CAREER: A systems engineering approach to elucidate and treat multi-factorial pathology

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

Millions of patients suffer worldwide from multi-factorial disease, which is a disease with no single cause but rather numerous contributing factors. Due to their complex nature, most multi-factorial diseases are currently incurable. Examples include the devastating neurodegenerative diseases of Alzheimer’s Dementia (AD), frontotemporal dementia (FTD), and Amyotrophic Lateral Sclerosis (ALS), which all impact brain function and the ability to perform normal daily tasks. Effectively measuring multiple simultaneous contributing factors throughout the disease course is extremely challenging in a traditional lab or clinical setting. The goal of this CAREER project is to develop new complex computer models that integrate and simultaneously analyze data from thousands of studies examining individual disease factors measured in the lab or clinic. The developed computer models prioritize the most promising factors and develop optimal combination treatment strategies. Computer prioritization increases the likelihood of clinical trial success and expedites the rate of new treatment availability to patients. While this project focuses on predicting treatments for AD, FTD, and ALS, the developed new technology can be applied to numerous other multi-factorial diseases. Educational activities for this project focus on undergraduate research internship curricula to increase opportunities; professional mentoring of students with disabilities; integrated advocacy of patients with AD, FTD, and ALS through local and national organizations; and improved collegiate education for neuroengineering via development of a new integrative class that integrates therapy design with medical school lectures on clinical neurologic disease. In addition to graduate research assistants, this project is estimated to provide STEM research internships for about 100 undergraduates and high school interns with an emphasis on under-represented groups. The investigator’s long-term goal is to use “pathology dynamics” (a branch of pathophysiology that deals with the motion, equilibrium, or homeostasis of physiological systems under the action of pathological forces) to enhance predictive medicine, whose primary purpose is to improve, expedite and personalize healthcare by developing computer models that forecast disease progression and treatment response. Towards this goal, the goal of this CAREER project is to construct new literature mining and predictive medicine models that leverage pathology dynamics to tackle multi-factorial disease(s). Most multi-factorial diseases are intractable and not responsive to traditional therapeutic approaches. The project’s driving hypothesis is that pathology dynamics is the key to unlocking unique signatures that can differentiate a spectrum of multi-factorial diseases that share similar symptoms, etiology, and biomarkers, but are currently clinical “diagnoses of exclusion” due to the lack of sensitive and specific clinical diagnostic tests. Three multi-factorial neuropathology test cases--Alzheimer’s Disease (AD), Amyotrophic Lateral Sclerosis (ALS), and frontotemporal dementia (FTD)--will be used to characterize the ability of pathology dynamics-based models to improve diagnostic, prognostic, and therapeutic prediction. The Research Plan is organized under three Aims. The FIRST Aim is to construct databases to capture, quantify, and aggregate entire fields’ literature. The investigator’s optimized student-driven assembly line (a hierarchy of high school and undergraduate students trained for biocuration tasks) will fully recapture journal article data along with key experimental methods/protocols, which enable meaningful data aggregation and analysis. The assembly line will first complete full published preclinical data recapture and corresponding databases for ALS, AD, and FTD (approximately 40,000 articles), which will be followed by construction of integrative clinical databases that consist of de-identified patient data for AD, ALS, and FTD. Efforts will also be made to develop and integrate additional biocuration automation for full data recapture with a goal of increasing biocuration automation from 40% to >75%. The SECOND Aim is to develop protocols for literature relationship extraction and ranking. Text mining with semantic inference networks will be used to identify multi-scalar relationships from 30+ million PubMed articles using the United Medical Language System ontology for keyword categorization and adapted unsupervised rank aggregation to prioritize relationships of interest. The THIRD Aim is to construct “pathology dynamics” models for the multi-factorial diseases using the data curated in Aim 1 and the relationships identified and ranked in Aim 2. Unsupervised models will be constructed for pathology dynamics phenotyping and supervised machine learning models will be constructed for diagnostic and therapeutic prediction. The models will be used in comparing rankings of literature relationships to experimentally measured relationships. In summary, the deliverables of this project include: novel multi-scalar databases for full curation of the AD, ALS, and FTD corpuses; new biocuration automation and relationship-based literature mining technology; and de novo systems-dynamics based preclinical and clinical predictive medicine models of AD, ALS, and FTD that can be used to predict etiology, diagnosis, treatment, and prognosis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.