- Email: [email protected]
Pharmacokinetics-Pharmacodynamics During Drug Development – An Example from Servier: Ivabradine
PHARMACOCINETIQUE
Thérapie 2004 Mar-Avr; 59 (2): 173-177 0040-5957/04/0002-0173/$31.00/0 © 2004 Société Française de Pharmacologie
Pharmacokinetics-Pharmacodynamics During Drug Development – An Example from Servier: Ivabradine Les relations PK-PD au cours du développement, un exemple Servier : l’ivabradine Christian Laveille and Roeline Jochemsen Institut de Recherches Internationales Servier, Courbevoie, France
Abstract
A short introduction to the principles of pharmacokinetic-pharmacodynamic (PK-PD) modelling and population approaches is provided in this article. The importance of implementing these techniques in the drug development process is illustrated by an example from experience at the Servier International Research Institute. This example demonstrates how the use of PK-PD modelling can rationalise the development process and save valuable time. Population approaches significantly contribute to the integration of PK-PD modelling into the different drug development phases by expanding the possibilities of application. Keywords: ivabradine, pharmacokinetic/pharmacodynamic modelling, population approaches, drug development
Résumé
Une courte introduction des principes de la modélisation pharmacocinétique-pharmacodynamique (PK-PD) ainsi que de l’approche de population est donnée. L’importance de l’implémentation de ces techniques dans le développement de médicaments est illustrée par un exemple de la recherche Servier. Cet exemple démontre que grâce à l’utilisation de la modélisation PK-PD, le processus de développement peut être rationalisé et permettre un gain de temps précieux. L’approche de population contribue de manière significative à l’intégration de la modélisation PK-PD dans les différentes phases du développement en accroissant ces possibilités d’application. Mots clés : ivabradine, modélisation pharmacocinétique/pharmacodynamique, approche de population, développement médicamenteux Texte reçu le 7 octobre 2003 ; accepté le 4 décembre 2003
1. Introduction Drug development is a time-consuming and expensive process. It has been proposed that pharmacokinetic-pharmacodynamic (PK-PD) modelling is of value in all stages of this process.[1] It has the potential to facilitate decision-making by demonstrating early dose-concentration-response relationships in well controlled experiments. The possibilities of the application of PK-PD principles to the drug development process have been greatly expanded by the association with population ap-
proaches. In this article, the principles of PK-PD modelling and population approaches are explained, and their importance to the drug development process is illustrated by an example from experience at the Servier International Research Institute.
2. Pharmacokinetic-Pharmacodynamic (PK-PD) Modelling The objective of PK-PD modelling is to estimate the key parameters of a drug in vivo to allow characterisation and predic-
174
Laveille & Jochemsen
tion of the time-course of the intensity of drug effects under physiological and pathological conditions. It thereby provides the basis for rational clinical trial designs and individualisation of drug therapy. The introduction of indirect link models[2] and indirect pharmacodynamic response models[3,4] has significantly contributed to a better recognition of the relationships between drug concentration and response. Modern techniques, such as nonlinear mixed-effect modelling, have made it possible to study PK-PD relationships in the clinical situation using clinically relevant PD parameters. 2.1 Population Approaches
The population approach offers the possibility of gaining integrated information on pharmacokinetics and response from relatively sparse observational data obtained directly in patients who are being treated with the drug under development. Since only a few data per subject are needed, this approach can be directly applied to phase II and phase III studies, and therefore the patients studied are more relevant to the population of interest than in the traditional case when only small groups of patients were studied. The approach allows the analysis of data from a variety of unbalanced designs, as well as from studies that are normally excluded because they do not lend themselves to the usual form of pharmacokinetic analysis, such as concentration data obtained from paediatric or frail elderly patients, or from data obtained during the evaluation of the relationships between dose or concentration and efficacy or safety.[5] 2.2 Pharmacological (Clinical) Effect Measures
