机器学习优化自身免疫疗法，重点关注 1 型糖尿病-猫眼课题宝

机器学习优化自身免疫疗法，重点关注 1 型糖尿病

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

项目状态：

未结题

起止时间：

2023-09-15 至 2025-08-31

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

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中文摘要

翻译

项目摘要我们建议开发一种新的免疫原性测定和基于机器学习的方法。用于产生具有改进的免疫原性的自身免疫性疾病的耐受化疫苗的框架人口覆盖率。与哈佛大学和马萨诸塞州陈医学院糖尿病卓越中心，我们将开发一个一种新的检测方法，多重多抗原激活检测（MMAA），用于发现自我- 在1型糖尿病（T1 D）中被细胞毒性T细胞识别的抗原（目的1）。我们将使用我们已经证实的自身抗原来设计新的多表位耐受疫苗，并测试疫苗在PBMC中扩增CD 4 + TReg细胞的能力， T1 D供体（目标2）。我们将利用新的机器学习方法来修改和选择疫苗表位，以显著提高耐受性疫苗的人群覆盖率。我们产品将是由此产生的疫苗。

英文摘要

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.

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