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Kinetic Phenotype Discovery Informatics for Neurological Diseases

神经系统疾病的动力学表型发现信息学

基本信息

批准号：
9769172
负责人：
Shih-Jong J Lee
金额：
$ 60.19万
依托单位：
DRVISION TECHNOLOGIES, LLC
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2020-11-16
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9769172
关键词：
Address Adopted Adoption Affect Age of Onset Area Beds Biological Models Biological Products Cell model Cell physiology Cells Characteristics Clinical Collaborations Color Complement Data Diagnosis Diagnostic Disease Drug Industry Evaluation Event Failure Fluorescent Probes Gene Expression Profiling Genomics Government Healthcare Image In Vitro Incidence Informatics Investigation Kinetics Methods Microscopy Morphology Neurons Pathogenicity Patient Representative Patients Pattern Persons Pharmaceutical Preparations Phase Phenotype Process RNA Reporter Research Resolution Sampling Scientist Sensitivity and Specificity Stratification System Technology Testing Therapeutic Time Translational Research Update Validation analytical tool base blind cellular imaging disease phenotype imaging study innovation instrument interest microscopic imaging nervous system disorder next generation patient population patient variability prototype quantum therapy development time use tool

项目摘要

Project Summary Neurological disorders significantly outnumber diseases in other therapeutic areas and are growing in incidence faster than any other disease classes. However, the pharmaceutical industry has been unsuccessful in coming up with effective drugs. A big factor in these failures has been a lack of adequate model systems for fundamental disease understanding affecting both diagnosis and treatment. There is therefore a strong, emerging interest in the use of patient-derived cell models to understand the pathogenic mechanisms underlying neurological disease phenotypes. To gain a true understanding of these mechanisms and phenotypes, it is necessary to analyze dynamic events using time-lapse microscopy. These tools complement RNA profiling studies by enabling single-cell resolution of pathogenic processes at high-throughput, enabling investigation of highly diverse or largely replicative patient sets at an unprecedented scale. To discover predictive disease phenotypes across a large representative patient sample, a systematic, unbiased approach is needed to mine time-lapse microscopy image sequences, patient clinical and concomitant data. There is therefore a critical need for a next-generation analytical tool to enable the discovery of disease predictive phenotypes robust to patient variations. Thus, we propose to develop a teachable kinetic informatics discovery (KID) tool based on a hierarchical inference framework. If proven, the KID tool would be rapidly adopted for translational research using patient-derived cell models in many diseases. It could facilitate a paradigm shift towards broad adoption of patient-cell models for therapeutics discovery, optimization, stratification and diagnostic discovery. Our immediate objective for this Fast-Track project is to develop and validate the KID tool by showing that it can classify patients on the basis of disease and disease characteristics such as age-of-onset. In Phase I we will develop the prototype KID tool and preliminary patient panel. We will prove feasibility by discovering phenotypes and then accurately scoring patients based on the phenotypes in blind tests. In Phase II we will develop the full patient panel and pre-product KID tool. We will validate by scoring patients in blind tests and validate phenotypes through corroboration with targeted transcriptional profiling and genomic tests. We will reduce the phenotypes to a single time point and show efficacy in a targeted compound screen. The Specific Aims are: Phase 1, (Aim 1): Develop the prototype KID tool and preliminary patient panel incorporating neuronal firing reporter. (Aim 2): Verify the prototype KID tool in a preliminary patient panel. Phase II, (Aim 1): Complete the beta prototype KID tool and patient panel for KID tool validation. (Aim 2): Disease phenotype discovery and verification in the full patient panel. (Aim 3): Disease phenotype validation.

项目总结