The choice of the pharmacological measure is very important in PK-PD strategies. There has been much debate in the past few years on the selection and validation of intermediate drug effect parameters in drug development.[6-8] Recently, the following definitions have been suggested:[6] an intermediate parameter becomes a surrogate endpoint if complete validation with respect to its clinical relevance has taken place. A surrogate endpoint can often be taken as an appropriate alternative to a clinical endpoint and serve for regulatory approval.[7] All other markers for therapeutic effect should preferably be called biomarkers, although it is the current practice for most markers, independently of the level of validation, to be called surrogates. A marker is valid for its intended use when changes in the marker are correlated with the desired changes in the disease state or clinical outcome.[8] Biomarkers have a variety of potential applications during the drug development process. In the early stages of development, they can be used to assess direct mechanistic evidence to support 2004 Société Française de Pharmacologie
the proof-of-concept for the drug substance. Later in the development process, the marker may be used to predict outcomes. Ultimately, they may recognised as surrogate endpoints and serve for regulatory approval. Both biological and statistical criteria have been proposed to evaluate the suitability of biomarkers to serve as surrogate endpoints. 3. Ivabradine 3.1 Preclinical and Phase I
Ivabradine is a heart rate-lowering agent under development by Servier for the treatment of myocardial ischaemia. It is extensively metabolised, mainly by cytochrome P450 (CYP) 3A4. One of its circulating metabolites, the N-desmethyl derivative, which is also mainly metabolised by CYP3A4, has been shown to be active in animal experiments and in vitro. A PK-PD study in the rat using resting heart rate as the pharmacodynamic parameter showed that, after oral administration of the parent compound, the PK-PD relationship was best described by an indirect link model. The N-desmethyl metabolite does not circulate in the rat, but a comparative PK-PD study after intravenous administration suggested that the intrinsic activity of this metabolite was similar to that of the parent compound, using heart rate at rest as the biomarker (Vilaine J.P., unpublished data). In humans, a PKPD model was constructed from the combined data gathered in the phase I single-dose and multiple-dose tolerance studies, as well as in a more specific PK-PD study (figure 1 [a and b]),[9] using heart rate at exercise as the biomarker. This parameter is considered to be a good intermediate parameter for anti-angina activity. The fit of the data to the model improved significantly when the N-desmethyl metabolite concentrations were incorporated using a agonist/partial agonist model, thus suggesting that this metabolite also contributes to (pharmacological) activity in humans. When the results of the modelling in the rat were compared with results from human subjects, it was observed that similar concentrations produced similar effects on heart rate in the rat (resting heart rates) and in man (heart rate during exercise). The PK-PD model in healthy volunteers has been used in several clinical pharmacology studies to assist in determining the dose necessary to achieve a predetermined heart rate-lowering effect. Furthermore, it assisted in the choice of doses for the phase II study. 3.2 Phase II: Pharmacokinetics
In the phase II study, for the majority of the patients, four blood samples were collected after the morning administration at steady state. A prospective strategy was developed, using samThérapie 2004 Mar-Avr; 59 (2)
PK-PD Relationships and Ivabradine
175
a 10 5 0 −5
Observed heart rate during exercise (%)
−10 −15 −20 −25 −30 b 10 5 0 −5 −10 −15 −20 −25 −30 0
4
8
12
16
20
24
28
Time (h)
Fig. 1. Pharmacological response versus time curves for ivabradine after oral administration to healthy volunteers. (a) Single administration of 10mg (solid circles) and 20mg (open circles). (b) Repeated administration of 10mg (solid circles) and 20mg (open circles).
pling time windows, to obtain a good distribution of blood sampling times across the pharmacokinetic profile. Furthermore, in order to investigate the pharmacokinetic profile after the evening administration (the compound is given twice daily), additional samples were collected in a subset of patients on the basis of the strategy defined above. It was shown that age was the major covariate influencing the pharmacokinetics of both compounds: for a patient with a mean age of 58 years in this study, exposure increased by about 30–40% and 40–50% compared with healthy young volunteers for ivabradine and its N-desmethyl metabolite, respectively. Because of the metabolic characteristics of both compounds, a potential interaction with CYP3A4 substrates was investigated. Statins and antihistamine H2 drugs were the only substrates coadministered in sufficient patients to allow detection of a significant interaction, if it did occur (more than ten patients). No significant interaction was detected with either the parent drug or its metabolite. The exploration of drug-drug interactions 2004 Société Française de Pharmacologie
will continue in the phase III studies, where the larger number of patients included, as well as the greater variety of coadministered drugs allowed, will give a particular importance to the population kinetic study in that phase.
