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 ] S20 remainder of the six-week period. Subjects were given devices with the AI application downloaded and asked to use the a pplication for dosing administrations. 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. Methodology and management of clinical trials with adaptive designs Presenters: Laage T, Barag J Affiliations: Premier Research Group Objective: Our objective was to present practical techniques for implementing and managing adaptive design clinical trials to preserve study integrity and validity Design: We reviewed two clinical trial design principles (and their practical application): controlling the chance of erroneous conclusions (minimizing Type I and Type II error by controlling "multiplicity" of data examination at interim assessments and by adjusting for study adaptations) and minimizing operational bias (some or all participants i n the study have access to study results by treatment group, influencing the ongoing operations of the study). Results: Trials are planned using experienced statisticians for analytical m ethods and computer simulations to evaluate the study design operating characteristics and ensure control of Type I error rate and adequate power. Trials are conducted to minimize operational bias, including the following: masking investigators from knowledge of interim analyses and of study adaptations (sample size increase, longer enrollment, and changed endpoints or randomization ratios); ensuring an adequate firewall (i.e., a process or procedure to ensure and document sufficient restrictions on information flow to control statistical and operational bias); and separating the precise details of the adaptation algorithm from the investigator-shared protocol and placing them in a detailed Statistical Analysis Plan (for institutional review boards [IRBs] and United States Food and Drug Administration [FDA] only). Conclusion: With careful planning and execution, adaptive trials can realize efficiencies in time and resources. Disclosures/funding: None reported. 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 specific 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 placebo 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 t hat 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 p opulations. 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. A reanalysis of the effects of protocol design and subject stipend on completion rates in Phase 1 studies in subjects with stable schizophrenia or schizoaffective disorder: Does money matter? Presenters: Krefetz DG Affiliations: PRA Health Sciences, Early Development Services, Marlton, New Jersey Objective: The objective of this study was to evaluate the impact of both protocol design and subject stipend on individual completion in Phase 1 studies in subjects with stable schizophrenia or schizoaffective disorder. Design: In a poster presented at the central nervous system (CNS) Summit in 2015, the author examined the effect of nine trial design variables on individual completion in 11 Phase 1 trials in subjects

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