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.
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. There is interest in using patient-derived cells as test beds for finding better ways to cure neurological diseases. However there are two critical challenges to overcome in order for patient- derived cells to become a powerful new paradigm in health care: 1) the in vitro differences between patients are large and it is difficult to know which differences are disease-relevant and which are normal inter-patient differences, and 2) how to come up with good methods to image cellular functions and infer from the imaging data the reliable metrics which can quantify the disease-relevant differences and generate disease predictive metrics. This project proposes to take a quantum leap forward in translational disease research by combining patient-specific cell models, innovative fluorescent probes and a next generation kinetic informatics discovery tool that will well equip scientists to efficiently discover the meaningful phenotypic differences between disease and healthy patients.