3.3 Phase II: Pharmacodynamics
For the patients, three exercise tolerance tests were used for the PK-PD modelling: before the first administration (baseline), at steady-state just before the morning administration (trough), and approximately 3−4 hours after administration. Using the PK and PD data obtained in the phase II study (described above), together with the previously defined healthy volunteer model, a model was constructed representing patients. It was shown that the relationship between drug concentrations and the heart ratelowering effect in patients was similar to that in healthy volunteers. This model was used to simulate the heart rate-lowering Thérapie 2004 Mar-Avr; 59 (2)
176
Laveille & Jochemsen
a
b
960
Simulation 10mg
Simulation placebo
900 840 780
Time to limiting angina (sec)
720 660 600 540 480 420 360 300 240 180 120 60 0 1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Simulation
Simulation
Fig. 2. Simulations of the phase IIb results. On these graphs are represented the clinical endpoint (time to limiting angina [TLA]), function of the dose group (placebo or 10mg, 2a and 2b, respectively), and the number of simulations. The dotted lines show the phase IIb results, with the 5th percentile, the median, and the 95th percentile, respectively. The box-and-whisker plots show the results obtained with the simulations. Each of the ten box-and-whisker plots describes the variable TLA for a replicate. The plot elements and the statistics they represent are as follows: the length of the box represents the interquantile range (the distance between the 25th and the 75th percentile); the horizontal line in the box interior represents the median; the vertical lines issuing from the box extend to the 5th and the 95th percentile; the simulated data lower than the 5th percentile and greater than the 95th percentile are represented by the solid dots.
effect at intermediate doses, to support the choice of dose for the phase III studies. Subsequently, the relationship between heart rate during exercise and the clinical endpoint, time to limiting angina (TLA), was explored. A survival analysis was used, since the data were censored: patients sometimes stopped the exercise before getting pain. This analysis demonstrated that both heart rate and age influenced the probability of getting angina pain: when heart rate and/or age decreases, the probability of pain also decreases, confirming that the clinical activity of ivabradine is mainly due to its heart rate-lowering activity. The different models, i.e. the PK model, the PK-PD model relating concentrations with heart rate during exercise, and the survival model relating heart rate during exercise to TLA, will be used to simulate the phase III trial. In this case, the simulations will be performed parallel to the actual phase III studies, with the aim of acquiring expertise. With the same objective, the phase IIb study was simulated retrospectively (figure 2). In our opinion, clinical trial simulations will become a very important decision tool in the design of clinical trials in the near future. Relevant PK-PD models and 2004 Société Française de Pharmacologie
parameters will prove to be of great value as regards the predictive power of such simulations.
4. Conclusion When the correct effect measures are used, the application of PK-PD modelling, assisted by preclinical data, can streamline the drug development process by rationalising dose selection at a relatively early stage of clinical development. Population approaches, in particular mixed-effect modelling, significantly contribute to the (ongoing) integration of PK-PD into the different phases of drug development, by expanding the application possibilities, e.g. in experimental animals, clinical trials, special populations, etc. The combination of these techniques permits a better understanding of the drug during development and reduces the number of studies required, which ultimately leads to a reduction in development time. For this integrated strategy, it is mandatory that communication and collaboration between the different disciplines – such as pharmacology, safety, clinical development Thérapie 2004 Mar-Avr; 59 (2)
PK-PD Relationships and Ivabradine
and pharmacokinetics – be excellent, and that these principles are integrated into the development plan and built prospectively into the study protocols. The quality of the data and validation of the biomarkers and the surrogate endpoints utilised, as well as of the mathematical models, should be ensured if a positive contribution is to be achieved. References 1.
