Pharmacokinetics And Pharmacodynamics Of Quinidine Biology Essay

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The translational sciences aim to transfer results from basic research to the treatment of animals or patients. One of the approaches that could be utilized to achieve this goal is the in vitro - in vivo extrapolation (IVIVE) of pharmacokinetic (PK) and pharmacodynamic (PD) effects by using in silico methods presented here, using quinidine as an example. Such methodology, if properly applied, could help to reduce use of animals in pre-clinical research substantially. Results of nine published clinical studies describing the PK (plasma concentration) and PD (QTcB/ΔQTcB) effect, were mimicked by the combination of the IVIVE platform Simcyp (pharmacokinetics prediction) with the ToxComp (cardiac effect prediction) system, based exclusively on in vitro data. The results show that reliable QT prediction as a manifestation of the clinical effect using the mechanistic IVIVE of the PK and PD effects is possible. This can be considered a proof-of-concept that also could be applied as a drug safety evaluation procedure.

The scope of translational science ranges from a general description of the drug and medical device development process from bench to bedside, through translating research into practice, culminating in the complex approach where the multidisciplinary collaboration of translational science accelerates the specific scientific application (Wolf, 2008; Zerhouni, 2005). Translation science divides the drug development process into a series of incremental steps (http://www.tuftsctsi.org). Regardless of the translational step under consideration, all the stages contribute to a more effective use of the available information; and thus an efficient transfer of the developed therapies from the bench to the bedside. In vitro - in vivo extrapolation (IVIVE) of the pharmacokinetic (PK) and pharmacodynamic (PD) effects, utilized with the use of the developed in silico methods, provides a model-based drug development method which facilitates progression to the bedside end-point (Lalonde et al., 2007). PK and PD models used in this study are mechanistic models working exclusively on the in vitro data, therefore no clinical study data was used at the results simulation level to fit parameters and improve the prediction. The mechanistic models are widely utilized in the 'PK arena' and are becoming more prominent in the 'PD arena' and vital areas of toxicology and drug safety (Marshall et al., 2006). This study unites the concepts of PK and PD mechanistic modeling and simulation to highlight the importance of assessing drug effect and safety in the preliminary phases of the drug development process. The IVIVE application approach necessitates the provision of three data sets: 1) drug related (ADME processes and activity), 2) system data (describing population and variability of the chosen parameters), and 3) simulated trial design (Rostami-Hodjegan and Tucker, 2007). The IVIVE methodology is a robust evaluation tool which assesses inter-individual variability, based on the virtual population characteristics, in the population study group.

To assess the application value of the described approach, quinidine was selected as the model drug in the virtual study. The study endpoints covered the parent compound and its main metabolite, 3-OH quinidine plasma concentration from the pharmacokinetic side. Either QTc interval or the drugs triggered change as compared to the baseline (ΔQT/ΔQTc) were used as the pharmacodynamics effect descriptors. This pharmacodynamic effect dictates that drug cardiac safety should be regarded as a pivotal focal point of this study. Pre-clinical studies routinely use in vitro approaches to assess cardiac safety, however non-rodent species (e.g. dogs, monkeys) are commonly used in the assessment procedure. This study proposes a novel concept based on a combination of the mechanistic PBPK/PD modeling and simulation to predict the cardiac effects of drugs and thus help to incorporate the 3Rs concept into practice, either by waiving or reducing animal studies.

Aim of the study

The aim of this study was to exclusively use available in vitro data to simulate the in vivo effects of drugs. Both arms of the clinical study, pharmacokinetics and pharmacodynamics, were mimicked. The focus of the study was to assess the inter-individual variability and to establish accurate simulation methodology.

Materials and Methods

Data

A wide literature search to find papers describing combined pharmacokinetic and pharmacodynamic effects of quinidine was implemented. The study inclusion criteria were: a) Caucasian healthy volunteers, b) availability of information about the quinidine pharmacokinetics, ideally presented as a drug plasma concentration change in time, c) PD results presented as QT/QTc or ΔQT/ΔQTc (regardless of the correction type) thus comparable with the simulation outputs. Nine papers fulfilling such conditions were identified and used for the study (Belz et al., 1982; Ching et al., 1991; El-Eraky and Thomas, 2003; Fieldman et al., 1977; Kaukonen et al., 1997; Laganiere et al., 1996; Min et al., 1996; Olatunde and Price Evans, 1982; Shin et al., 2007). Characteristics of the clinical studies derived from the identified papers are presented in Table .

Table 1

When applicable, data from the selected manuscripts were used directly or were derived from the graphs after digitization. The latter was derived using the GetData Graph Digitizer tool (http://www.getdata-graph-digitizer.com).

Simulation study

All simulations were implemented using two complementary pieces of software - Simcyp platform version 12 for the in vitro - in vivo extrapolation of the ADME processes (www.simcyp.com) and ToxComp version 1.6 (www.tox-portal.net) for the cardiac effect prediction.

