Early detection of multiple sclerosis is key to limiting neurological damage but monitoring patient progression and response to therapy is of arguably similar if not greater importance due to the chronic nature of disease. Moreover, rates of non-adherence to therapy has been reported to be as high as 25% to 40% in the patient population suggesting the need to provide continuous monitoring and selection of optimal therapy. Identification of novel actionable biomarkers would provide clinicians with additional information for the purposes of diagnosis, prognosis, clinical subtyping as well as for the selection and monitoring of therapy. Initiation of sub-optimal therapy can be both detrimental to the patients health and financial well-being. To date, the general approach to selecting a disease modifying treatment (DMT) is to weigh the risks and benefits while considering the aggressiveness of disease, efficacy of the drug and the potential side effects of treatment in a trial and error fashion. This approach is quite unsettling when understanding that treatment failure or inadequacy can cause irreversible neurological damage. Furthermore, many of these drugs are associated with serious adverse drug reactions such as cardiac events, opportunistic infections and secondary autoimmunity. Selection of the best therapy for a particular patient as well as the ability to identify if/when efficacy of a particular DMT dwindles is highly desirable and would be of great benefit throughout the healthcare spectrum. The course of MS disease does not manifest identically in all patients nor do all patients respond to treatment the same way. Identification of actionable biomarkers to serve as a surrogate for the efficacy of a particular therapy would allow clinicians to identify nonresponsive patients as early as possible and potentially evaluate dosing or administration to optimize patient outcomes. Our previous work has explored lncRNAs as candidate biomarkers that can be measured in peripheral whole blood to accurately classify MS. The preliminary data provided in support of our fast track application highlights the potential for lncRNA expression levels analyzed with machine learning to not only classify MS but also indicate treatment responses.
Public Health Relevance Statement: The emerging view by specialists that take care of patients with autoimmune diseases is that early diagnosis and early, aggressive and effective therapy is critical to produce the best long-term outcomes. This is very evident for multiple sclerosis with the goal of reducing damage to the central nervous system, producing longer and better disease remission, and mitigating long-term disability. Long non-coding RNAs (lncRNAs) are newly discovered classes of RNAs with an array of regulatory functions. Our hypothesis to test is that classifiers can be built based upon differential expression of novel lncRNAs in blood. These classifiers will possess high levels of accuracy to monitor treatment responses in multiple sclerosis.
NIH Spending Category: Autoimmune Disease; Brain Disorders; Clinical Research; Clinical Trials and Supportive Activities; Genetics; Machine Learning and Artificial Intelligence; Multiple Sclerosis; Neurodegenerative; Neurosciences; Patient Safety; Prevention
Project Terms: Adopted; adverse drug reaction; Advocate; aggressive therapy; Autoimmune Diseases; Autoimmunity; base; Benefits and Risks; Biological; Biological Markers; Biological Process; Blood; Brain; brain health; candidate marker; Cardiac; cell type; Cerebrospinal Fluid; Chronic; Clinical; clinical subtypes; Code; cohort; Complex; cost; Data; Detection; Diagnosis; differential expression; disability; Disease; Disease remission; disorder control; Dose; drug efficacy; Early Diagnosis; Early Intervention; Early treatment; effective therapy; Europe; Event; Evoked Potentials; Exhibits; experimental group; Expressed Sequence Tags; Expression Profiling; Face; Foundations; Genes; Genetic Transcription; Goals; Health; health organization; Healthcare; Healthcare Systems; Human; human disease; Inflammatory; Invertebrates; Laboratories; Machine Learning; Magnetic Resonance Imaging; Measures; Messenger RNA; Monitor; Multiple Sclerosis; multiple sclerosis patient; Nature; nervous system disorder; Nervous System Trauma; Neuraxis; Neurologist; novel; Oligoclonal Bands; Opportunistic Infections; optimal treatments; Organism; Outcome; outcome forecast; Paper; Patient Care; Patient Monitoring; patient population; Patient-Focused Outcomes; Patients; Pattern; Peripheral; Personal Satisfaction; Persons; Pharmaceutical Preparations; Phase; phase 2 study; Positioning Attribute; preservation; Probability; Proteins; Reporting; Research; response; Rest; RNA; Site; Specialist; Spinal Cord; Suggestion; Symptoms; Testing; Therapeutic; Time; Tissues; Treatment Failure; treatment response; Treatment Side Effects; United States; Untranslated RNA; Vertebrates; Whole Blood; Work