2.
3. 4.
Peck C, Barr WH, Benet LZ, et al. Opportunities for integration of pharmacokinetics, pharmacodynamics, and toxicokinetics in rational drug development. Clin Pharmacol Ther 1992; 51 (4): 465-73 Sheiner LB, Stanski DR, Vozeh S, et al. Simultaneous modelling of pharmacokinetics and pharmacodynamics: application to d-tubocurarine. Clin Pharmacol Ther 1979; 25: 358-71 Jusko WJ, Ko HC. Physiologic indirect response models characterize diverse types of pharmacodynamic effects. Clin Pharmacol Ther 1994; 56: 406-19 Dayneka NL, Garg V, Jusko WJ. Comparison of four basic models of indirect pharmacodynamic responses. J Pharmacokinet Biopharm 1993; 21: 457-78
2004 Société Française de Pharmacologie
177
5.
6.
7. 8. 9.
Guidance for Industry: population pharmacokinetics. Draft guidance, US Department of Health and Human Services, Food and Drug Administration. Fed Regist 1999 Feb 10; 64 (27): 6663-4 Atkinson AJ. The value of biomarkers and surrogate endpoints in early drug development [abstract]. 5th EUFEPS Conference; 1998 Dec 7-9; Wiesbaden, Germany Boissel JP, Collet JP, Moleur P, et al. Surrogate endpoints: a basis for a rational approach. Eur J Clin Pharmacol 1992; 43: 235-44 Colburn WA. Selecting and validating biologic markers for drug development. J Clin Pharmacol 1997; 37: 355-62 Ragueneau I, Laveille C, Jochemsen R, et al. Pharmacokinetic-pharmacodynamic modelling of the effects of ivabradine, a direct sinus node inhibitor, on heart rate in healthy volunteers. Clin Pharmacol Ther 1998; 64: 192-203
Correspondence and offprints: Christian Laveille, I.R.I.S, 6 place des Pléïades, 92415 Courbevoie Cedex, France. E-mail: [email protected]
Thérapie 2004 Mar-Avr; 59 (2)
Thérapie 2004 Mar-Avr; 59 (2): 173-177 0040-5957/04/0002-0173/$31.00/0 © 2004 Société Française de Pharmacologie
Pharmacokinetics-Pharmacodynamics During Drug Development – An Example from Servier: Ivabradine Les relations PK-PD au cours du développement, un exemple Servier : l’ivabradine Christian Laveille and Roeline Jochemsen Institut de Recherches Internationales Servier, Courbevoie, France
Abstract
A short introduction to the principles of pharmacokinetic-pharmacodynamic (PK-PD) modelling and population approaches is provided in this article. The importance of implementing these techniques in the drug development process is illustrated by an example from experience at the Servier International Research Institute. This example demonstrates how the use of PK-PD modelling can rationalise the development process and save valuable time. Population approaches significantly contribute to the integration of PK-PD modelling into the different drug development phases by expanding the possibilities of application. Keywords: ivabradine, pharmacokinetic/pharmacodynamic modelling, population approaches, drug development
Résumé
Une courte introduction des principes de la modélisation pharmacocinétique-pharmacodynamique (PK-PD) ainsi que de l’approche de population est donnée. L’importance de l’implémentation de ces techniques dans le développement de médicaments est illustrée par un exemple de la recherche Servier. Cet exemple démontre que grâce à l’utilisation de la modélisation PK-PD, le processus de développement peut être rationalisé et permettre un gain de temps précieux. L’approche de population contribue de manière significative à l’intégration de la modélisation PK-PD dans les différentes phases du développement en accroissant ces possibilités d’application. Mots clés : ivabradine, modélisation pharmacocinétique/pharmacodynamique, approche de population, développement médicamenteux Texte reçu le 7 octobre 2003 ; accepté le 4 décembre 2003
1. Introduction Drug development is a time-consuming and expensive process. It has been proposed that pharmacokinetic-pharmacodynamic (PK-PD) modelling is of value in all stages of this process.[1] It has the potential to facilitate decision-making by demonstrating early dose-concentration-response relationships in well controlled experiments. The possibilities of the application of PK-PD principles to the drug development process have been greatly expanded by the association with population ap-
proaches. In this article, the principles of PK-PD modelling and population approaches are explained, and their importance to the drug development process is illustrated by an example from experience at the Servier International Research Institute.
