0090-9556/07/3510-1766–1780$20.00
`DRUG METABOLISM AND DISPOSITION
`Copyright © 2007 by The American Society for Pharmacology and Experimental Therapeutics
`DMD 35:1766–1780, 2007
`
`Vol. 35, No. 10
`15644/3252832
`Printed in U.S.A.
`
`Prediction of Human Pharmacokinetics Using Physiologically
`Based Modeling: A Retrospective Analysis of 26 Clinically
`Tested Drugs
`
`Stefan S. De Buck, Vikash K. Sinha, Luca A. Fenu, Marjoleen J. Nijsen, Claire E. Mackie, and
`Ron A. H. J. Gilissen
`
`Johnson & Johnson Pharmaceutical Research and Development, Discovery ADME-Tox Department,
`Beerse, Belgium
`
`Received March 5, 2007; accepted July 3, 2007
`
`ABSTRACT:
`
`The aim of this study was to evaluate different physiologically
`based modeling strategies for the prediction of human pharmaco-
`kinetics. Plasma profiles after intravenous and oral dosing were
`simulated for 26 clinically tested drugs. Two mechanism-based
`
`from
`predictions of human tissue-to-plasma partitioning (Ptp)
`physicochemical input (method Vd1) were evaluated for their abil-
`ity to describe human volume of distribution at steady state (V
`ss).
`This method was compared with a strategy that combined pre-
`dicted and experimentally determined in vivo rat P
`tp data (method
`
`Vd2). Best Vss predictions were obtained using method Vd2, pro-
`viding 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 metabo-
`
`lism 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 concen-
`tration-time profile, oral apparent volume of distribution, and max-
`imum 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 phar-
`macokinetics.
`
`In the drug discovery process considerable resources are required to
`assess the pharmacokinetic (PK) properties of potential drug candi-
`dates 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
`
`Article, publication date, and citation information can be found at
`
`http://dmd.aspetjournals.org.
`
`doi:10.1124/dmd.107.015644.
`
`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 dispo-
`sition model are derived from the anatomical structure of the organ-
`ism; therefore, the model structure is predetermined and basically
`independent of the drug of interest. The PBPK model input parameters
`include both a drug-independent and a drug-specific subset. The first
`subset comprises data underlying the physiological processes (e.g.,
`blood flow), and the second subset comprises drug-specific biochem-
`
`ABBREVIATIONS: PK, pharmacokinetic(s); ACAT, advanced compartmental absorption and transit model; ADME, absorption, distribution,
`metabolism, and excretion; AUC, area under the plasma concentration-time curve; AUMC, area under the first moment curve; BCS, Biopharma-
`ceutical Classification Scheme; CL, total body clearance from plasma; CL/F, total body clearance from plasma after oral administration; CLH,
`hepatic plasma clearance; CLH,blood, hepatic blood clearance; CLint, intrinsic clearance; CLR, renal clearance from plasma; Cmax, peak plasma
`concentration after oral administration; D, dose; F, absolute oral bioavailability; fuinc, unbound fraction in microsomal or hepatocyte incubation; fup,
`unbound fraction in plasma; GFR, glomerular filtration rate; in vivo t1/2, in vivo terminal half-life; log Pow, n-octanol:water partition coefficient of the
`non-ionized species; PBPK, physiologically based pharmacokinetics; Ptp, tissue-to-plasma partition coefficient; Ptpu, tissue-to-plasma partition
`coefficient of the unbound drug; Qh, hepatic blood flow; RA, ratio of albumin concentration found in tissue over plasma; RB, blood-to-plasma
`concentration ratio; SF, scaling factor; SIF, simulated intestinal fluid; Vd/F, apparent volume of distribution after oral administration; Vss, apparent
`volume of distribution at steady state.
`
`1766
`
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`PREDICTION OF HUMAN PHARMACOKINETICS
`
`1767
`
`Arterial
`Arterial
`Supply
`Supply
`
`Venous
`Venous
`Return
`Return
`
`LungLung
`
`Spleen
`Spleen
`
`GutGut
`
`MuscleMuscle
`
`BrainBrain
`
`SkinSkin
`
`LiverLiver
`
`Adipose
`Adipose
`
`HeartHeart
`
`Kidney
`Kidney
`
`Testes
`Testes
`
`Bone marrow
`Bone marrow
`
`Rest of Body
`Rest of Body
`
`FIG. 1. Scheme of the generic disposition PBPK model for simulation of full
`plasma and tissue concentration-time profiles in rat and human. An overview of all
`physiological values is given in Table 3. Estimation of rate and extent of oral
`absorption from the gut was obtained using ACAT (Yu and Amidon, 1999; Agoram
`et al., 2001). For more details on all methods used, refer to Materials and Methods.
`
`ical parameters. The latter consists of the drug’s in vivo intrinsic
`clearance (CLint) of each organ involved in its elimination, in addition
`to estimates of the drug’s tissue-to-plasma coefficient (Ptp) for each
`model compartment. Prediction of the rate and extent of absorption
`can be obtained using semiphysiologically based absorption models,
`such as the advanced compartmental absorption and transit (ACAT)
`model (Yu and Amidon, 1999; Agoram et al., 2001). As depicted in
`Fig. 1, the ACAT model may serve as a time-dependent input function
`to the disposition model, thereby creating a combined absorption-
`distribution PBPK model.
`Although PBPK models have been widely used in areas such as risk
`assessment to predict the PK behavior of toxic chemicals, their ap-
`plication in support of drug discovery and development has remained
`limited, most probably as a result of their mathematical complexity
`and the labor-intensive drug-specific input data required. However,
`more recently, a variety of in vitro based prediction tools have been
`developed for the estimation of PBPK model input parameters (Theil
`et al., 2003). Such prediction tools require commonly determined
`biochemical and physicochemical drug-specific input and thus allow
`for the prediction of ADME parameters before any in vivo experi-
`ment. As examples of such prediction tools, mechanistic equations
`have been developed for the prediction of fraction of oral dose
`absorbed (Agoram et al., 2001; Willmann et al., 2004), tissue parti-
`tioning (Ptp) (Poulin and Theil, 2000; Poulin et al., 2001; Rodgers et
`al., 2005a), apparent volume of distribution at steady state (Vss)
`(Poulin and Theil, 2002), and hepatic plasma clearance (CLH) (Hous-
`ton and Carlile, 1997; Austin et al., 2002; Ito and Houston, 2004). In
`
`a previous study, we also evaluated a variety of physiologically based
`prediction tools for the prediction of rat PK (De Buck et al., 2007).
