Manager, Applied Data Science
Eric joined MetrumRG in May of 2017. He holds an M.S. in Mathematics from the University of Connecticut. Prior to joining MetrumRG, Eric worked in the areas of Analytics and Economic Research in the insurance industry for over five years. His experience includes statistical programming, creating insight-driven data visualizations, and developing application based user interfaces.
October 6, 2026
Brian W. Corrigan, Eric Anderson, Andrew Tredennick, Luis Martinez Lomeli, Megan Cala Pane, Tyler Dunlap, Brian Davis, James Rogers, Marc R. Gastonguay
First published 05 September 2026 in Clinical and Translational Science, Volume 19, Issue 9
Despite integration of artificial intelligence (AI) into drug discovery and an expanding global medicines pipeline, new drug approvals have declined, highlighting a paradox in biopharma: More drugs discovered, but less approvals, higher costs, and longer timelines. Reasons for the decreased research and development (R&D) efficiency are multifactorial, in part driven by the complexity of new modalities, difficult targets and indications, and persistence of cognitive biases in clinical decision-making. One proposed solution to address R&D productivity challenges includes the adoption of organization-wide quantitative decision frameworks (QDFs). QDFs have the potential to increase R&D productivity by integrating quantitative assessments of program risk and value, clinical development costs, time, and probability of success into product valuations.
March 18, 2026
Project Rationale
QDF Components QDF Components
Competitive Landscape
MIDD Enhanced Valuations
Rising costs, uncertain reimbursement, competition, and declining success rates have
reduced drug R&D productivity and investment over the last decade.
Proposed strategies to improve R&D productivity include four key factors: 1) leveraging all
data sources; 2) utilizing quantitative models; 3) elimination of information silos across R&D
and commercial organizations; and 4) application of decision frameworks to reduce
cognitive bias and improve decision making.1
A QDF for a drug development program in atopic dermatitis (AD) was developed to: 1) link
MIDD models aligned with a target product profile (TPP) to risk-adjusted net present value
(rNPV); and 2) integrate context-sensitive large language models (LLMs) to incorporate
non-structured data from novel sources into the decision-making framework in a responsible
manner.
May 27, 2025
A companion poster to the ASCPT print copy, this visual presentation emphasizes the simulation approach used to evaluate individualized dosing strategies and their economic implications. Results underscore the importance of reducing discontinuation rates to enhance value.