Simcyp

The Simcyp Population-based Simulator streamlines drug development through the modeling and simulation of pharmacokinetics (PK) and pharmacodynamics (PD) in virtual populations. The Simcyp Simulator is the platform for the prediction of pharmacokinetic outcomes in clinical populations with the use of the human physiology, genetics and epidemiology information. Integration of this information with in vitro data allows the prediction of PK drug behavior in 'real-world' populations. The Simcyp Simulator can also identify key pre-clinical data requirements, which are extremely valuable for redefining and optimizing early drug development processes and procedures.

ToxComp

ToxComp is a user-friendly, systems biology driven, modeling and simulation based platform for the proarrhythmic potency assessment of chemicals at the population level. The system utilizes the in vitro - in vivo extrapolation approach, thus by default the input data comes from the in vitro ionic currents inhibition studies (Polak et al., 2012c). The model describing the electrophysiology of the human left ventricular cardiomyocyte, applied in the current study, was based on the work reported by ten Tusscher, with minor modifications (ten Tusscher et al., 2004). The reasoning behind this selection was that the majority of the data used for the model development was of human origin (Niederer et al., 2009). The ToxComp system contains a module for the virtual population development which is subsequently used as a basis for the simulation. Randomly picked individuals carry unique demographic and physiological characteristics, the specific parameters include inter alia: cardiomyocyte area, electric capacitance and volume (all parameters are age dependent), ions plasma concentration, left ventricle heart wall thickness and heart rate with the latter following the circadian rhythm (Polak and Fijorek, 2012; Polak et al. 2012a). An additional parameter is a genetic status described by the potential modification of the hERG potassium channel gating parameters (Glinka and Polak, 2012). Stable version 1.2 is currently available at www.tox-comp.net either for download or for a live run. This version of the platform is freely available and distributed under the GNU GPLv3 license.

Pharmacokinetic simulation preceded the heart electrophysiological simulation and during the latter simulation the same group of individuals, carrying the demographic and physiological parameters, were involved in the drug pharmacodynamics simulation together with the predicted drugs plasma concentration for all virtual individuals involved in the study.

Input data and modeling assumptions

The utilized drug related input data includes two elements - the in vitro information, describing the ADME processes used to run the Simcyp simulation, and the in vitro data describing various cardiac ionic currents inhibition. For the PK simulation, default quinidine (parent) and 3-OH quinidine (main metabolite) compound files were utilized. The major ADME parameters are listed in Table and their values are presented in the Supplementary materials.

Table 2

The in vitro inhibition of various cardiac ionic currents were taken from the literature. If this data was not available it was predicted with the previously developed and described QSAR models (Polak et al., 2012b; Polak et al., 2012d; Polak et al., 2012e, Wisniowska et al. 2012). It was assumed, and confirmed, in a subsequent QSAR based simulation that 3-OH quinidine carries the ionic currents inhibition activity. As multiple results from various sources were available those best mimicking the human physiology were selected. The IC50 and n values are the parameters of the Hill equation which were used for the concentration - inhibitory effect scaling, to account for the drugs triggered ionic currents modifications. The specific equation, part of the ten Tusscher model, describing current of interest was multiplied by the inhibition factor calculated with the use of the Hill equation [Equation ].

Equation

where:

IC50 - concentration responsible for the 50% inhibition of the ionic current

n - Hill equation parameter

DRUG CONCENTRATION - active drug concentration [µM]

Total inhibition was a sum of inhibitions triggered by both drugs - quinidine and its metabolite.

Table presents the relevant information for both drugs.

Table 3

The above listed input parameters, which were used to feed the appropriate QSAR model, were selected to match the parameters used during the measurement of different currents. It was assumed that neither quinidine, nor its main active metabolite 3-OH quinidine, does not influence the human physiological parameters including plasma ions concentration and heart rate. Two different scenarios were tested where either the total, or unbound, concentrations of both compounds were used for the cardiac electric effects simulation and compared with the clinically observed data for the pharmacodynamics endpoints. For all virtual studies the sampling time points were repeated as in the simulated clinical study. If the time of day the study commenced was provided, the simulation was set to start at the same time. In all other situations the virtual study was assumed to start at 8:00 a.m.

Output and data analysis

The results were presented in the form of the observed versus predicted graphs for the pharmacokinetic (drug plasma concentration) and pharmacodynamic (either QTc or ΔQTc with Bazzet correction applied or both where applicable) effects. The goodness of prediction measures included absolute RMSE (absRMSE - root mean squared error over difference between maximal and minimal observed value currently analyzed), Pearson correlation coefficient r and Rescigno ξ2 index in accordance with the formula presented in Equation (Rescigno, 1992).