`The aim of the present work was to further evaluate these prediction
`tools for their ability to predict human PK parameters by simulation of
`full plasma concentration-time profiles after both intravenous and oral
`administration. Although recent studies have addressed a similar
`question, the overall prediction accuracy obtained was in the lower
`range, particularly for predictions of Vss and in vivo terminal half-life
`(in vivo t1/2) (Parrott et al., 2005b; Jones et al., 2006a). In the present
`study, a more comprehensive range of approaches toward the predic-
`tion of Vss and CLH was explored, including two mechanism-based
`Vss predictions from physicochemical input, as well as approaches that
`combine the use of both predicted and experimentally determined in
`vivo rat Ptp. For each of the approaches tested, the influence of
`interspecies differences in plasma protein binding on prediction ac-
`curacy was investigated. The role of relative drug binding in plasma
`and in vitro drug matrices was also considered with respect to CLH
`projection from in vitro metabolism data. Whereas the basic tenet of
`pharmacokinetics states that the unbound drug concentration in the
`plasma dictates clearance, our previous report in rat using microsomes
`has suggested that in vitro CLint may provide a better estimate of in
`vivo CLH of total rather than unbound drug (De Buck et al., 2007). To
`further investigate the effect of relative drug binding, predictions of
`human CLH were performed each time under two variations, either by
`incorporating or disregarding such binding factors. Methods to predict
`Vss and CL were combined to predict in vivo t1/2, and the ACAT
`model was tested for its ability to predict the area under the oral
`concentration-time profile (AUC), the oral apparent volume of distri-
`bution (Vd/F), and peak plasma concentration (Cmax). To determine
`whether a successful prediction in rat correlates with a successful
`prediction in human, the accuracy of each method was assessed within
`both species.
`
`Materials and Methods
`
`Compounds and Sources of in Vitro and in Vivo Parameters. The set of
`compounds (n ⫽ 26) included in this analysis were taken from those brought
`into clinical development at Johnson & Johnson Pharmaceutical Research and
`Development (Beerse, Belgium). Compounds were selected based on the
`availability of historical data on the in vivo preclinical (rat) and clinical PK, as
`well as of each of the following experimentally determined biochemical and
`physicochemical parameters: unbound fraction in plasma (fup), unbound frac-
`tion in microsomal or hepatocyte incubation (fuinc), basic and acidic dissoci-
`ation constants (pKa), n-octanol:water partition coefficient of the non-ionized
`species (log Pow), aqueous solubility at defined pH conditions or solubility in
`simulated intestinal fluid (SIF), in vitro CLint determined in hepatic micro-
`somes or hepatocyte suspension cultures, and the blood-to-plasma concentra-
`tion ratio (RB). Summaries of the available in vitro and in vivo PK data are
`shown in Tables 1 and 2, respectively.
`The 26 compounds in the data set cover a broad range of small molecules
`from a variety of discovery programs. The majority of compounds (n ⫽ 19)
`were moderate-to-strong bases (pKa of protonated base ⬎7.0); three were
`neutral or weakly ionized at physiological pH (weak base). The remaining
`compounds were one weak acid, one strong acid, and two zwitterions. The
`lipophilicity (log Pow) ranged between 1.11 and 5.5, and fup ranged from 0.001
`to 0.867. Aqueous solubility was highly variable with values at physiological
`pH ranging from 0.003 mg/ml to 74 mg/ml. Vss in humans varied from limited
`(30 L) to widespread (⬎1000 L). In the rat, major elimination pathways
`included hepatic metabolism, renal excretion, or a combination of the two. In
`humans, total body clearance from plasma (CL) varied from less than 10% of
`hepatic blood flow (Qh) to more than 70% of Qh.
`Model Structure. The Gastroplus 5.1.0 generic PBPK model and its
`built-in mass balance differential equations were used for all simulations
`(Simulations Plus Inc., Lancaster, CA). In brief, the model (Fig. 1) was
`composed of 14 tissue compartments, including lung, spleen, liver, gut, adi-
`pose tissue, muscle, heart, brain, kidney, skin, testes, red marrow, yellow
`
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`1768
`
`Solubility
`
`mg/ml
`
`In vitro and in silico physicochemical and biochemical properties of the 26 compounds
`
`TABLE 1
`
`JNJ No.
`
`Generic Name
`
`mol. wt.
