Research and methods
Regulatory Science Lab
Building the evidence regulators and health systems need to decide which new health technologies reach patients, how fast, and at what cost.
Led by Dr. Dean Regier at the UBC Academy of Translational Medicine
What the lab does
New therapies are arriving faster than the evidence to judge them
Before a new medicine or genetic test reaches patients, regulators decide whether it is safe and effective, and health systems decide whether it is worth paying for. For many precision therapies, the trials are small, the populations are rare, and the evidence those decisions depend on is incomplete.
The Regulatory Science Lab develops methods to close that gap, so promising therapies can reach all Canadians sooner, at a cost the health system can sustain.
Research themes
Four areas of research
Economic valuation
Measuring the value of genomic and precision technologies, including multi-gene panel sequencing and tumour-agnostic therapies.
Real-world evidence
Drawing reliable conclusions about effectiveness and cost from routine patient care data, using methods such as target trial emulation.
Regulatory evidence and flexibility
Defining what counts as substantial evidence when trials are small, and how regulators can be flexible without lowering the standard of proof.
Equity and patient preferences
Examining who benefits and who is left out when a technology is funded, and how patient preferences can be measured at scale.
Capabilities and infrastructure
The data, tools, and partners to answer hard questions
AI built for regulatory work
A large language model in development to speed up evidence review and regulatory submissions.
Population-level data
BC Cancer population data lets the lab measure how new therapies perform for real patients, and at what cost, outside the controlled setting of a trial.
Evidence that feeds back into care
Learning health system infrastructure connects what the lab learns to decisions made in routine care.
Direct lines to decision-makers
Working relationships with Health Canada, Canada’s Drug Agency, and INESSS keep the research tied to the questions regulators and payers are asking.
Reach beyond Canada
Research and industry partnerships with institutions in the United Kingdom and Australia.
Across the Academy
Research, practice, and training strengthen one another
Research informs practice
The lab’s methods strengthen the evidence strategies and decision pathways the Academy builds with innovators.
Practice shapes research
Real-world cases reveal recurring barriers and raise new research questions.
Knowledge builds capability
Research and practical experience feed the Academy’s training programs and strengthen the system’s ability to support translation.
Latest research
Recent work from 2025 to 2026
Selected research from the lab and its collaborators.
JCO Precision Oncology
Determining the Causal Effect of Neurotrophic Tropomyosin Receptor Kinase Gene Fusions on Survival Prognosis for Adult Patients With Advanced Cancers
Krebs, E., Tamvada, N., Weymann, D., Lim, H. J., Chia, S., Ho, C., & Regier, D. A. (2026). Determining the causal effect of neurotrophic tropomyosin receptor kinase gene fusions on survival prognosis for adult patients with advanced cancers. JCO Precision Oncology, 10, e2600281. https://ascopubs.org/doi/full/10.1200/PO-26-00281
Lab researchers found that adults with advanced cancers carrying rare NTRK gene fusions had more than twice the risk of death in the first year without targeted treatment. The findings give clinicians a clearer baseline for judging the drugs that target these fusions.
The Lancet Oncology
Affordability in the era of precision oncology
Weymann, D., & Regier, D. A. (2026). Affordability in the era of precision oncology. The Lancet Oncology. https://doi.org/10.1016/S1470-2045(26)00436-5
Global cancer drug spending is projected to reach US$441 billion by 2029, driven largely by rising prices. Weymann and Regier argue that lifecycle regulation and value-based pricing are needed to keep precision oncology affordable.
PLOS Digital Health
Advancing the science of qualitative patient preference assessment using large language models
Grover T, Krebs E, Weymann D, Ehman M, Regier DA. PLOS Digital Health.
Tests whether an open-source large language model can replicate human thematic analysis of focus-group data on patient attitudes toward health data-sharing platforms, finding moderate to strong overlap with human-coded themes across three prompting strategies.
Genetics in Medicine
Forced resilience: Indigenous perspectives on systemic barriers and humanizing genomic medicine in British Columbia, Canada
Ehman M, Montour L, Pollard S, Weymann D, Kirk D, Brown K, Epp S, Wadsworth D, Garrison N, Laberge AM, Caron N, Arbour L, Regier DA. Genetics in Medicine. https://doi.org/10.1016/j.gim.2026.102555.
Examines Indigenous families’ perspectives on diagnostic genomic sequencing in BC, finding that physical and relational access barriers, racism, and persistent demands for self-advocacy impose “forced resilience” as an unspoken condition of care.
JCO Precision Oncology
Clinical effectiveness and cost-effectiveness of multigene panel sequencing in advanced melanoma: A population-level real-world target trial emulation
Krebs E, Weymann D, Ho C, Weppler A, Bosdet I, Karsan A, Hanna TP, Pollard S, Regier DA. 2025; 9:e2400631:1–12.
Uses target trial emulation to estimate effectiveness and cost-effectiveness from population health data.
Frontiers in Medicine
Putting the substance in substantial evidence: An evidence-based approach to flexible drug regulation
Krebs E, Bubela T, McPhail M, McCabe C, Regier DA. 2025; 12:133789.
Sets out how regulators can exercise flexibility without lowering the standard of proof.
Frontiers in Medicine
How Life-Cycle Real-World Evidence Can Bridge Evidentiary Gaps in Precision Oncology
Krebs E, Weymann D, Bubela T, Regier DA. 2025; 12:1563950.
Shows where real-world data can close the evidence gaps that trials in precision oncology leave open.
Health Services Research
Determining the Survival Impact and Cost-Effectiveness of Multi-Gene Panel Sequencing in Metastatic Colorectal Cancer With Super Learning Approaches
Krebs E, Weymann D, Lim H, Yip S, Regier DA. 2025; e70009. Advance online publication.
Applies machine learning methods to population data to estimate survival and cost-effectiveness outcomes.
Contact
Work with the lab
Team members, current projects, and open positions are listed on the lab’s BC Cancer Research page.
For research collaboration, data partnerships, or media enquiries, contact the Academy.