Species Differences in Distribution and Prediction of Human Vss

from Preclinical Data

Loren M. Berry, Chao Li, and Zhiyang Zhao

Pharmacokinetics and Drug Metabolism, Amgen Inc., Cambridge, Massachusetts

Received May 17, 2011; accepted August 5, 2011

ABSTRACT:

Prediction of human volume of distribution at steady state (Vss)before first administration of a new drug candidate to humans hasbecome an important part of the drug development process. Thisstudy examines the assumptions behind interspecies scaling tech-niques used to predict human Vss from preclinical data, namely theequivalency of Vss,u and/or fut across species. In addition, severalinterspecies scaling techniques are evaluated side by side using aset of 67 reference compounds where observed Vss from rats,dogs, monkeys, and humans were compiled from the literature andwhere plasma protein binding was determined across speciesusing an ultracentrifugation technique. Species similarity in Vss,u orfut does not appear to be the norm among rats, dogs, monkeys, orhumans. Despite this, interspecies scaling from rats, dogs, and

monkeys is useful and can provide reasonably accurate predic-tions of human Vss, although some interspecies scaling ap-proaches were better than others. For example, the performanceof the common Vss,u or fut equivalency approaches using averageVss,u or fut across three preclinical species was superior to allo-metric scaling techniques. In addition, considering data from sev-eral preclinical species, using the equivalency approach, was su-perior to scaling from any single species. Although the mechanistictissue composition equations available in the Simcyp population-based pharmacokinetic simulator did not necessarily provide themost accurate predictions, and the equations used likely needrefinement, they still provide the best opportunity for a mechanisticunderstanding and prediction of human Vss.

Introduction

Prediction of human pharmacokinetics has become an importantpart of the drug development process, to aid in estimating the potentialtherapeutic dose and safety margins before the first dose to humans.The volume of distribution at steady state (Vss) is typically one of thekey parameters predicted and, along with clearance, governs theeffective half-life and dosing interval of the prospective drug.

A number of approaches to predicting Vss have been proposedrecently or have become widely used (Sui et al., 2008). Some of themost commonly used approaches attempt to predict human Vss fromanimal data through interspecies scaling techniques such as allometry.Allometry is the extrapolation of pharmacokinetic parameters to onespecies by fitting a power function to the relationship between thepharmacokinetic parameter from other species and a measure ofthe size of the species, such as body weight (Boxenbaum, 1982). Themain assumption in this approach is that the factors or mechanismsgoverning the pharmacokinetics of a drug scale proportionally to bodysize. For example, since the volumes organisms occupy correlate withtheir body weights, it is logical to conclude that the Vss of a drug givento these organisms will also correlate with their body weights, if thevolume of each organism is the major determinant of Vss. However, itis understood that Vss is an apparent term that often bears littleresemblance to the actual volume of an organism. The observed Vss

for drugs has ranged from �3 liters (plasma volume) to as high as�7000 liters in adult humans (Obach et al., 2008), whereas humanbody volume is �70 liters. Therefore, body volume is not the onlyfactor governing drug distribution.

Another factor governing drug distribution is protein binding. Perthe free drug hypothesis, it is thought that only unbound drug isable to equilibrate between blood and tissues, and for noneliminatingtissues, relative total drug concentrations observed in blood versustissues (CT/Cb) are determined, in part, by relative binding to bloodand tissue components (fub/fut). This realization led to various forms ofa popular physiological definition of volume of distribution based onrelative protein binding (Oie and Tozer, 1979). Because plasma pro-tein binding and tissue binding are biochemical processes that bearlittle relation to body weight, for allometric scaling of Vss to be valid,one must make the assumption that fub/fut will be similar acrossspecies. The same assumption of similarity across species must bemade for other nonscalable processes that influence drug distribution,such as permeability, active transport, pH-dependent partitioning, andenterohepatic recycling (Roberts et al., 2002; Gong et al., 2007;Grover and Benet, 2009). It is well known that plasma protein bindingcan vary between species. However, limited studies have indicatedthat tissue binding (as measured in vitro) could be similar acrossspecies (Fitchl and Schulmann, 1986). This led to the notion thatscaling unbound volume of distribution at steady state (Vss,u) orcalculated fraction unbound in tissues (fut) might be more accurate orscientifically justifiable as ways to scale distribution across species, aslong as the assumptions of species similarity in other processes are

Article, publication date, and citation information can be found athttp://dmd.aspetjournals.org.

doi:10.1124/dmd.111.040766.

ABBREVIATIONS: Vss, volume of distribution at steady state; Vss,u, unbound volume of distribution at steady state; fup, fraction unbound inplasma; fut, fraction unbound in tissues; MPA, mobile phase A; MPB, mobile phase B; clogP, calculated log P; clogD, calculated log D.