`
`pKa
`
`Log Pow
`
`Species
`
`fup
`
`a
`fuinc
`
`RB
`
`JNJ1
`
`Lorcainide
`
`407
`
`B 9.44
`
`JNJ2
`
`Domperidone
`
`425
`
`B 7.89 B 2.50
`
`JNJ3
`
`Nebivolol
`
`405
`
`B 8.40
`
`4.16
`
`3.96
`
`4.03
`
`Rat
`Human
`
`Rat
`Human
`
`Rat
`Human
`
`0.260
`0.150
`
`0.092
`0.061
`
`0.015
`0.020
`
`0.45
`
`0.34
`
`0.12e
`
`1.2
`0.70
`
`1.3
`0.74
`
`1.2
`1.2
`
`In Vivo
`b
`CLint
`
`ml/min/kg
`
`624
`31.5
`
`178
`69.3
`
`89.1
`11.2
`
`Test System
`
`c,d
`
`Peff
`
`10⫺4 cm/s
`
`4.78
`
`1.88
`
`1.86
`
`RLMic
`HLMic
`
`RLMic
`HLMic
`
`RLMic
`HLMic
`
`JNJ4
`
`Galantamine
`
`287
`
`B 8.20
`
`JNJ5
`
`Alfentanil
`
`JNJ6
`
`Sufentanil
`
`JNJ7
`
`Ketanserin
`
`JNJ8
`
`Ritanserin
`
`JNJ9
`
`Sabeluzole
`
`416
`
`386
`
`395
`
`478
`
`415
`
`B 6.50
`
`B 8.10
`
`B 7.50
`
`B 8.20 B 2.07
`
`B 7.60 B 3.40
`
`1.11
`
`2.21
`
`4.02
`
`3.30
`
`5.20
`
`4.63
`
`Rat
`Human
`
`Rat
`Human
`Rat
`Human
`Rat
`Human
`
`Rat
`Human
`Rat
`Human
`
`0.755
`0.822
`
`0.164
`0.079
`0.069
`0.075
`0.012
`0.049
`
`0.015
`0.008
`0.016
`0.014
`
`0.86e
`
`0.97
`
`0.87
`
`0.32
`
`0.45
`
`0.06
`
`1.0
`1.2
`
`0.69
`0.63
`0.74
`0.74
`0.65
`0.70
`
`0.74
`0.65
`0.84
`0.82
`
`20.8
`2.49
`
`416
`190
`250
`184
`10.0
`31.5
`
`139
`4.91
`43.0
`5.10
`
`RLMic
`HLMic
`
`RLMic
`HLMic
`RLMic
`HLMic
`RLMic
`HLMic
`
`RLMic
`HLMic
`RLMic
`HLMic
`
`5.43
`
`7.14
`
`12.0d
`
`2.93
`
`265, 214, 192, 2.4, 0.18 in aqueous
`buffer at pH 2.2, 4.2, 5.9, 7.7 and 9.5,
`respectively
`0.31, 1.5, 0.057, 0.006, 0.001 in aqueous
`buffer at pH 2.3, 4.2, 6.0, 7.2, and 8.0,
`respectively
`0.046, 0.071, 0.91, 0.031, 0.12 in
`aqueous buffer at pH 1.9, 4.0, 5.4, 6.1,
`and 8.1, respectively
`35, 39, 33, 38, 37, 41 in aqueous buffer
`at pH 2.0, 4.9, 5.2, 6.8, 7.5, and 7.7,
`respectively
`
`DE BUCK ET AL.
`
`0.72, 1.30, 16, 15, 11, 0.050, 0.001 in
`aqueous buffer at pH 1.2, 2.6, 3.1, 3.5,
`4.6, 5.7, and 8.0, respectively
`1.4, 0.063, 0.037in aqueous buffer at pH
`2.2, 4.1, and 6.1, respectively
`13, 5.8, 1.3, 3.9, 0.19, 0.01 in aqueous
`buffer at pH 2.7, 3.3, 4.2, 4.6, 6.0, and
`6.9, respectively
`29, 11, 4.7, 2.9, 0.14, 0.061 in aqueous
`buffer at pH 3.4, 3.5, 4.5, 7.5, 9.14, and
`12.8, respectively
`0.013 in aqueous buffer at pH 6.9
`
`JNJ10
`
`297
`
`B 9.47
`
`JNJ11
`
`Lubeluzole
`
`JNJ12
`
`JNJ13
`
`Ridogrel
`
`JNJ14
`
`Laniquidar
`
`433
`
`296
`
`366
`
`584
`
`B 7.60 B 4.27
`
`B 9.88 B 3.00
`
`A 4.90 B 3.84
`
`B 7.90 B 3.30
`
`JNJ15
`
`Mazapertine
`
`421
`
`B 7.06
`
`Rat
`Human
`
`Rat
`Human
`Rat
`Human
`
`Rat
`Human
`Rat
`Human
`
`0.141
`0.115
`
`0.008
`0.003
`0.820
`0.867
`
`0.049
`0.033
`0.002
`0.001
`
`0.12e
`
`0.05e
`
`0.85e
`
`1.0f
`
`0.08
`
`2.0
`1.4
`
`0.76
`0.58
`1.5
`1.5
`
`0.80
`0.77
`0.79
`0.62
`
`312
`10.5
`
`52.0
`3.90
`20.8
`0.570
`
`5.10
`2.20
`51.7
`99.0
`
`RLMic
`HLMic
`
`RLMic
`HLMic
`RLMic
`HLMic
`
`RLHep
`HLHep
`RLMic
`HLMic
`
`4.03
`
`4.88
`
`1.18
`
`3.54
`
`5.50
`
`3.96
`
`0.321
`
`2.79
`
`0.05
`
`4.73
`
`4.56d
`
`5.70d
`
`JNJ16
`
`JNJ17
`
`JNJ18
`
`Risperidone
`
`JNJ19
`
`Levocabastine
`
`JNJ20
`
`Norcisapride
`
`686
`
`B 7.20 B 3.10
`
`558
`
`411
`
`420
`
`313
`
`B 7.26 B 6.18 B 4.00 A 8.28
`
`B 8.24 B 3.11
`
`B 9.90 A 3.20
`
`B 9.10 B 3.00
`
`Rat
`Human
`
`Rat
`Human
`
`Rat
`Human
`Rat
`Human
`
`Rat
`Human
`Rat
`Human
`
`0.030
`0.011
`
`0.036
`0.034
`
`0.028
`0.009
`0.118
`0.100
`
`0.465
`0.453
`0.650
`0.625
`
`0.13e
`
`0.08
`
`0.14e
`
`0.34
`
`1.0f
`
`0.79e
`
`0.63
`0.52
`
`0.78
`0.75
`
`1.0
`1.0
`0.85
`0.67
`
`1.1
`1.2
`1.5
`1.6
`
`623
`231
`
`28.2
`20.3
`
`416
`231
`250
`7.96
`
`1.25
`0.33
`2.43
`0.88
`
`4.12
`
`3.90
`
`3.04
`
`1.75
`
`1.51
`
`RLMic
`HLMic
`
`RLMic
`HLMic
`
`RLMic
`HLMic
`RLMic
`HLMic
`
`RLHep
`HLHep
`RLMic
`HLMic
`
`1.85
`
`5.70
`
`2.10
`
`1.16
`
