Prediction of Pharmacokinetics Prior to In Vivo Studies. II. Generic Physiologically Based Pharmacokinetic Models of Drug Disposition

Prediction of Pharmacokinetics prior to In Vivo Studies. II. Generic Physiologically Based Pharmacokinetic Models of Drug Disposition PATRICK POULIN, FRANK-PETER THEIL F. Hoffmann-La Roche Ltd., Pharmaceuticals Division, Non-Clinical Development - Drug Safety,PRNS Bau: 69/101, CH-4070 Basel, Switzerland

Received 11 July 2001; revised 10 December 2001; accepted 17 January 2002

ABSTRACT: Many in vitro data on physicochemical properties and specific absorption, distribution, metabolism, and elimination (ADME) processes are already available at early stages of drug discovery. These data about new drug candidates could be integrated/ connected in physiologically based pharmacokinetic (PBPK) models to estimate a priori the overall plasma and tissue kinetic behaviors under in vivo conditions. The objective of the present study was to illustrate that generic PBPK models integrating such data can be developed in drug discovery prior to any in vivo studies. This approach was illustrated with three example compounds, including two lipophilic bases (diazepam, propranolol) and one neutral more hydrophilic drug (ethoxybenzamide). Distribution and liver metabolism were the processes integrated in the generic rat PBPK models of disposition. Tissue:plasma partition coefficients (Pt:ps) used for description of distribution were estimated from established tissue composition-based equations, which need only in vitro data on drug lipophilicity and plasma protein binding as sole input parameters. Furthermore, data on intrinsic clearance (CLint) determined in vitro with hepatocytes were scaled to the in vivo situation to estimate hepatic metabolic clearance. These prediction approaches were both incorporated in the PBPK models to enable automated estimation of distribution and liver metabolism for each drug studied. The generic PBPK models suggested can simulate a priori concentration–time profiles of plasma and several tissues after intravenous administrations to rat. The results indicate that most of the simulated concentration– time profiles of plasma and 10 tissues are in reasonable agreement with the corresponding experimental data determined in vivo (less than a factor of two). However, some more relevant deviations were observed for specific tissues (brain and gut for diazepam; liver and gut for ethoxybenzamide; lung for propranolol) because of important ADME processes were probably neglected in the PBPK models of these drugs. In this context, generic PBPK models were also used for mechanistic evaluations of pharmacokinetics for generating research hypotheses to understand these deviations. Overall, the present generic and integrative PBPK approach of drug disposition suggested as a tool for a priori simulations and mechanistic evaluations of pharmacokinetics has the potential to improve the selection and optimization of new drug candidates. ß 2002 Wiley-Liss, Inc. and the American Pharmaceutical Association J Pharm Sci 91:1358–1370, 2002

Keywords: animal alternatives; disposition; drug discovery; in silico; metabolism; pharmacokinetics; physiologically based pharmacokinetic (PBPK) modeling; quantitative structure–activity relationships (QSAR); toxicokinetics

Correspondence to: P. Poulin (Telephone:41-61-688-0625; Fax: 41-61-688-2908; E-mail: [email protected]) Journal of Pharmaceutical Sciences, Vol. 91, 1358–1370 (2002) ß 2002 Wiley-Liss, Inc. and the American Pharmaceutical Association

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INTRODUCTION Physiologically based pharmacokinetic (PBPK) models are built on physiology (e.g., tissue blood flows and volumes) and allow prediction of plasma and tissue kinetics of drugs with the advantages of a mechanism-based model.1,2 These models describe the mechanistic interrelationships between the pharmacokinetic processes, including absorption, distribution, metabolism, and elimintion (ADME). During the development of new drug candidates, specific ADME processes are studied and quantified under in vitro conditions. Furthermore, in silico-based tools can be developed to predict specific ADME processes by taking into account their physiological and physicochemical determinants. The data accumulated for each new drug should therefore be evaluated together to estimate the overall pharmacokinetic behavior. In this context, such data could be integrated/connected in generic physiologically based pharmacokinetic (PBPK) models to provide a priori simulations of plasma and tissue kinetics under in vivo conditions. Such generic PBPK models are attractive tools to provide predictions of plasma and tissue concentration–time profiles of novel drugs prior to any in vivo studies in mammals. During the drug-development process, the experimental data on concentration–time profiles in vivo become available and can be used as confirmatory in vivo trials in relation to the PBPK model simulations based on in silico and in vitro input data only. The comparison of simulated with experimental data on plasma and tissue concentration–time profiles can provide mechanistic information whether the key ADME processes are covered within the models. If substantial deviations between simulated and experimental data are observed, further ADME processes need to be described in the PBPK models. In this sense, generic PBPK models can also be used as diagnostic tools for mechanistic evaluations of the overall ADME of new drug candidates. Several PBPK models were developed and validated in the environmental sciences and pharmaceutical academia.1–6 So far, however, PBPK models were rarely used in drug discovery. The main reason was that the estimation of essential drug-specific input parameters of PBPK models required additional resources and compound supply for each novel drug candidate. The commonly described processes in PBPK models of disposition are distribution and liver metabolism. The drugspecific input parameters of distribution refer to