Equation .

where:

ωj - weight (for this study assumed to be 1)

cr(tj) - observed value (i.e. plasma concentration) in time t

cx(tj) - predicted value (i.e. plasma concentration) in time t

The latter measure is commonly used during the results analysis of bioequivalence studies but was applied in this study as a useful measure enabling the two curves' comparison (in this case - predicted and observed for quinidine concentration and QTc/ΔQTc change over time).

Results

Results presented in Figure . contain data from all studies presented together as a comparison. The observed vs. predicted graph for quinidine plasma concentration (all studies) is linked with the appropriate goodness of prediction measures. Please refer to the Supplementary materials for the individually presented, detailed results containing additional set of information.

Figure 1

Figure presents the ΔQTcB and QTcB simulation results (5 and 6 studies respectively) compared with observed values presented in a similar format as above.

Figure 2

The clinical endpoints were directly compared with the predicted values for all time points characteristic of the clinical studies protocols. Figure presents a comparison between the observed and predicted ΔQTc calculated values based on the free and total compound concentrations.

Figure 3

Discussion

The current study was conducted to test the ability of simulating the in vivo activity of drugs based exclusively on the in vitro data, mimicking both the pharmacokinetic and pharmacodynamic arms of the clinical study. According to this assumption, the simulation results are presented as both the plasma concentration and QTc/ΔQTc change over time. It is worth noting that the endpoint choice for particular simulated study depends exclusively on the data presented in the original paper. Comparison between the predicted and observed values of the heart rate corrected QT intervals gives more information relating to the quality of the human left ventricular myocyte electrophysiology model and its ability to mimic human electrophysiology. ΔQTc is more model independent and gives information about the ability of the model to react on the factors influencing the QT lengths (in our case - drugs).

Considering that both systems, namely Simcyp for the PK simulation and ToxComp for the PD simulation, utilized no in vivo clinical data, with the exception of the 3-OH quinidine CLpo value which could be replaced either by the whole organ metabolic clearance scaled from the in vitro systems or in vitro measured enzyme kinetics, the obtained results are consistent with the clinically observed data. Analysis of the clinical study results shows one characteristic feature, namely the high heterogeneity of the results even for the studies with relatively similar protocols. The observed differences could be a result of the various analytical methods applied, sampling times etc. and may not necessarily reflect the real variability. This is an important factor influencing the predictiveness of the applied methods which have a tendency to under-predict the clinical results. Such an effect is not obvious for the PK endpoint prediction (plasma concentration) and ΔQTc from the PD perspective but it seems to be systematic for the QTc measure. The explanation for this difference lies in the characteristics of the model used to describe the human cardiac myocytes electrophysiology which tends to under predict the cardiomyocyte action potential duration. This was accounted for at the ToxComp system planning level where focus was put on mimicking the human situation, the ten Tusscher approach offered a suitable model. However, there are novel models based exclusively on the human data which can help with the under-prediction issue and their application is planned for the future (O'Hara and Rudy, 2011).

As previously mentioned, the verification of viable simulation methodology was one of the additional study goals. The author is confident that this is the first published study where the utilized methods and techniques were applied in parallel. From a practical perspective, the main concern is applying an accurate operational drugs concentration. There are two major elements which need to be considered - drug target (plasma concentration is a surrogate for the drug meeting the ionic channels in the heart) and binding (free or total concentration). The extracellular water in the heart tissue is regarded as the drug target and hence the preferred location for measuring the active drug concentration. However, considering that the vascular wall is not an efficient barrier, the measurement of plasma drug concentration is a realistic and acceptable surrogate. There are some drugs (physico-chemical character) and/or pathophysiological (i.e. arteriosclerosis triggered changes in the vascular wall permeability) related factors which could potentially influence the balance and change the plasma-to-heart tissue extracellular water concentration ratio. Moreover, of greater importance from the in silico realized in vitro - in vivo extrapolation perspective is the use of the unbound drugs as the active one (driving the pharmacological effect). In this study both options, total and unbound plasma concentrations, were tested and the results are presented. The quality of prediction assessed, based on a visual check and the goodness of prediction measures analysis, is more accurate for the free plasma concentration study arm. Graphical inspection demonstrates that for the free concentration setup either most (ΔQTcB) or all of the QTcB values for the various time points only deviate +/-20% from the identity line range. Simulations run in parallel using the total plasma concentration as the operational concentration, significantly over-predicted the cardiac effect. As this cannot be considered as proof, additional analysis was performed where the observed PD endpoints were directly compared with the predicted values for all time points characteristic of the clinical studies protocol. The graphs presented in Figure verify the hypothesis and show the problems associated with the suitable mimicking of the effect over time curve shape. For the ElEraky study it could be noted that switch from free to total concentration results in drastic increase of the predicted ΔQTc values. Considering relatively low drugs concentration values, an increase of these values results in the significantly larger IKr current inhibition as compared with higher concentrations observed in other studies as the concentration points lie on the slope of the Hill equation where maximum in vitro effect change is observed. This is subsequently transferred to the simulation and results in a large difference between the free and total concentrations scenarios. Similar effect can be observed for the Olatunde study where the concentrations are comparable to those from the work of El-Eraky. There is up to 30 ms of difference in the ΔQTc between free and total scenarios although it is connected with the underprediction of the plasma concentration in the Olatunde study which aligns and mitigates the PD effect and makes it less spectacular.

