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

• 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
• 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

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。