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Abstract Item response theory (IRT) models are usually the best way to analyze composite or rating scale data. Standard methods to evaluate covariate or treatment effects in IRT models do not allow to identify item‐specific effects. Finding subgroups of patients who respond differently to certain items could be very important when designing inclusion or exclusion criteria for clinical trials, and aid in understanding different treatment responses in varying disease manifestations. We present a new method to investigate item‐specific effects in IRT models, which is based on inspection of residuals. The method was investigated in a simulation exercise with a model for the Epworth Sleepiness Scale. We also provide a detailed discussion as a guidance on how to build a robust covariate IRT model.

Abstract Obstructive sleep apnea (OSA) is a sleep disorder which is linked to many health risks. The gold standard to evaluate OSA in clinical trials is the Apnea‐Hypopnea Index (AHI). However, it is time‐consuming, costly, and disregards aspects such as quality of life. Therefore, it is of interest to use patient‐reported outcomes like the Epworth Sleepiness Scale (ESS), which measures daytime sleepiness, as surrogate end points. We investigate the link between AHI and ESS, via item response theory (IRT) modeling. Through the developed IRT model it was identified that AHI and ESS are not correlated to any high degree and probably not measuring the same sleepiness construct. No covariate relationships of clinical relevance were found. This suggests that ESS is a poor choice as an end point for clinical development if treatment is targeted at improving AHI, and especially so in a mild OSA patient group.

Abstract Copanlisib dose selection was established under the maximum tolerated dose paradigm, and no dedicated dose‐finding studies have investigated copanlisib dose selection when used in combination with rituximab. In CHRONOS‐3, copanlisib plus rituximab demonstrated significantly improved progression‐free survival versus placebo plus rituximab in patients with relapsed indolent non‐Hodgkin lymphoma (iNHL). We conducted a comprehensive investigation of copanlisib population pharmacokinetics (PopPK) from a pooled analysis of 712 patients across nine copanlisib phase I–III studies and exposure–response (ER) relationships for efficacy and safety from the 1‐year follow‐up of CHRONOS‐3. PopPK analyses examined the impact of demographic, laboratory, and comedication covariates on copanlisib between‐patient PK variability. Individual static and time‐varying exposure estimates were derived to investigate exposure–efficacy and exposure–safety relationships. Multivariate Cox proportional hazards and logistic regression analyses examined ER relationships with consideration of predefined potentially prognostic demographic‐, laboratory‐, and/or disease‐related baseline covariates. Copanlisib PK were best described by a three‐compartment model with first‐order elimination. Individual identified covariates had modest effects on copanlisib PK and were generally in line with known copanlisib disposition properties. In CHRONOS‐3, ER analyses showed a significant relationship between time‐varying exposure estimates and progression‐free survival, and no significant exposure–safety relationships. Thus, lower copanlisib doses may result in reduced efficacy but not necessarily improved safety or tolerability. These outcomes substantiate the current intermittent dosing regimen of copanlisib 60 mg on days 1, 8, and 15 of a 28‐day cycle and support the observed clinical results of copanlisib in combination with rituximab in the iNHL population.

Abstract Copanlisib is an intravenously administered phosphatidylinositol 3‐kinase (PI3K) inhibitor which was investigated in pediatric patients with relapsed/refractory solid tumors. A model‐informed approach was undertaken to support and confirm an empirically selected starting dose of 28 mg/m2 for pediatric patients ≥1 year old, corresponding to 80% of the adult recommended dose adjusted for body surface area. An adult physiologically based pharmacokinetic (PBPK) model was initially established using copanlisib physicochemical and disposition properties and clinical pharmacokinetics (PK) data and was shown to adequately capture clinical PK across a range of copanlisib doses in adult cancer patients. The adult PBPK model was then extended to the pediatric population through incorporation of age‐dependent anatomical and physiological changes and used to simulate copanlisib exposures in pediatric cancer patient age groups. The pediatric PBPK model predicted that the copanlisib 28 mg/m2 dose would achieve similar copanlisib exposures across pediatric ages when compared with historical adult exposures following the approved copanlisib 60 mg dose administered on Days 1, 8, and 15 of a 28‐day cycle. Clinical PK were collected from a phase I study in pediatric patients with relapsed/refractory solid tumors (aged ≥4 years). An established adult population PK model was extended to incorporate an allometrically‐scaled effect of body surface area and confirmed that the copanlisib maximum tolerated dose of 28 mg/m2 was appropriate to achieve uniform copanlisib exposures across the investigated pediatric age range and consistent exposures to historical data in adult cancer patients. The model‐informed approach successfully supported and confirmed the copanlisib pediatric dose recommendation.

Bayesian forecasting for dose individualization of prophylactic factor VIII replacement therapy using pharmacokinetic samples is challenged by large interindividual variability in the bleeding risk. A pharmacokinetic‐repeated time‐to‐event model‐based forecasting approach was developed to contrast the ability to predict the future occurrence of bleeds based on individual (i) pharmacokinetic, (ii) bleeding, and (iii) pharmacokinetic, bleeding and covariate information using observed data from the Long‐Term Efficacy Open‐Label Program in Severe Hemophilia A Disease (LEOPOLD) clinical trials (172 severe hemophilia A patients taking prophylactic treatment). The predictive performance assessed by the area under receiver operating characteristic (ROC) curves was 0.67 (95% confidence interval (CI), 0.65–0.69), 0.78 (95% CI, 0.76–0.80), and 0.79 (95% CI, 0.77–0.81) for patients ≥ 12 years when using pharmacokinetics, bleeds, and all data, respectively, suggesting that individual bleed information adds value to the optimization of prophylactic dosing regimens in severe hemophilia A. Further steps to optimize the proposed tool for factor VIII dose adaptation in the clinic are required.

Prothrombin time (PT) is a measure of coagulation status and was assessed in the majority of patients in the rivaroxaban phase II and III clinical trials as a pharmacodynamic marker. In the absence of sufficient phase III pharmacokinetic (PK) data to provide individual exposure measures for input into rivaroxaban exposure–response analyses, the aim of the present study was to investigate the use of PT‐adjustment approaches (i.e., the use of observed individual PT measurements) to enhance the prediction of individual rivaroxaban exposure metrics (derived using a previously developed integrated population PK model) based on the observed linear relationship between PT and rivaroxaban plasma concentrations. The PT‐adjustment approaches were established using time‐matched PK and PT measurements, which were available from 1,779 patients across four phase II trials and one phase III trial of rivaroxaban. PT‐adjusted exposure estimates improved the identification of statistically significant effects when compared with covariate‐only exposure estimates.