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Using Bayesian-PBPK modeling for assessment of inter-individual variability and subgroup stratification.

Krauss M, Burghaus R, Lippert J, Niemi M, Neuvonen P, Schuppert A, Willmann S, Kuepfer L, Görlitz L - In Silico Pharmacol (2013)

Bottom Line: Inter-individual variability in clinical endpoints and occurrence of potentially severe adverse effects represent an enormous challenge in drug development at all phases of (pre-)clinical research.Moreover, clinically relevant homogeneous subpopulations can be mechanistically identified.The large scale PBPK model separates physiological and drug-specific knowledge which allows, in combination with Bayesian approaches, the iterative assessment of specific populations by integrating information from several drugs.

View Article: PubMed Central - PubMed

Affiliation: Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, 51368 Germany ; RWTH Aachen, Schinkelstr, Aachen Institute for Advanced Study in Computational Engineering Sciences, Aachen, 2, 52062 Germany.

ABSTRACT

Purpose: Inter-individual variability in clinical endpoints and occurrence of potentially severe adverse effects represent an enormous challenge in drug development at all phases of (pre-)clinical research. To ensure patient safety it is important to identify adverse events or critical subgroups within the population as early as possible. Hence, a comprehensive understanding of the processes governing pharmacokinetics and pharmacodynamics is of utmost importance. In this paper we combine Bayesian statistics with detailed mechanistic physiologically-based pharmacokinetic (PBPK) models. On the example of pravastatin we demonstrate that this combination provides a powerful tool to investigate inter-individual variability in groups of patients and to identify clinically relevant homogenous subgroups in an unsupervised approach. Since PBPK models allow the identification of physiological, drug-specific and genotype-specific knowledge separately, our approach supports knowledge-based extrapolation to other drugs or populations.

Methods: PBPK models are based on generic distribution models and extensive collections of physiological parameters and allow a mechanistic investigation of drug distribution and drug action. To systematically account for parameter variability within patient populations, a Bayesian-PBPK approach is developed rigorously quantifying the probability of a parameter given the amount of information contained in the measured data. Since these parameter distributions are high-dimensional, a Markov chain Monte Carlo algorithm is used, where the physiological and drug-specific parameters are considered in separate blocks.

Results: Considering pravastatin pharmacokinetics as an application example, Bayesian-PBPK is used to investigate inter-individual variability in a cohort of 10 patients. Correlation analyses infer structural information about the PBPK model. Moreover, homogeneous subpopulations are identified a posteriori by examining the parameter distributions, which can even be assigned to a polymorphism in the hepatic organ anion transporter OATP1B1.

Conclusions: The presented Bayesian-PBPK approach systematically characterizes inter-individual variability within a population by updating prior knowledge about physiological parameters with new experimental data. Moreover, clinically relevant homogeneous subpopulations can be mechanistically identified. The large scale PBPK model separates physiological and drug-specific knowledge which allows, in combination with Bayesian approaches, the iterative assessment of specific populations by integrating information from several drugs.

No MeSH data available.


Related in: MedlinePlus

Schematic representation of the combined Bayesian-PBPK approach. A block-wise Metropolis-Hastings Markov chain Monte Carlo algorithm was used to sample the posterior distribution of individual patients’ physiology on the one hand and global compound parameters on the other hand. The underlying model kernel was provided by detailed mechanistic physiologically-based pharmacokinetic models.
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Fig1: Schematic representation of the combined Bayesian-PBPK approach. A block-wise Metropolis-Hastings Markov chain Monte Carlo algorithm was used to sample the posterior distribution of individual patients’ physiology on the one hand and global compound parameters on the other hand. The underlying model kernel was provided by detailed mechanistic physiologically-based pharmacokinetic models.

Mentions: In this work, we present a new approach applying MCMC to Bayesian-PBPK modeling for the assessment of inter-individual variability in groups of patients (Figure 1). Notably, we use a highly detailed and mechanistic PBPK model, where every organ is divided into four sub-compartments describing the intracellular space, the interstitial space, the blood plasma and the blood cells (Willmann et al., 2003a). Due to a segregated representation of the physiology of the patient and the underlying distribution model which is related to the physicochemistry of the drug, physiological parameters, genotype-specific (in the following also referred to as physiological parameters) and drug-specific parameters, respectively, can be considered separately. This model representation together with Bayesian approaches allows direct inference of physiological and drug-specific information, such as the variability in organ volumes or the uncertainty in the lipophilicity of a drug. Therefore, the main sources of variability within the PK of a drug may be quantified by analyzing the posterior distribution. Moreover, we present a way to analyze the posterior to identify clinically relevant homogenous subgroups, such as patients with a specific genotype linking to a PK (or PD) phenotype. Additionally, the use of such a mechanistic model bears great extrapolation capacity, by enabling an iterative use of the posterior as the prior distribution of a new run. Since physiology and drug are treated independently, posterior physiological information of a run with a known drug can be used for the investigation of a new drug candidate in the same group of patients such that physiological knowledge is conserved. The same applies for using the same drug in different populations. This allows for example the construction of a large database, wherein prior information about physiological as well as drug-specific parameters may be updated with experimental data of lots of experiments. Since only little literature information about specific parameters is available, informative prior distributions can be ‘learned’ after several MCMC runs with different experimental data.Figure 1


Using Bayesian-PBPK modeling for assessment of inter-individual variability and subgroup stratification.

