Journal of Pharmacological and Toxicological Methods
Original articleTranslational pharmacokinetic–pharmacodynamic modeling of QTc effects in dog and human
Introduction
A crucial part in evaluating the safety of new chemical entities (NCE) is the assessment of the effect of drugs on the electrocardiogram (ECG). One of the measurements that is routinely assessed is the QT interval, which is an index of ventricular cell action potential durations (Shah, 2002). Prolongation of QT interval represents a delay in ventricular repolarization and it has been associated with a potentially fatal arrhythmia Torsades de Pointes (Moss, 1999). Therefore, QT prolongation has been accepted as an important biomarker and the assessment of a NCE's liability to prolong QT interval in preclinical models is currently mandatory.
It has been shown that the prolongation of the QT interval is caused primarily by a blockage of the delayed rectifier potassium current (IKr), encoded by human ether-a-go-go-related gene (hERG) (Curran et al., 1995). hERG is an ion channel that controls potassium efflux in cardiac myocytes, through changes between open, inactivated and closed states (for a review see (Sanguinetti & Tristani-Firouzi, 2006)). As a result, the hERG assay is widely used as an in-vitro screen to detect a delayed repolarization risk (Anon, 2005a, Brown, 2004). Apart from the hERG screen, there are other preclinical studies that are listed in the FDA guideline, which are used to evaluate QT prolongation liability, including in-vitro, in-vivo and ex-vivo methods (Anon, 2005a, Fermini and Fossa, 2003, Shah, 2002). In addition to preclinical assessment, the FDA also requires rigorous characterization of the NCEs' effects on the QT interval during clinical development. This includes a “thorough QT/QTc study” (TQT), specifically designed to detect potential QT effect (Anon, 2005b). QT interval values are usually corrected for changes in heart-rate and currently there are numerous approaches that have been described to calculate QTc (i.e. heart-rate corrected QT interval, see for example (Bazett, 1920, Fridericia, 1920, Ollerstam, Persson, et al., 2007, Van de Water et al., 1989).
In contrast to the well characterized QT prolongation effects and the role of hERG inhibition, less is known about safety implications of QT shortening despite a recent survey suggesting an increase preclinically in the number of drugs with this effect (Holbrook, Malik, Shah, & Valentin, 2009). Although the clinical significance of QT shortening is not fully understood, it has been suggested that a short QT interval may be linked to proarrhythmia (for a review see (Shah, 2010)). The mechanism of QT-shortening is unfortunately poorly understood, although there is some evidence that it can be caused by blocking calcium channels or activation of hERG (Antzelevitch et al., 2007, Holbrook et al., 2009).
Since QT changes pose a safety risk for human volunteers, it is beneficial to detect them in drug discovery, i.e. before the NCE is tested in human. If the undesired QT effect is discovered early, it can result in an early discontinuation of a compound. This would be beneficial from a safety point of view (i.e. human volunteers will not be unnecessarily exposed to a potentially harmful substance) but also from a financial perspective, since it will avoid further costly development of an unsuccessful candidate. It is therefore important to additionally assess the QT liability of NCEs in in-vivo models, in order to accurately calculate safety margins and select appropriate doses for clinical trials which will not cause undesired QT effects in human volunteers. An example of such a model is in-vivo conscious dog telemetry (Ollerstam et al., 2006), which can be used to assess both drug-induced QT prolongation as well as QT shortening. Other animal models have also been characterized using guinea pigs, anesthetized dogs and conscious monkeys (Hammond et al., 2001). In the conscious dog model, animals are free to move while the cardiovascular measurements are taken. This however can lead to a high variability in heart rate and QT interval and can therefore complicate detection of the drug-induced changes. It is therefore important to use a heart rate correction method (see for example (Ollerstam et al., 2006)). Since plasma samples are also collected during the study, it is possible to relate the observed cardiovascular effects to the drug concentration using pharmacokinetic–pharmacodynamic (PKPD) modeling. Such analysis of concentration–effect relationships can provide an accurate assessment of drug-induced QT interval in animal models (Dubois et al., 2011, Ollerstam, Persson, et al., 2007, Ollerstam, Visser, et al., 2007, Ollerstam et al., 2006, Watson et al., 2011), while taking into account time delay between plasma concentrations and QT effects (hysteresis, see for example (Danhof, de Lange, Della Pasqua, Ploeger, & Voskuyl, 2008)). The advantage of having a mathematical model to explain a concentration–effect relationship is the ability to predict the response for alternative dosing schedules and if the model is combined with a well understood and quantified translational link between animal and human, it can also be used to predict clinical effects. As a result, it could improve prediction of safety margins and can help in clinical study design.
