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Related Articles Variability and Impact on Design of Bioequivalence Studies. Basic Clin Pharmacol Toxicol. 2009 Nov 11; Authors: Van Peer A In 2008, the European Agency for the Evaluation of Medicinal Products released a draft guidance on the investigation of bioequivalence for immediate release dosage forms with systemic action to replace the former guidance of a decade ago. Revisions of the regulatory guidance are based upon many questions over the past years and sometimes continuing scientific discussions on the use of the most suitable statistical analysis methods and study designs, particularly for drugs and drug products with high within-subject variability. Although high within-subject variability is usually associated with a coefficient of variation of 30% or more, new approaches are available in the literature to allow a gradual increase and a levelling off of the bioequivalence limits to some maximum wider values (e.g. 75-133%), dependent on the increase in the within-subject variability. The two-way, cross-over single dose study measuring parent drug is still the design of first choice. A partial replicate design with repeating the reference product and scaling the bioequivalence for the reference variability are proposed for drugs with high within-subject variability. In case of high variability, more regulatory authorities may accept a two-stage or group-sequential bioequivalence design using appropriately adjusted statistical analysis. This review also considers the mechanisms why drugs and drug products may exhibit large variability. The physiological complexity of the gastrointestinal tract and the interaction with the physicochemical properties of drug substances may contribute to the variation in plasma drug concentration-time profiles of drugs and drug products and to variability between and within subjects. A review of submitted bioequivalence studies at the Food and Drug Administration’s Office of Generic Drugs over the period 2003-2005 indicated that extensive pre-systemic metabolism of the drug substance was the most important explanation for consistently high variability drugs, rather than a formulation factor. These scientific efforts are expected to further lead to revisions of earlier regulatory guidance in other regions as is the current situation in Europe. PMID: 20041877 [PubMed - as supplied by publisher]

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Variability and Impact on Design of Bioequivalence Studies.

Related Articles Analysis, Modeling, and Simulation of the Accuracy of Continuous Glucose Sensors. J Diabetes Sci Technol. 2008 Sep 1;2(5):853-862 Authors: Breton M, Kovatchev B BACKGROUND: Continuous glucose monitors (CGMs) collect a detailed time series of consecutive observations of the underlying process of glucose fluctuations. To some extent, however, the high temporal resolution of the data is accompanied by increased probability of error in any single data point. Due to both physiological and technical reasons, the structure of these errors is complex and their analysis is not straightforward. In this article, we describe some of the methods needed to obtain a description of the sensor error that is detailed enough for simulation. METHODS: Data were provided by Abbott Diabetes Care and included two data sets collected by the FreeStyle Navigator CGM: The first set consisted of 1032 time series of glucose readings from 136 patients with type 1 diabetes and parallel time series of reference blood glucose (BG) collected via self-monitoring at irregular intervals. The average duration of a time series was 5 days; the total number of sensor-reference data pairs was approximately 20,600. The second data set consisted of 56 time series of glucose readings from 28 patients with type 1 diabetes and a parallel time series of reference BG measured via the YSI 2300 Stat Plus analyzer every 15 minutes. The average duration of a time series was 2 days; the total number of sensor-reference data pairs was approximately 7000. RESULTS: Three sets of results are discussed: analysis of sensor errors with respect to the BG rate of change, mathematical modeling of sensor error patterns and distribution, and computer simulation of sensor errors: Sensor errors depend nonlinearly on the BG rate of change: Errors tend to be positive (high readings) when the BG rate of change is negative and negative (low readings) when the BG rate of change is positive, which is indicative of an underlying time delay. In addition, the sensor noise is non-white (non-Gaussian) and the consecutive sensor errors are highly interdependent.Thus, the modeling of sensor errors is based on a diffusion model of blood-to-interstitial glucose transport, which accounts for the time delay, and a time-series approach, which includes autoregressive moving average (ARMA) noise to account for the interdependence of consecutive sensor errors.Based on modeling, we have developed a computer simulator of sensor errors that includes both generic and sensor-specific error components. A chi(2) test showed that no significant difference exists between the observed and the simulated distribution of sensor errors and the distribution of errors of the FreeStyle Navigator (p > .46). CONCLUSIONS: CGM accuracy was modeled via diffusion and additive ARMA noise, which allowed for designing a computer simulator of sensor errors. The simulator, a component of a larger simulation platform approved by the Food and Drug Administration in January 2008 for pre-clinical testing of closed-loop strategies, has been successfully applied to in silico testing of closed-loop control algorithms, resulting in an investigational device exemption for closed-loop trials at the University of Virginia. PMID: 19750186 [PubMed - as supplied by publisher]

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