`20, 20, 20, 7.56, 3.09 in aqueous buffer
`at pH 1.8, 3.8, 4.3, 7.45, and 12.6,
`respectively
`0.26, 0.02, 0.65, 9.8 in aqueous buffer at
`pH 2.1, 5.4, 7.0, and 8.1, respectively
`12.4, 0.58, 0.10, 0.064 in aqueous buffer
`at pH 2.21, 2.78, 3.62, and 7.05,
`respectively
`80, 43, 0.54, 0.21, 0.22 in aqueous buffer
`at pH 3.8, 4.7, 6.9, 8.9, and 11.5,
`respectively
`13, 1.1, 0.75, 0.04, 0.01 in aqueous
`buffer at pH 2.2, 3.7, 5.7, 7.5, and 8.6,
`respectively
`
`40, 4.1, 1.8, 0.25, 0.064 in aqueous
`buffer at pH 5.4, 6.0, 6.2, 7.5, and 8.7,
`respectively
`0.06, 0.05, 0.02, 0.02 in aqueous buffer
`at pH 2.0, 3.2, 6.0, and 8.0, respectively
`80, 92, 93, 74, 41 in aqueous buffer at
`pH 2.1, 4.8, 6.6, 7.8, and 8.0,
`respectively
`
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`PREDICTION OF HUMAN PHARMACOKINETICS
`
`1769
`
`marrow, and rest of the body, which were linked by the venous and arterial
`blood circulation. It was assumed that drug distributes instantaneously and
`homogenously within each tissue compartment, and uptake of drug within each
`tissue compartment was limited by the blood flow (perfusion rate-limited
`uptake). The default Gastroplus settings of all physiological data used in the rat
`and human PBPK models are summarized in Table 3. The methods used for
`estimating the PBPK model input data on CLH, renal plasma clearance (CLR),
`Ptp values, and absorption rate are described below.
`ss: Method Vd1. Predicted values
`Prediction of Human and Rat P
`tp and V
`of rat and human Ptp for each tissue compartment of Fig. 1 were obtained from
`drug-specific physicochemical parameters using the following mechanistic
`tissue composition-based equation developed by Poulin and coworkers (Poulin
`and Theil, 2002):
`
`Ptp
`
`⫽
`
`关P 䡠 共VNLT
`关P 䡠 共VNLp
`
`⫹ 0.3 䡠 VPHT兲 ⫹ 共VWT
`⫹ 0.3 䡠 VPHp兲 ⫹ 共VWp
`
`⫹ 0.7 䡠 VPHT兲兴 䡠 fup
`⫹ 0.7 䡠 VPHp兲兴 䡠 fut
`
`(1)
`
`where P is the anti-logged value of log Pow for a nonadipose tissue or is the
`vegetable oil/buffer partition coefficient for both the ionized and nonionized
`species at pH 7.4 (Dvow) for adipose tissue. Dvow was calculated from log Pow
`using the Henderson-Hasselbalch equations and the following relationship: log
`Pvow ⫽ 1.115 䡠 log Pow ⫺ 1.35 (Leo et al., 1971). V is the fractional tissue
`volume content of neutral lipids (NL), phospholipids (PH), or water (W) in
`tissue (T) and plasma (p). The physiological data on human and rat values used
`for VNLT, VNLp, VPHT, VPHp, VWT, and VWp have been described in the literature
`(Poulin and Theil, 2002). The fraction unbound in tissue (fut) in eq. 1 was
`estimated as follows:
`
`fut
`
`⫽ 1/共1 ⫹ 共共共1 ⫺ fup兲/fup兲 䡠 RA兲兲
`
`(2)
`
`atpH2.3,4.5,5.9,and7.5
`2.3,0.18,0.014,0.005inaqueousbuffer
`respectively
`bufferatpH2.1,4.4,5.0,7.0,and9.0,
`1.6,2.43,0.52,0.02,0.01inaqueous
`and0.005SIFatpH7.4
`pH1.4,4.4,5.2,and6.0,respectively
`20,10.2,2.19,0.026inaqueousbufferat
`respectively
`bufferatpH3.0,4.2,5.1,6.0,and8.1,
`10.3,3.9,0.42,0.035,0.002inaqueous
`SIFatpH7.5
`6.5and8.7,respectivelyand0.249in
`0.002and100inaqueousbufferatpH
`inSIFatpH7.53
`0.05inaqueousbufferatpH1.2,0.003
`
`mg/ml
`
`Solubility
`
`2.07
`
`4.54d
`
`2.00
`
`3.41
`
`0.751
`
`1.96
`
`10⫺4cm/s
`
`HLHep
`RLHep
`
`HLMic
`RLMic
`
`HLHep
`RLHep
`
`HLMic
`RLMic
`
`HLMic
`RLMic
`HLMic
`RLMic
`
`9.03
`
`24.8
`
`7.28
`
`19.9
`
`8.97
`
`371
`
`10.2
`
`208
`
`116
`156
`
`77.0
`35.6
`
`ml/min/kg
`
`1.5
`1.3
`
`0.72
`0.70
`
`0.59
`0.75
`
`0.61
`0.80
`
`0.55
`0.74
`1.5
`1.5
`
`1.0f
`
`0.05e
`
`1.0f
`
`0.06
`
`0.90
`
`0.23
`
`c,d
`
`Peff
`
`TestSystem
`
`b
`
`InVivoCLint
`
`RB
`
`a
`
`fuinc
`
`fup
`
`where RA is the ratio of albumin concentration found in tissue over plasma.
