# 1/2 Prospective tReatment EffiCacy in IPF uSlng genOtype for Nac Selection (PRECISIONS) trial and Molecular Endophenotyping in Idiopathic Pulmonary Fibrosis and Interstitial Lung Diseases study

> **NIH NIH UH3** · WEILL MEDICAL COLL OF CORNELL UNIV · 2021 · $2,748,291

## Abstract

ABSTRACT
Despite being the most frequent and deadly of the interstitial lung diseases (ILD), Idiopathic Pulmonary Fibrosis
(IPF) remains challenging to diagnose and treat. The diagnostic process for IPF relies on subjective
interpretations of clinical data while current antifibrotic therapies employ a “one size fits all” paradigm.
Members of our team have been at the forefront of developing `omics approaches to diagnose and define
prognosis in ILDs. Importantly, we identified the first pharmacogenomic interaction suggesting that IPF patients
with TOLLIP rs3750920 T/T genotype strongly benefited from NAC. There is a critical need for molecular
classifications that define IPF, thus allowing precision-based management. Our long-term goals are to move
ILD diagnosis and therapy into the “era of precision medicine.” In a highly innovative approach we have
partnered with the Pulmonary Fibrosis Foundation (PFF) Clinical Care Network (CCN) and Registry. This group
has recruited ILD patients who have provided extensive baseline phenotypic and longitudinal outcome data,
biological samples and have consented to be re-contacted for future research. Our overall objective is to
efficiently conduct a novel, precision genotype-based trial in IPF while leveraging the CCN and its unique
biospecimen collection to characterize a broad range of ILDs molecularly and identify genetic variants of IPF
risk. To address our goal of precision-based ILD management, we will complete three complementary Specific
Aims. In Aim 1 we will determine if NAC is an effective treatment in IPF patients characterized by a precision
genotype approach. In partnership with the PFF, we will identify PFF registry subjects with the TOLLIP T/T
genotype to begin randomizing 200 IPF patients followed by enrolling new subjects at the same clinical sites to
receive NAC or placebo in a double-blind fashion. This study, the “Prospective tReatment EffiCacy in IPF
uSIng genOtype for Nac Selection (PRECISIONS)” trial, will document the benefits of an innovative “precision”
genotype-specific study design of a well-tolerated and inexpensive therapy. In Aim 2 we will distinguish IPF
from non-IPF ILDs using unbiased combinations of blood transcriptomics and proteomics. We propose to
conduct RNA-seq and proteomics to characterize gene expression and protein biomarkers on the entire PFF
registry cohort. We will define “signatures” for distinguishing IPF from non-IPF ILDs. Our unbiased approaches
to `omics traits will be integrated to reveal `omics risk scores that define individual diseases, predict disease
course, and response to therapy. In Aim 3 we will identify genetic variants playing a role in IPF risk. We will
conduct whole genome sequencing of the entire PFF cohort to detect novel genetic associations for IPF and
ILD risk. With sufficient power, we will assess both common and rare/infrequent variants in comparison to
ethnically matched un-afflicted cases, and between ILD cohorts to meet our objective. ...

## Key facts

- **NIH application ID:** 10026438
- **Project number:** 4UH3HL145266-02
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Fernando J Martinez
- **Activity code:** UH3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $2,748,291
- **Award type:** 4N
- **Project period:** 2020-09-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10026438

## Citation

> US National Institutes of Health, RePORTER application 10026438, 1/2 Prospective tReatment EffiCacy in IPF uSlng genOtype for Nac Selection (PRECISIONS) trial and Molecular Endophenotyping in Idiopathic Pulmonary Fibrosis and Interstitial Lung Diseases study (4UH3HL145266-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10026438. Licensed CC0.

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