2. Pharmacokinetic-Pharmacodynamic (PK-PD) Modelling The objective of PK-PD modelling is to estimate the key parameters of a drug in vivo to allow characterisation and predic-
174
Laveille & Jochemsen
tion of the time-course of the intensity of drug effects under physiological and pathological conditions. It thereby provides the basis for rational clinical trial designs and individualisation of drug therapy. The introduction of indirect link models[2] and indirect pharmacodynamic response models[3,4] has significantly contributed to a better recognition of the relationships between drug concentration and response. Modern techniques, such as nonlinear mixed-effect modelling, have made it possible to study PK-PD relationships in the clinical situation using clinically relevant PD parameters. 2.1 Population Approaches
The population approach offers the possibility of gaining integrated information on pharmacokinetics and response from relatively sparse observational data obtained directly in patients who are being treated with the drug under development. Since only a few data per subject are needed, this approach can be directly applied to phase II and phase III studies, and therefore the patients studied are more relevant to the population of interest than in the traditional case when only small groups of patients were studied. The approach allows the analysis of data from a variety of unbalanced designs, as well as from studies that are normally excluded because they do not lend themselves to the usual form of pharmacokinetic analysis, such as concentration data obtained from paediatric or frail elderly patients, or from data obtained during the evaluation of the relationships between dose or concentration and efficacy or safety.[5] 2.2 Pharmacological (Clinical) Effect Measures
The choice of the pharmacological measure is very important in PK-PD strategies. There has been much debate in the past few years on the selection and validation of intermediate drug effect parameters in drug development.[6-8] Recently, the following definitions have been suggested:[6] an intermediate parameter becomes a surrogate endpoint if complete validation with respect to its clinical relevance has taken place. A surrogate endpoint can often be taken as an appropriate alternative to a clinical endpoint and serve for regulatory approval.[7] All other markers for therapeutic effect should preferably be called biomarkers, although it is the current practice for most markers, independently of the level of validation, to be called surrogates. A marker is valid for its intended use when changes in the marker are correlated with the desired changes in the disease state or clinical outcome.[8] Biomarkers have a variety of potential applications during the drug development process. In the early stages of development, they can be used to assess direct mechanistic evidence to support 2004 Société Française de Pharmacologie
the proof-of-concept for the drug substance. Later in the development process, the marker may be used to predict outcomes. Ultimately, they may recognised as surrogate endpoints and serve for regulatory approval. Both biological and statistical criteria have been proposed to evaluate the suitability of biomarkers to serve as surrogate endpoints. 3. Ivabradine 3.1 Preclinical and Phase I
Ivabradine is a heart rate-lowering agent under development by Servier for the treatment of myocardial ischaemia. It is extensively metabolised, mainly by cytochrome P450 (CYP) 3A4. One of its circulating metabolites, the N-desmethyl derivative, which is also mainly metabolised by CYP3A4, has been shown to be active in animal experiments and in vitro. A PK-PD study in the rat using resting heart rate as the pharmacodynamic parameter showed that, after oral administration of the parent compound, the PK-PD relationship was best described by an indirect link model. The N-desmethyl metabolite does not circulate in the rat, but a comparative PK-PD study after intravenous administration suggested that the intrinsic activity of this metabolite was similar to that of the parent compound, using heart rate at rest as the biomarker (Vilaine J.P., unpublished data). In humans, a PKPD model was constructed from the combined data gathered in the phase I single-dose and multiple-dose tolerance studies, as well as in a more specific PK-PD study (figure 1 [a and b]),[9] using heart rate at exercise as the biomarker. This parameter is considered to be a good intermediate parameter for anti-angina activity. The fit of the data to the model improved significantly when the N-desmethyl metabolite concentrations were incorporated using a agonist/partial agonist model, thus suggesting that this metabolite also contributes to (pharmacological) activity in humans. When the results of the modelling in the rat were compared with results from human subjects, it was observed that similar concentrations produced similar effects on heart rate in the rat (resting heart rates) and in man (heart rate during exercise). The PK-PD model in healthy volunteers has been used in several clinical pharmacology studies to assist in determining the dose necessary to achieve a predetermined heart rate-lowering effect. Furthermore, it assisted in the choice of doses for the phase II study. 