`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 Vss values were calculated by Gastroplus software
`according to the equation of Sawada et al. (1984) in which Vss equals the
`plasma volume in addition to the sum of each Ptp multiplied by its respective
`tissue volume.
`ss: Method Vd2. For rat Ptp and
`Prediction of Human and Rat P
`tp and V
`Vss, experimental rat Ptp 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. All experimentally
`determined in vivo rat Ptp values used within this study are summarized in
`Table 2. In instances where the in vivo Ptp was not available for a compound,
`the value for that tissue compartment (Fig. 1) was predicted using the tissue
`composition-based equation as described by Rodgers et al. (2005a). In brief,
`for strong bases (pKa ⬎ 7.0), Ptp of unbound drug (Ptpu) was calculated using
`eq. 3:
`
`Ptpu
`
`⫽
`
`Ptp
`fup
`
`⫽冤
`
`VEW
`
`⫹
`
`1 ⫹ 10pKa-7.0
`1 ⫹ 10pKa-7.4 䡠 VIW
`
`⫹
`
`Ka 䡠 关AP兴t 䡠 10pKa-7.0
`1⫹10pKa-7.4
`
`Pvow 䡠 VNL
`
`⫹
`
`⫹ 共共0.3 䡠 Pvow
`1⫹10pKa-7.4
`
`⫹ 0.7兲 䡠 VNP兲
`
`冥
`
`(3)
`
`where V is the fractional tissue volume of neutral lipids (NL), neutral phos-
`pholipids (NP), extracellular water (EW), and intracellular water (IW), [AP]t is
`the concentration of acidic phospholipids in tissue, all physiological data on
`VEW, VIW, VNL, VNP and [AP]t for both adipose and nonadipose tissue have
`been described in the literature (Rodgers et al., 2005a), pKa represents the
`dissociation constant of the protonated base, and Pvow is the anti-logged value
`of log Pvow (calculated from Pow as described above). Ka is the association
`constant of the compound with the acidic phospholipids, and was calculated
`from eq. 4:
`
`fHepatocyteincubationwasperformedinprotein-freemedium(fuinc⫽1).
`ePredictedfuincvalueinmicrosomeswasdeterminedaccordingtothemethodofAustinetal.(2002).
`dInsilicopredictedPeff(ADMETPredictorsoftwareversion1.30.2;SimulationsPlusInc.).
`cPermeabilitywasmeasuredusingaCaco-2assayandconvertedtoPeffusingthereportedcorrelationlogPeff,human⫽0.6532䡠logPapp,caco-2⫺0.3036(Sunetal.,2002).
`bInvivoCLintwascalculatedusingeq.6asdescribedunderMaterialsandMethods.
`aExperimentalvaluesoffuincinhumanmicrosomalproteinweredeterminedaccordingtothemethodofGiulianoetal.(2005).Ratfuincwasassumedtoequalhumanfuinc.
`A,acid;B,base;HLHep,humanliverhepatocytes;HLMic,humanlivermicrosomes;logRLHep,ratliverhepatocytes;RLMic,ratlivermicrosomes.
`
`0.023
`0.036
`
`0.016
`0.015
`
`0.006
`0.007
`
`0.016
`0.082
`
`0.001
`0.001
`0.012
`0.015
`
`Human
`Rat
`
`Human
`Rat
`
`Human
`Rat
`
`Human
`Rat
`
`Human
`Rat
`Human
`Rat
`
`4.00
`
`4.84
`
`5.24
`
`3.40
`
`4.78
`
`3.55
`
`B5.95B3.67
`
`500
`
`B6.80B2.86
`
`660
`
`B7.23B5.20
`
`380
`
`B7.00B3.10
`
`359
`
`A8.21
`
`B7.27
`
`570
`
`481
`
`JNJ26
`
`JNJ25
`
`JNJ24
`
`JNJ23
`
`JNJ22
`
`JNJ21
`
`Species
`
`LogPow
`
`pKa
`
`mol.wt.
`
`GenericName
`
`JNJNo.
`
`TABLEI—Continued
`
`Apotex v. Cellgene - IPR2023-00512
`Petitioner Apotex Exhibit 1039-0004
`
`

`

`1770
`
`Summary of the preclinical (rat) and clinical pharmacokinetic data for the 26 compounds
`
`TABLE 2
`
`JNJ No.
`
`Species
`
`Dose
`
`Route
`
`CL or CL/F
`
`CLR
`
`Vss or Vd/F
`
`In Vivo t1/2
`
`Cmax
`
`AUC
`
`mg
`
`100
`100
`2.50
`1.88
`
`Human
`Human
`Rat
`Rat
`
`l/h
`
`71.6
`202
`1.55
`4.24
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`JNJ1
`
`JNJ2
`
`liters
`
`413
`1.49E ⫹ 03
`3.92
`
`h
`
`5.10
`
`2.91
`
`7.59
`
`ng/ml
`
`ng 䡠 h/ml
`
`1.40E ⫹ 03
`494
`1.61E ⫹ 03
`442
`
`60.1
`
`102
`
`Experimentally Determined In Vivo Rat Ptp
`
`a
`
`Lung Adipose Muscle
`
`Liver
`
`Spleen
`
`Heart
`
`Brain Kidney
`
`Skin
`
`Testes
`
`Bone
`
`19.4
`
`5.27
`
`6.50
`
`0.571 10.3
`
`2.91
`
`1.52
`
`5.68
`
`3.21
`
`3.45 13.8
`
`3.87
`
`22.5
`
`4.35
`
`Human
`Human
`Rat
`Rat
`
`Human
`Human
`Rat
`Rat
`
`Human
`Human
`Rat
`Rat
`
`10.0
`60.0
`0.625
`0.625
`
`0.500
`5.00
`0.313
`0.313
`
`8.00
`8.00
`0.625
`0.625
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`34.3
`232
`1.30
`6.01
`
`80.5
`192
`0.736
`0.925
`
`17.8
`18.7
`0.473
`0.803
`
`JNJ3
`
`JNJ4
`
`157
`2.54E ⫹ 03
`1.39
`
`1.14E ⫹ 03
`2.87E ⫹ 03
`1.55
`
`3.93
`
`0.100
`
`175
`200
`1.30
`
`0.871
`
`10.40
`
`1.37
`
`7.40
`
`3.48
`
`2.01
`
`42.6
`
`292
`259
`480
`104
`
`6.20
`26.1
`425
`338
`
`482
`427
`1.32E ⫹ 03
`778
`
`28.8
`
`1.37
`
`510
`
`10.9
`
`99.7
`
`2.67
`
`2.95 14.1
`
`15.6
`
`4.71
`
`3.73
`
`10.6
`
`7.65
`
`5.32
`
`7.87;14.1
`
`4.42 0.476
`
`2.14
`
`2.53
`
`2.92
`
`2.28
`
`1.51
`
`14.5
`
`1.14
`
`1.46
`
`4.79;4.81
`
`DE BUCK ET AL.
