A theoretical framework for estimation of AUCs in complete and incomplete sampling designs.

Nonclinical in vivo animal studies have to be completed before starting clinical studies of the pharmacokinetic behavior of a drug in humans. The drug exposure in animal studies is often measured by the area under the concentration versus time curve (AUC). The classic complete data design, where each animal is sampled for analysis once per time point, is usually only applicable for large animals. In the case of rats and mice, where blood sampling is restricted, the batch design or the serial sampling design needs to be considered. In batch designs samples are taken more than once from each animal, but not at all time points. In serial sampling designs only one sample is taken from each animal. In this paper we present an estimator for the AUC from 0 to the last time point that is applicable to all three designs. The variance and asymptotic distribution of the estimator are derived and confidence intervals based upon the asymptotic results are discussed and evaluated in a simulation study. Further, we define an estimator for linear combinations of AUCs and investigate its asymptotic properties mathematically as well as in simulation

Non-compartmental estimation of pharmacokinetic parameters for flexible sampling designs

Pharmacokinetic (PK) studies aim to understand the kinetics of absorption, distribution, metabolism and elimination of a drug. Typically, such studies involve measuring the concentration of the drug in the plasma or blood at several time points after drug administration. In studying the PK behaviour, either the non-compartmental approach or alternatively a modelling approach can be utilized. Traditionally, the non-compartmental approach makes minimal assumptions about the data-generating process but requires the data to be collected in a very structured way. Conversely, the modelling approach depends heavily on assumptions about the data-generating process but does not impose a specific data structure. In this paper, we will discuss non-compartmental methods for estimating the area under the concentration versus time curve and other common PK parameters that use minimal assumptions about the data structure making it applicable to a wide range of PK studies. We will evaluate the methods using simulation and give an illustrative example

Estimation of AUC from 0 to Infinity in Serial Sacrifice Designs.

Nonclinical in vivo animal studies have to be completed before starting clinical studies of the pharmacokinetic behavior of a drug in humans. The drug exposure in animal studies is often measured by the area under the concentration time curve (AUC). The classical complete data design where each animal is sampled for analysis once per time point is usually only applicable for large animals. In the case of rats and mice, where blood sampling is restricted, the batch design or the serial sacrifice design need to be considered. In batch designs samples are taken more than once from each animal, but not at all time points. In serial sacrifice designs only one sample is taken from each animal. This paper presents an estimator for AUC from 0 to infinity in serial sacrifice designs, the corresponding variance and its asymptotic distribution

Recurrent events modelling of haemophilia bleeding events

A pharmacokinetic-pharmacodynamic (PK-PD) approach is developed for modelling recurrent bleeding events in patients with severe haemophilia to investigate the relationship between factor VIII plasma activity level and the instantaneous risk of a bleed. The model incorporates patient-level pharmacokinetic (PK) information obtained through measurements taken prior to the study which are used to fit a non-linear mixed effects two-compartment PK model. Dosing times within the study are combined with the PK model to provide the estimated factor VIII plasma level for all patients, which is used as a time dependent covariate within the recurrent events model. Methods are developed to correct the attenuation in covariate effects that would otherwise arise due to the discrepancy between estimated and true factor VIII. In contrast to existing methods proposed for such data, such as count data regression or time-to-event analysis, the new method allows all the bleeding times to be used to investigate the relationship between current factor VIII and risk of a bleed. The performance of the proposed estimators are assessed via simulation and found to outperform the naive estimator, which treats the estimated factor VIII levels as if they were measured without error, both in terms of bias and mean squared error

Simultaneous confidence intervals by iteratively adjusted alpha for relative effects in the one-way layout.

A bootstrap based method to construct 1 � α simultaneous confidence intervals for relative effects in the one-way layout is presented. This procedure takes the stochastic correlation between the test statistics into account and results in narrower simultaneous confidence intervals than the application of the Bonferroni correction. Instead of using the bootstrap distribution of a maximum statistic, the coverage of the confidence intervals for the individual com- parisons are adjusted iteratively until the overall confidence level is reached. Empirical coverage and power estimates of the introduced procedure for many-to-one comparisons are presented and compared with asymptotic procedures based on the multivariate normal distribution

A note on statistical analysis of organ weights in non-clinical toxicological studies.

Statistical comparison of organ weights between treated and untreated animals have traditionally been used to predict potential toxicity for patients. The manner of presentation of organ weight data, and the value of statistical analyses have been topics of discussion. Historically, a decision tree approach has been applied for statistical comparison of organ weights which does not control the overall error rate and can lead to different statistical tests being used by chance for identical settings causing confusion. This paper proposes a simple nonparametric approach for assessing treatment effects on organ weights in terms of ratios based on the Hodges-Lehmann estimator. This allows for simple interpretation of results and aids in the identification of potential target organs as the evaluation is based on effect sizes and not on p-values allowing a robust proof of effect as well as a robust proof of no effect. The proposed estimate and the corresponding nonparametric confidence interval applied to a rank-sum score can be used as a confirmatory test for difference and as a confirmatory test for equivalence. Exploratory analyses can be performed calculating the proposed estimates for each organ weight separately to be summarized graphically in a confidence interval plot

A comparison of methods for classifying samples as truly specific with confirmatory immunoassays

Biotechnology-derived therapeutics may induce an unwanted immune response leading to the formation of anti-drug antibodies (ADAs) which can result in altered efficacy and safety of the therapeutic protein. Anti-drug antibodies may, for example, affect pharmacokinetics of the therapeutic protein or induce autoimmunity. It is therefore crucial to have assays available for the detection and characterization of ADAs. Commonly, a screening assay is initially used to classify samples as either ADA positive or negative. A confirmatory assay, typically based on antigen competition, is subsequently employed to separate false positive samples from truly positive samples. In this manuscript we investigate the performance of different statistical methods classifying samples in competition assays through simulation and analysis of real data. In our evaluations we do not find a uniformly best method although a simple t-test does provide good results throughout. More crucially we find that very large differences between uninhibited and inhibited measurements relative to the assay variability are required in order to obtain useful classification results questioning the usefulness of competition assays with high variability

Establishing bioequivalence in complete and incomplete data designs using AUCs.

Nonclinical in vivo animal studies have to be completed before starting clinical studies of the pharmacokinetic behavior of a drug in humans. The drug exposure in animal studies is often measured by the area under the concentration versus time curve (AUC). The classical complete data design where each animal is sampled for analysis at every time point is applicable for large animals only. In the case of small animals, where blood sampling is restricted, the batch design or the serial sampling design need to be considered. In batch designs, samples are taken more than once from each animal, but not at all time points. In serial sampling designs, only one sample is taken from each animal. In this article we derive the asymptotic distribution for the ratio of two AUCs and construct different confidence intervals, which are frequently used to assess bioequivalence. The performance of these intervals is then evaluated between the different designs in a simulation study. Additionally, the sample sizes required for the different designs are compared