Exposure-response analysis of adverse events associated with molibresib and its active metabolites in patients with solid tumors

Molibresib (GSK525762) is an investigational orally bioavailable small‐molecule bromodomain and extraterminal (BET) protein inhibitor for the treatment of advanced solid tumors. In the first‐time‐in‐human BET115521 study of molibresib in patients with solid tumors, thrombocytopenia was the most frequent treatment‐related adverse event (AE), QT prolongation was an AE of special interest based on preclinical signals, and gastrointestinal (GI) AEs (nausea, vomiting, diarrhea, and dysgeusia) were often observed. The aims of this analysis were the following: (i) develop a population pharmacokinetic (PK)/pharmacodynamic (PD) model capable of predicting platelet time courses in individual patients after administration of molibresib and identify covariates of clinical interest; (ii) evaluate the effects of molibresib (and/or its two active metabolites [GSK3529246]) exposure on cardiac repolarization by applying a systematic modeling approach using high‐quality, intensive, PK time‐matched 12‐lead electrocardiogram measurements; (iii) evaluate the exposure–response (ER) relationship between molibresib and/or GSK3529246 exposures and the occurrence of Grade 2 or higher GI AEs. Overall, the PK/PD model (including a maximal drug effect model and molibresib concentration) adequately described platelet counts following molibresib treatment and was used to simulate the impact of molibresib dosing on thrombocytopenia at different doses and regimens. ER analyses showed no clinically meaningful QT interval prolongation with molibresib at up to 100 mg q.d., and no strong correlation between molibresib exposure and the occurrence of Grade 2 or higher GI AEs. The models described here can aid dosing/schedule and drug combination strategies and may support a thorough QT study waiver request for molibresib.

Concentration-QTc analysis with two or more correlated baselines

The relationship between drug concentration and QTc interval is typically evaluated by applying the standard analysis model proposed in a scientific whitepaper by Garnett et al. ( https://doi.org/10.1007/s10928-017-9558-5 ). The model is a mixed effects model in which a baseline QTc interval is included as a covariate. Two or more baseline QTc intervals are sometimes observed for a study participant, such as time-matched baselines on a baseline day in parallel studies, or pre-dose baselines in each period in crossover studies. In such situations, the baseline adjustments are not straightforward because these baselines correlate with not only the corresponding QTc intervals after drug administration, but also other QTc intervals at different timepoints for parallel studies, or those in different periods for crossover studies. In this study, we compared three analysis models through simulations and clinical study examples in settings in which two or more baselines were observed for a subject. We compared a model without baseline adjustment, a model with baseline adjustment, and a model in which baseline and baseline mean were included as covariates. In the simulations and clinical study examples, the model with baseline and baseline mean as covariates demonstrated higher accuracy and power than the other models. This model assumed a specific covariance structure in QTc intervals, which well approximated the correlations between QTc intervals within and between days. When there are two or more baselines in concentration-QTc analyses, the baseline mean should be included as a covariate in addition to the corresponding baseline.

Concentration-QTc analysis for single arm studies

Concentration-QTc (C-QTc) modeling is being increasingly used in phase 1 studies. For studies without a placebo arm (single arm studies), the scientific whitepaper by Garnett et al. ( https://doi.org/10.1007/s10928-017-9558-5 ) states that time-matched baseline adjustments may minimize the effect of diurnal variation in QTc intervals, and categorical time effects are not needed in the model. However, how diurnal variations can be accounted for when only pre-dose baselines are available is unclear. This research investigates whether including categorical time effects in the model can adjust diurnal variation in single arm studies with pre-dose baselines, where QTc prolongation is evaluated at a concentration of interest based on ΔQTc at 24 h and ΔΔQTc (a model-derived difference in ΔQTc from concentration zero). To understand the operating characteristics for the models with and without categorical time effects, simulations were conducted under various scenarios considering oncology early phase studies. When the C-QTc relationship is linear, models without categorical time effects provided biased estimates for model parameters and inflated or decreased false negative rates (FNRs) depending on the pattern of diurnal variations in QTc intervals, whereas models with categorical time effects caused no biases and controlled the FNRs. For non-linear C-QTc relationships, ΔΔQTc estimations made using the model with categorical time effects were not robust. Thus, for single arm studies where only pre-dose baselines are available, we recommend collecting QTc measurements at 24 h and estimating ΔQTc at a concentration of interest at 24 h using the C-QTc model with categorical time effects.

