# Toward improved understanding of sex differences in drug response: developing gene and pathway-based informatics methods to examine sex-differential genetic effects

> **NIH NIH F31** · STANFORD UNIVERSITY · 2021 · $17,117

## Abstract

RESEARCH SUMMARY
Women are at more than 1.5-fold higher risk for adverse events, including serious adverse events such as
drug induced liver injury. Between 1997 and 2000, eight out of the ten drugs withdrawn from the market
proposed greater health risks to women. While some of this increased risk is due to women being overdosed, a
large portion of these differences are also related to biological sex differences; however, the mechanisms
behind these differences are poorly understand. In addition, an often-cited reason for not studying women is
the presence of within-sex variability due to the menstrual cycle. Throughout the drug development pipeline,
sex is rarely considered, and labels are routinely left out of analysis, even at the computational level. Genetic
data, in the forms of Genome-Wide Association Studies (GWAS) and gene expression levels, provide unique
opportunities for analyzing the effects of sex; they allow for insights into biological function and examination of
unlabeled data is possible in this case because sex can be easily imputed. Additionally, network-based
analysis of these data has the benefit of increasing the signal-to-noise ratio by relying on prior information
about gene-gene interactions and pathways, and also aids in the biological interpretation of results. To improve
understanding of sex-differential effects, I propose to leverage genetic data to accomplish following specific
aims: 1) use gene expression data to investigate the molecular effects of between-sex differences at the
organ-level and within-sex differences due to menstrual cycle hormone variability, 2) develop network-based
methods for detecting sex-differential effects in GWAS, and 3) link identified between- and within-sex variability
to drug response. My work will improve understanding of interactions between sex and drug response, and
provide insight into the mechanisms behind these interactions. With success and further evaluation, this
analysis will improve the drug development process by taking sex-related variability into account, decreasing
adverse events.
My long-term career goal is to become an independent academic researcher developing informatics methods
to study between and within sex variability in biology, disease, and drug response. During my fellowship
training, I will work toward this goal by deepening my research skills, building collaborations, publishing papers,
attending seminars and conferences, taking additional relevant coursework, and teaching and mentoring
students. I am exceptionally well-poised to achieve these goals; my training will take place with Dr. Russ
Altman, who has an extremely successful track record of mentoring students, and at Stanford University, which
has incredible educational resources and a collaborative cutting-edge research environment.

## Key facts

- **NIH application ID:** 10181072
- **Project number:** 5F31LM013053-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Emily Flynn
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $17,117
- **Award type:** 5
- **Project period:** 2019-04-01 → 2021-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10181072

## Citation

> US National Institutes of Health, RePORTER application 10181072, Toward improved understanding of sex differences in drug response: developing gene and pathway-based informatics methods to examine sex-differential genetic effects (5F31LM013053-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10181072. Licensed CC0.

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