`
`Human
`Human
`Rat
`Rat
`
`Human
`Human
`Rat
`Rat
`
`8.75
`
`4.00E02
`
`0.350
`
`6.25E04
`
`JNJ5
`
`JNJ6
`
`JNJ7
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`21.2
`
`0.464
`
`49.6
`
`1.04
`
`33.9
`71.7
`5.75E-02
`9.82E02
`
`0.110
`
`0.146
`
`86.2
`
`1.11 3.01
`
`0.440 1.43
`
`1.05
`
`0.791
`
`0.181
`
`1.18
`
`0.512 0.481
`
`128
`
`0.967
`
`268
`1.48E ⫹ 03
`0.168
`
`2.47
`
`1.05
`
`14.3
`
`2.00
`
`8.10
`
`0.604
`
`71.4
`
`298
`279
`4.35E ⫹ 04
`2.55E ⫹ 04
`
`6.18 7.72
`
`1.71
`
`0.370
`
`2.80
`
`1.80
`
`2.08
`
`1.17
`
`1.97
`
`1.49 0.562
`
`0.284 2.60
`
`0.911
`
`0.354
`
`0.194
`
`1.53
`
`0.463 0.495 0.19;0.18
`
`Human
`Human
`Rat
`Rat
`
`Human
`Human
`Rat
`Rat
`
`Human
`Human
`Rat
`Rat
`
`Human
`Human
`Rat
`Rat
`
`10.0
`20.0
`2.50
`2.50
`
`5.00
`10.0
`0.625
`0.625
`
`10.0
`5.00
`0.313
`0.625
`
`1.00
`8.00
`2.50
`10.0
`
`JNJ8
`
`JNJ9
`
`JNJ10
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`2.14
`2.33
`0.400
`0.918
`
`17.0
`22.7
`0.538
`1.24
`
`149
`950
`2.02
`5.26
`
`99.0
`134
`2.00
`
`385
`621
`1.46
`
`1.33E ⫹ 03
`9.72E ⫹ 03
`8.37
`
`40.0
`
`2.52
`
`18.9
`
`2.13
`
`7.09
`
`2.77
`
`164
`
`14.5
`
`2.51E ⫹ 03
`4.30E ⫹ 03
`1.56E ⫹ 03
`681
`
`594
`220
`581
`506
`
`0.590
`
`6.58
`8.42
`1.24E ⫹ 03
`1.90E ⫹ 03
`
`27.8
`
`4.29
`
`3.02 21.8
`
`10.5
`
`14.1
`
`29.2
`
`8.41
`
`0.831 37.7
`
`5.48
`
`2.45
`
`5.37
`
`10.4
`
`2.95
`
`4.62
`
`1.83;7.76
`
`400
`
`20.1 150
`
`40.2
`
`80.3
`
`80.1
`
`75.1
`
`Apotex v. Cellgene - IPR2023-00512
`Petitioner Apotex Exhibit 1039-0005
`
`

`

`TABLE 2—Continued
`
`JNJ No.
`
`Species
`
`Dose
`
`Route
`
`CL or CL/F
`
`CLR
`
`Vss or Vd/F
`
`In Vivo t1/2
`
`Cmax
`
`AUC
`
`Human
`Human
`Rat
`Rat
`
`Human
`Human
`Rat
`Rat
`
`Human
`Human
`Rat
`Rat
`
`mg
`10.0
`10.0
`0.158
`
`30.0
`30.0
`1.25
`0.625
`
`100
`400
`2.50
`2.50
`
`Human
`Human
`
`50.0
`200
`
`JNJ11
`
`JNJ12
`
`JNJ13
`
`JNJ14
`
`JNJ15
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`i.v.
`p.o.
`
`8.46
`13.1
`0.375
`
`16.6
`184
`0.550
`78.1
`
`4.41
`4.61
`3.00E-02
`4.07E-02
`
`59.4
`568
`
`l/h
`
`liters
`181
`333
`1.06
`
`9.24
`
`0.300
`
`122
`2.18E ⫹ 03
`1.77
`
`30.3
`50.2
`0.194
`
`422
`8.69E ⫹ 03
`
`ng/ml
`
`52.6
`
`15.6
`
`1.71E ⫹ 04
`
`ng 䡠 h/ml
`1.22E ⫹ 03
`763
`420
`
`196
`163
`2.27E ⫹ 03
`8.00
`
`2.29E ⫹ 03
`8.67E ⫹ 04
`8.33E ⫹ 04
`6.15E ⫹ 04
`
`187
`
`882
`352
`
`h
`17.6
`
`2.05
`
`8.20
`
`2.90
`
`7.54
`
`5.10
`
`10.6
`
`4.60
`
`Experimentally Determined In Vivo Rat Ptp
`
`a
`
`Lung Adipose Muscle Liver
`
`Spleen
`
`Heart
`
`Brain Kidney
`
`Skin
`
`Testes
`
`Bone
`
`18.1
`
`13.8
`
`2.04
`
`31.7
`
`7.51
`
`3.66
`
`4.13
`
`9.91
`
`4.62
`
`6.32
`
`2.67;11.1
`
`7.17
`
`1.01
`
`45.9
`
`2.81
`
`1.02 10.7
`
`1.03
`
`0.371
`
`0.111
`
`1.39
`
`0.389
`
`0.178 0.251
`
`8.01
`
`1.49
`
`20.5
`
`1.55
`
`1.52
`
`0.620 7.36
`
`1.12
`
`0.762
`
`PREDICTION OF HUMAN PHARMACOKINETICS
`
`1771
`
`Human
`Human
`Rat
`Rat
`
`2.80
`40.0
`1.25
`7.50
`
`Human
`Human
`Rat
`
`50.0
`200
`0.500
`
`Human
`Human
`Rat
`Rat
`
`Human
`Human
`Rat
`Rat
`
`Human
`Human
`Rat
`Rat
`
`Human
`Human
`Rat
`Rat
`
`75.0
`
`0.500
`2.50
`
`1.00
`1.00
`0.313
`0.313
`
`0.200
`2.00
`2.50E02
`0.625
`
`15.0
`1.25
`2.50
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`i.v.
