Andrew Tredennick, Ph.D.

Principal Scientist I

Andrew joined MetrumRG in September 2022 as a Research Scientist. He earned his PhD in Ecology from Colorado State University, followed by a National Science Foundation Postdoctoral Research Fellowship in Biology and Mathematics at Utah State University. Nearly all of Andrew’s work relies on confronting mechanistic models with data to make predictions and inference. His interest in fitting dynamical models to data led to a postdoctoral appointment at the University of Georgia’s Center for the Ecology of Infectious Diseases. Andrew then worked for four years as a statistical consultant in the environmental space before joining MetrumRG. Andrew has completed projects on early warning signals for disease re-emergence and forecasting models of emerging infectious diseases, like COVID-19.

Recent publications by this scientist

Expanding Quantitative Medicines’ Reach: Systems and Methods to Enhance R&D Productivity and Decision-Making in the Agentic Era

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.

Read Article

Comparison of Virtual Population Generation Methods for Quantitative Systems Pharmacology Models

October 6, 2026

Andrew Tredennick¹, Matthew Weins¹, Timothy Knab¹, Cole Zmurchok¹, Ahmed Elmokadem²
¹ Metrum Research Group, Boston, MA, USA; ² GlaxoSmithKline (GSK), Collegeville, PA, USA

Presented at the 2026 American Conference on Pharmacometrics (ACoP 2026), an International Conference on Pharmacometrics in the US (ICoP – US), on October 14th, 2026.

Virtual populations (VPops) are widely used in quantitative systems pharmacology (QSP) to represent variability in clinical populations and support model calibration and simulation of treatment outcomes. Practical guidance on selecting among algorithms to generate VPops, and understanding their relative performance, remains limited. The objective of this work was to compare commonly used and emerging VPop generation approaches based on data-simulation matching and computation time.

Download PDF

A MODEL INFORMED DRUG DEVELOPMENT (MIDD)-BASED QUANTITATIVE DECISION FRAMEWORK (QDF) FOR IMPROVING R&D PRODUCTIVITY: PROOF OF CONCEPT FOR ATOPIC DERMATITIS (AD)

March 18, 2026

A MODEL INFORMED DRUG DEVELOPMENT (MIDD)-BASED QUANTITATIVE DECISION FRAMEWORK (QDF) FOR IMPROVING R&D PRODUCTIVITY: PROOF OF CONCEPT FOR ATOPIC DERMATITIS (AD)
E. Anderson¹, BW. Corrigan¹, M. Cala Pane¹, A. Tredennick¹, T. Dunlap¹, L. Lomeli¹, B. Davis¹, MR.Gastonguay¹1Metrum Research Group, Boston, MA

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.

Download PDF