The acute respiratory distress syndrome (ARDS) is a common cause of respiratory failure in critically ill patients, with nearly 200,000 cases per year in the US alone and mortality rates of 30-40%. Clinical trials of novel pharmacotherapies in ARDS have met with nearly ubiquitous failure, a dismal track record which has been attributed at least in part to the considerable clinical and biological heterogeneity within this syndrome. With this strong evidence of significant clinical and biological heterogeneity and a major need for new therapies to improve poor clinical outcomes, ARDS is a ripe target for the application of precision medicine, yet little has been done to move from our current one-size-fits-all approach to ARDS clinical care and trials to a more targeted and personalized approach. We recently identified and validated the presence of two distinct subphenotypes (also known as ?endotypes?) of ARDS in four large randomized controlled trials. In an independent analysis of all four datasets, there was strong evidence for two different endotypes within ARDS: a hyper-inflammatory endotype and a hypo-inflammatory endotype. These endotypes had strikingly different (1) clinical characteristics, (2) biomarker profiles, (3) clinical outcomes, and (4) treatment responses. Most notably, significant endotype- specific treatment responses were identified within three clinical trials previously thought to be ?negative.? While these data are highly promising, we have only a basic understanding of the biology of these endotypes, of the full range of differential treatment responses they exhibit, of the impact of environmental exposures on endotypes, and of how best to translate this growing knowledge base into practical tools for application at the bedside and in clinical trials. In this application, we describe a research program that brings together expertise in molecular phenotyping of critical illness, environmental exposures assessment, advanced statistical approaches to analysis of complex multi-dimensional data, an experimental human lung model of ARDS, and access to clinical trial networks and diverse heterogeneous patient cohorts, in order to determine the optimal approach to applying precision therapeutics in human ARDS. This program has the potential to be paradigm-shifting by developing practical models for personalized medicine for patients with ARDS, targeted to the biology of an individual patient's disease, with a significant impact on both clinical trials and ultimately clinical care. In addition, these studies will have a high impact via identification of endotype-specific therapeutic responses in completed and ongoing ARDS clinical trials and by improving our understanding of the diverse biology of human ARDS, enhancing the likelihood that successful new therapeutics will be identified for each endotype. Finally, this program will develop a framework by which the principles of precision medicine can be applied to the fast-paced, rapidly evolving setting of critical care medicine.

Public Health Relevance

The acute respiratory distress syndrome (ARDS) remains a common and frequently fatal cause of acute respiratory failure in critically ill patients, with no specific preventative strategies or therapies available. The goal of this project is to identify and deepen our understanding of subtypes of ARDS that may respond differently to treatments, in order to tailor therapy to individual ARDS patients and thereby improve clinical outcomes from ARDS.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Unknown (R35)
Project #
5R35HL140026-04
Application #
10072072
Study Section
Special Emphasis Panel (ZHL1)
Program Officer
Reineck, Lora A
Project Start
2018-01-16
Project End
2024-12-31
Budget Start
2021-01-01
Budget End
2021-12-31
Support Year
4
Fiscal Year
2021
Total Cost
Indirect Cost
Name
University of California San Francisco
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94143
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