Innovations In Clinical Neuroscience

CNS Summit 2016

A peer-reviewed, evidence-based journal for clinicians in the field of neuroscience

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Innovations in CLINICAL NEUROSCIENCE [ V O L U M E 1 4 , N U M B E R 1 – 2 , J A N U A R Y – F E B R U A R Y 2 0 1 7 , S U P P L E M E N T ] S16 were given devices with the AI application downloaded and asked to use the application for dosing a dministrations. The primary adherence measure for the study was based on scheduled pill counts; AI platform adherence data were tested for exploratory purposes. R esults: The AI platform was used by 26 subjects for 372 subject days; 744 adherence parameters were collected. Five subjects discontinued early (19.2%). For subjects who completed the trial, mean (standard deviation [SD]) cumulative adherence rates based on visual confirmation of drug ingestion (AI application) and on pill count were 82.5 percent and 99.7 percent, respectively. The mean time to use the AI platform was 86.8 seconds per pill. Conclusion: Subjects with acutely exacerbated schizophrenia who were eligible for discharge from the inpatient setting and who completed the study demonstrated high rates of adherence using the mobile AI application. Subjects were able to easily use the technology. Use of the platform did not appear to increase the dropout rate. This study demonstrates the feasibility of using AI platforms to ensure high adherence, provide reliable adherence data, and rapidly detect nonadherence in CNS trials. Disclosures/funding: Adam Hanina and Laura Shafner are employees of AiCure, New York, New York, and consultants to Takeda. Xinxin Dong, Elizabeth Hanson, Thomas A. Macek, Atul Mahableshwarkar, and Maggie McCue are employees of Takeda Development Center Americas, Inc., Deerfield, Illinois. Anne Rubin is a former employee of Takeda. NetraMark: predicting placebo and drug response for pharma Presenters: Geraci J Affiliations: NetraMark Corp., Ontario, Canada Objective: We wished to showcase precision medicine through novel machine learning models based on molecular and psychiatric scale data that can impact clinical trial success through placebo response and drug efficacy prediction. Design: Utilizing data sets, we discovered models of placebo response robust enough to reproduce (within the s pecific treatment paradigm of a trial) and models that demonstrated the patient population purification process for predicting response in a precise way. Results: We found two models of p lacebo response that had an accuracy of over 85 percent. For some situations we were able to predict if someone was a placebo responder with an accuracy of over 95 percent. Clinical scales alone were used for one of these models. We also based models on miRNA expression data that were capable of accurately predicting response for a particular subpopulation of major depression patients. This provided evidence that our technology is capable of dealing with heterogeneous patient populations. This could be valuable during Phase 3 scenarios for drug protocols that only work for a portion of the treatment population. Conclusion: NetraMark developed a machine learning system to help pharmaceutical companies who must deal with complex patient populations. NetraMark accomplished this by providing accurate models of placebo and drug response. In situations where the efficacy of a drug is limited to a subpopulation whose effect could be "washed out" by the United States Food and Drug Administration (FDA) statistical paradigm, we provided what we call a Patient Population Purification process. This process is capable of predicting who these patients are with a high level of accuracy. Our unique system has the ability to help companies get potential medications on the market through our approach to precision medicine. Disclosures/funding: We are now working with several pharmaceutical companies. At this time, we are unable to disclose all our current relationships. The NetSCID: a validated web-based adaptive version of the SCID that improves diagnostic accuracy in mental health research Presenters: Brodey BB 1 , Zweede L 1 Affiliations: 1 TeleSage, Inc., Chapel Hill, North Carolina Background: The Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders ( SCID, DSM) is the gold standard for research-based mental health diagnoses. It is the most widely used comprehensive tool for assessing DSM diagnoses. Its direct adherence to DSM c riteria provides strong test-retest and high interrater reliability; however, administration of the full research version averages two hours and requires considerable clinician training, making it impractical for many protocols. Objective: Our objective was to develop and validate a highly configurable and secure web-based version of the SCID—the NetSCID— thereby making the gold standard in mental health diagnostics available for clinical trials. Methods: The validation research included 24 clinicians who administered the SCID to 230 participants that completed the paper SCID and/or the NetSCID. Data-entry errors, branching errors, and clinician satisfaction were quantified. Results: Ninety-seven percent of error rates occurred among clinicians administering the paper SCID; all errors that occurred when utilizing the NetSCID were subsequently corrected by our programmers. Administration time was reduced by over 30 percent. Clinicians found it easier to administer (p< 0.05), easier to navigate (p< 0.05), and simpler to score (p< 0.01). Ninety percent of clinicians preferred the NetSCID to its paper counterpart. Conclusion: Because of its unique ability to record symptoms in a standardized database, the NetSCID facilitates characterization of patient populations and assists with identification of sub-groups that may respond to interventions. Adoption of this tool has shown to improve diagnostic accuracy and will increase the power in central nervous system (CNS) clinical trials worldwide. Disclosures/funding: This research was supported in part by a grant from the National Institutes of Mental Health, which was awarded to TeleSage, Inc. Dr. Brodey is Chief Executive Officer of TeleSage, Inc., and Lisa Zweede is

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