0090-9556/11/3911-2103–2116$25.00DRUG METABOLISM AND DISPOSITION Vol. 39, No. 11Copyright © 2011 by The American Society for Pharmacology and Experimental Therapeutics 40766/3725941DMD 39:2103–2116, 2011 Printed in U.S.A.

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acceptable. However, the equivalency of Vss,u or fut across species hasnot been systematically evaluated for a wide range of reference drugs,an aim of the present study.

Despite the empirical nature of interspecies scaling, and the re-quired assumptions, it has been a valuable tool for predicting humanVss, and there are many success stories. Ward and Smith (2004)evaluated the ability to predict human Vss from several commonpreclinical species used during drug development in the pharmaceu-tical industry and suggested that scaling from monkey data was themost accurate approach. Unfortunately, their analysis of 103 com-pounds did not consider potential species differences in plasma pro-tein binding. Other studies have examined single- or multiple-speciesscaling based on the assumption of Vss,u or fut equivalency acrossspecies using proprietary compounds, with limited species, or onlyhaving oral half-life data available in humans for comparison (Obachet al., 1997; McGinnity et al., 2007; Hosea et al., 2009). In suchstudies, scaling Vss from rats or dogs is proposed to be more accurate(Hosea et al., 2009; Sui et al., 2010), depending on the study. Theseconflicting reports about the best scaling methods or species to userequire some reconciliation. In addition, the aforementioned interspe-cies scaling methods for predicting Vss have not been evaluated sideby side using a sizable dataset of diverse reference compounds thatalso considers plasma protein binding. The present work aims to doso. Finally, interspecies scaling methods were compared with themechanistic tissue partition methods for prediction of human Vss

proposed by Poulin and Theil (2002) and Rodgers and Rowland(2007).

Materials and Methods

Materials. All compounds were obtained from commercial sources asappropriate. Sprague-Dawley rat, beagle dog, cynomolgus monkey, and humanplasma (pooled) were purchased from Bioreclamation, Inc. (Hicksville, NY).

Source of In Vivo Data. Published literature was searched for reportedvalues for volume of distribution at steady state (Vss) in rats, dogs, monkeys,and humans. Sources include reviews and original articles. If Vss was reportedin liters, it was converted to l/kg using the mean body weight of the animalsas reported in the source. In some cases in which Vss was not specificallyreported, it was estimated by noncompartmental analysis of a digitally ex-tracted version of the plasma concentration time plot provided in the source.The final dataset contains 67 drug-like molecules of diverse structures andchemical properties. The dataset included 16 acidic, 8 neutral, 31 basic, and 12zwitterionic compounds. The molecular weight in this dataset ranged from 144to 811 Da. Polar surface area ranged from 6 to 321 Å. Calculated solubility atpH 7.4 ranged from 6 � 10�6 to �1000 mg/ml. Calculated log P (clogP) rangedfrom �1.1 to 7.8. Calculated log D (clogD) at pH 7.4 ranged from �5.1 to 6.2.The compounds represented a number of therapeutic classes, including but notlimited to, antibiotic, anti-inflammatory, anticancer, antiarrhythmic, antide-pressant, sedative, anticoagulant, antihypertension, antiviral, anticonvulsant,antipsychotic, and anesthetic drugs.

Determination of Fraction Unbound in Plasma (fup). Fractions unboundin plasma (fup) were determined in triplicate using an ultracentrifugationtechnique. Blank plasma was spiked with compound to achieve a final con-centration of 5 �M, and was centrifuged at 600,000g for 5 h at 37°C. Halfwaythrough the spin, at 2.5 h, the topmost (lipid) layer was removed by aspiration.After the complete 5-h spin, aliquots of the middle (water) layer were trans-ferred into an equal volume of blank plasma and extracted with 5 volumes ofacetonitrile containing internal standard. Aliquots of the original spiked plasmawere mixed with an equal volume of blank plasma and 2 volumes of plasmaultrafiltrate and extracted with 10 volumes of acetonitrile. Extracts werecentrifuged to precipitate proteins and analyzed by the liquid chromatography-tandem mass spectrometry method described below. Observed fraction un-bound was calculated from the ratio of concentration observed in the water layerfollowing centrifugation relative to total concentration in original spiked plasma.

Sample Analysis. Sample extracts from in vitro experiments were analyzedby multiple reaction monitoring on a liquid chromatography-tandem mass

spectrometry system consisting of dual Shimadzu LC-10AD high-performanceliquid chromatography pumps and a DGU-14A degasser (Shimadzu, Colum-bia, MD), a CTCPAL autoinjector (LEAP Technologies, Carrboro, NC), andan API3000 or API4000 LC-MS/MS system, equipped with an electrosprayion source and operated by the Analyst software package (Applied Biosystems,Foster City, CA). Chromatography was conducted on a Sprite Armor C18 (20�2.1 mm, 10 �m) analytical column (Analytical Sales and Products, PomptonPlains, NJ) with a 0.5 �m guard filter, using the following mobile-phasegradient program: mobile phase A (MPA), H20 with 0.1% formic acid; mobilephase B (MPB), acetonitrile with 0.1% formic acid; 0 min � 98% MPA, 2%MPB; 0.3 min � 98% MPA, 2% MPB; 0.7 min � 5% MPA, 95% MPB; 1.3min � 5% MPA, 95% MPB; 1.4 min � 98% MPA, 2% MPB; 1.7 min � endof run; there were approximately 2 min between sample injections. For the first0.5 min of each sample run, the LC eluent was diverted from the ion source towaste. Each compound was detected in either positive or negative ion modeafter tuning the MS electronics to the mass transition with the largest intensity.

