# Lung fibrosis modeling and compound testing platform using fibrotic lung ECM that recreates the fibrotic disease environment to improve predictiveness and accelerate anti-fibrotic drug development

> **NIH NIH R44** · XYLYX BIO, INC. · 2021 · $297,924

## Abstract

PROJECT ABSTRACT
Xylyx is developing a pulmonary fibrosis disease modeling and anti-fibrotic compound testing platform aimed at
improving the physiological relevance and predictive value of in-vitro models for idiopathic pulmonary fibrosis
(IPF) to power the investigation of IPF disease biology and accelerate development of drugs to treat IPF.
Devastating, intractable, and life-threatening, IPF is an interstitial lung disease characterized by obliteration of
pulmonary alveoli and progressive loss of respiratory function. Over 55,000 new cases of IPF are diagnosed
each year. Median survival is 3–4 years, and annual mortality in the US exceeds 40,000. The etiology and
pathogenesis of IPF remain unknown. Predictive animal and in-vitro models of IPF for basic science research
and drug development are severely lacking, leaving a significant unmet need and market opportunity for a
physiologically-relevant in-vitro platform that enables high-fidelity cell-based phenotypic studies of IPF. This
SBIR Fast Track will support development and validation studies for commercialization of an IPF disease
modeling and compound testing platform that recapitulates in vitro key features of the human IPF disease
environment and has been shown to support fibrotic phenotype of human lung fibroblasts to improve cell-based
assays in early-stage anti-fibrotic drug discovery. The technological innovation is the product’s human IPF fibrotic
lung specificity stemming from proprietary methods for isolating acellular human IPF lung extracellular matrix
(ECM) with the composition and biomechanics of human IPF lung tissue. Our ‘physiomimetic approach’ yields
standardized human fibrotic lung cell culture substrates for predictive in-vitro models of IPF that enable more
physiologic and thus more predictive studies, providing a major competitive advantage over existing products
like collagen-coated polystyrene plates. The goal is validation and commercialization of standard human IPF
lung ECM disease modeling and compound testing platform for predictive in-vitro models of IPF to greatly reduce
dependence on animal models and enable more relevant results for IPF drug developers. Specific aims are to:
(i) determine transcriptomic and metabolomic profiles of lung fibroblasts in human IPF and normal lung ECM
hydrogels, (ii) evaluate quality and consistency of human IPF and normal lung ECM hydrogels, (iii) perform
compound testing studies with IPF standard-of-care drugs. After successful completion of the Fast Track project,
Xylyx will commercialize the IPF compound testing platform to scientists in pharmaceutical companies in need
of predictive IPF disease models for drug discovery and screening, thus reducing the significant costs associated
with late-stage attrition due to poor efficacy, and facilitating the development of improved treatment options for
the more than 3 million sufferers of IPF worldwide. The product of this SBIR Fast Track will immediately enter
the rapidly growing ce...

## Key facts

- **NIH application ID:** 10323494
- **Project number:** 1R44HL158364-01A1
- **Recipient organization:** XYLYX BIO, INC.
- **Principal Investigator:** John David O'Neill
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $297,924
- **Award type:** 1
- **Project period:** 2021-09-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10323494

## Citation

> US National Institutes of Health, RePORTER application 10323494, Lung fibrosis modeling and compound testing platform using fibrotic lung ECM that recreates the fibrotic disease environment to improve predictiveness and accelerate anti-fibrotic drug development (1R44HL158364-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10323494. Licensed CC0.

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