Krauss M, Burghaus R, Lippert J, Niemi M, Neuvonen P, Schuppert A, Willmann S, Kuepfer L, Görlitz L - In Silico Pharmacol (2013)

Schematic representation of the combined Bayesian-PBPK approach. A block-wise Metropolis-Hastings Markov chain Monte Carlo algorithm was used to sample the posterior distribution of individual patients’ physiology on the one hand and global compound parameters on the other hand. The underlying model kernel was provided by detailed mechanistic physiologically-based pharmacokinetic models.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230716&req=5

Fig1: Schematic representation of the combined Bayesian-PBPK approach. A block-wise Metropolis-Hastings Markov chain Monte Carlo algorithm was used to sample the posterior distribution of individual patients’ physiology on the one hand and global compound parameters on the other hand. The underlying model kernel was provided by detailed mechanistic physiologically-based pharmacokinetic models.
Mentions: In this work, we present a new approach applying MCMC to Bayesian-PBPK modeling for the assessment of inter-individual variability in groups of patients (Figure 1). Notably, we use a highly detailed and mechanistic PBPK model, where every organ is divided into four sub-compartments describing the intracellular space, the interstitial space, the blood plasma and the blood cells (Willmann et al., 2003a). Due to a segregated representation of the physiology of the patient and the underlying distribution model which is related to the physicochemistry of the drug, physiological parameters, genotype-specific (in the following also referred to as physiological parameters) and drug-specific parameters, respectively, can be considered separately. This model representation together with Bayesian approaches allows direct inference of physiological and drug-specific information, such as the variability in organ volumes or the uncertainty in the lipophilicity of a drug. Therefore, the main sources of variability within the PK of a drug may be quantified by analyzing the posterior distribution. Moreover, we present a way to analyze the posterior to identify clinically relevant homogenous subgroups, such as patients with a specific genotype linking to a PK (or PD) phenotype. Additionally, the use of such a mechanistic model bears great extrapolation capacity, by enabling an iterative use of the posterior as the prior distribution of a new run. Since physiology and drug are treated independently, posterior physiological information of a run with a known drug can be used for the investigation of a new drug candidate in the same group of patients such that physiological knowledge is conserved. The same applies for using the same drug in different populations. This allows for example the construction of a large database, wherein prior information about physiological as well as drug-specific parameters may be updated with experimental data of lots of experiments. Since only little literature information about specific parameters is available, informative prior distributions can be ‘learned’ after several MCMC runs with different experimental data.Figure 1

Bottom Line: Inter-individual variability in clinical endpoints and occurrence of potentially severe adverse effects represent an enormous challenge in drug development at all phases of (pre-)clinical research.Moreover, clinically relevant homogeneous subpopulations can be mechanistically identified.The large scale PBPK model separates physiological and drug-specific knowledge which allows, in combination with Bayesian approaches, the iterative assessment of specific populations by integrating information from several drugs.

View Article: PubMed Central - PubMed

Affiliation: Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, 51368 Germany ; RWTH Aachen, Schinkelstr, Aachen Institute for Advanced Study in Computational Engineering Sciences, Aachen, 2, 52062 Germany.

ABSTRACT

Purpose: Inter-individual variability in clinical endpoints and occurrence of potentially severe adverse effects represent an enormous challenge in drug development at all phases of (pre-)clinical research. To ensure patient safety it is important to identify adverse events or critical subgroups within the population as early as possible. Hence, a comprehensive understanding of the processes governing pharmacokinetics and pharmacodynamics is of utmost importance. In this paper we combine Bayesian statistics with detailed mechanistic physiologically-based pharmacokinetic (PBPK) models. On the example of pravastatin we demonstrate that this combination provides a powerful tool to investigate inter-individual variability in groups of patients and to identify clinically relevant homogenous subgroups in an unsupervised approach. Since PBPK models allow the identification of physiological, drug-specific and genotype-specific knowledge separately, our approach supports knowledge-based extrapolation to other drugs or populations.

Methods: PBPK models are based on generic distribution models and extensive collections of physiological parameters and allow a mechanistic investigation of drug distribution and drug action. To systematically account for parameter variability within patient populations, a Bayesian-PBPK approach is developed rigorously quantifying the probability of a parameter given the amount of information contained in the measured data. Since these parameter distributions are high-dimensional, a Markov chain Monte Carlo algorithm is used, where the physiological and drug-specific parameters are considered in separate blocks.

Results: Considering pravastatin pharmacokinetics as an application example, Bayesian-PBPK is used to investigate inter-individual variability in a cohort of 10 patients. Correlation analyses infer structural information about the PBPK model. Moreover, homogeneous subpopulations are identified a posteriori by examining the parameter distributions, which can even be assigned to a polymorphism in the hepatic organ anion transporter OATP1B1.

Conclusions: The presented Bayesian-PBPK approach systematically characterizes inter-individual variability within a population by updating prior knowledge about physiological parameters with new experimental data. Moreover, clinically relevant homogeneous subpopulations can be mechanistically identified. The large scale PBPK model separates physiological and drug-specific knowledge which allows, in combination with Bayesian approaches, the iterative assessment of specific populations by integrating information from several drugs.

No MeSH data available.


Related in: MedlinePlus