Browsing by Subject "Dose response"

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    Optimization of a human induced pluripotent stem cell-derived sensory neuron model for the in vitro evaluation of taxane-induced neurotoxicity
    (Springer Nature, 2024-08-17) Cantor, Erica L.; Shen, Fei; Jiang, Guanglong; Philips, Santosh; Schneider, Bryan P.; Medicine, School of Medicine
    Human induced pluripotent stem cell-derived sensory neuron (iPSC-dSN) models are a valuable resource for the study of neurotoxicity but are affected by poor replicability and reproducibility, often due to a lack of optimization. Here, we identify experimental factors related to culture conditions that substantially impact cellular drug response in vitro and determine optimal conditions for improved replicability and reproducibility. Treatment duration and cell seeding density were both found to be significant factors, while cell line differences also contributed to variation. A replicable dose-response in viability was demonstrated after 48-h exposure to docetaxel or paclitaxel. Additionally, a replicable dose-dependent reduction in neurite outgrowth was demonstrated, demonstrating the applicability of the model for the examination of additional phenotypes. Overall, we have established an optimized iPSC-dSN model for the study of taxane-induced neurotoxicity.
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    Penalized spline modeling of the ex-vivo assays dose-response curves and the HIV-infected patients' bodyweight change
    (2015-06-05) Sarwat, Samiha; Harezlak, Jaroslaw; Yiannoutsos, Constantin T.; Li, Xiaochun; Wools-Kaloustian, Kara K.
    A semi-parametric approach incorporates parametric and nonparametric functions in the model and is very useful in situations when a fully parametric model is inadequate. The objective of this dissertation is to extend statistical methodology employing the semi-parametric modeling approach to analyze data in health science research areas. This dissertation has three parts. The first part discusses the modeling of the dose-response relationship with correlated data by introducing overall drug effects in addition to the deviation of each subject-specific curve from the population average. Here, a penalized spline regression method that allows modeling of the smooth dose-response relationship is applied to data in studies monitoring malaria drug resistance through the ex-vivo assays.The second part of the dissertation extends the SiZer map, which is an exploratory and a powerful visualization tool, to detect underlying significant features (increase, decrease, or no change) of the curve at various smoothing levels. Here, Penalized Spline Significant Zero Crossings of Derivatives (PS-SiZer), using a penalized spline regression, is introduced to investigate significant features in correlated data arising from longitudinal settings. The third part of the dissertation applies the proposed PS-SiZer methodology to analyze HIV data. The durability of significant weight change over a period is explored from the PS-SiZer visualization. PS-SiZer is a graphical tool for exploring structures in curves by mapping areas where rate of change is significantly increasing, decreasing, or does not change. PS-SiZer maps provide information about the significant rate of weigh change that occurs in two ART regimens at various level of smoothing. A penalized spline regression model at an optimum smoothing level is applied to obtain an estimated first-time point where weight no longer increases for different treatment regimens.