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data on tissue:plasma partition coefficients (Pt:p), whereas those of liver metabolism correspond to data on hepatic intrinsic clearance (CLint). In particular, the estimation of Pt:p of each tissue by using conventional in vitro or in vivo techniques7,8 is not adapted for efficient screening efforts. Nevertheless, the Pt:p determined in vitro may potentially be used in PBPK models, but no general rules exist to scale in vitro Pt:p to the in vivo situation7. Thus, the development of PBPK models still represents a major challenge in drug discovery. Recent advances in the development of methods for predicting Pt:p and CLint from input parameters routinely determined in drug discovery would permit development of PBPK models of drugs. In this context, it was demonstrated for several classes of drugs that (i) their in vivo Pt:p of several nonadipose and adipose tissues (and the overall volume of distribution) can be predicted by using in vitro data on drug lipophilicity and plasma protein binding as sole input parameters in established mechanistic tissue compositionbased equations,9–11and (ii) their hepatic metabolic clearance can be estimated by scaling the CLint determined in vitro with hepatocytes or microsomes to the in vivo situation.12–14 Because the prediction of important pharmacokinetic parameters such as distribution and hepatic clearance can already be performed for several drugs, the next logical step is to integrate the prediction methods of distribution and clearance in PBPK models to simulate the full concentration–time profiles of plasma and tissues prior to in vivo studies. Because the prediction methods of distribution and hepatic clearance have been extensively evaluated in the past for their accuracy with certain classes of drugs, the integration of predicted values of distribution (Pt:ps) and clearance (CLint) into a generic PBPK model framework appears to be feasible. To prove the applicability of such an approach, the development of generic PBPK models for compound examples was necessary. In this context, it is of interest to note that previous modeling studies in environmental sciences present generic PBPK models of solvents and pollutants in which mechanistic tissue composition-based equations and CLint in vitro were both incorporated to enable automated calculation of the Pt:ps and hepatic metabolic clearance under in vivo conditions, respectively.3,15,16 Such mechanism-based PBPK models of solvents and pollutants can therefore be developed prior to JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 91, NO. 5, MAY 2002

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in vivo studies and could be refined for their use with drugs. The tissue composition-based equations initially applied to solvents and pollutants were recently adapted to drugs for efficient screening efforts as mentioned earlier.9–11 Furthermore, the scaling of CLint determined in vitro to the in vivo situation is already implemented in drug discovery of several pharmaceutical companies. Thus, mechanism-based PBPK models of drug disposition using only in vitro and in silico input data can potentially be developed also in the early drug discovery. In contrast, the conventional PBPK models of drug disposition available in the literature4,5 could not be used in drug discovery because their input parameters were estimated by optimization requiring extensive collection of in vivo data including tissue kinetics. The objective of the present study was to illustrate that generic PBPK models integrating prediction methods of tissue distribution and metabolic clearance can be developed in drug discovery prior to in vivo studies. Thus, physiological information and in vitro data on drug lipophilicity, plasma protein binding, and intrinsic hepatic clearance were incorporated in PBPK models to enable automated calculation of the essential drug-specific input parameters of distribution (Pt:ps) and liver metabolism (CLint) under in vivo conditions. These models were used with three example compounds, including two lipophilic bases (diazepam, propranolol) and one neutral more hydrophilic drug (ethoxybenzamide).

METHODS The method consists of developing generic rat PBPK models to study drug disposition of diazepam, ethoxybenzamide, and propranolol. These generic models should potentially be applicable to other compounds. The drugs studied were chosen based on the facts that (i) their disposition in rat under in vivo conditions after intravenous (iv) administration appears mainly to be governed by distribution and liver metabolism,6,19–21 (ii) tissue composition-based equations and CLint determined in vitro with hepatocytes can reasonably predict their distribution and hepatic clearance for the in vivo conditions, respectively,9–12,18,22 and (iii) the availability of literature data on in vivo concentration–time profiles of plasma and several common rat tissues.6,19–21 As a first step, the present study verifies if prediction methods of distribution and liver metabolism can be integrated in conventional PBPK model frameworks to provide reasonable a priori simulations of plasma and tissue kinetics of drugs. The overall method is described in more detail later.

Development of Generic PBPK models of Diazepam, Ethoxybenzamide, and Propranolol Conceptual Representation of Generic PBPK Models of Rat The PBPK model framework of each drug is conceptually presented in Figure 1. A conventional

Figure 1. Conceptual representation of the generic PBPK model framework of rat used for diazepam, ethoxybenzamide, and propranolol. See the Methods section for the mathematical relationships concerning each compartment. JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 91, NO. 5, MAY 2002

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Figure 2. Schematic representation of the procedures used for developing generic PBPK models of rat for diazepam, ethoxybenzamide, and propranolol without conducting in vivo studies. The procedure is also detailed in the Methods section.

model framework of 12 compartments (blood, adipose, bone, brain, gut, heart, liver, lung, kidney, muscle, skin, and spleen) were used.1,2,17 The liver was considered as the only site of clearance by metabolism based on the literature of these drugs.6,12,18 –22 Organization of Generic PBPK Models The feasibility of the method was explored using tissue composition-based PBPK models of rat. The mathematical model was written in the simulation software ModelMaker1, version 3.04 (Cherwell Scientific Inc., Oxford). The procedures used for constructing the generic PBPK models of each drug are schematically presented in Figure 2. The generic models integrate physiological data with prediction methods of distribution and liver metabolism. Equations and Scaling Factors used in Generic PBPK Models

in the equations refer to blood flow (Q), concentration (C), volume (V), extraction ratio (E), infusion rate constant (K), tissue:plasma ratios (Pt:p), and blood:plasma ratio (B:P). The subscripts refer to tissue (t), hepatic/liver (h), gut (g), spleen (sl), lung (l), cardiac output (c), arterial blood (ab), mixed venous blood (vb), venous blood leaving tissue (vbt), and mixed venous plasma (vp), whereas the term dC/dt refers to the concentration of drug per unit of time. Mass balance differential equations are presented as follows, based on Figure 1:


for non-eliminating tissue: dCt =dt ¼ ½ðQt  ðCab  Cvbt ÞÞ=Vt 

ð1Þ




ð2Þ

Cvbt ¼ Ct


Pt:p =B:P



for eliminating tissue (hepatic):

dCh =dt ¼ ½ððQhQg  Qsl ÞCab Conventional Mass Balance Differential Equations Of Drug Disposition. A well-stirred distribution into each tissue limited by the blood perfusion was hypothesized. The parameters used

