Physiologically Based Pharmacokinetic Model for Composite Nanodevices: Effect of Charge and Size on In Vivo Disposition

Mager, Donald E.; Mody, Vidhi; Xu, Chao; Forrest, Alan; Lesniak, Wojciech G.; Nigavekar, Shraddha S.; Kariapper, Muhammed T.; Minc, Leah D.; Khan, Mohamed K.; Balogh, Lajos

doi:10.1007/s11095-012-0784-7

Cited by 39 publications

(44 citation statements)

References 31 publications

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“…59 In any case, most, if not all, investigators incorporate partition coefficients into their nano PBPK models. 10,[17][18][19][20][21][22][24][25][26][27] Here, we found that at least in the case of PAA-PEG, PAA, and gold NPs, the biokinetics cannot be accurately described without introducing a P factor and a similar conclusion was reached by Lin et al 25 The exchange of NPs between blood and organs is strongly influenced by the ability to cross the endothelium. This endothelial permeability varies between organs as a result of differences in the degree of fenestration, as well as phagocytic capacity.…”

supporting

confidence: 71%

“…However, several investigators have emphasized the importance of corona dynamics and this aspect of modeling needs to be explored further, especially in relationship to alterations in NP partitioning between blood and tissues and the rate of phagocytic uptake with time. 21,23,57 The incorporation of partition coefficients (sometimes referred to as distribution coefficients) into our model can be questioned since, in contrast to small, nonionized molecules, NPs form thermodynamically unstable, nonheterogenous suspensions so that partitioning equilibrium may not be attained. 58 Thus, NPs tend to agglomerate/aggregate, conditions in the body may cause a corona to change with time, and most preparations contain NPs with a range of sizes.…”

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confidence: 99%

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Toward a general physiologically-based pharmacokinetic model for intravenously injected nanoparticles

Carlander

Jolliet

et al. 2016

IJN

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To assess the potential toxicity of nanoparticles (NPs), information concerning their uptake and disposition (biokinetics) is essential. Experience with industrial chemicals and pharmaceutical drugs reveals that biokinetics can be described and predicted accurately by physiologically-based pharmacokinetic (PBPK) modeling. The nano PBPK models developed to date all concern a single type of NP. Our aim here was to extend a recent model for pegylated polyacrylamide NP in order to develop a more general PBPK model for nondegradable NPs injected intravenously into rats. The same model and physiological parameters were applied to pegylated polyacrylamide, uncoated polyacrylamide, gold, and titanium dioxide NPs, whereas NP-specific parameters were chosen on the basis of the best fit to the experimental time-courses of NP accumulation in various tissues. Our model describes the biokinetic behavior of all four types of NPs adequately, despite extensive differences in this behavior as well as in their physicochemical properties. In addition, this simulation demonstrated that the dose exerts a profound impact on the biokinetics, since saturation of the phagocytic cells at higher doses becomes a major limiting step. The fitted model parameters that were most dependent on NP type included the blood:tissue coefficients of permeability and the rate constant for phagocytic uptake. Since only four types of NPs with several differences in characteristics (dose, size, charge, shape, and surface properties) were used, the relationship between these characteristics and the NPdependent model parameters could not be elucidated and more experimental data are required in this context. In this connection, intravenous biodistribution studies with associated PBPK analyses would provide the most insight.

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supporting

confidence: 71%

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confidence: 99%

Toward a general physiologically-based pharmacokinetic model for intravenously injected nanoparticles

Carlander

Jolliet

et al. 2016

IJN

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“…Temporal exposure and elimination of five golddendrimer composite nanodevices in mice bearing melanoma was evaluated using a PBPK model (Mager et al, 2012). The authors concluded that, as specific binding ligands were lacking, size and charge of composite nanodevices governed most of their in vivo interactions.…”

Section: Pbpk and Nanotechnology: Current Examplesmentioning

confidence: 99%

Optimizing nanomedicine pharmacokinetics using physiologically based pharmacokinetics modelling

Moss

Siccardi

2014

British J Pharmacology

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The delivery of therapeutic agents is characterized by numerous challenges including poor absorption, low penetration in target tissues and non-specific dissemination in organs, leading to toxicity or poor drug exposure. Several nanomedicine strategies have emerged as an advanced approach to enhance drug delivery and improve the treatment of several diseases. Numerous processes mediate the pharmacokinetics of nanoformulations, with the absorption, distribution, metabolism and elimination (ADME) being poorly understood and often differing substantially from traditional formulations. Understanding how nanoformulation composition and physicochemical properties influence drug distribution in the human body is of central importance when developing future treatment strategies. A helpful pharmacological tool to simulate the distribution of nanoformulations is represented by physiologically based pharmacokinetics (PBPK) modelling, which integrates system data describing a population of interest with drug/nanoparticle in vitro data through a mathematical description of ADME. The application of PBPK models for nanomedicine is in its infancy and characterized by several challenges. The integration of property-distribution relationships in PBPK models may benefit nanomedicine research, giving opportunities for innovative development of nanotechnologies. PBPK modelling has the potential to improve our understanding of the mechanisms underpinning nanoformulation disposition and allow for more rapid and accurate determination of their kinetics. This review provides an overview of the current knowledge of nanomedicine distribution and the use of PBPK modelling in the characterization of nanoformulations with optimal pharmacokinetics. LINKED ARTICLESThis article is part of a themed section on Nanomedicine. To view the other articles in this section visit http://dx

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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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Physiologically Based Pharmacokinetic Model for Composite Nanodevices: Effect of Charge and Size on In Vivo Disposition

Abstract: Since the PBPK model can capture the diverse temporal profiles of non-targeted nanoparticles, we propose that when specific binding ligands are lacking, size and charge of nanodevices govern most of their in vivo interactions.

Cited by 39 publications

References 31 publications

Toward a general physiologically-based pharmacokinetic model for intravenously injected nanoparticles

Toward a general physiologically-based pharmacokinetic model for intravenously injected nanoparticles

Optimizing nanomedicine pharmacokinetics using physiologically based pharmacokinetics modelling

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

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