Prediction of Vss from Preclinical Species. Several methods were comparedfor their ability to predict human Vss from that in rats, dogs, and monkeys.

Allometric Scaling of Vss. For each drug, Vss in preclinical species wasplotted in log-log scale versus body weight. The allometric power function wasfit to the data (eq. 1).

Vss � aWb (1)

where Vss is in liters, W is body weight in kilograms, and a and b are theallometric coefficient and exponent, respectively. The Vss in humans wasextrapolated using the fitted function for each drug. Body weights for rats,dogs, monkeys, and humans were set to 0.25, 10, 5, and 70 kg, respectively.

Allometric Scaling of Vss,u. Allometric scaling of Vss,u uses the sameprinciple as allometric scaling of Vss, only substituting Vss with Vss,u, whereVss,u � Vss/fup.

Vss,u Equivalency Approach. This approach assumes Vss,u in humans willbe the same as Vss,u in animals. The Vss,u equivalency approach was conductedfor each individual species (i.e., single-species scaling) and used the mean Vss,u

across all three species (i.e., multispecies scaling).fut Equivalency Approach. This approach assumes fut in humans will be

the same as fut in animals. Two common approaches to calculating fut wereconsidered, eqs. 2 and 3 (collectively termed the “Oie-Tozer-style equations”here for simplicity). Both use observed Vss and fup and some physiological datato calculate apparent fut.

fut � Vt � fup/Vss � Vp (2)

where Vp is the total volume of plasma in the animal and Vt is the restof the animal volume (which can be considered as essentially 1 � Vp,assuming 1 kg of body weight equals 1 liter of volume); or

fut � Vr � fup/Vss � Vp � fup � Ve � (1�fup) � Re/i � Vp (3)

where, Ve is the extracellular fluid volume, Vr is the remaining fluid volume,and Re/i is the ratio of protein binding in extracellular fluid to that in plasma.Re/i is assumed to be 1.4 and the same across species. Physiological volumesused in the calculations are shown in Table 1 (Obach et al., 1997). The fut

equivalency approach was conducted for eqs. 2 and 3 using each individualspecies (i.e., single-species scaling) and using the mean fut across species (i.e.,multispecies scaling). Negative values for fut or fut values of �1 were notexcluded from subsequent calculations.

Simcyp. For comparison, Vss was also calculated according to thetissue composition equations proposed by Poulin and Theil (2002) and

TABLE 1

Physiological volumes (l/kg) used in the calculation of fut

Data are from Obach et al. (1997).

Rat Dog Monkey Human

Vp 0.0313 0.0515 0.0448 0.0436Ve 0.265 0.216 0.208 0.154Vr 0.364 0.45 0.485 0.38

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corrected by Berezhkovskiy (2004) (or “method 1”) and Rodgers andRowland (2007) (or “method 2”), as programmed in Simcyp version8.0 (Simcyp Ltd., Sheffield, UK). Please refer to these publications for

a detailed description of their approaches. In brief, both methods 1 and2 calculate total partitioning of a drug into a particular tissue as thesum of partitioning into individual tissue components, such as neutral

TABLE 2

Physicochemical properties, plasma protein binding (fup), blood plasma concentration ratio (RB), and volume of distribution at steady state for the test compounds (Vss)

Physicochemical properties were calculated using ACD Labs Chemistry version 12. The source for in vitro and in vivo data is as follows. Values for fup were determined for the presentwork, except where noted and reference included.

