Clinical and Translational Science Pilot Award Round 3 Awardees
The Michigan Institute of Clinical & Health Research (MICHR)’s Clinical and Translational Science Pilot Award supports innovative research projects in clinical & translational science (CTS). CTS is a field of investigation focused on understanding and removing barriers that slow translational research so that new treatments and other health solutions reach people faster. Projects typically propose addressing a common cause of inefficiency or failure in research projects at any stage of translation, with the goal of producing foundational knowledge that will ultimately improve health.
We are pleased to announce that the following proposals have been funded in the third round:
A General Computational Deconvolution Method of Bulk DNA Methylation Data for Epigenetic Biomarker Research
Investigators: Lana Garmire, PhD
DNA methylation is a process that controls how our genes are turned on or off, and it can be a crucial marker for diagnosing and understanding many diseases. By studying these changes in DNA, scientists can gain important insights into how diseases develop and progress, which helps in creating personalized treatments for patients. One way to find these DNA changes is through large studies that analyze thousands of sites on DNA to link changes in methylation to specific diseases.
However, a big challenge in these studies is that tissues are made up of many different cell types in different proportions, and the mixture of these cells can make it hard to pinpoint disease-specific DNA changes. Without accounting for the different proportions of cell types in the tissue, the results for DNA methylation biomarkers might be misleading.
To compuationally solve this, researchers use a process called deconvolution, which estimates the different cell types in a sample. Traditional methods rely on limited reference data from a technique called FACS, but this approach does not capture all the cell types, which can lead to inaccuracies.
We are proposing a new, more advanced deconvolution method called Molecular Sibling Matching (MSM). This method uses data from single-cell RNA sequencing, which identifies more cell types than previous techniques. By comparing new DNA methylation data to a large repository of samples with known profiles, we can more accurately estimate cell type proportions and make better conclusions about disease-related DNA changes.
This new method will help improve the accuracy of disease biomarker discovery, which is essential for predicting patient outcomes and finding new treatments, ultimately advancing personalized medicine.
Building Capacity for the Unexpected: Addressing Psychological Adverse Events in Clinical Research
Investigators: Viktoryia Kalesnikava, PhD; Vikas Kotagal, MD; Cynthia Ewell Foster, PhD; Briana Mezuk, PhD; Richard Gonzalez, PhD
The success of any clinical study depends in part on how effectively research teams can respond to unexpected or unusually severe adverse events. In research, unexpected or serious psychological adverse events (USPAEs) are events related to self-harm that include disclosure of severe distress, non-suicidal self-harm, and suicidal ideation or behavior.
The Institutional Review Board mandates that every clinical study reports USPAEs and provides reporting guidelines. Despite mandatory requirements, research teams may face system-level challenges due to insufficient infrastructure, lack of standardized procedures for evaluating USPAEs, and the absence of best clinical action plans post-USPAE. These structural and knowledge gaps can compromise the quality and continuity of research, lead to its early termination, and increase clinical staff burnout and turn-over.
This pilot proposal responds to these challenges by seeking to create a comprehensive and flexible framework for managing USPAEs in clinical and behavioral research at UM. We aim to leverage the diverse expertise and resources across UM scientific units to: 1) identify critical infrastructure gaps and systematically learn from the experiences of study teams with USPAE events; and 2) streamline existing practices and develop new best-practice solutions targeted for effective USPAE risk management. Developed in collaboration with UM research teams, mental health clinicians, and IRB consultants, our framework will accommodate disciplinary differences while providing actionable guidelines and supportive infrastructure. In the long-term, this framework will help seed new research efforts aiming to enhance clinical research infrastructure and efficiency through the NIH-funded CTSA centers and support suicide prevention in high-risk populations.
Development of Preclinical Approaches to Understand The Impact of Airway Mucus on The Efficacy of Inhaled Drug Products
Investigators: Ashlee Brunaugh, PharmD, PhD; Yvonne Huang, MD; Jozef Al-Gousous, PhD
A major roadblock in the development of drugs delivered to the body by inhalation is that we cannot directly determine the amount of drug present in the lungs after delivery. This can make it challenging to select an appropriate dose for therapeutic effect or to understand how small changes in the product could impact it’s ability to work in the lungs. Our airway surfaces are covered by mucus, which helps protect our lungs from exposure to inhaled toxins or pathogens. However, we hypothesize that mucus could also limit exposure to drugs in the lungs, and impact the ability of the drug to work properly following inhalation. This may be further worsened in certain lung diseases in which mucus is more sticky or over-produced. In this one year project, we will test this hypothesis by determining if mucus reduces the amount of drug that can reach the airway surface. We will compare mucus obtained from healthy airway cells and healthy mice to mucus from cells and mice that have inflammation present, and determine if those differences also impact drug exposure. Completion of this project will provide improved understanding on how mucus in our body impacts drugs, and could help generate new screening tools to test for mucus-drug interactions, and improve dose selection in the clinic.
Instrumenting Quality Reporting Systems to Enhance Trials of Audit and Feedback
Investigators: Zach Landis-Lewis, PhD, MLIS; Andrew Krumm, PhD; Seifi Farid, PhD
All healthcare organizations collect performance data in quality reporting systems. These systems are frequently studied because they deliver feedback to providers that is effective for improving the quality of care for patients. Delivering feedback as a strategy for improving care is moderately effective but unreliable. Rapid changes in the technology used for quality reporting systems have made it possible for many trials to be conducted at large-scale. However, evidence from hundreds of trials has yielded limited insight into how to make feedback more reliably effective.
A community of researchers who study clinical feedback have made progress to generate better evidence around common theories of feedback across diverse clinical settings and populations. However, this community lacks shared software tools for collecting data about what works based on their theories of interest. Shared tools for these analyses could reduce the effort required for data collection in trials, and could enable quality reporting systems to more easily conduct trials that build on each others’ findings. Our research team developed software for a trial in an anesthesia care network that we anticipate could broadly support feedback researchers in this goal.
We hypothesize that it is feasible to create shared software tools to reduce the effort required to collect data for trials of feedback that are embedded in quality and reporting systems. We will evaluate these tools with collaborators in three quality networks, to understand their ability to support a common workflow and performance data analyses for improving patient care in innovative studies of feedback.