Prediction of the volumes of distribution of basic drugs in humans based on data from animals

Sawada, Yasufumi; Hanano, Manabu; Sugiyama, Yuichi; Harashima, Hideyoshi; Iga, Tatsuji

doi:10.1007/bf01059554

Cited by 112 publications

(75 citation statements)

References 36 publications

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“…A second clear trend was that correction of rat P tp data for interspecies differences in plasma protein binding yielded better predictions compared with when binding differences were ignored (84% versus 53% within 2-fold). This observation was anticipated because in scaling tissue distribution from rat to human, the unbound human P tp values are generally assumed to be identical to those of rat (Sawada et al, 1984). Nevertheless, in the case of basic drugs, the accuracy of this assumption remains uncertain since electrostatic interactions with acidic phospholipids have been identified as a major factor controlling tissue distribution (Rodgers et al, 2005b), and an interspecies variability in the acidic phospholipids has been indicated (Rodgers et al, 2005a).…”

Section: Discussionmentioning

confidence: 99%

“…For lipophilic and highly protein-bound compounds, it has been assumed that for adipose tissue, RA equals 0.15, whereas for nonadipose tissue, RA equal 0.5 (Ellmerer et al, 2000;Poulin and Theil, 2002). Finally, rat and human V ss values were calculated by Gastroplus software according to the equation of Sawada et al (1984) in which V ss equals the plasma volume in addition to the sum of each P tp multiplied by its respective tissue volume.Prediction of Human and Rat P tp and V ss : Method Vd2. For rat P tp and V ss , experimental rat P tp values were determined under in vivo conditions (single oral or intravenous dose) as the ratio of the AUC calculated over a minimum of five time points, assuming pseudoequilibrium.…”

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confidence: 99%

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Prediction of Human Pharmacokinetics Using Physiologically Based Modeling: A Retrospective Analysis of 26 Clinically Tested Drugs

Buck¹,

Sinha²,

Fenu³

et al. 2007

Drug Metab Dispos

239

177

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ABSTRACT:The aim of this study was to evaluate different physiologically based modeling strategies for the prediction of human pharmacokinetics. Plasma profiles after intravenous and oral dosing were simulated for 26 clinically tested drugs. Two mechanism-based predictions of human tissue-to-plasma partitioning (P tp ) from physicochemical input (method Vd1) were evaluated for their ability to describe human volume of distribution at steady state (V ss ). This method was compared with a strategy that combined predicted and experimentally determined in vivo rat P tp data (method Vd2). Best V ss predictions were obtained using method Vd2, providing that rat P tp input was corrected for interspecies differences in plasma protein binding (84% within 2-fold). V ss predictions from physicochemical input alone were poor (32% within 2-fold). Total body clearance (CL) was predicted as the sum of scaled rat renal clearance and hepatic clearance projected from in vitro metabolism data. Best CL predictions were obtained by disregarding both blood and microsomal or hepatocyte binding (method CL2, 74% within 2-fold), whereas strong bias was seen using both blood and microsomal or hepatocyte binding (method CL1, 53% within 2-fold). The physiologically based pharmacokinetics (PBPK) model, which combined methods Vd2 and CL2 yielded the most accurate predictions of in vivo terminal half-life (69% within 2-fold). The Gastroplus advanced compartmental absorption and transit model was used to construct an absorption-disposition model and provided accurate predictions of area under the plasma concentration-time profile, oral apparent volume of distribution, and maximum plasma concentration after oral dosing, with 74%, 70%, and 65% within 2-fold, respectively. This evaluation demonstrates that PBPK models can lead to reasonable predictions of human pharmacokinetics.In the drug discovery process considerable resources are required to assess the pharmacokinetic (PK) properties of potential drug candidates in vivo in animals. To optimize the use of such in vivo testing, there has been a growing interest in predicting the PK behavior of drug candidates (Theil et al., 2003;van de Waterbeemd and Gifford, 2003). If sufficiently reliable, such simulations could also help to select the most promising candidates for development and reject those with a low probability of success (van de Waterbeemd and Gifford, 2003).The majority of the approaches to predict human PK developed to date typically focus on the drug's behavior in individual processes of absorption, distribution, metabolism and excretion (ADME). The characterization of a drug's PK in a complex biological system is best described by assembling these processes in one global model. In this context, physiologically based pharmacokinetics (PBPK) models have been developed (Bischoff, 1986). PBPK models map the complex drug transport scheme onto a physiologically realistic compartmental structure (Fig. 1). The major structural elements of the PBPK disposition model are derived from the anato...

