Abstract
In this chapter, we introduce statistical applications used in the design and analysis of a high throughput in vitro screening assay, QTiSA-HT (an acronym for QT-inotropy-Screening Assay-High Throughput), a proprietary in vitro platform used to characterize concentration-dependent effects of drugs that affect cardiac repolarization and contractility. Specifically, we discuss the design and analysis of cumulative, ascending dose concentration response studies, calculation of appropriate sample sizes, and the use of statistical significance tests and equivalence margins to provide robust estimates of true drug effects based on both concurrent and historical vehicle-control data. The goal of this chapter is to showcase how we search for solutions to real scientific problems arising in early phases of drug safety screening using statistical methods and tools.
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References
Cavero I (2009) Exploratory safety pharmacology: a new safety paradigm to de-risk drug candidates prior to selection for regulatory science investigations. Expert Opin Drug Saf 8(6):627–647
Gintant G (2011) An evaluation of hERG current assay performance: translating preclinical safety studies to clinical QT prolongation. Pharmacol Ther 129(2):109–119
Gintant GA, Su Z, Martin RL, Cox BF (2006) Utility of hERG assays as surrogate markers of delayed cardiac repolarization and QT safety. Toxicol Pathol 34(1):81–90
Sager PT, Gintant G, Turner JR, Pettit S, Stockbridge N (2014) Rechanneling the cardiac proarrhythmia safety paradigm: a meeting report from the Cardiac Safety Research Consortium. Am Heart J 167(3):292–300
Littell RC, Henry PR, Ammerman CB (1998) Statistical analysis of repeated measures data using SAS procedures. J Anim Sci 76:1216–123
Littell RC, Pendergast J, Natarajan R (2000) Modelling covariance structure in the analysis of repeated measures data. Stat Med 19:1793–1819
Hedeker D, Gibbons RD, Waternaux C (1999) Sample size estimation for longitudinal designs with attrition. J Educ Behav Stat 24:70–93
Basagana X, Spiegelman D (2010) Power and sample size calculations for longitudinal studies comparing rates of change with a time-varying exposure. Stat Med 29(2):181–192
Comulada WS, Weiss RE (2010) Sample size and power calculations for correlations between bivariate longitudinal data. Stat Med 29(27):2811–2824
Phillips KF (1990) Power of the Two One-Sided Tests Procedure in Bioequivalence. J Pharmacokinet Biopharm 18(2):137–144
Blackwelder WC (1998) Equivalence trials. In: Encyclopedia of biostatistics, vol 2. Wiley, New York, pp 1367–1372
Schuirmann D (1987) A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. J Pharmacokinet Biopharm 15(6): 657–680
Stroup W (2010) Introduction to generalized linear mixed models using SAS® PROC GLIMMIX, ASA GLMM workshop
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Shu, L., Gintant, G., Zhang, L. (2016). Statistical Applications in Design and Analysis of In Vitro Safety Screening Assays. In: Zhang, L. (eds) Nonclinical Statistics for Pharmaceutical and Biotechnology Industries. Statistics for Biology and Health. Springer, Cham. https://doi.org/10.1007/978-3-319-23558-5_8
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DOI: https://doi.org/10.1007/978-3-319-23558-5_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23557-8
Online ISBN: 978-3-319-23558-5
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