IQuity Labs, Inc. — Department of Health and Human Services SBIR Phase I: NIAID

Description

ABSTRACTEarly 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 diseaseMoreoverrates of non adherence to therapy has been reported to be as high astoin the patient population suggesting the need to provide continuous monitoring and selection of optimal therapyIdentification of novel actionable biomarkers would provide clinicians with additional information for the purposes of diagnosisprognosisclinical subtyping as well as for the selection and monitoring of therapyInitiation of sub optimal therapy can be both detrimental to the patient s health and financial well beingTo datethe general approach to selecting a disease modifying treatmentDMTis to weigh the risks and benefits while considering the aggressiveness of diseaseefficacy of the drug and the potential side effects of treatment in atrial and errorfashionThis approach is quite unsettling when understanding that treatment failure or inadequacy can cause irreversible neurological damageFurthermoremany of these drugs are associated with serious adverse drug reactions such as cardiac eventsopportunistic infections and secondary autoimmunitySelection 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 spectrumThe course of MS disease does not manifest identically in all patients nor do all patients respond to treatment the same wayIdentification 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 outcomesOur previous work has explored lncRNAs as candidate biomarkers that can be measured in peripheral whole blood to accurately classify MSThe 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 The emerging view by specialists that take care of patients with autoimmune diseases is that early diagnosis and earlyaggressive and effective therapy is critical to produce the best long term outcomesThis is very evident for multiple sclerosis with the goal of reducing damage to the central nervous systemproducing longer and better disease remissionand mitigating long term disabilityLong non coding RNAslncRNAsare newly discovered classes of RNAs with an array of regulatory functionsOur hypothesis to test is that classifiers can be built based upon differential expression of novel lncRNAs in bloodThese classifiers will possess high levels of accuracy to monitor treatment responses in multiple sclerosis