A Physiologically Based Pharmacokinetic Model for Strontium Exposure in Rat

Pertinez, Henry; Chenel, Marylore; Aarons, Leon

doi:10.1007/s11095-013-0991-x

Cited by 13 publications

(8 citation statements)

References 46 publications

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“…Coding of the PBPK model, as well as simulations were conducted in Berkeley Madonna™ (version 8.3.18; University of California, Berkeley, CA, USA). To estimate the chemical‐specific parameters (PerC, PC, B Max , K d , CL), a simplified ‘open loop’ model (Nestorov, ; Pertinez et al ., ) with only one tissue compartment (as shown in Figure B), was developed. This ‘open loop’ model was used to estimate the chemical‐specific parameters in a tissue‐by‐tissue manner.…”

Section: Methodsmentioning

confidence: 99%

“…The mouse body weight was assumed to be 0.022 kg, and the cardiac blood flow was set as 13.98 mL·min −1 (Brown et al, 1997). Other physiological parameters, such as blood flow to different organs, tissue volumes, fractions of vascular space in tissues, are listed in Table 1 (Nestorov, 2007;Pertinez et al, 2013) with only one tissue compartment (as shown in Figure 1B), was developed. This 'open loop' model was used to estimate the chemical-specific parameters in a tissue-by-tissue manner.…”

Section: Liver Vascular Spacementioning

confidence: 99%

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A physiologically based pharmacokinetic model of alvespimycin in mice and extrapolation to rats and humans

Lü

Zhao

2014

British J Pharmacology

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BACKGROUND AND PURPOSEAlvespimycin, a new generation of heat shock protein 90 (Hsp90) inhibitor in clinical trial, is a promising therapeutic agent for cancer. Pharmacokinetic models of alvespimycin would help in the understanding of drug disposition, predicting drug exposure and interpreting dose-response relationship. In the present study we aimed to develop a physiologically based pharmacokinetic (PBPK) model of alvespimycin in mice and evaluate the utility of the model for predicting alvespimycin disposition in other species. EXPERIMENTAL APPROACHA literature search was performed to collect pharmacokinetic data for alvespimycin. A PBPK model was initially constructed to demonstrate the disposition of alvespimycin in mice, and then extrapolated to rats and humans by taking into account the interspecies differences in physiological-and chemical-specific parameters. KEY RESULTSA PBPK model, employing a permeability-limited model structure and saturable tissue binding, was built in mice. It successfully characterized the time course of the disposition of alvespimycin in mice. After extrapolation to rats, the model simulated the alvespimycin concentration-time profiles in rat tissues with acceptable accuracies. Likewise, a reasonable match was found between the observed and simulated human plasma pharmacokinetics of alvespimycin. CONCLUSIONS AND IMPLICATIONSThe PBPK model described here is beneficial to the understanding and prediction of the effects of alvespimycin in different species. It also provides a good basis for further development, which necessitates additional studies, especially those needed to clarify the in-depth mechanism of alvespimycin elimination. A refined PBPK model would benefit the understanding of dose-response relationships and optimization of dosing regimens.

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Section: Methodsmentioning

confidence: 99%

Section: Liver Vascular Spacementioning

confidence: 99%

A physiologically based pharmacokinetic model of alvespimycin in mice and extrapolation to rats and humans

Lü

Zhao

2014

British J Pharmacology

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“…Finally, it should be pointed out that as with any optimized parameter, the fitted estimate itself is always accompanied with a level of uncertainty which derives from imperfect data or any model mis‐specifications . It is striking that a large fraction of the recently published PBPK models (in the pharmaceutical arena) that performed parameter optimization estimation do not report any uncertainty on the fitted estimate, while only a few do . Reporting a single value for an estimated parameter gives no idea how reliable this estimate is.…”

Section: Parameter Estimation Approaches Uncertainty and Variabilitymentioning

confidence: 99%

Combining the ‘bottom up’ and ‘top down’ approaches in pharmacokinetic modelling: fittingPBPKmodels to observed clinical data

Tsamandouras

Rostami‐Hodjegan

Aarons

2014

Brit J Clinical Pharma

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219

186

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Pharmacokinetic models range from being entirely exploratory and empirical, to semi-mechanistic and ultimately complex physiologically based pharmacokinetic (PBPK) models. This choice is conditional on the modelling purpose as well as the amount and quality of the available data. The main advantage of PBPK models is that they can be used to extrapolate outside the studied population and experimental conditions. The trade-off for this advantage is a complex system of differential equations with a considerable number of model parameters. When these parameters cannot be informed from in vitro or in silico experiments they are usually optimized with respect to observed clinical data. Parameter estimation in complex models is a challenging task associated with many methodological issues which are discussed here with specific recommendations. Concepts such as structural and practical identifiability are described with regards to PBPK modelling and the value of experimental design and sensitivity analyses is sketched out. Parameter estimation approaches are discussed, while we also highlight the importance of not neglecting the covariance structure between model parameters and the uncertainty and population variability that is associated with them. Finally the possibility of using model order reduction techniques and minimal semi-mechanistic models that retain the physiological-mechanistic nature only in the parts of the model which are relevant to the desired modelling purpose is emphasized. Careful attention to all the above issues allows us to integrate successfully information from in vitro or in silico experiments together with information deriving from observed clinical data and develop mechanistically sound models with clinical relevance.

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“…To obtain the remaining parameter values for AMD, a simplified ''open loop'' model [53,54] consisting of two compartments (blood and one single tissue) was developed. In this ''open loop'' model, the blood kinetics was fixed, and the chemical-specific parameters for each tissue were estimated individually.…”

Section: Model Parametersmentioning

confidence: 99%

A Physiologically Based Pharmacokinetic Model of Amiodarone and its Metabolite Desethylamiodarone in Rats: Pooled Analysis of Published Data

Cai

Chen

et al. 2015

Eur J Drug Metab Pharmacokinet

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A Physiologically Based Pharmacokinetic Model for Strontium Exposure in Rat

Abstract: The model describes Strontium exposure in a physiologically rationalized manner and has the potential for future uses in modelling the PK-PD of Strontium, and/or other bone seeking agents, and for scaling to model human Strontium bone exposure.

Cited by 13 publications

References 46 publications

A physiologically based pharmacokinetic model of alvespimycin in mice and extrapolation to rats and humans

A physiologically based pharmacokinetic model of alvespimycin in mice and extrapolation to rats and humans

Combining the ‘bottom up’ and ‘top down’ approaches in pharmacokinetic modelling: fittingPBPKmodels to observed clinical data

A Physiologically Based Pharmacokinetic Model of Amiodarone and its Metabolite Desethylamiodarone in Rats: Pooled Analysis of Published Data

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