3.2 Phase II: Pharmacokinetics
In the phase II study, for the majority of the patients, four blood samples were collected after the morning administration at steady state. A prospective strategy was developed, using samThérapie 2004 Mar-Avr; 59 (2)
PK-PD Relationships and Ivabradine
175
a 10 5 0 −5
Observed heart rate during exercise (%)
−10 −15 −20 −25 −30 b 10 5 0 −5 −10 −15 −20 −25 −30 0
4
8
12
16
20
24
28
Time (h)
Fig. 1. Pharmacological response versus time curves for ivabradine after oral administration to healthy volunteers. (a) Single administration of 10mg (solid circles) and 20mg (open circles). (b) Repeated administration of 10mg (solid circles) and 20mg (open circles).
pling time windows, to obtain a good distribution of blood sampling times across the pharmacokinetic profile. Furthermore, in order to investigate the pharmacokinetic profile after the evening administration (the compound is given twice daily), additional samples were collected in a subset of patients on the basis of the strategy defined above. It was shown that age was the major covariate influencing the pharmacokinetics of both compounds: for a patient with a mean age of 58 years in this study, exposure increased by about 30–40% and 40–50% compared with healthy young volunteers for ivabradine and its N-desmethyl metabolite, respectively. Because of the metabolic characteristics of both compounds, a potential interaction with CYP3A4 substrates was investigated. Statins and antihistamine H2 drugs were the only substrates coadministered in sufficient patients to allow detection of a significant interaction, if it did occur (more than ten patients). No significant interaction was detected with either the parent drug or its metabolite. The exploration of drug-drug interactions 2004 Société Française de Pharmacologie
will continue in the phase III studies, where the larger number of patients included, as well as the greater variety of coadministered drugs allowed, will give a particular importance to the population kinetic study in that phase.
3.3 Phase II: Pharmacodynamics
For the patients, three exercise tolerance tests were used for the PK-PD modelling: before the first administration (baseline), at steady-state just before the morning administration (trough), and approximately 3−4 hours after administration. Using the PK and PD data obtained in the phase II study (described above), together with the previously defined healthy volunteer model, a model was constructed representing patients. It was shown that the relationship between drug concentrations and the heart ratelowering effect in patients was similar to that in healthy volunteers. This model was used to simulate the heart rate-lowering Thérapie 2004 Mar-Avr; 59 (2)
176
Laveille & Jochemsen
a
b
960
Simulation 10mg
Simulation placebo
900 840 780
Time to limiting angina (sec)
720 660 600 540 480 420 360 300 240 180 120 60 0 1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Simulation
Simulation
Fig. 2. Simulations of the phase IIb results. On these graphs are represented the clinical endpoint (time to limiting angina [TLA]), function of the dose group (placebo or 10mg, 2a and 2b, respectively), and the number of simulations. The dotted lines show the phase IIb results, with the 5th percentile, the median, and the 95th percentile, respectively. The box-and-whisker plots show the results obtained with the simulations. Each of the ten box-and-whisker plots describes the variable TLA for a replicate. The plot elements and the statistics they represent are as follows: the length of the box represents the interquantile range (the distance between the 25th and the 75th percentile); the horizontal line in the box interior represents the median; the vertical lines issuing from the box extend to the 5th and the 95th percentile; the simulated data lower than the 5th percentile and greater than the 95th percentile are represented by the solid dots.