Novel concentration-QTc models for early clinical studies with parallel placebo controls: A simulation study

The QTc interval of the electrocardiogram is a pharmacodynamic biomarker for drug-induced cardiac toxicity. The ICH E14 guideline Questions and Answers offer a solution for evaluating a concentration-QTc relationship in early clinical studies as an alternative to conducting a thorough QT/QTc study. We focused on covariance structures of QTc intervals on the baseline day and dosing day (two-day covariance structure,) and proposed a two-day QTc model to analyze a concentration-QTc relationship for placebo-controlled parallel phase 1 single ascending dose studies. The proposed two-day QTc model is based on a constrained longitudinal data analysis model and a mixed effects model, thus allowing various variance components to capture the two-day covariance structure. We also propose a one-day QTc model for the situation where no baseline day or only a pre-dose baseline is available and models for multiple ascending dose studies where concentration and QTc intervals are available over multiple days. A simulation study shows that the proposed models control the false negative rate for positive drugs and have both higher accuracy and power for negative drugs than existing models in a variety of settings for the two-day covariance structure. The proposed models will promote early and accurate evaluation of the cardiac safety of new drugs.

A Randomized, Single-Blind, Placebo-Controlled, 3-Way Crossover Study to Evaluate the Effect of Therapeutic and Supratherapeutic Doses of Edaravone on QT/QTc Interval in Healthy Subjects

This randomized, single-blind, 3-way crossover study assessed the effect of edaravone on QT interval, including an exposure-response analysis. Twenty-seven healthy Japanese male volunteers, aged 20 to 49 years, were randomly assigned to receive a single intravenous dose of each treatment in 1 of 3 sequences (n = 9 each): ACB, BAC, and CBA, where A was edaravone 60 mg (therapeutic dose), B was edaravone 300 mg (supratherapeutic dose), and C was normal saline (placebo). Electrocardiographs were collected to assess treatment effects. In an exposure-response analysis, a linear model was determined to be valid and indicated no statistically significant positive slope for the relationship between change from baseline in QTcF (ΔQTcF) and edaravone concentration (0.000155 ms/(ng/mL); P = .1478); upper bounds of 2-sided 90% confidence intervals after placebo adjustment (ΔΔQTcF) were <10 milliseconds at the geometric mean maximum concentration for each edaravone dose. Overall estimated values by time point of ΔΔQTcF ≤0.9 milliseconds, no outlier values, and no morphologic changes suggestive of repolarization abnormalities were observed. Analysis of heart rate, PR interval, and QRS duration also revealed no adverse findings. These data indicate that edaravone, even at supratherapeutic doses, does not produce clinically meaningful QT prolongation and has no clinically relevant cardiac effects.

Number of ECG Replicates and QT Correction Formula Influences the Estimated QT Prolonging Effect of a Drug

INTRODUCTION

The present analysis addressed the effect of the number of ECG replicates extracted from a continuous ECG on estimated QT interval prolongation for different QT correction formulas.

METHODS

For 100 healthy volunteers, who received a compound prolonging the QT interval, 18 ECG replicates within a 3-minute window were extracted from 12-lead Holter ECGs. Ten QT correction formulas were deployed, and the QTc interval was controlled for baseline and placebo and averaged per dose level.

RESULTS

The mean prolongation difference was >4 ms for single and >2 ms for triplicate ECG measurements compared with the 18 ECG replicate mean values. The difference was <0.5 ms after 14 replicates. By contrast, concentration-effect analysis was independent of replicate count and also of the QT correction formula.

CONCLUSION

The number of ECG replicates impacted the estimated QT interval prolongation for all deployed QT correction formulas. However, concentration-effect analysis was independent of both the replicate number and correction formula.

A Study to Assess the Proarrhythmic Potential of Mirtazapine Using Concentration-QTc (C-QTc) Analysis

Most new chemical entities with systemic availability are required to be tested in a study specifically designed to exclude drug-induced corrected QT interval (QTc) effects, the so-called thorough QT/QTc study. Mirtazapine (Remeron™) is an antidepressant indicated for the treatment of episodes of major depression, which was originally approved in 1994 without a thorough QT study. To evaluate the proarrhythmic potential of mirtazapine, we performed a QT/QTc study with a novel design including implementation of an analysis of the relationship between drug concentration and the QTc interval as the primary assessment of proarrhythmic potential of mirtazapine. The least squares mean differences of the corrected QT interval between mirtazapine and placebo at the geometric mean maximum concentration of drug in blood plasma (90% confidence interval) were 2.39 milliseconds (0.70, 4.07) at the 45-mg dose and 4.00 milliseconds (1.18, 6.83) at the 75-mg dose level of mirtazapine. Modeling of the concentration/QTc relationship for moxifloxacin confirmed that the assay method was adequately sensitive. This trial showed a positive relationship between mirtazapine concentrations and prolongation of the QTc interval. However, the degree of QT prolongation observed with both 45-mg and 75-mg doses of mirtazapine was not at a level generally considered to be clinically meaningful. This study further demonstrates that analysis of the relationship between drug concentration and the QTc interval may be a reasonable alternative to traditional TQT studies to assess risk of QT prolongation.