DrugPhysicochemical Property fup RB Vss Vss

SourceClass clogP cpKa1 cpKa2 Rat Dog Monkey Human Human Rat Dog Monkey Human

l/kg

Cefotetan A 1.3 1.7 2.6 0.41 0.48 0.10 0.15 0.65s 0.25 0.25 0.17 0.13 a

Tiludronate A 2.0 1.1 2.6 0.30 0.30 0.20 0.10 0.60z 15 3.0 7.0 1.0 a

Captopril A 2.0 3.6 0.73 0.78 0.38 0.71 0.70s 1.7 2.5 3.6 0.81 a

Cefazolin A �0.7 2.6 0.13 0.43 0.058 0.091 0.64s 0.23 0.27 0.13 0.11 a

Cefoperazone A �1.1 2.6 0.36 0.38 0.029 0.066 0.64s 0.39 0.28 0.13 0.14 a

Diclofenac A 4.6 4.2 0.008 0.007 0.007 0.004 0.71b 0.75 0.23 0.17 0.17 a

Fluvastatin A 4.6 4.3 0.016 0.003 0.007 0.007 0.70s 1.0 0.7 3.7 0.42 a

Furosemide A 2.3 3.0 0.013 0.062 0.025 0.010 0.70b 0.16 0.23 0.11 0.11 a

Gemfibrozil A 4.3 4.8 0.010b 0.004b 0.007b 0.005b 0.75b 0.28 1.6 0.16 1.1 b

Indomethacin A 4.3 4.0 0.003 0.006 0.004 0.007 0.98b 1.9 5.6 1.8 0.93 b

Ketoprofen A 2.9 4.2 0.011b 0.024b 0.014b 0.004b 1.1b 0.42 0.28 0.25 0.13 b

Naproxen A 2.9 4.8 0.008 0.004 0.003 0.003 0.55x 0.18 0.12 0.10 0.10 a

Troglitazone A 4.7 6.3 0.010 0.004 0.005 0.006 0.55w 1.0 0.77 0.71 0.71 a

Valproic acid A 2.6 4.8 0.37 0.22 0.080 0.052 0.58w 0.66 0.31 0.15 0.15 a

Warfarin A 3.1 4.5 0.005 0.033 0.005 0.006 0.59s 0.22 0.28 0.11 0.11 a

Phenytoin A, pKa �7 1.4 8.3 0.17 0.19 0.19 0.13 0.61w 0.64 1.0 7.8 1.4 a

Antipyrine N 0.4 0.69 0.65 0.73 0.72 1.0x 0.97 0.62 0.74 0.87 a

Caffeine N �0.6 0.70 0.90 0.95 0.78 1.0v 0.92 0.76 0.83 0.73 a

Coumarin N 1.4 0.32 0.28 0.24 0.17 1.1s 1.3 6.5 4.5 2.2 a

Flunisolide N 2.0 0.22 0.24 0.26 0.24 0.76s 3.0 3.7 4.0 1.8 a

Isosorbide dinitrate N 1.0 0.55 0.87 0.62 0.55 1.5s 4.9 3.0 1.7 1.9 a,d

Isosorbide mononitrate N �0.2 0.42 1.0 0.77 0.72 1.0s 0.97 0.80 0.50 0.60 a

Lorazepam N 2.4 0.090b 0.073b 0.10b 0.057b 1.1b 1.0 7.0 1.2 1.3 b

Zidovudine N 0.1 0.73b 0.82b 0.71b 0.70b 1.1b 1.2 1.1 1.2 1.8 b

Cyclophosphamide B, pKa �7 0.7 2.8 0.76 0.62 0.66 0.81 0.96s 0.80 0.70 1.3 0.70 a

Felodipine B, pKa �7 4.8 2.7 0.005 0.006 0.021 0.034 0.70w 12 7.8 2.2 9.7 k

Lamivudine B, pKa �7 �0.5 4.2 1.0 0.88 1.0 0.95 1.0u 1.8 0.87 1.2 1.3 a

Midazolam B, pKa �7 3.8 6.0 0.028 0.011 0.035 0.011 0.55w 2.5 1.1 2.9 1.1 e,f,g,d

