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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

Inter-individual variability of pravastatin pharmacokinetics. (A) Simulations were performed for each patient, simulating the pravastatin PBPK model with each of the 200 parameter vectors which were subsampled out of the posterior distribution. Next, the 5–95% quantile was calculated over all patients (with all 2000 samples) and plotted. Additionally, the mean value PK curve was monitored for every patient together with the experimental data. (B) Simulations were performed for three exemplary patients by simulating the pravastatin model with each of the 200 parameter vectors which were subsampled out of the posterior distribution. The 5% and 95% quantiles were calculated and plotted out of the respective subsample for each patient, together with the mean value curve and the experimental data.
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Fig4: Inter-individual variability of pravastatin pharmacokinetics. (A) Simulations were performed for each patient, simulating the pravastatin PBPK model with each of the 200 parameter vectors which were subsampled out of the posterior distribution. Next, the 5–95% quantile was calculated over all patients (with all 2000 samples) and plotted. Additionally, the mean value PK curve was monitored for every patient together with the experimental data. (B) Simulations were performed for three exemplary patients by simulating the pravastatin model with each of the 200 parameter vectors which were subsampled out of the posterior distribution. The 5% and 95% quantiles were calculated and plotted out of the respective subsample for each patient, together with the mean value curve and the experimental data.

Mentions: Next, the PK which described the inter-individual variability of the whole population and the mean PK of the corresponding patients were simulated (Figure 4A). The inter-individual variability was estimated by calculating the 5–95% range of all patients. To demonstrate that the depicted inter-individual variability did not already result from large variability and uncertainty of the single patients, the 5% and 95% quantiles and mean values for three exemplary patients were illustrated (Figure 4B). Additionally, the patient-specific mean value curves show good agreement to the experimental data (Figure 5). Notably, beside the PK range which is kind of a ‘macroscopic’ result of the posterior parameter distribution a lot of other information can be obtained by directly analyzing the posterior. The calculation of correlations between the 8 individual parameters provided information about dependencies between the various parameters in the model. For example, a strong correlation between Pint and kcat,M was observed (Figure 6).Figure 4


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)

Inter-individual variability of pravastatin pharmacokinetics. (A) Simulations were performed for each patient, simulating the pravastatin PBPK model with each of the 200 parameter vectors which were subsampled out of the posterior distribution. Next, the 5–95% quantile was calculated over all patients (with all 2000 samples) and plotted. Additionally, the mean value PK curve was monitored for every patient together with the experimental data. (B) Simulations were performed for three exemplary patients by simulating the pravastatin model with each of the 200 parameter vectors which were subsampled out of the posterior distribution. The 5% and 95% quantiles were calculated and plotted out of the respective subsample for each patient, together with the mean value curve and the experimental data.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig4: Inter-individual variability of pravastatin pharmacokinetics. (A) Simulations were performed for each patient, simulating the pravastatin PBPK model with each of the 200 parameter vectors which were subsampled out of the posterior distribution. Next, the 5–95% quantile was calculated over all patients (with all 2000 samples) and plotted. Additionally, the mean value PK curve was monitored for every patient together with the experimental data. (B) Simulations were performed for three exemplary patients by simulating the pravastatin model with each of the 200 parameter vectors which were subsampled out of the posterior distribution. The 5% and 95% quantiles were calculated and plotted out of the respective subsample for each patient, together with the mean value curve and the experimental data.
Mentions: Next, the PK which described the inter-individual variability of the whole population and the mean PK of the corresponding patients were simulated (Figure 4A). The inter-individual variability was estimated by calculating the 5–95% range of all patients. To demonstrate that the depicted inter-individual variability did not already result from large variability and uncertainty of the single patients, the 5% and 95% quantiles and mean values for three exemplary patients were illustrated (Figure 4B). Additionally, the patient-specific mean value curves show good agreement to the experimental data (Figure 5). Notably, beside the PK range which is kind of a ‘macroscopic’ result of the posterior parameter distribution a lot of other information can be obtained by directly analyzing the posterior. The calculation of correlations between the 8 individual parameters provided information about dependencies between the various parameters in the model. For example, a strong correlation between Pint and kcat,M was observed (Figure 6).Figure 4

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