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 ] S18 do not complete trials. Identifying those patients who show a tendency to respond early to treatment may assist research s ites in recruiting subjects of a different population as well as improving overall success of clinical trials. Methods: Factors including subjects' age, sex, BMI, prior hospitalizations, and P ANSS scores were collected on 90 patients with schizophrenia who were experiencing an acute exacerbation of symptoms and presented for inclusion in an eight-week inpatient trial. These factors were analyzed to determine whether there is a correlation in response rate in PANSS scores in relation to these factors. Results: Correlational analyses were used to examine the relationship between demographic factors (age, sex, BMI, and prior hospitalizations) and response rate in PANSS scores. There was no significant relationship found between demographic factors and percent change in total PANSS score. However, preliminary correlational analyses indicated an inverse relationship between PANSS early onset schizophrenia (EOS) scores and overall percent change (r= - 0.618, p=0.000). This suggests subjects with higher EOS scores showed less change overall. Additionally, an inverse relationship between PANSS ET score and overall percent change (r= -.397, p=0.009) also suggesting a higher ET score indicated a lower overall percent change. Conclusion: These data indicated the demographic factors of our subset of patients did not significantly relate to their overall response rate in the trials. Information that would assist in a stronger analysis of these factors includes a greater number of subjects for analysis, total number of previous clinical trial exposure, and site outcome data. This topic of discussion is particularly relevant to the continued success in recruitment and retention of subjects and overall positive trial outcome data. Financial disclosure/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 m olecular and psychiatric scale data that can impact clinical trial success through placebo response and drug efficacy prediction. Design: Utilizing data sets, we d iscovered 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 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. RATER TRAINING AND ASSESSMENT U nderstanding rater preferences in services used to increase the reliability of clinical trials: a multi-national survey Presenters: Komorowsky A 1 , DiCindio L 1 , Rock C 1 , Lobb J 1 , Avrumson R 1 , Carbo M 1 , Baldwin K 1 , Rohleder L 1 , Lytle D 1 , Murphy M 1 Affiliations: 1 Worldwide Clinical Trials, King of Prussia, Pennsylvania Objective: Industry literature supports the use of independent, expert clinicians to train and monitor data quality for psychiatric/neurological assessments in order to reduce bias and variability in outcomes data. As the number of clinical trials continues to increase, so does the frequency with which site raters undergo training and monitoring using different modalities and standards. The authors investigated raters' perceptions and preferences with common rater training and data surveillance programs as well as the potential impact of rater engagement on data quality. Methods: A nonprobability-based, purposive survey was employed in which site staff engaged in interventional psychiatry/neurology studies was contacted from the Worldwide Clinical Trials database of site raters and coordinators. Over 2,000 surveys were deployed using a proprietary survey engine. Results: Reponses from the survey revealed key rater preferences in methods of training and surveillance. Specific preferences were found for training via video demonstration with a practice quiz as well as preferences for in-person investigators' meetings. Data surveillance preferences were found for some methodologies, including source document review. The survey also identified pre-conceived perceptions of the raters regarding the purpose of rater training and surveillance. Conclusion: Understanding site rater preferences is paramount to enriching the current rater training and data monitoring methods, which can directly impact study outcomes. Implications for training and quality assurance methodology were also outlined.

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