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Prediction of Human Pharmacokinetics Using Physiologically Based Modeling: A Retrospective Analysis of 26 Clinically Tested Drugs

Buck¹,

Sinha²,

Fenu³

et al. 2007

Drug Metab Dispos

239

177

View full text Add to dashboard Cite

show abstract

“…Clearance and V ss were calculated as described below for input into the disposition part of the model. V ss was calculated from predicted P tp (tissue to plasma partition coefficient) [5]. Predicted values of P tp for each tissue compartment were obtained from drug-specific physicochemical parameters using the tissue composition-based equation described by Rodgers et al [6,7].…”

Section: Pbpk Model Verification In the Ratmentioning

confidence: 99%

From preclinical to human – prediction of oral absorption and drug–drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach in an industrial setting: a workflow by using case example

Sinha

Snoeys

Osselaer

et al. 2012

Biopharm & Drug Disp

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Purpose: A case example is presented in which the physiologically based modeling approach has been used to model the absorption of a lipophilic BCS Class II compound predominantly metabolized by CYP3A4, and to assess the interplay of absorption related parameters with the drug-drug interaction (DDI) potential. Methods: The PBPK model was built in the rat using Gastroplus W to study the absorption characteristics of the compound. Subsequently relevant model parameters were used to predict the non-linear human PK observed during first-in-human study after optimizing the absorption model for colonic absorption, bile micelle solubilization and unbound fraction in gut enterocytes (fu gut ) using SIMCYP W simulator. The model fitted absorption parameters were then used to assess the drug-drug interaction (DDI) potential of the test compound when administered along with multiple doses of a potent CYP 3A4 inhibitor, ketoconazole. The impact of fu gut in the extent of DDI was assessed using parameter sensitivity analysis. Results and Conclusions: After optimizing the preclinical model and taking into consideration bile micelle solubilization and colonic absorption, the non-linear pharmacokinetics of the test compound was satisfactorily predicted in man. Sensitivity analysis performed with the absorption parameter fu gut indicated that it could be an important parameter in predicting oral absorption. In addition, DDI simulations using SIMCYP W suggest that C max and AUC ratios may also be sensitive to the fu gut input in the model. Since fu gut cannot be measured experimentally, sensitivity analysis may help in assessing the importance of fu gut in human PK and DDI prediction using SIMCYP W .

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“…Data collection R b and f p values were collected for 96 compounds in humans and 60 compounds in rat, mainly from Goodman & Gilman's The Pharmacological Basis of Therapeutics; Tenth Edition and from reports on the prediction of pharmacokinetic parameters [6][7][8][9][10][11][12][13][14][15][16]. Prolog D (log D) was calculated using the Pallas software for Windows (version 1.1; Infocom, Berkshire, UK).…”

Section: Theorymentioning

confidence: 99%

Prediction of human blood‐to‐plasma drug concentration ratio

Uchimura¹,

Kato²,

Saito³

et al. 2010

Biopharm & Drug Disp

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The objective of this study was to predict R(b) (blood/plasma ratio) in humans using a simple method. Human and rat R(b) and free fraction in plasma (f(p)) values were obtained from the literature. The ratio of total red blood cell concentration to the free concentration in plasma (K(b)) was calculated using f(p) and R(b). Four methods were used for the prediction of R(b): (A) use of rat R(b); (B) use of R(b) calculated from rat K(b) and human f(p); (C) correlation of human log ((1-f(p))/f(p)) and human log K(b); and (D) correlation of log D with human log K(b). The R(b) of 96 compounds in humans ranged from 0.52 to 2.00, with an average of 0.89. A significant correlation was observed among human log K(b), human log ((1-f(p))/f(p)), and log D; however, no obvious correlation was observed among human R(b), human log ((1-f(p))/f(p)), and log D. The errors within 1.25-fold for methods A-D were 68.3%, 77.6%, 61.5% and 64.8%, respectively. All predictive methods considered here were superior to the use of the average value of human R(b) or R(b)=1. Rat R(b) corrected by human f(p) improved the accuracy of the prediction. Method B was the most accurate of the four methods.

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Prediction of the volumes of distribution of basic drugs in humans based on data from animals

Cited by 112 publications

References 36 publications

Prediction of Human Pharmacokinetics Using Physiologically Based Modeling: A Retrospective Analysis of 26 Clinically Tested Drugs

Prediction of Human Pharmacokinetics Using Physiologically Based Modeling: A Retrospective Analysis of 26 Clinically Tested Drugs

From preclinical to human – prediction of oral absorption and drug–drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach in an industrial setting: a workflow by using case example

Prediction of human blood‐to‐plasma drug concentration ratio

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