Mifepristone B, pKa �7 6.2 5.5 0.013 0.023 0.039 0.013 1.0u 4.3 10.5 33 1.5 k,y

Nifedipine B, pKa �7 3.6 2.7 0.004 0.13 0.065 0.12 0.59x 0.13 1.2 0.5 0.78 k

Indinavir B, pKa �7 3.4 5.2 4.9 0.25 0.30 0.40 0.45 1.0u 2.2 0.73 1.5 0.82 A,d

Amiodarone B 7.8 9.4 0.058 0.043 0.062 0.050 0.73w 72 4.6 5.1 66 k

Biperiden B 4.3 9.3 0.11 0.096 0.077 0.14 0.95v 14 9.5 3.6 6.2 k

Bupivacaine B 3.3 8.1 0.23 0.061 0.23 0.16 0.68v 1.3 0.51 0.66 0.84 h,i,j,d

Chlorpromazine B 5.2 9.4 0.062 0.073 0.26 0.12 1.2v 29 16 8.0 11 k

Cimetidine B 0.6 7.1 0.69 0.72 0.85 0.75 0.97w 3.3 1.8 2.0 1.2 p,q,r,d

Citalopram B 3.5 9.6 0.40 0.32 0.37 0.32 1.3s 21 10 10 14 a

Diltiazem B 4.7 8.9 0.21 0.28 0.38 0.22 1.0v 3.6 21 3.5 3.1 k

Erythromycin B 1.9 8.2 0.48 0.45 0.30 0.34 0.91s 9.3 2.7 0.93 0.89 a

Haloperidol B 3.8 8.0 0.12 0.29 0.18 0.13 1.1v 10 37 6.6 18 k

Hydrodolasetron B 2.8 8.8 0.20 0.28 0.27 0.25 1.0u 15 9.6 28 11 a

Imipramine B 4.4 9.5 0.15 0.16 0.14 0.13 1.1v 11 6.0 9.7 13 b

Lidocaine B 2.2 8.0 0.38 0.44 0.42 0.31 0.87v 2.5 2.1 0.98 1.1 a

Metoprolol B 1.6 9.4 0.80 0.61 0.77 0.62 1.1v 7.0 4.5 3.0 4.2 a

Propafenone B 3.4 9.3 0.077 0.088 0.18 0.099 0.70v 4.9 5.0 8.2 2.2 m,o,k,d

Propranolol B 2.9 9.5 0.28 0.20 0.21 0.21 0.80v 5.3 2.3 5.6 3.6 a,l

Venlafaxine B 2.5 9.3 0.69 1.0 1.0 1.0 1.0u 6.6 3.3 3.5 4.4 C,d

Verapamil B 4.0 9.0 0.20 0.19 0.19 0.17 0.84v 4.0 5.8 4.5 3.7 m,n,k,d

Disopyramide B 2.3 10.1 4.5 0.61 0.62 0.48 0.45 1.0w 6.2 2.3 2.0 0.78 a

Imatinib B 2.9 7.6 3.0 0.030 0.12 0.063 0.097 1.1s 3.2 9.2 8.0 3.9 s,t,d

Irinotecan B 3.7 9.3 5.1 0.19 0.067 0.26 0.22 1.2s 3.2 1.0 2.1 3.8 a

Nicardipine B 4.9 7.3 2.6 0.014 0.019 0.038 0.021 0.71v 1.3 1.3 1.9 0.64 a

Nicotine B 0.9 8.0 3.2 1.0 1.0 1.0 1.0 0.8u 4.7 3.3 1.5 2.6 a

Quinidine B 2.8 9.3 4.8 0.17 0.11 0.059 0.17 0.96v 6.0 4.8 0.55 3.0 a

Vinblastine B 5.9 7.9 5.6 0.13 0.059 0.16 0.18 1.0u 12 2.3 1.5 24 a

Cefpiramide Z 0.2 3.5 8.7 0.31 0.30 0.010 0.018 0.65s 0.26 0.45 0.10 0.11 a

Ciprofloxacin Z 1.6 6.4 8.7 0.57 1.0 0.56 0.80 0.75w 4.6 3.1 1.8 2.3 a

Doxorubicin Z 0.2 7.4 8.7 0.067 0.092 0.12 0.13 2.0u 25 44 24 25 a

Gabapentin Z 1.1 4.7 10.3 1.0 1.0 1.0 1.0 1.0u 1.4 0.16 0.68 0.82 a

Lamifiban Z �0.3 3.4 10.8 0.92c 0.89c �1.0u 0.94c 1.0u 0.24 0.75 0.29 0.29 c

Levofloxacin Z 1.9 5.2 7.4 0.83b 0.92b 0.87b 0.78b 1.5b 3.6 1.3 1.9 1.2 b

Methotrexate Z �0.5 3.5 5.6 0.41 0.31 0.36 0.21 1.0u 0.4 0.54 1.2 0.67 a

Morphine Z 0.9 9.5 8.3 0.70 0.67 0.61 0.63 1.0v 2.9 4.0 12 3.3 a

Naltrexone Z 2.1 9.2 7.5 0.57 0.46 0.72 0.58 1.2s 3.5 11 5.1 7.6 a,d

Telmisartan Z 6.5 3.9 5.0 0.007b 0.018b 0.008b 0.003b 1.2b 2.0 2.7 1.5 5.3 b

Topotecan Z 0.3 8.9 7.7 0.21 0.17 0.88 0.72 1.0u 0.52 1.4 1.8 1.8 B,d

Verlukast Z 3.4 4.3 2.2 0.003 0.012 0.002 0.0007 1.0u 0.81 0.24 0.33 0.11 D,E

A, acid; N, neutral; B, base; Z, zwitterions; zwitterions pKa1, acid; zwitterions pKa2, base.a Jolivette and Ward, 2005; b Deguchi et al., 2011; c Lave et al., 1996; d Obach et al., 2008; e Gueorguieva et al., 2004; f Kuroha et al., 2002; g Nishimuta et al., 2010; h Wu et al., 2010; i Mazoit

et al., 1988; j Thompson et al., 1986; k Evans et al., 2006; l Hayes and Cooper, 1971; m Poulin and Theil, 2002; n Bai et al., 1993; o Puigdemont et al., 1987; p Kaka et al., 1989; q Viernstein et al.,1990; r Tahara et al., 2006; s Amgen data on file; t Ishizuka et al., 2007; u Assumed value; v Rogers and Rowland, 2007; w Uchimura et al., 2010; x Brown et al., 2007; y Vz/F reported for humansby Liu et al. (1988); z Sansom et al., 1995; A Lin et al., 1996; B Ahlawat et al., 2008; C Howell et al. 1994; D Tocco et al., 1990; E Depré et al., 1992.