Evaluation of dependent variable, time effect, covariates, and covariation structure in concentration-QTc modeling: A simulation study

The revised ICH E14 Question and Answer (R3) document issued in December 2015 enables pharmaceutical companies to use concentration-QTc (C-QTc) modeling as the primary analysis for assessing QTc prolongation risk of new drugs. A new approach by including the time effect into the current C-QTc model is introduced. Through a simulation study, we evaluated performances of different C-QTc modeling with different dependent variables, covariates, and covariance structures. This simulation study shows that C-QTc models with ΔQTc being dependent variable without time effect inflate false negative rate and that fitting C-QTc models with different dependent variables, covariates, and covariance structures impacts the control of false negative and false positive rates. Appropriate C-QTc modeling strategies with good control of false negative rate and false positive rate are recommended.

Drug-induced Proarrhythmia and Torsade de Pointes: A Primer for Students and Practitioners of Medicine and Pharmacy

Multiple marketing withdrawals due to proarrhythmic concerns occurred in the United States, Canada, and the United Kingdom in the late 1980s to early 2000s. This primer reviews the clinical implications of a drug's identified proarrhythmic liability, the issues associated with these safety-related withdrawals, and the actions taken by the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) and by regulatory agencies in terms of changing drug development practices and introducing new nonclinical and clinical tests to asses proarrhythmic liability. ICH Guidelines S7B and E14 were released in 2005. Since then, they have been adopted by many regional regulatory authorities and have guided nonclinical and clinical proarrhythmic cardiac safety assessments during drug development. While this regulatory paradigm has been successful in preventing drugs with unanticipated potential for inducing the rare but potentially fatal polymorphic ventricular arrhythmia torsade de pointes from entering the market, it has led to the termination of drug development programs for other potentially useful medicines because of isolated results from studies with limited predictive value. Research efforts are now exploring alternative approaches to better predict potential proarrhythmic liabilities. For example, in the domain of human electrocardiographic assessments, concentration-response modeling conducted during phase 1 clinical development has recently become an accepted alternate primary methodology to the ICH E14 "thorough QT/QTc" study for defining a drug's corrected QT interval prolongation liability under certain conditions. When a drug's therapeutic benefit is considered important at a public health level but there is also an identified proarrhythmic liability that may result from administration of the single drug in certain individuals and/or drug-drug interactions, marketing approval will be accompanied by appropriate directions in the drug's prescribing information. Health-care professionals in the fields of medicine and pharmacy need to consider the prescribing information in conjunction with individual patients' clinical characteristics and concomitant medications when prescribing and dispensing such drugs.

Assessing QT/QTc interval prolongation with concentration-QT modeling for Phase I studies: impact of computational platforms, model structures and confidence interval calculation methods

Modeling the relationship between drug concentrations and heart rate corrected QT interval (QTc) change from baseline (C-∆QTc), based on Phase I single ascending dose (SAD) or multiple ascending dose (MAD) studies, has been proposed as an alternative to thorough QT studies (TQT), in assessing drug-induced QT prolongation risk. The present analysis used clinical SAD, MAD and TQT study data of an experimental compound, AZD5672, to evaluate the performance of: (i) three computational platforms (linear mixed-effects modeling implemented via PROC MIXED in SAS, as well as in R using LME4 package and linear quantile mixed models (LQMM) implemented via LQMM package; (ii) different model structures with and without treatment- or time-specific intercepts; and (iii) three methods for calculating the confidence interval (CI) of QTc prolongation (analytical and bootstrap methods with fixed or varied geometric mean concentrations). We show that treatment- and time-specific intercepts may need to be included into C-∆QTc modeling through PROC MIXED or LME4, regardless of their statistical significance. With the intersection union test (IUT) in the TQT study as a reference for comparison, inclusion of these intercepts increased the feasibility for C-∆QTc modelling of SAD or MAD to reach the same conclusion as the IUT analysis based on TQT study. Compared to PROC MIXED or LME4, the LQMM method is less dependent on inclusion of treatment- or time-specific intercepts, and the bootstrap CI calculation methods provided higher likelihood for C-∆QTc modeling of SAD and MAD studies to reach the same conclusion as the IUT based on the TQT study.

Leveraging model-informed approaches for drug discovery and development in the cardiovascular space

Cardiovascular disease remains a significant global health burden, and development of cardiovascular drugs in the current regulatory environment often demands large and expensive cardiovascular outcome trials. Thus, the use of quantitative pharmacometric approaches which can help enable early Go/No Go decision making, ensure appropriate dose selection, and increase the likelihood of successful clinical trials, have become increasingly important to help reduce the risk of failed cardiovascular outcomes studies. In addition, cardiovascular safety is an important consideration for many drug development programs, whether or not the drug is designed to treat cardiovascular disease; modeling and simulation approaches also have utility in assessing risk in this area. Herein, examples of modeling and simulation applied at various stages of drug development, spanning from the discovery stage through late-stage clinical development, for cardiovascular programs are presented. Examples of how modeling approaches have been utilized in early development programs across various therapeutic areas to help inform strategies to mitigate the risk of cardiovascular-related adverse events, such as QTc prolongation and changes in blood pressure, are also presented. These examples demonstrate how more informed drug development decisions can be enabled by modeling and simulation approaches in the cardiovascular area.