Data Science and Statistics in Pharmaceutical Engineering and Experimental Studies: Where Are They, Where Are They Going?

• Activity Number: 272 - Data Science and Statistics in Pharmaceutical Engineering and Experimental Studies: Where Is It? Where Is It Going?
• Type: Invited
• Date/Time: Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Mark Daniel Ward* and Terri Bui* and Ke Wang* and Jonathan E. Allen* and Huanyu Zhou*
• Companies: Purdue University and Merck & Co. and Pfizer, Inc. and Lawrence Livermore Nattional Laboratory and Teva Pharmaceutical Industries Ltd.
• Keywords: data science; pharmaceutical ; nonclinical

Abstract

The advent of Data Science (DS) presents intensive algorithmic approaches to data exploration and analysis of large, complex, structured, or unstructured databases. This is enabled by high performance computational tools and ‘highly scalable’ learning algorithms. The partnerships emerging at the boundaries of Statistics, DS and the Drug Development sciences driven by Artificial Intelligence, Machine Learning, and Computational Statistics, are bringing a new excitement to the drug development process and transforming the role of Statisticians in drug development. This session will discuss the changing landscape of quantitative tools : 1. What is DS, and how does it differ from traditional Statistics? 2. What are the appropriate roles of DS and Statistics in furthering a deeper understanding of Pharmaceutical Science and Engineering? 3. What are the hurdles preventing the Pharmaceutical Sciences from utilizing data science tools? 4. How can we improve the communication and collaboration capabilities, between Pharmaceutical domain experts and DS/Statistics colleagues? 5. Where will we be 10 years hence? A discussant will highlight the salient points brought out by the panelists.