Arne Ring

Pharmacokinetics and pharmacodynamics (PK/PD) similarity trials typically involve multiple coprimary endpoints and a 3‐way treatment comparison. The purpose of these trials is to demonstrate the similarity between a biosimilar candidate and two versions of the originator drug. The sample size for these trials is often based on point estimates of the expected treatment difference and/or variability, derived from historical reference data, without considering the uncertainty associated with these estimates. This uncertainty, especially when there are multiple comparisons, can lead to an unreliable estimate of study power. In this paper, we address the power and application of the assurance method in PK/PD similarity studies to account for the uncertainty surrounding treatment differences and/or variability in multiple coprimary endpoints when considering sample size. We introduce an assurance method that can handle multiple comparisons and propose a strategy to elicit joint prior distributions of parameters based on the availability of historical data. These methods are implemented in an R shiny app using the Monte Carlo method. Additionally, we provide a real data example to illustrate the practical application of these methods. Our findings demonstrate that the proposed methods significantly enhance our understanding of study power. Therefore, we recommend incorporating assurance methods as a complement to conditional power in sample size considerations.

Comparative bioavailability studies often involve multiple groups of subjects for a variety of reasons, such as clinical capacity limitations. This raises questions about the validity of pooling data from these groups in the statistical analysis and whether a group-by-treatment interaction should be evaluated. We investigated the presence or absence of group-by-treatment interactions through both simulation techniques and a meta-study of well-controlled trials. Our findings reveal that the test falsely detects an interaction when no true group-by-treatment interaction exists. Conversely, when a true group-by-treatment interaction does exist, it often goes undetected. In our meta-study, the detected group-by-treatment interactions were observed at approximately the level of the test and, thus, can be considered false positives. Testing for a group-by-treatment interaction is both misleading and uninformative. It often falsely identifies an interaction when none exists and fails to detect a real one. This occurs because the test is performed between subjects in crossover designs, and studies are powered to compare treatments within subjects. This work demonstrates a lack of utility for including a group-by-treatment interaction in the model when assessing single-site comparative bioavailability studies, and the clinical trial study structure is divided into groups. Graphical Abstract

Aims The aim of this work is the development of a mechanistic physiologically-based pharmacokinetic (PBPK) model using in vitro to in vivo extrapolation to conduct a drug-drug interaction (DDI) assessment of treosulfan against two cytochrome p450 (CYP) isoenzymes and P-glycoprotein (P-gp) substrates. Methods A PBPK model for treosulfan was developed de novo based on literature and unpublished clinical data. The PBPK DDI analysis was conducted using the U.S. Food and Drug Administration (FDA) DDI index drugs (probe substrates) midazolam, omeprazole and digoxin for CYP3A4, CYP2C19 and P-gp, respectively. Qualified and documented PBPK models of the probe substrates have been adopted from an open-source online model database. Results The PBPK model for treosulfan, based on both in vitro and in vivo data, was able to predict the plasma concentration-time profiles and exposure levels of treosulfan applied for a standard conditioning treatment. Medium and low potentials for DDI on CYP3A4 (maximum area under the concentration-time curve ratio (AUCR(max) = 2.23) and CYP2C19 (AUCR(max) = 1.6) were predicted, respectively, using probe substrates midazolam and omeprazole. Treosulfan was not predicted to cause a DDI on P-gp. Conclusion Medicinal products with a narrow therapeutic index (eg, digoxin) that are substrates for CYP3A4, CYP2C19 or P-gp should not be given during treatment with treosulfan. However, considering the comprehensive treosulfan-based conditioning treatment schedule and the respective pharmacokinetic properties of the concomitantly used drugs (eg, half-life), the potential for interaction on all evaluated mechanisms would be low (AUCR < 1.25), if concomitantly administered drugs are dosed either 2 hours before or 8 hours after the 2-hour intravenous infusion of treosulfan.

In late stage drug development, the experimental drug is tested in a diverse study population within the relevant indication. In order to receive marketing authorization, robust evidence for the therapeutic efficacy is crucial requiring investigation of treatment effects in well‐defined subgroups. Conventionally, consistency analyses in subgroups have been performed by means of interaction tests. However, the interaction test can only reject the null hypothesis of equivalence and not confirm consistency. Simulation studies suggest that the interaction test has low power but can also be oversensitive depending on sample size—leading in combination with the actually ill‐posed null hypothesis to findings regardless of clinical relevance. In order to overcome these disadvantages in the setup of binary endpoints, we propose to use a consistency test based on the interval inclusion principle, which is able to reject heterogeneity and confirm consistency of subgroup‐specific treatment effects while controlling the type I error. This homogeneity test is based upon the deviation between overall treatment effect and subgroup‐specific effects on the odds ratio scale and is compared with an equivalence test based on the ratio of both subgroup‐specific effects. Performance of these consistency tests is assessed in a simulation study. In addition, the consistency tests are outlined for the relative risk regression. The proposed homogeneity test reaches sufficient power in realistic scenarios with small interactions. As expected, power decreases for unbalanced subgroups, lower sample sizes, and narrower margins. Severe interactions are covered by the null hypothesis and are more likely to be rejected the stronger they are.

The recently finalised and published guideline ICH E9 (R1) introduced a new framework for the statistical analysis of clinical trials, namely that of “estimands”. While the framework was originally developed for the analysis of late phase trials, it could also provide a rigorous basis for the analysis of clinical pharmacology trials. We illustrate potential applications on two examples: A multiple dose pharmacology trial, and the interpretation of confirmatory bioequivalence (BE) trials according to the current FDA and EMA bioequivalence guidelines.

Aims: Bioequivalence (BE) trials aim to demonstrate that the 90% confidence interval of the T/R‐ratio of the pharmacokinetic metrics between two formulations (test [T] and reference [R]) of a drug is fully included in the acceptance interval [0.80, 1.25]. Traditionally, the sample size of BE trials is based on a power calculation based on the intrasubject variability coefficient of variation (CV) and the T/R‐ratio of the metrics. Since the exact value of the T/R‐ratio is not known prior to the trial, it is often assumed that the difference between the treatments does not exceed 5%. Hence, uncertainty about the T/R‐ratio is expressed by using a fixed value for the sample size calculation. We propose to characterise the uncertainty about the T/R‐ratio by a (normal) distribution for the log(T/R‐ratio), with an assumed mean of log θ = 0.00 (i.e. θ = 1.00) and a standard deviation σ_u, which quantifies the uncertainty. Evaluating this distribution leads to the statistical assurance of the BE trial.Methods: The assurance of a clinical trial can be derived by integrating the power over the distribution of the input parameters, in this case, the assumed distribution of the log(T/R)‐ratio. Because it is an average power, the assurance can be interpreted as a measure of the probability of success that does not depend on a specific assumed value for the log(T/R)‐ratio. The relationship between power and assurance will be analysed by comparing the numerical outcomes.Results: Using the assurance concept, values of the standard deviation for the distribution of potential log(T/R)‐ratios can be chosen to reflect the magnitude of uncertainty. For most practical cases (i.e. when 0.95 ≤ θ ≤ 1.05), the sample size is not, or only slightly, changed when σ = |log(θ)|.Conclusion: The advantage of deriving the assurance for BE trials is that uncertainty is directly expressed as a parameter of variability.