Machine learning optimized autoimmune therapeutics with a focus on Type 1 Diabetes

NIH RePORTER · NIH · R43 · $306,500 · view on reporter.nih.gov ↗

Abstract

Project Summary We propose to develop a new immunogenicity assay and machine learning based framework for creating tolerization vaccines for autoimmune diseases with improved population coverage. In collaboration with Harvard University and the University of Massachusetts Chan Medical School Diabetes Center of Excellence we will develop a new assay, the Multiplexed Multi-antigen Activation Assay (MMAA), to discover self- antigens that are recognized by cytotoxic T cells in Type 1 Diabetes (T1D) (Aim 1). We will use the self-antigens we have confirmed to design novel multi-epitope tolerization vaccines and test the vaccines for their ability to expand CD4+ TReg cells in PBMCs from T1D donors (Aim 2). We will utilize new machine learning methods to modify and select vaccine epitopes to substantially improve tolerization vaccine population coverage. Our products will be the resulting vaccines.

Key facts

NIH application ID
10929966
Project number
5R43AI177185-02
Recipient
THINK THERAPEUTICS, INC.
Principal Investigator
David K Gifford
Activity code
R43
Funding institute
NIH
Fiscal year
2024
Award amount
$306,500
Award type
5
Project period
2023-09-15 → 2025-08-31