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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

Exemplary representation of a subsample of the posterior distribution. After a burn-in period of 150000 steps, a subsample of 200 parameter vectors was drawn for each patient. The figure shows the traces for all eight individual parameters exemplarily for one patient as well as the four global parameters which were the same for all patients. The limits on the y-axis represent the physiological constraints (θmin, θmax).
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Fig3: Exemplary representation of a subsample of the posterior distribution. After a burn-in period of 150000 steps, a subsample of 200 parameter vectors was drawn for each patient. The figure shows the traces for all eight individual parameters exemplarily for one patient as well as the four global parameters which were the same for all patients. The limits on the y-axis represent the physiological constraints (θmin, θmax).

Mentions: During the first 150000 steps the parameter vectors have not been sampled from the correct distribution. For this so-called burn-in period the samples were discarded. By subsampling 200 parameter vectors of each patient from the remaining 150000 steps, an independent sample of the posterior distribution was drawn. The resulting traces are exemplarily shown for one patient (Figure 3). Notably, the four global parameters remain the same for every patient as described above, since they only depend on the physicochemistry of the drug.Figure 3


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)

Exemplary representation of a subsample of the posterior distribution. After a burn-in period of 150000 steps, a subsample of 200 parameter vectors was drawn for each patient. The figure shows the traces for all eight individual parameters exemplarily for one patient as well as the four global parameters which were the same for all patients. The limits on the y-axis represent the physiological constraints (θmin, θmax).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig3: Exemplary representation of a subsample of the posterior distribution. After a burn-in period of 150000 steps, a subsample of 200 parameter vectors was drawn for each patient. The figure shows the traces for all eight individual parameters exemplarily for one patient as well as the four global parameters which were the same for all patients. The limits on the y-axis represent the physiological constraints (θmin, θmax).
Mentions: During the first 150000 steps the parameter vectors have not been sampled from the correct distribution. For this so-called burn-in period the samples were discarded. By subsampling 200 parameter vectors of each patient from the remaining 150000 steps, an independent sample of the posterior distribution was drawn. The resulting traces are exemplarily shown for one patient (Figure 3). Notably, the four global parameters remain the same for every patient as described above, since they only depend on the physicochemistry of the drug.Figure 3

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