The future of AI and analytics in clinical trial protocol design holds great promise, especially when applied to real-world data. This webinar discusses how these advancements have the potential to enhance the efficiency, cost-effectiveness, and success rates of clinical trials, ultimately benefiting both patients and researchers.
Register Free: https://www.pharmexec.com/pe/artificial-intelligence
Event Overview:
Artificial Intelligence and advanced analytics have the potential to revolutionize clinical trial protocol design. Real-world patient data and AI can be leveraged together to facilitate data-driven decision making throughout the trial design continuum, including:
- Characterizing response groups and disease clusters to aid in subgroup selection and personalized treatment approached
- Generating and evaluating protocols via simulations to predict clinical outcomes, protocol feasibility, and commercial viability
- Analyzing novel protocol parameters to improve efficiency and inform future trial design
- Improving diversity in clinical trials by identifying barriers to participation and mitigating bias in trial design
These advancements have the potential to enhance the efficiency, cost-effectiveness, and success rates of clinical trials, ultimately benefiting patients, health systems and researchers.
Three key take-aways
In this webinar, data science experts will discuss how real-world data can be used to:
- Help identify patient subgroups that may respond differently to interventions
- Design protocols to more accurately reflect the actual patient population to generate more representative results
- Assess if the initial patient eligibility criteria can be adjusted to obtain a better approximation from efficacy to effectiveness
Speakers:
Lucas Glass
Vice President, Analytics Center of Excellence
IQVIA
Lucas Glass is the Vice President of the IQVIA Analytics Center of Excellence (ACOE). The ACOE is a team of over 200 data scientists, engineers, and product managers that research, develop, and operationalize machine learning and data science solutions within the R&D space. Lucas has launched over a dozen machine learning offerings within R&D such as site recommender systems, trial matching solutions, enrollment rate algorithms, drug target interactions, drug repurposing, molecular optimization. Lucas’ machine learning research which is dedicated to R&D has been accepted at AAAI, WWW, NIPS, ICML, JAMIA, KDD, and many others.
Lucas started his career in pharmaceutical data science 15 years ago at Center (Galt) working on pharmacovigilance data mining algorithms. Since then, he has worked at the US Department of Justice in healthcare fraud, several small startups, and TTC, llc which was acquired by IMS In 2012.
Lucas holds a BA in Physics from Boston University, a MS in biostatistics from Drexel University, and is a PhD Candidate at Temple University where he is researching deep learning embedding techniques on large scale healthcare data.
Pablo Aran Terol
Senior Product Manager, IQVIA Analytics Center of Excellence
IQVIA
Pablo Aran Terol began his career in life sciences nearly a decade ago. After obtaining his PhD in Biophysics from the University of Cambridge, he spent several years working as a postdoctoral researcher studying the biophysical processes responsible for Alzheimer’s Disease.
Pablo began his career in clinical research as a consultant at IQVIA, working in clinical development programs, pricing, market access and asset evaluation. After several years as a consultant, he worked in offering development and strategic planning before becoming a Senior Product Manager.
Currently, within the IQVIA Analytics Center of Excellence, Pablo leads the strategy and development of technology designed to analyze clinical trial protocol designs to mitigate design risks and ultimately bring therapies to patients faster.
Register Free: https://www.pharmexec.com/pe/artificial-intelligence
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