The complexity of initiating studies continues to grow, a confluence of complicated protocols, globalization, and regulatory changes, at a time when there is intense pressure to speed clinical trials and restrain costs. Key to reducing these complexities and the occurrence of mistakes is the ability to be able to use machine learning to gain critical operational insights, allowing organizations to learn and adapt. Ultimately, these insights allow organizations to transition away from subjective decisions to data-driven decisions, by leveraging these insights to optimize activities in the planning and execution of clinical trials.
Machine learning technologies can help predict outcomes in clinical trials, leading to faster drug approval times, lower costs, and more funding to develop new treatments. More accurate predictions can reduce the uncertainty in study execution by providing greater risk transparency and allowing informed data-driven decisions to be made in the risk assessment and portfolio management of investigational drugs in clinical trials.
How can machine learning deliver business intelligence at the outset of studies?
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JENNIFER GOLDSACK, Executive Director, Digital Medicine (DiMe)
Jennifer C. Goldsack co-founded and serves as the Executive Director of the Digital Medicine Society (DiMe), a 501(c)(3) non-profit organization dedicated to advancing digital medicine to optimize human health. Jen’s research focuses on applied approaches to the safe, effective, and equitable use of digital technologies to improve health, healthcare, and health research. She is a member of the Roundtable on Genomics and Precision Health at the National Academies of Science, Engineering and Medicine.
Previously, Jen spent several years at the Clinical Trials Transformation Initiative (CTTI), a public-private partnership co-founded by Duke University and the FDA. There, she led development and implementation of several projects within CTTI’s Digital Program and was the operational co-lead on the first randomized clinical trial using FDA’s Sentinel System.
Jen spent five years working in research at the Hospital of the University of Pennsylvania, first in Outcomes Research in the Department of Surgery and later in the Department of Medicine. More recently, she helped launch the Value Institute, a pragmatic research and innovation center embedded in a large academic medical center in Delaware.
Jen earned her master’s degree in chemistry from the University of Oxford, England, her masters in the history and sociology of medicine from the University of Pennsylvania, and her MBA from the George Washington University. Additionally, she is a certified Lean Six Sigma Green Belt and a Certified Professional in Healthcare Quality. Ms Goldsack is a retired athlete, formerly a Pan American Games Champion, Olympian, and World Championship silver medalist.
NECHAMA KATAN, Director of Data Science, DMM, Pfizer
Nechama Katan, Director of Data Science, DMM at Pfizer, is a “Data Wizard with Personality.” She helps organizations access and use their data to drive business decisions. Nechama blends an ability to write code and provide prototypes with an interest in driving the “business conversation.” In addition to working with various organizations, she is an experienced instructor in Statistics, Operations, Data presentation and Metrics development. Her education includes an MSc from NYU in Mathematics and an MA from Columbia University in Statistics.
ALEX ZHAVORONKOV, Founder and CEO, Insilico Medicine
Alex Zhavoronkov, PhD, is the founder and CEO of Insilico Medicine (insilico.com), a leader in next-generation artificial intelligence technologies for drug discovery, biomarker development. He is also the founder and CEO of Deep Longevity, Inc, a global company developing a broad range of artificial intelligence-based biomarkers of aging and longevity. Since 2015 he invented critical technologies in the field of generative adversarial networks (GANs) and reinforcement learning (RL) for generation of the novel molecular structures with the desired properties and generation of synthetic biological and patient data. He also pioneered the applications of deep learning technologies for prediction of human biological age using multiple data types, transfer learning from aging into disease, target identification, and signaling pathway modeling. Under his leadership Insilico raised over $50 million in multiple rounds from expert investors, opened R&D centers in 6 countries and regions, and partnered with multiple pharmaceutical, biotechnology, and academic institutions. Prior to founding Insilico, he worked in senior roles at ATI Technologies (acquired by AMD in 2006), NeuroG Neuroinformatics, Biogerontology Research Foundation. Since 2012 he published over 130 peer-reviewed research papers, and 2 books including “The Ageless Generation: How Biomedical Advances Will Transform the Global Economy” (Palgrave Macmillan, 2013). He serves on the editorial boards of Aging Research Reviews, Aging, Trends in Molecular Medicine, Frontiers in Genetics, and co-chairs the Annual Aging Research, Drug Discovery and AI Forum (7th annual in 2020) at Basel Life, one of Europe's largest industry events in drug discovery. He is the adjunct professor of artificial intelligence at the Buck Institute for Research on Aging.
ELVIN THALUND, Director, Industry Strategy, Oracle Health Sciences
Elvin is a recognized industry expert in clinical trials, having over twenty years of experience working as a Clinical Business Analyst Consultant at major pharmaceutical companies including, Hoffmann-La Roche and Johnson & Johnson. Elvin works as a product strategist and system architect in Oracle Health Sciences effort to optimize Study Startup. Elvin is the co-chair on the TMF Reference Model Exchange Mechanism and holds a Master of Science in Industrial Engineering from Aalborg Universitet.