`p.o.
`i.v.
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`JNJ16
`
`JNJ17
`
`JNJ18
`
`JNJ19
`
`JNJ20
`
`25.9
`56.4
`0.588
`2.96
`
`36.0
`68.1
`0.370
`
`31.2
`
`1.40
`
`23.6
`31.3
`0.962
`3.52
`
`1.82
`1.75
`4.05E02
`4.87E02
`
`56.4
`0.605
`1.11
`
`0.780
`
`1.26
`
`0.013
`
`25.2
`
`0.350
`
`108
`374
`0.433
`
`717
`1.86E ⫹ 03
`1.46
`
`172
`
`15.4
`
`81.0
`126
`0.443
`
`82.2
`83.4
`0.340
`
`646
`1.59
`
`1.94
`
`18.9
`
`2.92
`
`5.14
`
`6.67
`
`2.80
`
`0.600
`
`33.0
`
`6.00
`
`9.00
`2.30
`
`220
`
`7.90
`
`23.0
`
`36.1
`
`0.650
`2.13E ⫹ 03
`2.54E ⫹ 03
`
`2.31
`
`1.37E ⫹ 03
`2.94E ⫹ 03
`1.35E ⫹ 03 46.7
`
`2.45E ⫹ 03
`
`3.24
`
`35.5
`
`20.9
`
`7.73
`
`0.661 14.1
`
`2.35
`
`357
`
`56.1
`
`15.3
`
`20.5
`
`48.2
`
`50.4
`
`40.1
`
`1.12 92.3
`
`10.1
`
`2.11
`
`3.42
`
`0.581 12.3
`
`0.822
`
`0.233 6.43
`
`1.49
`
`0.840 0.883 14.0
`
`1.32
`
`1.19
`
`0.589 8.52
`
`0.978
`
`0.982
`
`0.52;1.56
`
`45.3
`32.0
`325
`88.8
`
`115
`1.14E ⫹ 03
`617
`1.28E ⫹ 04
`
`266
`2.07E ⫹ 03
`2.24E ⫹ 03
`
`Apotex v. Cellgene - IPR2023-00512
`Petitioner Apotex Exhibit 1039-0006
`
`

`

`1772
`
`TABLE 2—Continued
`
`JNJ No.
`
`Species
`
`Dose
`
`Route
`
`CL or CL/F
`
`CLR
`
`Vss or Vd/F
`
`In Vivo t1/2
`
`Cmax
`
`AUC
`
`Human
`Human
`Rat
`Rat
`Human
`Human
`Rat
`Rat
`
`mg
`
`150
`0.250
`1.25
`
`300
`6.25
`10.0
`
`i.v.
`p.o.
`i.v.
`p.o.
`i.v.
`p.o.
`i.v.
`p.o.
`
`JNJ21
`
`JNJ22
`
`JNJ23
`
`l/h
`
`773
`0.410
`1.10
`
`602
`0.175
`0.730
`
`17.9
`0.500
`1.65
`
`liters
`
`h
`
`3.04E ⫹ 03
`2.68
`
`21.2
`5.86
`
`687
`5.25E-02
`
`1.40
`1.20
`
`53.4
`0.369
`
`3.50
`0.545
`
`ng/ml
`
`39.3
`
`301
`
`314
`
`ng 䡠 h/ml
`
`194
`610
`1.14E ⫹ 03
`
`498
`3.57E ⫹ 04
`1.37E ⫹ 04
`
`894
`750
`803
`
`Experimentally Determined In Vivo Rat Ptp
`
`a
`
`Lung Adipose Muscle
`
`Liver
`
`Spleen
`
`Heart
`
`Brain Kidney
`
`Skin
`
`Testes
`
`Bone
`
`7.25
`
`23.3
`
`6.91
`
`4.35
`
`2.48
`
`2.53
`
`0.665
`
`8.64
`
`3.35
`
`1.34
`
`1.39
`
`4.03
`
`0.915
`
`1.72
`
`0.69;3.79
`
`DE BUCK ET AL.
`
`Human
`Human
`Rat
`Rat
`
`Human
`Human
`Rat
`Rat
`
`Human
`Human
`Rat
`Rat
`
`Human
`Human
`Rat
`Rat
`
`16.0
`0.375
`1.33
`
`80.0
`0.625
`1.25
`
`200
`0.625
`1.25
`
`20.0
`0.625
`2.50
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`i.v.
`p.o.
`i.v.
`p.o.
