Whole-body physiologically based pharmacokinetic models

Nestorov, Ivan

doi:10.1517/17425255.3.2.235

Cited by 203 publications

(185 citation statements)

References 168 publications

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“…A semi-PBPK model was chosen for rivaroxaban because its human pharmacokinetic profile exhibits an apparent one compartmental distribution behavior [11]. In contrast to a 'full' PBPK model in which organs and tissues are separately represented, a 'semi' PBPK model combines tissues having similar drug partitioning and distribution equilibrium with the plasma compartment [12]. These semi-PBPK models have been utilized to study the dynamics or time-based characteristics of drug-drug interactions [13][14][15].…”

Section: General Pbpk Model Buildingmentioning

confidence: 99%

Utility of a physiologically–based pharmacokinetic (PBPK) modeling approach to quantitatively predict a complex drug–drug–disease interaction scenario for rivaroxaban during the drug review process: implications for clinical practice

Grillo

Zhao

Bullock

et al. 2012

Biopharm & Drug Disp

102

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Background: Rivaroxaban is an oral Factor Xa inhibitor. The primary objective of this communication was to quantitatively predict changes in rivaroxaban exposure when individuals with varying degrees of renal impairment are co-administered with another drug that is both a P-gp and a moderate CYP3A4 inhibitor. Methods: A physiologically based pharmacokinetic (PBPK) model was developed to simulate rivaroxaban pharmacokinetics in young (20-45 years) or older (55-65 years) subjects with normal renal function, mild, moderate and severe renal impairment, with or without concomitant use of the combined P-gp and moderate CYP3A4 inhibitor, erythromycin. Results: The simulations indicate that combined factors (i.e., renal impairment and the use of erythromycin) have a greater impact on rivaroxaban exposure than expected when the impact of these factors are considered individually. Compared with normal young subjects taking rivaroxaban, concurrent mild, moderate or severe renal impairment plus erythromycin resulted in 1.9-, 2.4-or 2.6-fold increase in exposure, respectively in young subjects; and 2.5-, 2.9-or 3.0-fold increase in exposure in older subjects. Conclusions: These simulations suggest that a drug-drug-disease interaction is possible, which may significantly increase rivaroxaban exposure and increase bleeding risk. These simulations render more mechanistic insights as to the possible outcomes and allow one to reach a decision to add cautionary language to the approved product labeling for rivaroxaban. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: General Pbpk Model Buildingmentioning

confidence: 99%

Utility of a physiologically–based pharmacokinetic (PBPK) modeling approach to quantitatively predict a complex drug–drug–disease interaction scenario for rivaroxaban during the drug review process: implications for clinical practice

Grillo

Zhao

Bullock

et al. 2012

Biopharm & Drug Disp

102

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“…However, when aiming at analyzing the overall model or at estimating parameters, the increased complexity can become a problem. Here the relation between the detailed sub-compartment model and the lumped model can be exploited to reduce the complexity, and further reduction techniques may be applicable (cf., e.g., (32,33)). …”

Section: Discussionmentioning

confidence: 99%

“…K np:uc = fn c 0.3P * :w + 0.7 (32) assuming that neutral phospholipids behave like a mixture of 30% neutral lipids and 70% water (as above).…”

Section: Very Weak Bases Neutrals Acids and Type 2 Zwitterionsmentioning

confidence: 99%

Physiologically based pharmacokinetic modelling: a sub-compartmentalized model of tissue distribution

Kleist

Huisinga²

2007

J Pharmacokinet Pharmacodyn

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We present a sub-compartmentalized model of drug distribution in tissue that extends existing approaches based on the well-stirred tissue model. It is specified in terms of differential equations that explicitly account for the drug concentration in erythrocytes, plasma, interstitial and cellular space. Assuming, in addition, steady state drug distribution and by lumping the different sub-compartments, established models to predict tissue-plasma partition coefficients can be derived in an intriguingly simple way. This direct link is exploited to explicitly construct and parameterize the sub-compartmentalized model for moderate to strong bases, acids, neutrals and zwitterions. The derivation highlights the contributions of the different tissue constituents and provides a simple and transparent framework for the construction of novel tissue distribution models.

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“…Additionally, whole-body physiologically based pharmacokinetic (WB-PBPK) models were generated that treated an organism as a closed circulatory system consisting of compartments that are important for absorption, distribution, metabolism, and elimination. The development and application of WE-PBPK models in drug development are reviewed elsewhere (Edginton et al 2008;Nestorov, 2007).…”

Section: Pbpk Modelsmentioning

confidence: 99%

In silicoADME/T modelling for rational drug design

Wang

Xing

Yue

et al. 2015

Quart. Rev. Biophys.

247

155

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Abstract. In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.

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Whole-body physiologically based pharmacokinetic models

Cited by 203 publications

References 168 publications

Utility of a physiologically–based pharmacokinetic (PBPK) modeling approach to quantitatively predict a complex drug–drug–disease interaction scenario for rivaroxaban during the drug review process: implications for clinical practice

Utility of a physiologically–based pharmacokinetic (PBPK) modeling approach to quantitatively predict a complex drug–drug–disease interaction scenario for rivaroxaban during the drug review process: implications for clinical practice

Physiologically based pharmacokinetic modelling: a sub-compartmentalized model of tissue distribution

In silicoADME/T modelling for rational drug design

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