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Particle size distribution (PSD) equivalency using novel statistical comparators and PBPK input modelsNgeacharernkul, Pratak 01 December 2017 (has links)
For disperse system drug formulations, meaningful particle size distribution (PSD) comparators are essential in determining pharmaceutical equivalency and predicting biopharmaceutical equivalence in terms of the effect of particle size on the rate and extent of drug input. In formulation development and licensure, particle size characterization has been applied to establish relationships for bioequivalence of generic pharmaceutical drug products. The current approaches recommended by the US-FDA using median and span are not adequate to predict drug product performances or account for multi-modal PSD performance properties. The use of PSD similarity metric and the development and incorporation of drug release predictions based on PSD properties into PBPK models for various drug administration routes may provide a holistic approach for evaluating the effect of PSD differences on in vitro release of disperse systems and the resulting pharmacokinetic impact on drug product performance. The objectives of this study are to provide a rational approach for PSD comparisons by 1) developing similarity computations for PSD comparisons and 2) using PBPK-models to specifically account for PSD effects on drug input rates via a subcutaneous (SQ) administration route.
Two techniques for measuring PSDs of reference (reference-listed drug product) and test (generic) drug products were investigated: OVL and PROB, as well as the current standard measurements of median and span. In addition, release rate profiles of each product pair simulated from modified Bikhazi and Higuchi’s model were used to compute release rate comparators such as similarity factor (f2) and fractional time ratios. A subcutaneous input PBPK model was developed and used to simulate blood concentration-time profiles of reference and test drug products. Pharmacokinetic responses such as AUC, Cmax, and Tmax were compared using standard bioequivalence criteria. PSD comparators, release rate comparators, and bioequivalence metrics were related to determine their relationships and identify the appropriate approach for bioequivalence waiver.
OVL showed better predictions for bioequivalence compared to PROB, median, and span. For release profile comparisons, the f2 method was the best for bioequivalence prediction. The use of both release rate (e.g., f2) and PSD (e.g., OVL) comparison metrics significantly improved bioequivalence prediction to about 90%.
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Comparing Single-Case Design Non-Overlap Metrics and Visual Analysis Examining School-Based Interventions for Students with Autism Spectrum DisorderAlresheed, Fahad 11 January 2019 (has links)
High prevalence of individuals with autism spectrum disorder (ASD) and the legislation movement impacted the placement of students with ASD in general education settings. Hence, the increase raised the need to conduct research for ASD populations, and to examine the effectiveness of these interventions. With the increase of single case-design (SCD) studies, there is a demand to include SCD in the evaluation of evidence- based practices (EBPs), to analyze and interpret SCD results in meaningful ways beside visual analysis, and to generate effect size estimates. This dissertation contains four systematic literature reviews which examine single-case intervention research targeting academic, social communication, play, and functional life skills for children with ASD in school settings. 132 studies with 924 AB phase contrasts were analyzed using visual analysis and three non-overlap measures. Sensitivity and specificity of Tau-U, IRD, and Baseline Corrected Tau were tested on detecting intervention effects. Also, the three methods were examined in their agreement with interpretations based on the visual analysis and the effect of confounding factor on their scores. The analysis demonstrated that the three methods performed fairly well in distinguishing effective from non-effective interventions. The three non-overlap methods had a moderate to substantial level of agreement with visual analysis. The author recommended further research on the impact of confounding factors especially baseline trend and autocorrelation as well as the use of effect size methods with high sensitivity and visual aids to improve the reliability and accuracy of visual analysis.
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