Better Inform Clinical Development Decisions Leveraging AI/ML-Powered Analytics

There’s a new approach to informing clinical development choices and a way to make better decisions based on competitive insights. Discover how artificial intelligence and machine learning solutions can enable you to perform more rapid and objective assessments of asset and portfolio positioning within therapeutic and competitive landscapes in this webinar.

Register Free: https://www.pharmexec.com/pe_w/technology-powered-analytics

Event Overview:

Within the complex and evolving therapeutic and competitive landscapes, there is a need for better methods of rapidly and more objectively assessing assets and portfolio positioning. The traditional approach uses secondary research and is a lengthy, manual process that is not objective and is potentially more prone to errors due to outdated or missed information.

In this webinar, data science experts will discuss how using artificial intelligence can assist teams with these and other challenges by:

• De-risking development strategy by informing key decisions based on competitive positioning
• Offering an objective, data-driven perspective that is updated regularly based on market events
• Optimizing trial strategy based on factors that increase or decrease an asset’s probability of technical and regulatory success (PTRS)

Three key takeaways

Using AI/ML-based solutions, customers can:

• Perform fast, objective assessments of asset and portfolio positioning within therapeutic and competitive landscapes
• Gain competitive intelligence and rapid insights into approved assets, those which are soon to be in the market, and which are most likely to enter

Better forecast the outcomes of clinical developments, avoid running costly trials that will fail and devote more resources to assets with a higher chance of obtaining regulatory approval

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.

Greg Lever
AI Solutions Delivery Director, IQVIA Analytics Center of Excellence
IQVIA

Greg Lever began his career in life sciences and technology more than 13 years ago. After obtaining his PhD at the University of Cambridge for his work combining Quantum Physics and Machine Learning to develop new approaches for small-molecule drug discovery, he worked as a Postdoctoral Associate at MIT.

Shifting to industry, Greg has been an integral part at several technology startup companies in London and then joined Genomics England in the early stages of the 100,000 Genomes Project, seeing it through project completion.

As Director within the IQVIA Analytics Center of Excellence, Greg helps clients discover innovative ways to bring life-changing drugs and therapies to patients faster, leveraging IQVIA offerings and their team of expert ML engineers and scientists under his leadership.

Register Free: https://www.pharmexec.com/pe_w/technology-powered-analytics