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TABLE 3

Calculated Vss,u and fut

DrugVss,u from fut

a from futb from

Rat Dog Monkey Human Rat Dog Monkey Human Rat Dog Monkey Human

Cefotetan 0.61 0.52 1.7 0.85 1.8 2.3 0.79 1.7 1.8 3.7 1.1 5.0Tiludronate 50 10 35 10 0.019 0.097 0.027 0.10 0.007 0.048 0.014 0.043Captopril 2.3 3.2 9.5 1.1 0.42 0.30 0.10 0.89 0.18 0.16 0.054 0.42Cefazolin 1.8 0.63 2.2 1.2 0.61 1.9 0.65 1.3 0.36 2.3 2.0 �12Cefoperazone 1.1 0.74 4.5 2.1 0.96 1.6 0.33 0.66 0.55 1.7 0.77 0.85Diclofenac 96 32 23 40 0.011 0.038 0.056 0.032 0.004 0.031 0.058 0.025Fluvastatin 63 233 529 60 0.016 0.004 0.002 0.018 0.006 0.002 0.001 0.008Furosemide 12 3.7 4.5 11 0.10 0.33 0.36 0.15 0.058 0.29 �11 0.9Gemfibrozil 28 393 23 222 0.039 0.002 0.058 0.004 0.018 0.001 0.066 0.002Indomethacin 617 930 448 132 0.002 0.001 0.002 0.008 0.0006 0.0005 0.001 0.003Ketoprofen 38 12 18 33 0.027 0.10 0.065 0.044 0.012 0.071 0.048 0.061Naproxen 23 30 33 33 0.052 0.055 0.052 0.051 0.028 �0.43 �0.18 �0.23Troglitazone 100 193 139 118 0.010 0.005 0.007 0.009 0.004 0.003 0.004 0.004Valproic acid 1.8 1.4 1.9 2.9 0.56 0.79 0.73 0.47 0.26 0.62 1.3 0.49Warfarin 44 8.5 22 18 0.026 0.14 0.073 0.086 0.013 0.10 1.38 0.47Phenytoin 3.7 5.2 42 10 0.28 0.19 0.023 0.094 0.12 0.10 0.012 0.040Antipyrine 1.4 0.95 1.0 1.2 0.71 1.1 1.0 0.83 0.34 0.73 0.68 0.39Caffeine 1.3 0.84 0.87 0.94 0.76 1.2 1.2 1.1 0.37 0.80 0.79 0.53Coumarin 4.1 23 19 13 0.24 0.042 0.051 0.073 0.10 0.020 0.027 0.030Flunisolide 14 16 15 7.6 0.072 0.061 0.064 0.13 0.028 0.030 0.033 0.054Isosorbide dinitrate 8.9 3.5 2.7 3.5 0.11 0.28 0.36 0.28 0.043 0.14 0.20 0.12Isosorbide mononitrate 2.3 0.80 0.65 0.83 0.43 1.3 1.6 1.2 0.19 0.85 1.3 0.64Lorazepam 11 95 12 23 0.090 0.010 0.082 0.043 0.036 0.005 0.045 0.018Zidovudine 1.6 1.4 1.7 2.6 0.61 0.72 0.59 0.38 0.28 0.42 0.35 0.16Cyclophosphamide 1.1 1.1 2.0 0.86 0.96 0.91 0.50 1.2 0.50 0.58 0.29 0.59Felodipine 2400 1300 105 285 0.0004 0.001 0.009 0.003 0.0002 0.0004 0.005 0.001Lamivudine 1.8 0.99 1.2 1.4 0.55 1.0 0.83 0.72 0.24 0.64 0.51 0.32Midazolam 89 99 83 102 0.011 0.010 0.012 0.010 0.004 0.005 0.006 0.004Mifepristone 331 457 833 115 0.003 0.002 0.001 0.009 0.001 0.001 0.001 0.004Nifedipine 33 9.0 7.7 6.5 0.039 0.11 0.14 0.16 0.027 0.057 0.082 0.069Indinavir 8.8 2.4 3.8 1.8 0.11 0.42 0.26 0.55 0.044 0.24 0.15 0.25Amiodarone 1241 107 82 1320 0.001 0.009 0.012 0.001 0.0003 0.004 0.006 0.0003Biperiden 127 