þ ðQg Cvbg þ Qsl Cvbsl  Qh Cvbh ÞÞ=Vh   ½ðððQh  Qg  Qsl ÞCab þ ðQg Cvbg þ Qsl  Cvbsl ÞÞEh Þ=Vh

ð3Þ

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Table 1. Species-Specific Input Parameters used in Rat PBPK Models of Diazepam, Ethoxybenzamide and Propranolol Species-Specific Input Parameters Physiological Dataa

Tissue Composition Dataa

Blood Flow Rates (Qt) Volumes (Vt) Neutral Lipids Phospholipids [fraction of cardiac [fraction of total Water (Vw) [fraction (Vnl) [fraction of (Vph) [fraction of output in L/min] body volume] of wet tissue weight] wet tissue weight] wet tissue weight]

Tissues Adipose Bone Brain Gut Heart Kidney Liver Lung Muscle skeletal Skin Spleen Arterial blood Venous blood

0.07 0.122 0.02 0.131 0.049 0.141 0.175 —b 0.278 0.058 0.02 — —

0.076 0.04148 0.0057 0.027 0.0033 0.0073 0.0366 0.005 0.404 0.19 0.002 0.0272 0.0544

0.12 0.446 0.788 0.749 0.779 0.771 0.705 0.79 0.756 0.651 0.771 0.96c —

0.853 0.0273 0.0392 0.0292 0.014 0.0123 0.0138 0.0219 0.01 0.0239 0.0077 0.00147c —

0.002 0.0027 0.0533 0.0138 0.0118 0.0284 0.0303 0.014 0.009 0.018 0.0136 0.00083c —

a Mean data on tissue blood flow rates and volumes compiled from the literature,6,11,17 it was assumed that a change of body weight and tissue volumes follows a similar relationship; however, this was not the case for tissue blood flow rates (see footnote b). b Total cardiac output in L/min was calculated with an allometric equation (0.235  body weight0.75).17 For the PBPK models of diazepam and ethoxybenzamide, a rat body weight of 0.250 kg was considered, whereas for propranolol, rat body weights of 0.250 and 0.350 kg were used according to the experimental in vivo studies used to validate their PBPK models. c Composition provided for plasma. A similar composition was assumed for the arterial and venous plasma.




for lung: dCl =dt ¼ ½ðQc  ðCvb  Cvbl ÞÞ=Vl 




ð4Þ

for arterial blood: dCab =dt ¼ ½ðQc  ðCvbl  Cab ÞÞ=Vab 

ð5Þ




for mixed venous blood and plasma: X ½ðQt  Cvbt  Qc  Cvb þ Kiv Þ=Vvb  ð6Þ dCvb =dt ¼ Cvp ¼ Cvb =B : P

ð7Þ

Input Physiological Data of Differential Equations. The mean physiological data (i.e., Q and V ) were taken from a review of the literature6,11,17 and are summarized in Table 1 for a standard male adult rat. The data correspond to adipose tissue (subcutaneous), skeleton þ bone marrows, brain, gut, heart, kidney, hepatic artery, lung, muscle, and skin. The estimation of values of tissue volume from the literature were previously presented in a companion paper.11 The compilation of Brown et al.17 reports the mean values of JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 91, NO. 5, MAY 2002

tissue blood flow rates as fraction of total cardiac output in L/min. Note that the blood flow rate for liver corresponds to the summation of portal vein and hepatic artery. Therefore, the fraction of cardiac output of liver is 17.5%, of which the portal vein represents 15.1%.17 This value of 15.1% represents 13.1% for gut and others and 2% for spleen. The total cardiac output (L/min) used in the present rat PBPK models was estimated by the allometric equation 0.235body weight0.75, as reported by the compilation of Brown et al.17 The rat body weight was set equal to 0.250 or 0.350 kg based on the experimental in vivo studies used to validate the PBPK models of diazepam, ethoxybenzamide, and propranolol. Input Distribution Data Of Differential Equations. The in vivo Pt:ps for tissue:plasma ratios were calculated with established tissue composition-based equations for which the principles and assumptions are well presented in recent works of Poulin and Theil.9–11 Briefly, a homogeneous drug distribution into each tissue by a process of passive diffusion was assumed. The tissues and plasma were considered as mixtures of total lipids, water, and proteins with a global pH of

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7.4. Therefore, a drug partitioning into lipid and water fractions (nonspecific binding), as well as a specific reversible binding to main binding proteins present in plasma and tissue interstitial space, were the processes currently considered for calculating the Pt:ps at the organ level:

purpose, in contrast to the calculated/predicted values, and were not used in any of the model simulations. Input Metabolic Data of Differential Equations. The parameter Eh for the hepatic extrac-