In the work presented here, we have investigated four compounds that were tested in both conscious dog and human. These included 2 proprietary small molecules: AZD1305, which caused a prolongation; and AZD1386, which caused shortening of QT interval in both dog and human. In addition, we have included PKPD datasets from known QTc prolongers — moxifloxacin and dofetilide (data from in-house studies and the literature). The four compounds used in this analysis were characterized by different cardiac ion channel pharmacology — AZD1305 (a novel antiarrhythmic agent) is a mixed ion channel blocker (Carlsson et al., 2009, Rónaszéki et al., 2011); AZD1386 (TRPV1 antagonist, developed as a treatment for pain (Karlsten et al., 2010, Laird et al., 2010)) is an L-type calcium channel blocker; while moxifloxacin (antibacterial agent) and dofetilide (class III antiarrhythmic drug) are both selective hERG blockers (Alexandrou et al., 2006, Weerapura et al., 2002).
The purpose of this analysis was to investigate and quantify the translational relationship between QTc responses for both an increase and a decrease of the QTc interval in the conscious telemetered dog model and in humans. In order to achieve this, we have derived concentration–effect curves for each compound, using PKPD modeling, for both dog and human and compared the response in each species that was produced by the same free concentration of compound. Because of the diversity in cardiac ion channel pharmacology between compounds, we also assessed not only whether a dog is a good predictive model for humans but also if the potential translational relationship is independent of the underlying mechanism(s) of action. If this is the case, it would be possible to describe and quantify this single relationship and use it for predicting clinical outcome for all future NCEs based on the preclinical dog data. Alternatively, if the mechanism of action does have an influence on the translational relationship between dog and human, it is important to characterize its effect in order to accurately predict the clinical outcome for drugs with different cardiac ion channel pharmacology. In addition, we have also provided an example of how the translational relationship can be used in real life to predict outcome in a clinical study. This example is presented as a case study and can be found in the Supplementary Material.
Section snippets
Drugs
A summary of the information, doses, study design and data sources for all compounds used in this analysis are listed in Table 1. AZD1305 and AZD1386 are manufactured by AstraZeneca R&D Mölndal and AstraZeneca R&D Södertälje, Sweden, respectively.
In-vivo dog studies
All in-vivo studies were performed in-house, using beagle dogs (data for moxifloxacin and dofetilide has been previously published (Ollerstam et al., 2006, Ollerstam, Visser, et al., 2007, details are provided in Table 1). Telemetric recordings of the
Effects in conscious dogs and humans
When tested in conscious dogs, AZD1305 caused QTc prolongation during a single-dose escalating study. The highest observed average effect was 46.5 ms increase from the baseline (predose value) and was observed after 4 h after dosing (at the highest dose, 4.3 mg/kg). This was accompanied by an increase in QRS and PR intervals. AZD1386 caused a shortening of the QTc interval in dog. The maximum effect, 25.8 ms decrease from baseline was seen 1 h after dosing (at the 100 mg/kg dose). The compound also
Translational relationship between conscious dog and human
The aim of the work presented here was to describe and quantify the translational relationship between QTc response in conscious dog and human. This was done by performing retrospective PKPD analysis on two proprietary small molecules — QT prolonger, AZD1305 and QT shortener, AZD1386, using conscious dog and human data. In addition, we have also used concentration–effect data from two reference drugs, the well-characterized QT prolongers moxifloxacin and dofetilide.
The data for AZD1305 and
Conclusions
The questions that we aimed to answer by performing this work were: is there a translational relationship between dog and human and if yes, is it the same for all compounds, independent of their mechanism of action? To answer these questions, we have performed PKPD modeling using internal data from two proprietary AstraZeneca compounds as well as two reference drugs found in the literature. Our analysis confirmed that there is a translational relationship between the two species, i.e. the same
Acknowledgments
The authors would like to thank Harry Southworth for helpful discussions, Matt Skinner and Jackie Moors for their support with preclinical data, Matt Bridgland-Taylor for help with in-vitro data and Bart Ploeger for help with R scripts as well as AZD1305 and AZD1386 clinical teams for collection and support with clinical data.
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