99 47 44 0.008 0.010 0.021 0.022 0.003 0.005 0.011 0.009Bupivacaine 5.7 8.4 2.9 5.3 0.18 0.13 0.36 0.19 0.071 0.072 0.21 0.084Chlorpromazine 468 219 31 92 0.002 0.004 0.031 0.010 0.001 0.002 0.016 0.004Cimetidine 4.7 2.5 2.3 1.6 0.21 0.39 0.42 0.62 0.084 0.21 0.24 0.28Citalopram 52 31 27 44 0.019 0.031 0.035 0.022 0.007 0.015 0.018 0.009Diltiazem 17 75 9.2 14 0.057 0.013 0.11 0.069 0.022 0.006 0.055 0.028Erythromycin 19 6.0 3.2 2.6 0.050 0.16 0.32 0.38 0.019 0.081 0.18 0.17Haloperidol 83 128 37 138 0.012 0.007 0.026 0.007 0.004 0.004 0.014 0.003Hydrodolasetron 75 35 104 44 0.013 0.027 0.009 0.022 0.005 0.013 0.005 0.009Imipramine 71 38 69 100 0.014 0.025 0.014 0.010 0.005 0.012 0.007 0.004Lidocaine 6.6 4.8 2.3 3.5 0.15 0.20 0.43 0.28 0.059 0.10 0.25 0.12Metoprolol 8.8 7.4 3.9 6.8 0.11 0.13 0.25 0.14 0.043 0.063 0.13 0.058Propafenone 64 57 46 22 0.015 0.017 0.021 0.044 0.006 0.008 0.011 0.018Propranolol 19 12 27 17 0.051 0.084 0.036 0.056 0.020 0.042 0.019 0.023Venlafaxine 9.6 3.3 3.5 4.4 0.10 0.29 0.28 0.22 0.039 0.15 0.15 0.090Verapamil 20 31 24 22 0.049 0.031 0.041 0.044 0.019 0.015 0.021 0.018Disopyramide 10 3.7 4.2 1.7 0.10 0.26 0.23 0.58 0.037 0.13 0.13 0.27Imatinib 107 80 128 40 0.009 0.012 0.008 0.024 0.004 0.006 0.004 0.010Irinotecan 17 15 8.1 17 0.058 0.067 0.12 0.056 0.022 0.035 0.065 0.023Nicardipine 93 69 50 30 0.011 0.014 0.020 0.034 0.004 0.007 0.010 0.015Nicotine 4.7 3.3 1.5 2.6 0.21 0.29 0.66 0.37 0.083 0.15 0.39 0.16Quinidine 35 45 9.4 18 0.028 0.021 0.11 0.055 0.011 0.010 0.065 0.022Vinblastine 96 39 9.1 133 0.010 0.025 0.11 0.007 0.004 0.012 0.058 0.003Cefpiramide 0.84 1.5 10 6.1 1.3 0.70 0.17 0.26 0.97 0.47 -0.54 1.8Ciprofloxacin 8.1 3.1 3.2 2.9 0.12 0.31 0.30 0.34 0.047 0.16 0.17 0.14Doxorubicin 373 477 200 192 0.003 0.002 0.005 0.005 0.001 0.001 0.002 0.002Gabapentin 1.4 0.16 0.68 0.82 0.69 8.7 1.5 1.2 0.32 �4.2 1.1 0.61Lamifiban 0.26 0.84 0.29 0.31 4.3 1.2 3.9 3.6 �8.7 0.80 13 3.5Levofloxacin 4.4 1.4 2.2 1.5 0.22 0.68 0.45 0.65 0.090 0.39 0.25 0.29Methotrexate 0.98 1.7 3.3 3.2 1.1 0.61 0.30 0.32 0.64 0.38 0.17 0.15Morphine 4.2 6.0 20 5.2 0.24 0.16 0.049 0.19 0.10 0.080 0.025 0.076Naltrexone 6.1 24 7.1 13 0.16 0.040 0.14 0.073 0.063 0.019 0.071 0.030Telmisartan 286 151 193 1767 0.003 0.006 0.005 0.001 0.001 0.003 0.003 0.0002Topotecan 2.5 8.2 2.0 2.5 0.42 0.12 0.48 0.39 0.192 0.061 0.273 0.1678Verlukast 270 20 165 157 0.004 0.060 0.007 0.010 0.001 0.047 0.004 0.050