      Po:w  Vnlt þ 0:3Vpht þ 1  Vwt þ 0:7Vpht fu       p Pt:p non - adipose ¼  fut Po:w  Vnlp þ 0:3Vphp þ 1  Vwp þ 0:7Vphp        D Vnlt þ 0:3Vpht þ 1  Vwt þ 0:7Vpht fu      p Pt:p adipose ¼  vo:w 1 Dvo:w Vnlp þ 0:3Vphp þ 1  Vwp þ 0:7Vphp where, fut ¼ 1/(1 þ (((1  fup)/fup)  0.5)), fu ¼ unbound fraction, t ¼ tissue, p ¼ plasma, Po:w ¼ noctanol–buffer partition coefficient (PC) of the non-ionized species at pH 7.4, Dvo:w ¼ olive oil:buffer PC of both the non-ionized and ionized species at pH 7.4, V ¼ fractional tissue volume content of neutral lipids (nl), phospholipids (ph), and water (w). The input parameters of eqs. 8 and 9 concerning tissue composition data (i.e., Vn, Vph, Vw) were previously compiled in a companion paper11 (Table 1). Data on drug lipophilicity (Po:w, Dvo:w*) and plasma protein binding (fup) for each drug were obtained from in vitro studies published in the literature6,11,18,19 (Table 2). The resulting calculated values of Pt:p of diazepam, ethoxybenzamide, and propranolol used in the generic PBPK models were compared with corresponding experimental in vivo values obtained from the literature6,19–21 (Table 3). The experimental values are presented only for comparative

ð8Þ

ð9Þ

tion ratio was used in the differential equation of the liver as the parameter estimating the metabolic clearance in the generic PBPK models. The value of Eh in vivo for each drug was estimated by scaling CLint determined in vitro to the in vivo situations. Scaling Approach. The conventional description of Eh corresponds to (CLint  fup/fut)/(CLint  fup/ fut þ Qh) for a well-stirred liver model.13 A recent work of Obach13 demonstrates that neglecting the binding in liver subfractions [fut; i.e., Eh ¼ (CLint  fup)/(CLint  fup þ Qh)] yielded very poor estimates of Eh in vivo by using CLint determined in vitro of several lipophilic bases and neutral compounds, which are the types of drugs investigated in the current study (diazepam, ethoxybenzamide, propranolol). Obach13 also demonstrates that neglecting all binding in plasma and liver subfractions [i.e., Eh ¼ CLint/(CLint þ Qh)] can lead

Table 2. Drug-Specific Input Parameters used to Calculate Pt:ps and Eh in Rat PBPK Models of Diazepam, Ethoxybenzamide, and Propranolol Drug-Specific Input Parameters Physicochemical and biochemical data determined in vitro Log octanol:water PCs of non-ionized species (Po:w) Log olive oil:water PCs of non-ionized and ionized species (Dvo:w*) Ionization constant (pKa) Unbound fraction in plasma (fup) Blood:plasma ratio (B:P) Metabolic data determined in vitrob Intrinsic disappearance clearance in hepatocytes (CLint) (ml/min/106cells)

Diazepam

Ethoxybenzamide

Propranolol

2.99 2.07

0.77 0.491

3.20 0.164

3.5 0.14 1.04

— 0.67 1.00

9.5 0.13 0.80

a

83

1.30

1000

a Mean experimental data at pH 7.4 obtained from the literature.6,11,18–21 The log Dvo:w* represents either experimentally determined data (diazepam) or data calculated from log Po:w and pKa (ethoxybenzamide, propranolol).10,11 The log Po:w, Dvo:w*, and fup were used in the tissue composition-based equations to calculate tissue:plasma PCs (Pt:ps), whereas data on B:P were used to convert tissue:plasma PCs to tissue:blood PCs in the mass balance differential equations. b Mean experimental data determined in vitro from a suspension of rat hepatocytes.12,22 Data used to calculate Eh as detailed in the Methods section.

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Table 3. Comparison of Distribution and Metabolic Data Predicted From the Rat PBPK Models With Experimental Data Determined Under In Vivo Conditions Parameter Tissue:Plasma Ratio (Pt:p)a Adipose Bone Brain Gut Heart Kidney Liver Lung Muscle Skin Spleen Extraction ratio (Eh)b

Diazepam

Ethoxybenzamide

Propranolol

Predicted

Experimental

Predicted

Experimental

Predicted

Experimental

12.09 6.03 11.82 7.20 3.87 4.59 4.99 5.62 2.89 6.32 2.65 0.851

13.42 — 1.07 1.92 2.28 2.39 — 3.39 1.42 3.50 — 0.830

0.61 0.53 0.99 0.82 0.77 0.79 0.74 0.82 0.72 0.72 0.73 0.082

0.68 — 0.90 0.52 0.92 1.30 — 0.84 0.85 0.95 0.86 0.131

0.18 6.90 13.54 8.22 4.38 5.18 5.67 6.46 3.20 7.22 2.98 0.985

— — 9.20 — 4.97 3.80 — 54.90 2.20 — — 0.930

a

Data predicted for steady-state conditions with tissue composition-based equations as demonstrated in the Methods section, and mean experimental in vivo data obtained from the literature.6,19–21 For diazepam and ethoxybenzamide, the experimental Pt:p data were determined close to steady-state,6,19,20 whereas those for propranolol were determined at 20 min following an intravenous administration of 1.5 mg/kg.21 Experimental data on tissue:blood PCs were multiplied with data on blood:plasma PCs to estimate tissue:plasma PCs. The experimental values are presented only for comparative purpose in contrast to the present predicted values, and were not used in any of the model simulations. b Predicted from CLint/(CLint þ Qh) as explained in the Methods section. The in vitro data on CLint obtained from the literature12,22 and expressed in mL/min/106 cells (Table 2) were scaled to estimate CLint in L/min before predicting Eh. The mean experimental values of Eh, however, were previously derived from plasma concentration data obtained under in vivo conditions for each drug.12 The experimental values are presented only for comparative purpose, in contrast to the present predicted values, and were not used in any of the model simulations.

