Background macular Shape Background macular Shape Background macular Shape
Grants > Prescribing the Molecular Message of Retinal Health Updated On: Ene. 21, 2025
Macular Degeneration Research Grant

Prescribing the Molecular Message of Retinal Health

Innovative Approaches to Macular Degeneration Treatments
Yvette Wooff, PhD

Principal Investigator

Yvette Wooff, PhD

The Australian National University

Canberra, Australia

About the Research Project

Program

Macular Degeneration Research

Award Type

Postdoctoral Fellowship

Award Amount

$198,062

Active Dates

July 01, 2021 - June 30, 2023

Grant ID

M2021012F

Mentor(s)

Riccardo Natoli, PhD, The Australian National University

Goals

To harness and deliver the natural homeostatic cargo of retinal exosome vehicle (EV) as a lowly immunogenic and neuroprotective therapy for the treatment of age-related macular degeneration.

  • Aim 1: To understand the role of retinal EV in health and degeneration, the bioavailability and molecular composition of retinal EV will be profiled using both rodent models of AMD as well as human AMD donor retinas.
  • Aim 2: To determine the efficacy of microRNA-based therapeutics.
  • Aim 3: To develop lowly-immunogenic treatment strategies for the treatment of AMD. Patient-derived EV will be therapeutically loaded with key homeostatic EV cargo including miRNA and delivered to the degenerating retina.

Summary

Recent insights have demonstrated that the transfer of molecular material between cells is integral for maintaining tissue health. In the retina, extracellular vesicles (EV) are responsible for mediating this essential communication, and work by delivering molecular cargo, including small gene regulators called microRNA (miRNA) to target cells. However, in retinal degeneration we have shown that EV and their miRNA cargo are reduced in concentration, suggesting that their loss correlates with retinal cell death. We therefore hypothesize that if EV and their molecular cargo such as miRNA can be replenished, we may be able to restore the communication channels required for retinal health, and slow the progression of retinal degenerations, including age-related macular degeneration (AMD). To investigate this hypothesis, we will supplement the degenerating retina with essential retinal EV cargo and investigate the effect on retinal health. The cargo we use will be determined by profiling retinal EV in both health and disease using mouse models of AMD and human AMD donor retinas. Further, we will load and deliver this essential cargo using EV derived from stem cells; to reduce the risk of immune attack. Taken together, the outcomes of this work will enable us to pinpoint the essential EV cargo required for retinal health and to determine their therapeutic potential, which will pave the way for the development of EV-based gene therapies for the treatment of AMD.

Unique and Innovative

Our innovation is in harnessing the natural molecular cargo of retinal EV to develop therapeutics for AMD. To do this, we will load and deliver essential EV cargo using patient-derived EV, e.g. from blood or stem cells, to develop a lowly-immunogenic therapeutic platform by which patients can effectively “treat themselves”. The use of autologous EV as a therapeutic delivery agent would not only reduce the risk of adverse immune reactions to the patient, but would provide a more targeted and replenishable therapy that could be accessed and adapted over a patient’s lifetime.

Foreseeable Benefits

The outcomes of this study will fill gaps in our knowledge of fundamental retinal biology and address unmet clinical needs that are holding back the field of EV therapeutics. By harnessing and delivering the “molecular message of health”, this work will provide a framework for the development of EV-based therapeutics for the treatment of retinal degenerations including the currently untreatable AMD. The development of this therapeutic pipeline can further be used in the treatment of neurodegenerative diseases, and diseases in which EV dysregulation is a key pathogenic feature.