# Proteomics of human vitreous to investigate mechanisms underlying the variability in anti-VEGF treatment response in neovascular AMD patients

> **NIH NIH R21** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2022 · $306,250

## Abstract

PROJECT SUMMARY
 Age-related macular degeneration (AMD) is the leading cause of severe, irreversible vision loss in older
adults worldwide. Most vision loss in AMD is caused by the advanced neovascular form of the disease
(NVAMD). Intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections are effective in preserving
vision for many patients, but there is marked variability among NVAMD patients in treatment response.
Molecular factors contributing to this variability in anti-VEGF response represent a critical gap in knowledge.
 Our proposal addresses this knowledge gap directly by leveraging a unique and powerful resource. We
have an exclusive repository of deidentified vitreous samples collected in-office from more than 1,100 patients
treated for retinal diseases, including many with NVAMD. We will couple this resource with a highly sensitive,
high-throughput multiplex immunoassay-based proteomics technology to identify vitreous proteins associated
with anti-VEGF treatment response in NVAMD. We hypothesize that variation in vitreous levels of inflammatory
and angiogenic proteins impacts the fundamental mechanisms that underlie the variability in response to anti-
VEGF treatment. To test this hypothesis, we will measure levels of proteins involved in inflammation and
angiogenesis in vitreous samples collected from NVAMD patients both prior to and throughout the course of
treatment. We have identified a cohort of 83 treatment-naïve NVAMD patients who received the standard three
monthly loading doses of intravitreal bevacizumab, followed by monthly injections as needed based on visual
acuity, fundus examination, and OCT assessment. Using visual acuity and OCT measurements, each patient’s
primary (one month after three loading doses; Month 3) and secondary (6 months after treatment initiation;
Month 6) anti-VEGF treatment responses were classified as Good, Partial, Poor, or Non-Response.
 In Aim 1, we will use Olink Proteomics’ multiplex immunoassays to measure levels of 733 proteins involved
in inflammation and angiogenesis in vitreous samples collected from these NVAMD patients prior to their initial
bevacizumab injection. To compare between Good+Partial Responders and Poor+Non-Responders, we will
perform logistic regression of responder status (Good/Partial or Poor/Non-Response) against baseline levels of
each protein with and without adjustment for covariates at both primary and secondary response timepoints.
 In Aim 2, we will determine the longitudinal bevacizumab-induced changes in vitreous levels of
inflammatory and angiogenic proteins that correlate with clinical outcomes in NVAMD patients. For 58 of the
patients from Aim 1, we have additional vitreous samples collected at Month 1 (one month after treatment
initiation), Month 3, and Month 6. We will quantify levels of the same 733 inflammatory and angiogenic proteins
in these longitudinal vitreous samples. We will compare the protein fold-changes and the trend of protein
cha...

## Key facts

- **NIH application ID:** 10507176
- **Project number:** 1R21EY034313-01
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Milam A Brantley
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $306,250
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10507176

## Citation

> US National Institutes of Health, RePORTER application 10507176, Proteomics of human vitreous to investigate mechanisms underlying the variability in anti-VEGF treatment response in neovascular AMD patients (1R21EY034313-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10507176. Licensed CC0.

---

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