to reasonable estimates of Eh for these kinds of drugs. This observation from Obach was also supported by other observations with Roche compounds (T. Lave´, personal communication). The main reason for such an observation is still not fully understood at the present time. Nevertheless, it has been demonstrated that the assumptions on similar fup and fut lead to reasonable estimates of Eh in vivo for several lipophilic bases and neutral compounds. Consequently, the equation to estimate Eh in vivo simplifies to CLint/ (CLint þ Qh) for each drug investigated. It is essential to scale CLint determined in vitro to the in vivo situation. The scaling of CLint in vitro was presented as follows step-by-step: (i) providing the literature data on CLint for the disappearance clearance of each drug in fresh suspensions of rat hepatocytes (mL/min/106 cells; Table 2,12,22 (ii) scaling these in vitro data on CLint with a traditional scaling factor (109  106 cells/g liver)23 to estimate CLint in L/min under in vivo conditions, and (iii) calculating Eh in vivo [¼ CLint /(CLint þ Qh)] used in the mass balance differential equation of liver. In calculating Eh, the required values of Qh and liver weight were set equal to 0.0145 L/min and 9.15 g, respectiJOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 91, NO. 5, MAY 2002

vely (Table 1). The resulting scaled values of Eh were compared with corresponding experimental values previously derived from plasma concentration data determined under in vivo conditions for each drug12 (Table 3). The experimental values are presented only for comparative purpose, in contrast to the present scaled values, and were not used in any of the model simulations. Simulations of Pharmacokinetics of Three Compound Examples with Generic PBPK Models The simulations resulting from the generic and integrative PBPK modeling procedures according to Figure 2 were compared with corresponding experimental data determined in vivo. The experimentally determined data used for comparison were obtained from the literature for each drug.6,18–21 Specifically, for diazepam, the concentration–time profiles of plasma and nine tissues were simulated following a bolus administration of 1.2 mg/kg to male rats (250 g).6 For ethoxybenzamide, the concentration–time profiles of plasma and also nine tissues were simulated following a bolus administration of 20 mg/kg to male rats (250 g).19,20 For propra-

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nolol, the concentration–time profiles were either simulated for plasma and six tissues following a bolus administration of 1.5 mg/kg to male rats (250 g),21 or simulated for plasma only after a constant infusion (0.02 mg/kg min) to male rats (350 g;18 the average data of the racemic mixture were presented for propranolol).

RESULTS The concentration–time profiles of plasma and ten tissues both simulated with PBPK models and experimentally determined for each drug are depicted in Figures 3–5. Based on a visual comparison, the in vivo concentration–time profiles were reasonably simulated with the PBPK models

Figure 3. Comparison of PBPK model simulations (lines) with experimental data determined in vivo (circles) on concentration–time profiles of plasma, adipose, brain, gut, heart, kidney, liver, lung, muscle, and skin following an intravenous bolus administration of 1.2 mg/kg of diazepam to male rats (250 g). The mean experimental data on concentration–time profiles were taken from the literature.6

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in most cases. In other words, the simulated concentration–time profiles deviate generally by a factor of less than two from those experimentally determined under in vivo conditions. It is of interest to note that parameter optimization was omitted to improve the simulations. In fact, the current PBPK models present only first simulations of concentration–time profiles by using one set of specified data on each input parameter estimated, as described in the Methods section. Some more relevant deviations were particularly observed in specific tissues as detailed next. Diazepam Model simulations were presented for nine tissues and plasma (Figure 3). For seven tissues

Figure 4. Comparison of PBPK model simulations (lines) with experimental data determined in vivo (circles) on concentration–time profiles of plasma, brain, gut, heart, kidney, liver, lung, muscle, skin, and spleen following an intravenous bolus administration of 20 mg/kg of ethoxybenzamide to male rats (250 g). The mean experimental data on concentration– time profiles were taken from the literature.19,20 JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 91, NO. 5, MAY 2002

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(adipose, heart, kidney, liver, lung, muscle, skin) and plasma, the simulated concentration–time profiles were within a factor of two of the experimental data determined in vivo. The largest discrepancies were observed for brain and gut. In both cases, the in vivo concentration–time profiles were overestimated by a factor of at least three with the rat PBPK model of diazepam.

those of liver and kidney were underestimated. For skin, however, the slope of the terminal phase on the in vivo concentration–time profile differs from the one simulated with the rat PBPK model of ethoxybenzamide. However, the experimentally determined and simulated concentration– time profiles of this tissue deviate by a factor less than two. Note that the model simulation for skin is more accurate for diazepam (Figure 3).

Ethoxybenzamide Model simulations were presented for nine tissues and plasma (Figure 4). For all tissues (brain, gut, heart, kidney, liver, lung, muscle, skin, spleen) and plasma the simulated concentration–time profiles were within a factor of two of the experimental data determined in vivo. Nevertheless, the in vivo concentration–time profile of gut was overestimated with the PBPK model. Inversely,

Propranolol Model simulations were presented for six tissues and plasma (Figure 5). For heart, kidney, muscle, and plasma the simulated concentration–time profiles were generally within a factor of two of the experimental data determined in vivo. The largest discrepancy for propranolol was observed for lung for which the in vivo concentration–time profile was underestimated by a factor of at least four by its PBPK model. The in vivo concentration–time profile of liver appears to be reasonably simulated at the first time points, but not at the last time point. For brain, however, the in vivo concentration–time profile was overestimated by the PBPK model, especially at the first time point.

