# Development of a molecular-level skin condition diagnostic for precision medicine

> **NIH NIH R41** · FINALLY, LLC · 2022 · $275,764

## Abstract

PROJECT SUMMARY
The American Academy of Dermatology reports that 1 in 4 Americans (~84.5 million) are impacted by skin
disease. Skin disease is the fourth leading cause of disability worldwide, significantly impacts quality of life, and
costs ~$75 billion annually to treat. Skin conditions like atopic dermatitis (AD) are commonly diagnosed by
practitioners using clinical history and physical exam features; however, because of limited understanding of the
diverse pathophysiological mechanisms that underlie complex skin lesions, disease management still follows a
‘one-size-fits-all’ paradigm. This lack of evidence-based personalization or precision medicine leads to poor
treatment outcomes and patient frustration. The central objective of this proposal is the development of a
molecular-level skin assessment platform that will allow evidence-based diagnosis of skin conditions as well as
the delivery of supplementary information on the pathophysiological mechanisms of the disease state to aid
practitioners in choosing treatments and monitoring treatment progress. The final product skin assessment
platform includes: 1) a standardized sample collection kit which allows for easy, non-invasive collection of
material from a patient’s stratum corneum via tape-stripping, and 2) a pipeline to elucidate biomarker data
consisting of liquid chromatography-mass spectrometry (LC-MS/MS) analysis and big data artificial intelligence
approaches (i.e., deep neural networks, etc.). The test can be shipped through the mail and completed at home,
allowing for the technology to be used for remote dermatological care and expanding access to groups
historically underserved. Successful completion of Phase I will provide proof-of-principle of using skin biomarkers
for prediction of atopic dermatitis in samples collected at-home. In Aim 1, we will validate our sample collection
process to verity the robustness of at-home sample collection. In a study of 25 individuals, we will assess the
quality of data obtained from untrained (at-home) sample collection versus trained (in-office) sample collection
through assessing the protein content and similarity of compounds detected between these samples. In Aim 2,
we will identify predictive biomarkers of AD in a study of 75 healthy (control) and 75 individuals (patients)
diagnosed with AD. Feature selection and machine learning prediction analysis will be used to determine small
molecule biomarkers associated with AD, and success will be measured as 90% predictive ability (area under
curve (AUC) ≥ 0.90) of the biomarker set on an isolated cross validation dataset. These studies will demonstrate
proof of concept and prove product feasibility through the identification of diagnostic, monitoring and predictive
skin biomarkers associated with AD and AD therapy, provide critical analytical validation of the at-home sample
collection kit by users, and increase the success of a future Phase II program focused on the clinical validation
for t...

## Key facts

- **NIH application ID:** 10600694
- **Project number:** 1R41AR082172-01
- **Recipient organization:** FINALLY, LLC
- **Principal Investigator:** Stacy D. Sherrod
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $275,764
- **Award type:** 1
- **Project period:** 2022-09-20 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10600694

## Citation

> US National Institutes of Health, RePORTER application 10600694, Development of a molecular-level skin condition diagnostic for precision medicine (1R41AR082172-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10600694. Licensed CC0.

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