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

机器学习优化自身免疫疗法，重点关注 1 型糖尿病

基本信息

批准号：
10697204
负责人：
David K Gifford
金额：
$ 30.65万
依托单位：
THINK THERAPEUTICS, INC.
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-15 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10697204
关键词：
Admission activity Alleles Amino Acid Sequence Antigens Autoantigens Autoimmune Autoimmune Diseases Autoimmunity Biological Assay Businesses CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes CD8B1 gene Cell physiology Cell surface Cells Code Collaborations Computing Methodologies Cytotoxic T-Lymphocytes Data Databases Derivation procedure Diabetes Mellitus Epitopes FOXP3 gene Foundations Frequencies HLA-A gene Haplotypes Human Immune Immune system Immunologic Surveillance Individual Insulin-Dependent Diabetes Mellitus Killer Cells Legal patent Letters Literature Lymphocyte MHC Class I Genes MHC Class II Genes Machine Learning Mass Spectrum Analysis Massachusetts Messenger RNA Methods Normal Cell Peptide/MHC Complex Peptides Peripheral Blood Mononuclear Cell Persons Population Proteins Recovery Regulatory T-Lymphocyte Research Sampling Signal Transduction Symptoms T-Cell Activation T-Cell Receptor T-Lymphocyte T-cell receptor repertoire Testing Therapeutic Universities Vaccine Design Vaccines Work antigen test cross reactivity design high throughput screening immunogenicity improved machine learning framework machine learning method medical schools novel preproinsulin prevent receptor receptor binding response vaccination outcome vaccine evaluation

项目摘要

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.

项目摘要我们建议开发一种新的免疫原性测定法和基于机器学习改善自身免疫性疾病的耐受疫苗的框架人口覆盖范围。与哈佛大学和大学合作马萨诸塞州陈医学院糖尿病卓越中心我们将开发一个新测定，多重抗原激活测定法（MMAA），以发现自我在1型糖尿病（T1D）中被细胞毒性T细胞识别的抗原（AIM 1）。我们将使用我们确认的自我抗原来设计新型的多诊断耐受性疫苗并测试疫苗是否能够从PBMC中扩展CD4+ Treg细胞的能力 T1D捐助者（AIM 2）。我们将利用新的机器学习方法修改和选择疫苗代表大大改善了耐受疫苗的覆盖范围。我们的产品将是最终的疫苗。