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      Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials

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          Abstract

          Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a machine learning (ML) approach to improve phase II or proof‐of‐concept trials designed to address unmet medical needs in treating schizophrenia. Diagnostic data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trial were used to develop a binary classification ML model predicting individual patient response as either “improvement,” defined as greater than 20% reduction in total Positive and Negative Syndrome Scale (PANSS) score, or “no improvement,” defined as an inadequate treatment response (<20% reduction in total PANSS). A random forest algorithm performed best relative to other tree‐based approaches in model ability to classify patients after 6 months of treatment. Although model ability to identify true positives, a measure of model sensitivity, was poor (<0.2), its specificity, true negative rate, was high (0.948). A second model, adapted from the first, was subsequently applied as a proof‐of‐concept for the ML approach to supplement trial enrollment by identifying patients not expected to improve based on their baseline diagnostic scores. In three virtual trials applying this screening approach, the percentage of patients predicted to improve ranged from 46% to 48%, consistently approximately double the CATIE response rate of 22%. These results show the promising application of ML to improve clinical trial efficiency and, as such, ML models merit further consideration and development.

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          Random Forests

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            The positive and negative syndrome scale (PANSS) for schizophrenia.

            The variable results of positive-negative research with schizophrenics underscore the importance of well-characterized, standardized measurement techniques. We report on the development and initial standardization of the Positive and Negative Syndrome Scale (PANSS) for typological and dimensional assessment. Based on two established psychiatric rating systems, the 30-item PANSS was conceived as an operationalized, drug-sensitive instrument that provides balanced representation of positive and negative symptoms and gauges their relationship to one another and to global psychopathology. It thus constitutes four scales measuring positive and negative syndromes, their differential, and general severity of illness. Study of 101 schizophrenics found the four scales to be normally distributed and supported their reliability and stability. Positive and negative scores were inversely correlated once their common association with general psychopathology was extracted, suggesting that they represent mutually exclusive constructs. Review of five studies involving the PANSS provided evidence of its criterion-related validity with antecedent, genealogical, and concurrent measures, its predictive validity, its drug sensitivity, and its utility for both typological and dimensional assessment.
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              Innovation in the pharmaceutical industry: New estimates of R&D costs.

              The research and development costs of 106 randomly selected new drugs were obtained from a survey of 10 pharmaceutical firms. These data were used to estimate the average pre-tax cost of new drug and biologics development. The costs of compounds abandoned during testing were linked to the costs of compounds that obtained marketing approval. The estimated average out-of-pocket cost per approved new compound is $1395 million (2013 dollars). Capitalizing out-of-pocket costs to the point of marketing approval at a real discount rate of 10.5% yields a total pre-approval cost estimate of $2558 million (2013 dollars). When compared to the results of the previous study in this series, total capitalized costs were shown to have increased at an annual rate of 8.5% above general price inflation. Adding an estimate of post-approval R&D costs increases the cost estimate to $2870 million (2013 dollars).
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                Author and article information

                Contributors
                robstrat@iu.edu
                Journal
                Clin Transl Sci
                Clin Transl Sci
                10.1111/(ISSN)1752-8062
                CTS
                Clinical and Translational Science
                John Wiley and Sons Inc. (Hoboken )
                1752-8054
                1752-8062
                03 May 2021
                September 2021
                : 14
                : 5 ( doiID: 10.1111/cts.v14.5 )
                : 1864-1874
                Affiliations
                [ 1 ] Division of Clinical Pharmacology Department of Medicine Indiana University School of Medicine Indianapolis Indiana USA
                [ 2 ] Takeda Pharmaceuticals U.S.A., Inc Cambridge Massachusetts USA
                [ 3 ] Department of Pharmaceutical Sciences University at Buffalo, State University of New York Buffalo New York USA
                [ 4 ] Institute for Computational Data Science University at Buffalo, State University of New York at Buffalo Buffalo New York USA
                Author notes
                [*] [* ] Correspondence

                Robert E. Stratford Jr., Research II, 950 W. Walnut Street, Indianapolis, IN 46202, USA.

                Email: robstrat@ 123456iu.edu

                Author information
                https://orcid.org/0000-0003-0493-0603
                https://orcid.org/0000-0002-4918-0984
                https://orcid.org/0000-0003-3818-2252
                Article
                CTS13035
                10.1111/cts.13035
                8504834
                33939284
                365f02c9-2478-49a3-9f20-cf755e7f6b60
                © 2021 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 28 January 2021
                : 03 December 2020
                : 16 February 2021
                Page count
                Figures: 4, Tables: 3, Pages: 11, Words: 6632
                Categories
                Article
                Research
                Articles
                Custom metadata
                2.0
                September 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.8 mode:remove_FC converted:11.10.2021

                Medicine
                Medicine

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