Bioequivalence evaluation of sparse sampling pharmacokinetics data using bootstrap resampling method.

Bioequivalence studies are an essential part of the evaluation of generic drugs. The most common in vivo bioequivalence study design is the two-period two-treatment crossover design. The observed drug concentration-time profile for each subject from each treatment under each sequence can be obtained. AUC (the area under the concentration-time curve) and Cmax (the maximum concentration) are obtained from the observed drug concentration-time profiles for each subject from each treatment under each sequence. However, such a drug concentration-time profile for each subject from each treatment under each sequence cannot possibly be available during the development of generic ophthalmic products since there is only one-time point measured drug concentration of aqueous humor for each eye. Instead, many subjects will be assigned to each of several prespecified sampling times. Then, the mean concentration at each sampling time can be obtained by the simple average of these subjects' observed concentration. One profile of the mean concentration vs. time can be obtained for one product (either the test or the reference product). One AUC value for one product can be calculated from the mean concentration-time profile using trapezoidal rules. This article develops a novel nonparametric method for obtaining the 90% confidence interval for the ratio of AUCT and AUCR (or CT,max/CR,max) in crossover studies by bootstrapping subjects at each time point with replacement or bootstrapping subjects at all sampling time points with replacement. Here T represents the test product, and R represents the reference product. It also develops a novel nonparametric method for estimating the standard errors (SEs) of AUCh and Ch,max in parallel studies by bootstrapping subjects treated by the hth product at each time point with replacement or bootstrapping subjects treated by the hth product at all sampling time points with replacement, h = T, R. Then, 90% confidence intervals for AUCT/AUCR and CT,max/CR,max are obtained from the nonparametric bootstrap resampling samples and are used for the evaluation of bioequivalence study for one-time sparse sampling data.

Evaluating elevated release liner adhesion of a transdermal drug delivery system (TDDS): a study of Daytrana™ methylphenidate transdermal system.

BACKGROUND: Complaints from healthcare providers that the adhesive on the Daytrana™ methylphenidate transdermal drug delivery system (TDDS) adhered to the release liner to such an extent that the release liner could not be removed prompted this study. Daytrana™ has a packaging system consisting of a moisture-permeable pouch contained within a sealed tray containing a desiccant; the tray is impermeable to ambient moisture. The objective of this project was to determine if the Daytrana™ packaging system influenced the difficulty in removing the release liner. METHOD: Both a sealed tray and an open tray containing sealed pouches were placed into an environmental chamber at 25°C and 60% relative humidity for 30 days; afterwards, release liner removal testing using a peel angle of 90° and a peel speed of 300 mm/min was performed. RESULTS: TDDS from open chamber trays required less force to remove the release liner than did TDDS from closed chamber trays. For the 10 mg/9 h TDDS and the 15 mg/9 h TDDS (the dosages examined), there were substantial differences in release liner removal force between an old lot and a new lot for closed chamber trays but not for open chamber trays. CONCLUSION: The results demonstrate that for this particular TDDS, storage conditions such as humidity influence release liner adhesion. This project also demonstrates that, to ensure adequate product quality, adhesion needs to become an important design parameter, and the design of a TDDS should consider the ability to remove the release liner under anticipated storage conditions.

Release liner removal method for transdermal drug delivery systems (TDDS).

A release liner removal test is a valuable test for assessing the quality of a transdermal drug delivery system (i.e., TDDS, patch). This test measures the force required to remove the release liner from a patch. The objective of the present study was to establish sample preparation and instrument parameters for measuring release liner removal adhesion for TDDS. Ten TDDS were evaluated (six drugs for a total of 29 lots). Patches which had a rate-controlling membrane were run as-is, since they could not be cut to a precise width without sacrificing their structural integrity. Patches that were square or rectangular in shape were run as-is, and the width of these patches was determined using a digital caliper. Patches which were not square or rectangular in shape and did not have a rate-controlling membrane were cut to a precise width using a specimen cutter. Double-sided tape was used to adhere the liner side of the transdermal system to a clean stainless steel test panel. A release liner peel adhesion method for TDDS is proposed using a dwell time of approximately 3 min, a peel angle of 90 degrees , and a peel speed of 300 mm/min.

Statistical issues including design and sample size calculation in thorough QT/QTc studies.

After several drugs were removed from the market in recent years because of death due to ventricular tachycardia resulting from drug-induced QT prolongation (Khongphatthanayothin et al., 1998; Lasser et al., 2002; Pratt et al., 1994; Wysowski et al., 2001), the ICH Regulatory agencies requested all sponsors of new drugs to conduct a clinical study, named a Thorough QT/QTc (TQT) study, to assess any possible QT prolongation due to the study drug. The final version of the ICH E14 guidance (ICH, 2005) for "The Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential for Nonantiarrhythmic Drugs" was released in May 2005. The purpose of the ICH E14 guidance (ICH, 2005) is to provide recommendations to sponsors concerning the design, conduct, analysis, and interpretation of clinical studies to assess the potential of a drug to delay cardiac repolarization. The guideline, however, is not specific on several issues. In this paper, we try to address some statistical issues, including study design, primary statistical analysis, assay sensitivity analysis, and the calculation of the sample size for a TQT study.

Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA's spontaneous reports database.

Since 1998, the US Food and Drug Administration (FDA) has been exploring new automated and rapid Bayesian data mining techniques. These techniques have been used to systematically screen the FDA's huge MedWatch database of voluntary reports of adverse drug events for possible events of concern. The data mining method currently being used is the Multi-Item Gamma Poisson Shrinker (MGPS) program that replaced the Gamma Poisson Shrinker (GPS) program we originally used with the legacy database. The MGPS algorithm, the technical aspects of which are summarised in this paper, computes signal scores for pairs, and for higher-order (e.g. triplet, quadruplet) combinations of drugs and events that are significantly more frequent than their pair-wise associations would predict. MGPS generates consistent, redundant, and replicable signals while minimising random patterns. Signals are generated without using external exposure data, adverse event background information, or medical information on adverse drug reactions. The MGPS interface streamlines multiple input-output processes that previously had been manually integrated. The system, however, cannot distinguish between already-known associations and new associations, so the reviewers must filter these events. In addition to detecting possible serious single-drug adverse event problems, MGPS is currently being evaluated to detect possible synergistic interactions between drugs (drug interactions) and adverse events (syndromes), and to detect differences among subgroups defined by gender and by age, such as paediatrics and geriatrics. In the current data, only 3.4% of all 1.2 million drug-event pairs ever reported (with frequencies > or = 1) generate signals [lower 95% confidence interval limit of the adjusted ratios of the observed counts over expected (O/E) counts (denoted EB05) of > or = 2]. The total frequency count that contributed to signals comprised 23% (2.4 million) of the total number, 10.4 million of drug-event pairs reported, greatly facilitating a more focused follow-up and evaluation. The algorithm provides an objective, systematic view of the data alerting reviewers to critically important, new safety signals. The study of signals detected by current methods, signals stored in the Center for Drug Evaluation and Research's Monitoring Adverse Reports Tracking System, and the signals regarding cerivastatin, a cholesterol-lowering drug voluntarily withdrawn from the market in August 2001, exemplify the potential of data mining to improve early signal detection. The operating characteristics of data mining in detecting early safety signals, exemplified by studying a drug recently well characterised by large clinical trials confirms our experience that the signals generated by data mining have high enough specificity to deserve further investigation. The application of these tools may ultimately improve usage recommendations.