DISCUSSION

Figure 5. Comparison of PBPK model simulations (lines) with experimental data determined in vivo (circles) on concentration–time profiles of plasma, brain, heart, kidney, liver, lung, and muscle following an intravenous bolus administration of 1.5 mg/kg of propranolol to male rats (250 g). This comparison was also made following an infusion of 0.02 mg/kg/min to male rats (350 g). The mean experimental data for concentration–time profiles of the racemic mixture were taken from the literature.18,21 JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 91, NO. 5, MAY 2002

The development of PBPK models capable of predicting plasma and tissue kinetics of new drug candidates based on a limited set of input data is valuable for drug discovery. The present study demonstrates the potentials and limitations of generic PBPK model frameworks integrating in vitro data on liver metabolism with a prediction method of tissue distribution data. Simulation of plasma and tissue concentration–time profiles of drugs prior to any in vivo studies seems feasible after the integration of in vitro-derived metabolic data with predicted tissue distribution data. The needed in vitro data on metabolism, plasma protein binding, and lipophilicity used as sole inputs in the current generic PBPK models are generally available in drug discovery. Therefore, these model frameworks should allow efficient screening efforts by providing prospective simulations of the disposition behavior of novel drug candidates. The development of such an integrative-generic modeling approach has now been illustrated with three structurally unrelated drugs (diazepam, ethoxybenzamide, propranolol). The next logical

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step would be to perform further model validations and improvements. In this context, 20 drugs are currently being studied at Roche. At this time, however, a broader application of the generic PBPK models appears to be realistic. At this point, it becomes important to discuss the accuracy of the PBPK model simulations. In drug discovery, rapid first rough estimates of pharmacokinetics are required for efficient screening efforts. It is therefore considered, for the purpose of the present study, that deviations within a factor of two between sets of simulated and experimentally determined data are reasonably acceptable for early drug discovery. It is of interest to note that only the mean of each input parameter has been used in the PBPK models for the current simulations. Furthermore, the number of input parameters in PBPK models has been increased in the present study by incorporating the prediction methods of Pt:p and Eh compared with conventional PBPK models, which use only one set of experimental data of Pt:p and Eh. The uncertainty associated with each new input parameter is therefore of great interest. Sensitivity and variability analyses, however, are beyond the scope of the present study. Considering the given accuracy criteria, the in vivo concentration–time profiles of plasma and tissues of diazepam, ethoxybenzamide, and propranolol are generally well simulated by their generic PBPK model, either following a bolus administration or a constant infusion via the venous blood (Figures 3–5). However, the concentration–time profiles of some of the tissues investigated are less adequately simulated by PBPK models, but this has no relevant impact on the overall concentration–time profile of plasma, which is adequately simulated for each drug. Therefore, distribution and liver metabolism are probably the main processes determining the overall pharmacokinetics of these three drugs, because only these two processes have been considered in the generic PBPK models. It is effectively the case based on earlier rat pharmacokinetic studies.6,18–21 Nevertheless, the comparison of simulated and experimentally determined concentration–time profiles for specific tissues provides additional information on the disposition behavior of the drugs studied. At this point, the generic PBPK models are not used as pure simulation tools, but rather as tools for mechanistic evaluations, which can be an obvious matter of concern in latter stages of the drug development, as demonstrated next.

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Less accurate model simulations of the in vivo concentration–time profiles are particularly observed in the cases of brain and gut for diazepam, liver and gut for ethoxybenzamide, and lung for propranolol (Figures 3–5). In general, the largest deviations observed between the simulated and experimentally determined concentration–time profiles for these tissues are due to a lower accuracy of the prediction of Pt:p parameters used to estimate distribution within PBPK models (Table 3). This lower accuracy might be a consequence of unexpected distribution and clearance processes affecting these Pt:ps under in vivo conditions. In fact, the in vivo Pt:ps used in PBPK models have been predicted with tissue composition-based equations assuming a homogeneous distribution of drugs into each tissue by a process of passive diffusion, which is due to hydrophobic interactions with lipids and a reversible binding to main proteins present in plasma and tissue interstitial space. Furthermore, it was assumed that the metabolic clearance was present only in liver. Additional relevant processes that may be present in tissues are nonlinear and extrahepatic metabolisms, active transport processes, biliary excretion/enterohepatic recirculation, tissue permeability lowered by barriers, specific macromolecular binding, ionic lipid binding and lysosomal ionic trapping either reversible or irreversible. These processes have been not considered in estimating Pt:ps and Ehs within the current PBPK models, which may have led to less accurate simulations of concentration–time profiles in specific tissues. In this context, the in vivo concentration–time profile of brain of diazepam has been overestimated by the PBPK model by a factor of at least three (Figure 3). For benzodiazepines such as diazepam, a low uptake in brain was observed under in vivo conditions, probably because of a low permeability across the blood–brain barrier.24 In the present study, however, a rapid diffusion process only determined the uptake of diazepam in brain. In addition for diazepam, an enterohepatic circulation may be suspected because the two last time points of the experimentally determined concentration–time profile of liver are overestimated compared with the model simulations (Figure 3). Enterohepatic recirculation of diazepam in rodents has been reported in the literature.25 For ethoxybenzamide, the concentration–time profile of liver has been entirely underestimated with the model, but only by a factor of up to two (Figure 4). The explanation could be the inaccuracy of CLint alone JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 91, NO. 5, MAY 2002

