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Exposure‐response (ER) analyses of repeated time‐to‐event (RTTE) data can be confounded when treatment modifications occur due to the event of interest. One particularly challenging scenario is when an event is preceded by an undocumented clinical worsening leading to treatment discontinuation shortly before the event. Event‐related treatment modifications introduce potential reversed causality in the ER analysis and bias, as events may predict exposure, rather than exposure predicting events. We systematically evaluated three analysis approaches in multiple simulated placebo‐controlled studies to assess their capacity to estimate unbiased ER relationships: (1) average exposure (2) time‐varying exposure (3) average exposure + percentile‐rank of the average amount of blinded study treatment received. The novel Approach 3 was designed to remove confounding from event‐related treatment modifications by leveraging information on blinded study treatment received (including in the placebo treatment arm). Approach 1 was biased in all scenarios with event‐related treatment discontinuations. Approach 2 generally resulted in unbiased estimates, except in the scenarios in which events were sometimes preceded by a treatment discontinuation. In these scenarios, only Approach 3 provided unbiased estimates with linear ER, though it showed slight bias with pronounced nonlinear relationships. In conclusion, treatment modifications associated with events warrant careful consideration in ER analyses of RTTE data. Using a ranked measure of the average amount of blinded study treatment received may be used to lower the risk of confounding due to treatment modifications. Study Highlights What is the current knowledge on the topic? ○Exposure‐response analyses of repeated time‐to‐event data can be confounded when treatment modifications occur due to events. While solutions exist for continuous outcomes in titration studies, no systematic evaluation has been performed for repeated time‐to‐event data where events may cause treatment discontinuation. What question did this study address? ○We evaluated whether standard analysis approaches (average exposure, time‐varying exposure) provide unbiased exposure‐response estimates when event‐related treatment modifications occur, particularly when treatment discontinuation happens shortly before events due to unobserved clinical worsening. What does this study add to our knowledge? ○We identified that time‐varying exposure models become biased when discontinuations occur before events and proposed a novel method using a ranked measure of the amount of blinded study treatment received as covariate that reduces confounding across all tested scenarios with event‐related treatment modifications. How might this change drug discovery, development, and/or therapeutics? ○This methodology enables more accurate exposure‐response characterization in studies with event‐related treatment modifications, potentially improving dose selection decisions and regulatory submissions for drugs where events influence dosing patterns.

Asundexian is a potent, selective, and reversible inhibitor of activated clotting Factor XI currently under development for secondary prevention of recurrent ischemic stroke in the ongoing Phase III OCEANIC‐STROKE study (NCT05686070). Here, we report the development of a population pharmacokinetic (popPK) model for asundexian. Plasma concentration data were available from 2914 participants enrolled in nine Phase I and II studies of asundexian. The pharmacokinetics (PK) of asundexian were well described by the popPK model. Within the investigated dose range of asundexian 10–100 mg once daily, the PK of asundexian was dose‐proportional. The systemic apparent clearance (CL/F) of asundexian was estimated to be 2.25 L/h and the central volume of distribution (VC/F) was 35.3 L. Body weight, age, sex, concomitant administration of cytochrome P450 3A4 (CYP3A4) inhibitors, and renal function were identified as statistically significant covariates influencing the PK of asundexian. After accounting for differences in the distribution of these covariates, the PK of asundexian was comparable in healthy participants and participants at risk for thromboembolic/cardiovascular events. Similarly, no significant differences in PK were noted among participants with atrial fibrillation, ischemic stroke, or acute myocardial infarction. No clinically relevant covariates were identified that would warrant dose adjustments in various special populations of interest, including those defined by body weight, age, sex, and renal function, for the prevention of secondary ischemic strokes.

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.

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.

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.

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.

Background: This analysis aimed to evaluate the impact of rivaroxaban exposure and patient characteristics on efficacy and safety outcomes in patients with acute coronary syndrome (ACS) and to determine whether therapeutic drug monitoring might provide additional information regarding rivaroxaban dose, beyond what patient characteristics provide. Methods: A post hoc exposure–response analysis was conducted using data from the phase III ATLAS ACS 2 Thrombolysis in Myocardial Infarction (TIMI) 51 study, in which 15,526 randomized ACS patients received rivaroxaban (2.5 mg or 5 mg twice daily) or placebo for a mean of 13 months (maximum follow up: 31 months). A multivariate Cox model was used to correlate individual predicted rivaroxaban exposures and patient characteristics with time-to-event clinical outcomes. Results: For the incidence of myocardial infarction (MI), ischemic stroke, or nonhemorrhagic cardiovascular death, hazard ratios (HRs) for steady-state maximum plasma concentration (Cmax) in the 5th and 95th percentiles versus the median were statistically significant but close to 1 for both rivaroxaban doses. For TIMI major bleeding events, a statistically significant association was observed with Cmax [HR, 1.08; 95% CI, 1.06–1.11 (95th percentile versus median, 2.5 mg twice daily)], sex [HR, 0.56; 95% CI, 0.38–0.84 (female versus male)], and previous revascularization [HR, 0.62; 95% CI, 0.44–0.87 (no versus yes)]. Conclusions: The shallow slopes of the exposure–response relationships and the lack of a clear therapeutic window render it unlikely that therapeutic drug monitoring in patients with ACS would provide additional information regarding rivaroxaban dose beyond that provided by patient characteristics.

Anticoagulant plasma concentrations and patient characteristics might affect the benefit–risk balance of therapy. The study objective was to assess the impact of model-predicted rivaroxaban exposure and patient characteristics on outcomes in patients receiving rivaroxaban for venous thromboembolism (VTE) prophylaxis (VTE-P) after hip/knee replacement surgery. Post hoc exposure–response analyses were conducted using data from the phase 3 RECORD1–4 studies, in which 12,729 patients were randomized to rivaroxaban 10 mg once daily or enoxaparin for ≤ 39 days. Multivariate regression approaches were used to correlate model-predicted individual rivaroxaban exposures and patient characteristics with outcomes. In the absence of measured rivaroxaban exposure, exposure estimates were predicted based on individual increases in prothrombin time (PT) and by making use of the known correlation between rivaroxaban plasma concentration and dynamics of PT. No significant associations between rivaroxaban exposure and total VTE or major bleeding were identified. A significant association between exposure and a composite of major or non-major clinically relevant (NMCR) bleeding from day 4 after surgery was observed. The relationship was shallow, with an approximate predicted absolute increase in a composite of major or NMCR bleeding from 1.08 [95% confidence interval (CI) 0.76–1.54] to 2.18% (95% CI 1.51–3.17) at the 5th and 95th percentiles of trough plasma concentration, respectively. In conclusion, based on the underlying data and analysis, no reliable target window for exposure with improved benefit–risk could be identified within the investigated exposure range. Hence, monitoring rivaroxaban levels is unlikely to be beneficial in VTE-P.

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.