The obvious elements which can significantly influence the final results are the in vitro currents inhibition parameters. In this study a mixture of measured and predicted IC50 values were utilized. The predicted IC50 values, by default, are biased by the QSAR model error, but even if we consider the superiority of the measured over predicted values it still can be a source of uncertainty. It is mainly because of the lack of standard settings for the in vitro current inhibition studies which generates multiple results depending on the cell line, temperature and other settings applied (Polak et al., 2012e). Methodology applied in this work, where the in vitro studies best matching the human physiology were chosen, falsely minimizes the potential negative influence on the calculated endpoint but still may result in subsequent mis-prediction. It would be highly recommended to apply standard methodology for the in vitro measurements what could help to minimize the inter-lab differences in the obtained results and allow for reliable in vitro - in vivo scaling. In the current study HEK cells and currents measured in the physiological temperature were chosen as the standard in vitro system and the results were directly transferred to mimic the in vivo situation. It would be desirable to develop a standard in vitro system, possibly based on the results from human cardiomyocytes and then the application of scaling factors allowing for direct comparison with the results obtained with use of other cellular systems. This presented problem indicates also a need for widening of the measured membrane currents disrupted by the drugs. The additional currents which should be assessed include potassium (IKs), sodium (INa) and calcium (ICa) as potentially the most important from a drug safety assessment point of view.

One of the focal points of the study was to assess whether the set of IVIVE systems utilized is able to recover the inter-individual variability. The results are in general satisfactory however in this situation systems tend to slightly under predict the plasma concentration and to the higher degree the QTc values. A viable explanation for this under-prediction can lie both in the data defining the physiological parameters used during the virtual population random pick, in addition to the characteristics of the clinical studies. One of the most important factors would be plasma ion concentrations which undergoes diurnal fluctuation this was not accounted for during the simulation. Secondly the left ventricular heart wall thickness measurement used in this study was taken from the model proposed by Sjögren over 40 years ago and since then the quality of the analytical methods used for the wall thickness measurement changed significantly (Sjögren, 1971). It could be also expected that the intracellular ions concentration differs between individuals although such a factor was not considered due to the lack of data and a constant value was used. Additionally there is a 30 year period between the first (1977) and the last (2007) relevant study, in which time the healthy volunteers inclusion/exclusion criteria could have changed, which will subsequently influence the 'real' versus 'virtual' individual characteristics.

It is known that quinidine can influence the beta-adrenergic system and subsequently modify the heart rate (Darbar et al., 2001). There are other physiological parameters affecting the ECG characteristics which are likely to be modified by drugs. For example, the plasma ions concentration, which following the circadian rhythms can be also disrupted by administrated drugs (Sennels et al., 2012). The fact that none of these effects was considered during this study should be considered. This is however a direct consequence of the main study assumption - to utilize the in vitro data exclusively. All additional information regarding the drug-physiology relationship, regardless of the source (i.e. first-in-human studies), could be implemented and thus improve the - already accurate - predictiveness.

Conclusions

According to the ICH guidelines for drug studies, cardiac safety testing in animal models are widely utilized during the testing phase (ICH S7B, 2005). This study proposes a novel concept based on a combination of the mechanistic PBPK/PD modeling and simulation which could prove invaluable in the prediction of the cardiac effects of drugs and thus help to incorporate the 3Rs concept into practice by waiving the current animal studies. The presented results illustrate that reliable QTc and ΔQTc prediction by the combination of the mechanistic IVIVE of the PK and PD effects. It can be considered as a proof-of-concept that could also be applied as a reliable drug safety evaluation procedure.

Acknowledgements

I'd like to acknowledge Dr. Ruth Clayton from Simcyp Ltd. for professional help during manuscript preparation and Prof. Amin Rostami-Hodjegan from School of Pharmacy, University of Manchester, UK for his advice and comments on the manuscript.

Project financed by Polish National Center for Research and Development LIDER project number LIDER/02/187/L-1/09.

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Correspondence address

Sebastian Polak, PhD

Unit of Pharmacoepidemiology and Pharmacoeconomics, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9 Street, 30-688 Krakow, Poland

E-mail: spolak@cm-uj.krakow.pl

Tel.: 0048126205517

Fax: 0048126205519

Table . Characteristics of the clinical studies data used for the simulation.

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