multiple myeloma, lymphomas, and chronic lymphocytic leukemia, and provided scientific and policy leadership on early endpoint development in oncology. She was instrumental in advancing novel endpoints including minimal residual disease and championed efforts to improve clinical trial representation. Prior to the FDA, Dr. Gormley completed fellowship training in hematology and critical care at the National Institutes of Health and served as Deputy Clinical Director at the National Heart, Lung and Blood Institute.

Xun Chen is Global Head of Data and Statistical Sciences at AbbVie. The Data and Statistical Sciences department at AbbVie drives the integrated quantitative strategy and its execution across the lifecycle of drug development. Grounded in industry-leading statistical science, her organization has a strong focus on advancing AI-powered predictive modeling and simulation to strengthen evidence generation and decision-making. Xun is particularly passionate about translating emerging technologies into scientifically rigorous, decision enabling and scalable capabilities that unlock the full value of data, improve development decisions, and ultimately help bring innovative medicines to patients faster.

Dr. Danielle Bitterman is a physician-scientist focused on AI for clinical medicine. She is currently the Vice President of AI for Clinical Development at AstraZeneca, where she leads efforts to transform clinical trial design, decision-making, and operations with AI across Phase I-III. Prior to joining AstraZeneca, Dr. Bitterman was on faculty at Harvard Medical School, the Clinical Lead for Data Science and AI at Mass General Brigham, and a practicing attending radiation oncologist at the Dana-Farber Cancer Institute/Brigham Cancer Center. Her pioneering research focuses on leveraging natural language processing (NLP) and deep learning to unlock unstructured electronic health records, evaluate generative AI safety, and improve data-driven patient care. Dual board-certified in radiation oncology and clinical informatics, Dr. Bitterman has published extensively in high-impact venues spanning medicine and computer science, including Nature Medicine, JAMA Oncology, Lancet Digital Health, and NeurIPS. She holds an MD from the New York University School of Medicine and completed her residency training through the Harvard Radiation Oncology Program.

Mark Guinter is a Director of Research Sciences at Flatiron Health, where he leads research partnerships and the design of fit-for-purpose real-world evidence solutions. As an epidemiologist researching in oncology for over 10 years, Mark has extensive experience applying advanced methodological approaches to generate rigorous, high-quality evidence to support multiple phases of drug development, from early-stage target product profiling to post-market evaluation. Mark earned his Ph.D. in Epidemiology from the University of South Carolina Arnold School of Public Health and completed a postdoctoral fellowship at the American Cancer Society.