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to estimate the metabolism at the liver concentrations studied, which are close to the Km value of ethoxybenzamide.19 An additional explanation could be an inadequate in vitro–in vivo scaling of its liver metabolism. For propranolol, the in vivo concentration–time profile of liver has also been underestimated by the PBPK model, but especially at the last time point (Figure 5). Propranolol may potentially inhibit its own metabolism after a certain period of time under in vivo conditions, which may explain the underestimation by the PBPK model observed at the last time point.18 Also, the metabolic elimination of propranolol in the liver may be in direct competition with an active uptake process.21 For diazepam and ethoxybenzamide, the in vivo concentration–time profiles of gut have been overestimated by the PBPK models (Figures 3 and 4). Lower gut concentrations under in vivo conditions compared with simulated data may suggest the presence of an additional metabolic activity or an active export process in the enterocytes. The most pronounced discrepancy has been observed for lung in the case of propranolol (Figure 5). The in vivo concentration–time profile of lung was underestimated by the PBPK model by a factor of at least four. It has been reported that certain cationic-amphiphilic bases, such as propranolol, may significantly accumulate in the lung under in vivo conditions. This accumulation is probably caused by saturable ionic interactions with charged lipids present in membranes and/or by lysosomal ionic trapping in the cells.9,11,21,26 This cause may be true especially for the lung, which is the first organ exposed following intravenous bolus administrations. Such phenomena are supported by a significant underprediction of lung Pt:p of propranolol, as shown in Table 3. This underprediction is not surprising because saturable ionic interactions and ionic trapping were not considered in tissue composition-based equations used to predict the lung Pt:p. For propranolol, the underprediction of Pt:p of lung explains the in vivo concentration–time profile underestimated by the PBPK model for this tissue (Figure 5). It is of interest to note that for other cationic-amphiphilic bases (e.g., imipramine) a significant underprediction of their distribution in certain lean tissues from tissue composition-based equations has also been reported.9,11 At present, this aspect may limit the development of PBPK models for some cationic-amphiphilic compounds. In another context, it is also observed that the in vivo concentration–time profile of propranol in brain JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 91, NO. 5, MAY 2002

is overestimated by the PBPK model, but only at the first time point (Figure 5). The uptake of propranolol into brain might not be fully governed by an instantaneous diffusion process under in vivo conditions, in contrast to the present study. Further developments of the current generic PBPK models are therefore required to increase their use and accuracy in drug discovery and nonclinical development. It is evident that the inclusion of other ADME processes in PBPK models is needed, which should permit the integration of additional in vitro data on those processes. In particular, the integration of oral absorption and excretion models in the current PBPK models of drug disposition should get high priorities. Furthermore, concentration–time profiles of drugs at the organ level only may be simulated with the present PBPK models. Therefore, the tissue composition-based equations used in these models should be refined to provide simulations also for the tissue subfractions (e.g., cell, mitochondria), which could help the development of mechanism-based pharmacokinetic– pharmacodynamic (PK–PD) models. In the present study, generic PBPK models have been applied to rat only. However, the input data on physiology (Qt, Vt), tissue composition (Vn, Vph, Vw), metabolism (CLint), and plasma protein binding (fup) could be replaced with those of human. As an example, generic PBPK models of rat and also of human have already been successfully developed and validated for some lowmolecular weight volatile organic solvents.3,15,16 For drugs, however, such type of PBPK models for human has not been yet developed. The main reason was that a model validation with in vivo data on tissue concentration–time profiles was not possible because this kind of values for drugs is hardly accessible in human. Nevertheless, it would be of interest to develop generic PBPK models also for human and to validate them with in vivo data on plasma concentration–time profiles, which are available in the literature for many drugs. In summary, the present study demonstrates the feasibility of developing generic PBPK models of drug disposition based on an integration of in vitro data on liver metabolism and calculated data on tissue distribution. To simulate the pharmacokinetics of other drugs with these models, the only need is to change the value of each input parameter determined in vitro for lipophilicity (Po:w, Dvo:w*), protein binding (fup, B:P), and metabolism (CLint), because the rat physiological

PREDICTION OF PHARMACOKINETICS PRIOR TO IN VIVO STUDIES. II

values obtained from the literature should be constants. For a broader applicability of the PBPK models, Po:w may potentially be calculated from molecular structure information only.27 If the parameters for protein binding and clearance (fup, B:P, CLint) vary with the studied drug concentrations, this should be taken into account in PBPK models. During the drug development process, the experimentally determined PK data become available for comparisons with simulated data. In this context, generic PBPK models could also be used for mechanistic evaluations of pharmacokinetics for generating research hypotheses to understand the inaccurate simulations. Overall, the present generic and integrative PBPK approach of drug disposition suggested as a tool for a priori simulations and mechanistic evaluations of pharmacokinetics has the potential to improve the selection and optimization of new drug candidates.

ACKNOWLEDGMENTS We thank Dr. Wolfgang F. Richter and Prof. Theodor W. Guentert, at F. Hoffmann-La Roche Ltd., for the revision of this study.

