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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

Experimental data of ten patients which were considered for the assessment of their inter-individual variability. The patients have been chosen out of a dataset of 32 patients provided by Niemi et al. (Niemi et al., 2006) such that all three possible genotypes of the hepatic uptake transporter OATP1B1 occurred equally.
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Fig2: Experimental data of ten patients which were considered for the assessment of their inter-individual variability. The patients have been chosen out of a dataset of 32 patients provided by Niemi et al. (Niemi et al., 2006) such that all three possible genotypes of the hepatic uptake transporter OATP1B1 occurred equally.

Mentions: The experimental data was provided from previously published studies (Niemi et al., 2006). Out of the dataset of 32 patients, 10 patients have been chosen randomly to lower computational costs (Figure 2). Nevertheless, the three genotypes of OATP1B1 are distributed equally in the chosen population. To describe the variability in all relevant ADME processes, 8 individual parameters together with 4 global parameters were chosen for the Bayesian analysis (Table 1), which means the variation of 84 parameters in total. During the separation of the parameters into different blocks, it is very important to know if parameters are correlated, since correlated parameters have to be sampled in one block (Smith et al., 1992). Our block structure is driven by the clear separation between substance and individual physiology in the PBPK model, therefore, we can assume that all parameters of different blocks are independent and uncorrelated (see also the discussion)and we can assure that no lumped parameters exist which depend on physiological and substance-specific information.Figure 2


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)

Experimental data of ten patients which were considered for the assessment of their inter-individual variability. The patients have been chosen out of a dataset of 32 patients provided by Niemi et al. (Niemi et al., 2006) such that all three possible genotypes of the hepatic uptake transporter OATP1B1 occurred equally.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig2: Experimental data of ten patients which were considered for the assessment of their inter-individual variability. The patients have been chosen out of a dataset of 32 patients provided by Niemi et al. (Niemi et al., 2006) such that all three possible genotypes of the hepatic uptake transporter OATP1B1 occurred equally.
Mentions: The experimental data was provided from previously published studies (Niemi et al., 2006). Out of the dataset of 32 patients, 10 patients have been chosen randomly to lower computational costs (Figure 2). Nevertheless, the three genotypes of OATP1B1 are distributed equally in the chosen population. To describe the variability in all relevant ADME processes, 8 individual parameters together with 4 global parameters were chosen for the Bayesian analysis (Table 1), which means the variation of 84 parameters in total. During the separation of the parameters into different blocks, it is very important to know if parameters are correlated, since correlated parameters have to be sampled in one block (Smith et al., 1992). Our block structure is driven by the clear separation between substance and individual physiology in the PBPK model, therefore, we can assume that all parameters of different blocks are independent and uncorrelated (see also the discussion)and we can assure that no lumped parameters exist which depend on physiological and substance-specific information.Figure 2

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