Luis Martinez Lomeli, Ph.D.

Research Scientist II

Luis joined Metrum in July 2023. He earned his PhD in Mathematical and Computational Systems Biology from the University of California, Irvine where he was awarded a Fulbright fellowship. His doctoral dissertation was focused on developing new statistical and machine learning methods for mathematical models applied to infectious diseases and biological systems. He then acquired industry experience in the fields of artificial intelligence and statistics from Google X and Mythic-AI.

Recent publications by this scientist

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.

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