effect at intermediate doses, to support the choice of dose for the phase III studies. Subsequently, the relationship between heart rate during exercise and the clinical endpoint, time to limiting angina (TLA), was explored. A survival analysis was used, since the data were censored: patients sometimes stopped the exercise before getting pain. This analysis demonstrated that both heart rate and age influenced the probability of getting angina pain: when heart rate and/or age decreases, the probability of pain also decreases, confirming that the clinical activity of ivabradine is mainly due to its heart rate-lowering activity. The different models, i.e. the PK model, the PK-PD model relating concentrations with heart rate during exercise, and the survival model relating heart rate during exercise to TLA, will be used to simulate the phase III trial. In this case, the simulations will be performed parallel to the actual phase III studies, with the aim of acquiring expertise. With the same objective, the phase IIb study was simulated retrospectively (figure 2). In our opinion, clinical trial simulations will become a very important decision tool in the design of clinical trials in the near future. Relevant PK-PD models and 2004 Société Française de Pharmacologie
parameters will prove to be of great value as regards the predictive power of such simulations.
4. Conclusion When the correct effect measures are used, the application of PK-PD modelling, assisted by preclinical data, can streamline the drug development process by rationalising dose selection at a relatively early stage of clinical development. Population approaches, in particular mixed-effect modelling, significantly contribute to the (ongoing) integration of PK-PD into the different phases of drug development, by expanding the application possibilities, e.g. in experimental animals, clinical trials, special populations, etc. The combination of these techniques permits a better understanding of the drug during development and reduces the number of studies required, which ultimately leads to a reduction in development time. For this integrated strategy, it is mandatory that communication and collaboration between the different disciplines – such as pharmacology, safety, clinical development Thérapie 2004 Mar-Avr; 59 (2)
PK-PD Relationships and Ivabradine
and pharmacokinetics – be excellent, and that these principles are integrated into the development plan and built prospectively into the study protocols. The quality of the data and validation of the biomarkers and the surrogate endpoints utilised, as well as of the mathematical models, should be ensured if a positive contribution is to be achieved. References 1.
2.
3. 4.
Peck C, Barr WH, Benet LZ, et al. Opportunities for integration of pharmacokinetics, pharmacodynamics, and toxicokinetics in rational drug development. Clin Pharmacol Ther 1992; 51 (4): 465-73 Sheiner LB, Stanski DR, Vozeh S, et al. Simultaneous modelling of pharmacokinetics and pharmacodynamics: application to d-tubocurarine. Clin Pharmacol Ther 1979; 25: 358-71 Jusko WJ, Ko HC. Physiologic indirect response models characterize diverse types of pharmacodynamic effects. Clin Pharmacol Ther 1994; 56: 406-19 Dayneka NL, Garg V, Jusko WJ. Comparison of four basic models of indirect pharmacodynamic responses. J Pharmacokinet Biopharm 1993; 21: 457-78
2004 Société Française de Pharmacologie
177
5.
6.
7. 8. 9.
Guidance for Industry: population pharmacokinetics. Draft guidance, US Department of Health and Human Services, Food and Drug Administration. Fed Regist 1999 Feb 10; 64 (27): 6663-4 Atkinson AJ. The value of biomarkers and surrogate endpoints in early drug development [abstract]. 5th EUFEPS Conference; 1998 Dec 7-9; Wiesbaden, Germany Boissel JP, Collet JP, Moleur P, et al. Surrogate endpoints: a basis for a rational approach. Eur J Clin Pharmacol 1992; 43: 235-44 Colburn WA. Selecting and validating biologic markers for drug development. J Clin Pharmacol 1997; 37: 355-62 Ragueneau I, Laveille C, Jochemsen R, et al. Pharmacokinetic-pharmacodynamic modelling of the effects of ivabradine, a direct sinus node inhibitor, on heart rate in healthy volunteers. Clin Pharmacol Ther 1998; 64: 192-203
Correspondence and offprints: Christian Laveille, I.R.I.S, 6 place des Pléïades, 92415 Courbevoie Cedex, France. E-mail: [email protected]
Thérapie 2004 Mar-Avr; 59 (2)