# Utilizing a Lupus Clinical Trials Network to Advance Diversity and Representation in Clinical Trials: Perspectives, Preferences, and Unmet Needs of Patients, Providers, and Stakeholder Agencies

> **NIH FDA U01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2022 · $499,999

## Abstract

Title: Utilizing a Lupus Clinical Trials Network to Advance Diversity and Representation in Clinical Trials:
Perspectives, Preferences, and Unmet Needs of Patients, Providers, and Stakeholder Agencies
Project Abstract
Despite greater prevalence of systemic lupus erythematosus (SLE) among racial and ethnic minorities, marked
gaps exist between patients affected by the disease and those represented in lupus clinical trials. Advancing
enrollment of underrepresented populations is critical to ensure safety, efficacy, and equity in the process and
products from clinical trials leading to the development of novel lupus therapeutics. However, few studies have
explored the unique perspectives of patients and other key stakeholder groups to identify facilitators and
tangible solutions to increase representation of diverse racial and ethnic participants, particularly in the context
of lupus clinical trials. The goal of this proposal is to advance equity in lupus clinical trials by: a) leveraging
novel data sources to advance evidence for enrollment of underrepresented populations in clinical trials, b)
increasing understanding of diverse voices of key stakeholders in order to identify barriers, facilitators, and
tangible solutions, and c) developing patient-centered clinical trial communication strategies and skills training
for clinicians to improve participant diversity in lupus clinical trials. We will utilize a knowledge translation
framework and mixed-methods approach in order to identify, exchange, synthesize, and disseminate insights
to advance diversity in lupus clinical trials. We will leverage partnerships with the largest lupus clinical trials
network in North America, the Lupus Clinical Investigators Network (LuCIN), and collaboration with key
stakeholder groups to accomplish the proposed aims. The first specific aim of this project is to leverage
existing data to establish a multivariable dataset of participant- and site-level characteristics within the lupus
clinical trials network. The second specific aim is to describe the perspectives, preferences, and unmet needs
of diverse stakeholder groups to improve participation of underrepresented groups in Phase II and III lupus
clinical trials. Discussions will explore stakeholders’ perspectives on the barriers, facilitators, and tangible
solutions at the individual, interpersonal, organizational, and systems-level to improve representation in lupus
clinical trials; and assess stakeholders’ preferences for a practical patient-centered communication toolkit for
clinicians to integrate clinical trial discussions into clinical care. The third aim is to synthesize practical
approaches and resources to improve diversity and representation in lupus clinical trials. Using findings from
Aim 2, we will present a white paper summary outlining a framework of practical solutions to improve
representation in lupus clinical trials. As a tangible next step, we will develop a mockup of an online toolkit for
clini...

## Key facts

- **NIH application ID:** 10639164
- **Project number:** 1U01FD007781-01
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Saira Z Sheikh
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** FDA
- **Fiscal year:** 2022
- **Award amount:** $499,999
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10639164

## Citation

> US National Institutes of Health, RePORTER application 10639164, Utilizing a Lupus Clinical Trials Network to Advance Diversity and Representation in Clinical Trials: Perspectives, Preferences, and Unmet Needs of Patients, Providers, and Stakeholder Agencies (1U01FD007781-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10639164. Licensed CC0.

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