The role of concentration-effect relationships in the assessment of QTc interval prolongation

Abstract

Population pharmacokinetic and pharmacokinetic-pharmacodynamic (PKPD) modelling has been widely used in clinical research. Yet, its application in the evaluation of cardiovascular safety remains limited, particularly in the evaluation of pro-arrhythmic effects. Here we discuss the advantages of disadvantages of population PKPD modelling and simulation, a paradigm built around the knowledge of the concentration-effect relationship as the basis for decision making in drug development and its utility as a guide to drug safety. A wide-ranging review of the literature was performed on the experimental protocols currently used to characterize the potential for QT interval prolongation, both pre-clinically and clinically. Focus was given to the role of modelling and simulation for design optimization and subsequent analysis and interpretation of the data, discriminating drug from system specific properties. Cardiovascular safety remains one of the major sources of attrition in drug development with stringent regulatory requirements. However, despite the myriad of tests, data are not integrated systematically to ensure accurate translation of the observed drug effects in clinically relevant conditions. The thorough QT study addresses a critical regulatory question but does not necessarily reflect knowledge of the underlying pharmacology and has limitations in its ability to address fundamental clinical questions. It is also prone to issues of multiplicity. Population approaches offer a paradigm for the evaluation of drug safety built around the knowledge of the concentration-effect relationship. It enables quantitative assessment of the probability of QTc interval prolongation in patients, providing better guidance to regulatory labelling and understanding of benefit/risk in specific populations.

Keywords: ICH E14; PKPD modelling; QTc interval prolongation; cardiovascular safety; clinical trial simulations; drug development.

Figure 1

A) The cardiac sequelae of blockade of the HERG channel. Blockade of the HERG channel results in prolongation (in red) of the cardiac action potential leading to early after depolarizations (not shown). QT interval prolongation occurs secondary to these changes and ultimately leads to torsade de pointes (TdP). In the example above the TdP degenerates to ventricular fibrillation (VF). B) Summary of the potential areas of concern where significant impact can be made to the observed QT interval data, including the algorithm used for ECG recordings, the technique selected to correct measurement bias, the nature of the disease that influence ECG values, and various other covariates that can impact the recordings (modified from Totterman [68])

Figure 2

An example of the utility of understanding concentration−effect relationships during the evaluation of QT_c prolongation. In the upper panel (A), the supratherapeutic dose of 800 mg of MK-0431 (sitagliptin) crosses the 10 ms boundary at the fourth time point resulting in a positive TQT study, at exposure levels equivalent to an 11-fold higher dose than predicted clinically. A PK/QT_c model was used to describe the relationship between sitagliptin plasma concentrations and placebo-subtracted QT_cF change from baseline following single oral doses of 100 mg and 800 mg. The lower panel (B) illustrates that subsequent analysis of the concentration−effect relationship utilizing a linear PKPD model does not result in prolongation of the QT_c for therapeutic concentrations of sitagliptin (Reprinted with permission from Bloomfield et al. [69]). , 100 mg MK-0431; , 800 mg MK-0431; , moxifloxacin; , observed; , predicted mean; , slope = 0

Figure 3

A) Goodness-of-fit plots show the model-predicted vs. observed QT_c interval and B) the corresponding probability curves for QT_c interval prolongation ≥10 ms vs. the predicted plasma concentrations of cisapride, d,l-sotalol and moxifloxacin respectively. Black dots and dotted lines represent values for conscious dogs, grey dots and solid lines depict data in healthy subjects. (Reprinted with permission from Chain and Dubois et al. [40])

Figure 4

The diagram depicts the major components of a clinical trial simulation (CTS). In model-based drug development, CTS can be used to characterize the interactions between drug and disease, enabling among other things the assessment of disease-modifying effects, dose selection and covariate effects (e.g. age, body weight). In conjunction with a trial model, CTS allows the evaluation of such interactions, taking into account uncertainty and trial design factors, including the implications of different statistical methods for the analysis of the data (Reprinted with permission from Gobburu et al. [39])

Figure 5

Inferential methods are essential to establish causality during signal detection. The use of markers of exposure in conjunction with population PKPD modelling provides the basis for the characterization of concentration−effect relationships. On the other hand, epidemiological data can be used to establish the correlation between markers of risk and drug-induced treatment effects in subjects and experimental conditions which may not have been tested initially during drug development. In addition, evaluating risk by modelling and simulation also offers the possibility to discriminate between drug and systems or patient specific properties

Figure 6

A schematic flow chart demonstrating the steps required for integration of multiple sources of data in a Bayesian PKPD model to build a clinical trial simulation (CTS) for QT_c prolongation in a representative population. The diagram highlights the fact that clinical trials and in particular the TQT trial design yields estimates of drug effects on a restricted subset of the population. Simulation scenarios allow for inferences to be made about the true safety profile of a new chemical entity in the overall population (with different demographic characteristics, comorbidities and concomitant medication usage), i.e. discriminating drug-specific properties from other sources of variability in QT_c interval

Figure 7

A) Simulated plasma concentrations (········) and QT intervals (------) vs. time for a patient with typical PK and PD parameters after an overdose of citalopram (1200 mg). The RR interval was fixed at 760 ms for the purposes of this simulation exercise. B) Simulated probability over time for having a QT≥ 447 ms for a predefined RR interval of 760 ms. Ten different dose levels are shown, ranging from 100 mg to 1800 mg. In the simulations all patients were assumed to be 30-year-old women who were also taking citalopram therapeutically. (Slightly modified with permission from Friberg et al. [54])

Figure 8

Concentration-related effects of methadone on the QT interval (left panels). (A) Quantile plot showing the median and 90% CI for the linear concentration–QT_cF relationship with study-specific intercepts. (B) Quantile plot showing the median and 90% CIs for the linear concentration–ΔQT_cF relationship. Observed data from the Martell and DDI trials (squares) and Eap trial (circles) were grouped into eight quantiles and plotted as mean values± 90% CI. The shaded area encompasses the methadone concentration range observed from the pooled clinical trials. In addition, results of outlier analysis are shown as the percentages of simulated patients exceeding different QT-related risk thresholds for each methadone dose (right panels). (C) ΔQT_cF > 60 ms (dotted line) and QT_cF >480 (solid line). (D) QT_cF >500 ms for mean population baseline values of 407 ms (dotted line) and 421 ms (solid line). QT_cF, Fridericia rate-corrected QT. CI, confidence interval; DDI, drug–drug interaction; QT_cF, Fridericia rate corrected QT. (Slightly modified with permission from Florian et al. [55])

Figure 9

(A) Not-in-trial simulation results show overlapping distributions and discrepancies between observed and predicted QT_c interval in the male population (left panel) and female population (right panel). The darker colours represent the predicted drug-induced QT_c values and the light colours represent the observed overall QT_c intervals. The medium shades denote the overlapping areas. (B) QQ-plots comparing the distributions of the QT_c values for male population (left) and female population (right). The deviation from the identity line reflects the residual difference between the observed QT_c intervals and model-predicted sotalol effects under the assumption of comparable pharmacokinetic−pharmacodynamic relationship, as determined in phase I clinical trials (with permission from Chain et al. [42])