`
`JNJ24
`
`JNJ25
`
`JNJ26
`
`26.5
`0.829
`9.77
`
`19.9
`0.320
`0.373
`
`38.8
`0.365
`11.1
`
`635
`0.947
`
`26.6
`2.40
`
`413
`1.735
`
`22.7
`4.33
`
`292
`0.609
`
`9.80
`1.47
`
`551
`
`701
`
`99
`
`3.02E ⫹ 03
`754
`128
`
`1.01E ⫹ 04
`1.95E ⫹ 03
`3.35E ⫹ 03
`
`516
`1.71E ⫹ 03
`226
`
`3.43
`
`3.36
`
`4.36
`
`27.9
`
`2.53
`
`2.65
`
`1.00
`
`4.89
`
`0.936
`
`4.53
`
`0.58;2.54
`
`20.6
`
`3.80
`
`2.27
`
`11.7
`
`6.74
`
`4.27
`
`0.663
`
`2.96
`
`1.09
`
`0.666
`
`0.63;2.44
`
`2.51
`
`20.0
`
`1.20
`
`2.10
`
`1.10
`
`1.70
`
`0.500
`
`2.00
`
`2.51
`
`2.11
`
`2.72;20.0
`
`a Experimental rat Ptp 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. Underlined values refer to in vivo rat Ptp obtained
`using total radioactivity measurements.
`
`Apotex v. Cellgene - IPR2023-00512
`Petitioner Apotex Exhibit 1039-0007
`
`

`

`PREDICTION OF HUMAN PHARMACOKINETICS
`
`1773
`
`TABLE 3
`
`Physiological values for tissue volumes and blood flow in rat and human
`
`Default values taken from Gastroplus software 5.10.0 generic rat and human PBPK model.
`
`Tissue
`
`Lung
`Spleen
`Liver
`ACAT gut
`Adipose
`Muscle
`Heart
`Brain
`Kidney
`Skin
`Testes
`Red marrow
`Yellow marrow
`Rest of body
`Arterial blood
`Venous blood
`
`Rat
`
`Human
`
`Blood Flow
`
`Volume
`
`Blood Flow
`
`Volume
`
`ml/min
`
`53.0
`0.600
`13.8
`7.50
`0.400
`7.50
`3.90
`1.30
`9.20
`5.80
`0.500
`1.30
`0.275
`9.01
`53.0
`53.0
`
`ml
`
`2.10
`0.600
`10.3
`
`10.0
`122
`1.20
`1.24
`3.70
`40.0
`2.50
`1.33
`2.81
`41.5
`5.60
`11.3
`
`l/min
`
`6.08
`0.184
`1.50
`0.836
`0.605
`0.622
`0.230
`0.882
`1.02
`0.235
`0.007
`0.354
`0.098
`0.529
`6.08
`6.08
`
`liters
`
`1.11
`0.184
`1.63
`
`30.3
`20.7
`0.315
`1.73
`0.277
`1.96
`0.032
`1.18
`3.28
`17.6
`2.21
`4.42
`
`Second, the hepatic blood clearance (CLH,blood,) was calculated using the
`commonly used equation of the well stirred liver model:
`
`CLH,blood
`
`⫽
`
`共fup/RB兲 䡠 Qh 䡠 共in vivo CLint/fuinc兲
`⫹ 共in vivo CLint/fuinc兲 䡠 共fup/RB兲
`Qh
`
`(7)
`
`where Qh is the hepatic blood flow (human, 90 l/h; rat, 0.828 l/h). Experimental
`values for fup, fuinc, RB, and in vivo CLint are presented in Table 1. Third,
`because Gastroplus requires input data on CLH, CLH,blood was converted to
`CLH (CLH ⫽ RB 䡠 CLH,blood).
`For renally cleared compounds, the prediction of human CLR was obtained
`using the glomerular filtration rate (GFR) ratio approach as described by Lin
`(1998):
`
`Human CLR,unbound
`
`⫽
`
`Rat CLR,unbound
`GFR ratio
`
`(8)
`
`where rat CLR,unbound (l/h/kg) is the CLR corrected for rat fup (CLR/fup) and the
`GFR ratio between rat and human is 4.8 (Lin, 1998). Predicted CL was
`calculated as the sum of the predicted CLH and CLR.
`Prediction of CLH, CLR, and CL: Method CL2. Our previous study and
`those by others using in vitro metabolism data have suggested that in vitro
`CLint may provide a better estimate of in vivo CLH of total rather than unbound
`drug (Obach et al., 1997; De Buck et al., 2007). Therefore, CLH predictions
`were also assessed using method CL2 under the assumption that fup/RB and
`fuinc effectively nullify in the liver model calculation, negating the measure-
`ment of either process:
`
`CLH,blood
`
`⫽
`
`Qh 䡠 in vivo CLint
`Qh
`⫹ in vivo CLint
`
`(9)
`
`CLH,blood was converted to CLH as described above. The prediction of human
`CLR from rat data was identical to that found by method CL1. Predicted CL
`was calculated as the sum of the predicted CLH and CLR.
`Prediction of in Vivo t
`1/2: Method Vd1/CL1 and Method Vd2/CL2.
`Prediction of in vivo t1/2 relies on the prediction of both Vss and CL. Two
`different approaches were tested for their ability to predict in vivo t1/2. First,
`method Vd1 was combined with method CL1 (i.e., method Vd1/CL1) since
`this combination predicts CLH according to the most widely accepted approach
`toward the use of fup/RB and fuinc (eq. 7) (Jones et al., 2006a), and requires
`minimal data input for prediction of Vss. For comparison, method Vd2 was
`combined with method CL2 (i.e., method Vd2/CL2) since this combination
`predicts Vss and CL according to this approach, which was also found to
`provide the best results in rat. Predicted values of in vivo t1/2 were taken from
`the Gastroplus software interface (Simulations Plus Inc.).
`The ACAT Model and Prediction of Oral AUC. Prediction of oral AUC
`relies on the prediction of both CL and the extent of absorption. CL was
`predicted using either method CL1 or method CL2 as described above. The
`extent of absorption was predicted using the Gastroplus ACAT model (Yu and
`Amidon, 1999; Agoram et al., 2001). For all simulations, the ACAT model was
`provided with experimentally determined data on log Pow, pKa, aqueous buffer
`solubility or s