REFERENCES 1. Dedrick RL, Bischoff KB. 1969. Pharmacokinetics in applications of the artificial kidney. Chem Eng Progr Symp Ser 64:32–44. 2. Krishnan K, Andersen ME. 2001. Physiologically based pharmacokinetic modeling in toxicology. In: Hayes W, ed.; Principle and methods of toxicology, 4th ed. Philadelphia, PA: Taylor & Francis, pp.193– 241. 3. El-Masri HA, Portier CJ. 1998. Physiologically based pharmacokinetic model of primidone and its metabolites phenobarbital and phenylethylmalonamide in humans, rats, and mice. Drug Metab Dispos 26:585–594. 4. Yamaguchi T, Yabuki M, Saito S, Watanabe M. 1996. Research to develop a predicting system of mammalian subacute toxicity. III. Construction of a predictive toxicokinetic model. Chemosphere 33:2441–2468. 5. Blakey GE, Nestorov IA, Arundel PA, Aarons LJ, Rowland M. 1997. Quantitative structure-pharmacokinetic relationships. I. Development of a wholebody physiologically based model to characterize changes in pharmacokinetics across a homologous series of barbiturates in the rat. J Pharmacokinet Biopharm 25:277–311.

1369

6. Igari Y, Sugiyama Y, Sawada Y, Iga T, Hanano M. 1983. Prediction of diazepam disposition in the rat and man by a physiologically based pharmacokinetic model. J Pharmacokinet Biopharm 11:577–593. 7. Clausen J, Bickel MH. 1993. Prediction of drug distribution in distribution dialysis and in vivo from binding to tissues and blood. J Pharm Sci 82:345–349. 8. Lin JH, Sugiyama Y, Awazu S, Hanano M. 1982. In vitro and in vivo evaluation of the tissue-to-blood partition coefficients for physiological pharmacokinetic models. J Pharmacokinet Biopharm 10:637– 646. 9. Poulin P, Theil FP. 2000. A priori prediction of tissue:plasma partition coefficients of drugs to facilitate the use of physiologically-based pharmacokinetic models in drug discovery. J Pharm Sci 89:16–35. 10. Poulin P, Schoenlein K, Theil FP. 2001. Prediction of adipose tissue:plasma partition coefficients for structurally unrelated drugs. J Pharm Sci 90:436–445. 11. Poulin P, Theil FP. 2002. Prediction of pharmacokinetics prior to in vivo studies. I. Mechanismbased prediction of volume of distribution. J Pharm Sci 91:129–156. 12. Houston BJ. 1994. Utility of in vitro drug metabolism data in predicting in vivo metabolic clearance. Biochem Pharmacol 47:1469–1479. 13. Obach RS. 1999. Prediction of human clearance of twenty-nine drugs from hepatic microsomal intrinsic clearance data: An examination of in vitro half-life approach and non-specific binding to microsomes. Drug Metab Dipsos 27:1350–1359. 14. Schneider G, Coassolo P, Lave´ T. 1999. Combining in vitro and in vivo pharmacokinetic data for prediction of hepatic drug clearance in humans by artificial neural networks and multivariate statistical techniques. J Med Chem 42:5072–5076. 15. Poulin P, Be´liveau M, Krishnan K. 1999. Mechanistic animal-replacement approaches for predicting pharmacokinetics of organic chemicals. In: Salem H, Katz SA, editors. Toxicity assessments alternatives: Methods, issues, opportunities. Totowa, NJ: Humana Press, Inc., pp. 115–139. 16. Pelekis M, Poulin P, Krishnan K. 1995. An approach for incorporating tissue composition data into physiologically based pharmacokinetic models. Toxicol Ind Health 11:511–522. 17. Brown RP, Delp MD, Lindstedt SL, Rhomberg LR, Beliles RP. 1997. Physiological parameter values for physiologically based pharmacokinetic models. Toxicol Ind Health 13:407–484. 18. Singh K, Tripp SL, Dunton AE, Douglas FL, Rakhit A. 1991. Determination of in vivo hepatic extraction ratio from in vitro metabolism by rat hepatocytes. Drug Metab Dispos 19:990–998. 19. Lin JH, Sugiyama Y, Awazu S, Manabu H. 1982. Physiological pharmacokinetics of ethoxybenza-

JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 91, NO. 5, MAY 2002

1370

20.

21.

22.

23.

POULIN AND THEIL

mide based on biochemical data obtained in vitro as well as on physiological data. J Pharmacokinet Biopharm 10:649–661. Lin JH, Hayashi M, Awazu S, Hanano M. 1978. Correlation between in vitro and in vivo drug metabolism rate: oxidation of ethoxybenzamide in rat. J Pharmacokinet Biopharm 6:327–337. Schneck DW, Pritchard F, Hayes AH. 1977. Studies on the uptake and binding of propranolol by rat tissues. J Pharmocol Exp Ther 203:621– 629. Garie´py L, Fenyves D, Villeneuve JP. 1992. Propranolol disposition in the rat: variation in hepatic extraction ratio with unbound drug fraction. J Pharm Sci 81:255–258. Carlile DJ, Zomorodi K, Houston JB. 1997. Scaling factors to relate drug metabolism clearance in

JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 91, NO. 5, MAY 2002

24.

25.

26.

27.

hepatic microsomes, isolated hepatocytes and the interactions. Drug Metab Dispos 25:903–911. Arendt RM, Greenblatt DJ, Liebisch DC, Luu MD, Paul SM. 1987. Determinants of benzodiazepines brain uptake: Lipophilicity versus binding affinity. Psychopharmacology 93:72–76. Ma Y, Sun RY. 1993. Second peak of plasma diazepam concentration and enterogastric circulation. Zhongguo Yao Li Xue Bao 14:218–221. Ishizaki J, Yokogawa K, Nakashima E, Ohkuma S, Ichimura F. 1998. Uptake of basic drugs into rat lung granule fraction in vitro. Biol Pharm Bull 21:858–861 . Meylan WM, Howard PH. Log n-octanol:water partition coefficients. KOWWIN software. Syracuse Research Corporation, Environmental Science Center, Syracuse, NY 13210.