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      A Population Pharmacokinetic and Pharmacodynamic Analysis of RP5063 Phase 2 Study Data in Patients with Schizophrenia or Schizoaffective Disorder

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          Abstract

          Background and Objective

          RP5063 is a novel multimodal dopamine (D)–serotonin (5-HT) stabilizer possessing partial agonist activity for D 2/3/4 and 5-HT 1A/2A, antagonist activity for 5-HT 2B/2C/7, and moderate affinity for the serotonin transporter. Phase 2 trial data analysis of RP5063 involving patients with schizophrenia and schizoaffective disorder defined: (1) the pharmacokinetic profile; and (2) the pharmacokinetic/pharmacodynamic relationships.

          Methods

          Pharmacokinetic sample data (175 patients on RP5063; 28 doses/patient) were analyzed, utilized one- and two-compartment models, and evaluated the impact of covariates. Pharmacodynamic analysis involved development of an E max model.

          Results

          The pharmacokinetic analysis identified a one-compartment model incorporating body mass index influence on volume as the optimum construct, with fixed-effect parameters: (1) oral clearance ( Cl/ F), 5.11 ± 0.11 L/h; (2) volume of distribution ( V c/ F), 328.00 ± 31.40 L; (3) absorption constant ( ka) 0.42 ± 0.17 h −1; (4) lag time ( t lag) of 0.41 ± 0.02 h; and (5) a calculated half-life of 44.5 h. Pharmacokinetics were linear related to dose. An E max model for total Positive and Negative Syndrome Scale (PANSS) scores as the response factor against cumulative area under the curve (AUC) provided fixed-effect estimates: (1) E o = 87.3 ± 0.71 (PANSS Units; pu); (2) E max = − 31.60 ± 4.05 (pu); and (3) AUC 50 = 89.60 ± 30.10 (µg·h/mL). The predicted PANSS improvement reflected a clinical dose range of 5–30 mg.

          Conclusions

          Pharmacokinetics of RP5063 behaved predictably and consistently. Pharmacodynamics were characterized using an E max model, reflecting total PANSS score as a function of cumulative AUC, that showed high predictability and low variability when correlated with actual observations.

          Electronic supplementary material

          The online version of this article (10.1007/s13318-018-0472-z) contains supplementary material, which is available to authorized users.

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          Most cited references28

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          Inference from Iterative Simulation Using Multiple Sequences

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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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              How many bootstrap replicates are necessary?

              Phylogenetic bootstrapping (BS) is a standard technique for inferring confidence values on phylogenetic trees that is based on reconstructing many trees from minor variations of the input data, trees called replicates. BS is used with all phylogenetic reconstruction approaches, but we focus here on one of the most popular, maximum likelihood (ML). Because ML inference is so computationally demanding, it has proved too expensive to date to assess the impact of the number of replicates used in BS on the relative accuracy of the support values. For the same reason, a rather small number (typically 100) of BS replicates are computed in real-world studies. Stamatakis et al. recently introduced a BS algorithm that is 1 to 2 orders of magnitude faster than previous techniques, while yielding qualitatively comparable support values, making an experimental study possible. In this article, we propose stopping criteria--that is, thresholds computed at runtime to determine when enough replicates have been generated--and we report on the first large-scale experimental study to assess the effect of the number of replicates on the quality of support values, including the performance of our proposed criteria. We run our tests on 17 diverse real-world DNA--single-gene as well as multi-gene--datasets, which include 125-2,554 taxa. We find that our stopping criteria typically stop computations after 100-500 replicates (although the most conservative criterion may continue for several thousand replicates) while producing support values that correlate at better than 99.5% with the reference values on the best ML trees. Significantly, we also find that the stopping criteria can recommend very different numbers of replicates for different datasets of comparable sizes. Our results are thus twofold: (i) they give the first experimental assessment of the effect of the number of BS replicates on the quality of support values returned through BS, and (ii) they validate our proposals for stopping criteria. Practitioners will no longer have to enter a guess nor worry about the quality of support values; moreover, with most counts of replicates in the 100-500 range, robust BS under ML inference becomes computationally practical for most datasets. The complete test suite is available at http://lcbb.epfl.ch/BS.tar.bz2, and BS with our stopping criteria is included in the latest release of RAxML v7.2.5, available at http://wwwkramer.in.tum.de/exelixis/software.html.
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                Author and article information

                Contributors
                +1 (408) 816 1454 , lbhat@revivapharma.com
                Journal
                Eur J Drug Metab Pharmacokinet
                Eur J Drug Metab Pharmacokinet
                European Journal of Drug Metabolism and Pharmacokinetics
                Springer International Publishing (Cham )
                0378-7966
                2107-0180
                4 April 2018
                4 April 2018
                2018
                : 43
                : 5
                : 573-585
                Affiliations
                Reviva Pharmaceuticals, Inc., 1250 Oakmead Parkway, Suite 210, Sunnyvale, CA 94085 USA
                Author information
                http://orcid.org/0000-0003-0503-8537
                Article
                472
                10.1007/s13318-018-0472-z
                6133081
                29619682
                44ce36eb-131f-4793-b4f6-ce754c3a057b
                © The Author(s) 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                Funding
                Funded by: Reviva Pharmaceuticals, Inc
                Award ID: Not applicable
                Categories
                Original Research Article
                Custom metadata
                © Springer Nature Switzerland AG 2018

                Pharmacology & Pharmaceutical medicine
                Pharmacology & Pharmaceutical medicine

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