# Precision Monitoring of Treatment Response in Early Psoriatic Arthritis: Integrating at-Home RNA Microsampling into Ongoing, Remote, Smart Phone-Based, Digital Data Capturing

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $465,008

## Abstract

Psoriatic arthritis (PsA) is a complex, multifactorial, immune-mediated inflammatory disorder that affects ~1%
of the worldwide population (~1.5 million adults in the US alone). It is characterized by skin inflammation and
chronic synovitis that, when left untreated, can result in irreversible joint destruction and deformity, leading to
increased morbidity and all-cause mortality. The last three decades have witnessed impressive advances in the
understanding of disease pathogenesis and therapeutic outcomes. In fact, the use of anti-TNF (TNFi) and other
“biologics” have led to substantial improvements in PsA clinical outcomes, enhancing the quality of life for millions
of patients with inflammatory arthritis. Despite this progress, however, a significant question still remains
unanswered: why do over 50% of PsA patients with moderate to severe arthritis fail to respond appropriately to
these agents? Machine learning methods investigating the effect of inter-individual variations of molecular
features and digital data on drug response – promise to overcome these barriers and facilitate precision medicine
approaches in autoimmune disease.
 TNF inhibitors (TNFi), remain the anchor drugs for the treatment of PsA (and many autoimmune diseases)
and are used widely throughout the world. While quite effective, TNFi achieve significant results in less than 50%
of patients and remission in only a quarter of them. It is well established that patients’ response to TNFi and
other biologics is highly variable. The reasons for this are presumably multifactorial and while many biomarkers
have been studied, they have been unable to demonstrate significant predictive value for clinical use in PsA.
 Our multidisciplinary team composed of rheumatologists, experts in remote studies (homeRNA), digital
biomarkers, transcriptomics analysis, and immunoinformatics will address our overarching goal to study: a)
whether remote characterization of frequent (weekly) gene/module expression can promptly identify distinctive
dynamics of inter-individual variations in the blood transcriptional trajectories following treatment with two
cytokine-specific biologics in early PsA (i.e., TNFi and IL-17 inhibitors); and b) if a novel, remote precision
medicine approach based on the earliest detectable transcriptome and smartphone-based digital signatures can
be used to predict the immunomodulatory responses to TNFi in treatment-naïve, new-onset PsA (NOPA)
patients. We believe that the results of our highly translational, innovative studies will directly influence
therapeutic approaches for the treatment of PsA and offer a more personalized approach in which the clinical
efficacy response would be predicted early in any given patient about to initiate TNFi, limiting or preventing
disease progression and ultimately avoiding wasteful health expenditures (estimated as ~$60,000/year/patient
in direct costs). Importantly, we anticipate that our studies will establish generalizable approaches i...

## Key facts

- **NIH application ID:** 10901066
- **Project number:** 1R01AR084274-01
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Jose U. Scher
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $465,008
- **Award type:** 1
- **Project period:** 2024-06-15 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10901066

## Citation

> US National Institutes of Health, RePORTER application 10901066, Precision Monitoring of Treatment Response in Early Psoriatic Arthritis: Integrating at-Home RNA Microsampling into Ongoing, Remote, Smart Phone-Based, Digital Data Capturing (1R01AR084274-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10901066. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*