a Equation 2.b Equation 3.

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lipids, phospholipids, or tissue water. Partitioning of a drug into thesecomponents is assumed to be driven by a drug’s lipophilicity. Bothmethods use the octanol/water partition coefficient (Po:w) as a surro-gate for partitioning into neutral lipids, except in adipose tissue, whichuses the vegetable oil/water partition coefficient (Pvo:w). Becausephospholipids are composed of both hydrophilic and lipophilic prop-erties, partitioning into all phospholipids is described as a combinationof partitioning into water (70%) and neutral lipids (30%), assumingthe same hydrophilic/lipophilic balance as commercial lecithin. Adrug’s binding to specific macromolecular proteins common to theplasma and interstitial tissue space (such as albumin) is also consid-ered, as estimated from plasma protein binding experiments. Method2 also considers the ionization state of the drug (drug pKa versustissue pH), and interactions between acidic phospholipids and drugswith at least one basic pKa of �7.0 are estimated from blood-to-plasma concentration ratio data. Considering all these factors, differ-ences in distribution from one tissue type to the next will depend onthe abundance of these components in each tissue. Steady-state tissue-to-blood concentration ratio, Kp (Pt-b in some related citations), iscalculated as a function of total partitioning and binding in a tissueversus the total partitioning and binding in blood. Vss is then calcu-lated by incorporating the Kp values determined for each tissue into anOie-Tozer-style equation. Physicochemical properties used for thesemethods were calculated using ACD Labs Chemistry version 12(Advanced Chemistry Development, Inc., Toronto, Canada) and areshown in Table 2.

Prediction accuracy was measured by calculating the fold error aspredicted human Vss/observed human Vss. The various methods forpredicting Vss were compared by tallying the number of compoundsfalling within 1.5-, 2-, and 3-fold prediction error.

Results

Plasma Protein Binding and Calculation of Vss,u and fut. Rat,dog, monkey, and human plasma protein binding were determined for60 of the 67 reference compounds using an ultracentrifugation tech-nique. For the remaining seven compounds, plasma protein bindingvalues across species were obtained from the literature, where anultracentrifugation technique was also used (except for lamifiban).The values for free fraction are summarized in Table 2. Free fractionranged from �1% free in some species (diclofenac, fluvastatin, gem-fibrozil, indomethacin, ketoprofen, naproxen, troglitazone, warfarin,felodipine, telmisartan, and verlukast) to essentially 100% free acrossspecies (lamivudine, venlafaxine, nicotine, gabapentin, and lami-fiban). Among the compounds tested, 20 compounds (�30%) showedextensive species differences in plasma protein binding, with a greaterthan 3-fold ratio between the species with the highest and lowest freefractions. Compounds with the largest such difference (�5-fold) werecefazolin, cefoperazone, fluvastatin, furosemide, ketoprofen, valproicacid, warfarin, felodipine, nifedipine, cefpiramide, telmisartan, topo-tecan, and verlukast. Other compounds (70% of the total) showedmore moderate or minimal species differences in plasma proteinbinding (�3-fold ratio).

Plasma protein binding and reported Vss values for rat, dog, mon-key, and human were used to calculate Vss,u or fut for those species.Vss,u and fut (calculated by eq. 2 or 3) are summarized in Table 3.Calculated Vss,u ranged from 0.16 l/kg (gabapentin in dogs) to 2400l/kg (felodipine in rats). Among the compounds investigated, 26compounds (�39%) showed moderate or minimal species differences(�3-fold ratio between the species with the highest and lowest Vss,u).Calculation of fut by eq. 2 occasionally resulted in fut values of �1.0,the theoretical maximum. Values for fut, as calculated by eq. 2, rangedfrom 0.0004 (felodipine in rats) to 8.7 (gabapentin in dogs) and were

essentially inversely proportional to values for Vss,u. Considering fut ascalculated by eq. 2, 27 compounds (�40%) showed moderate orminimal species differences (�3-fold ratio between the species withthe highest and lowest fut). These compounds were essentially thesame as those showing moderate or minimal species differences inVss,u. Calculation of fut by eq. 3 also occasionally resulted in fut valuesof �1.0, as well as negative values for fut. Positive values for fut, ascalculated by eq. 3, ranged from 0.0002 (felodipine in rats) to 13(lamifiban in monkeys). Considering fut as calculated by eq. 3, 16compounds (�24%) showed moderate or minimal species differencesin fut. The compounds that showed moderate or minimal speciesdifferences in Vss,u and fut (regardless of calculation method) includetroglitazone, antipyrine, caffeine, flunisolide, zidovudine, cyclophos-phamide, lamivudine, midazolam, bupivacaine, citalopram, hyd-rodolasetron, propranolol, verapamil, irinotecan, and doxorubicin.Most of these (except for midazolam, bupivacaine, and irinotecan)also showed moderate or minimal species differences in plasmaprotein binding (�3-fold ratio).

FIG. 1. Vss and Vss,u for midazolam (A and B), propranolol (C and D), andfelodipine (E and F).

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Species Differences in Vss versus Species Differences in Vss,u.The impact of protein binding on the species difference in rat, dog,monkey, and human Vss was evaluated. Species differences in Vss

were compared with species differences in Vss,u. The majority ofcompounds showed little change in extent of species difference withVss,u compared with Vss, particularly when species difference in

TABLE 4

Predicted human Vss (l/kg) by allometric scaling of Vss or Vss,u and the corresponding allometric constants

DrugAllometry of Vss Allometry of Vss,u

Observed Humana

Predicted Vss a b R2 Predicted Vss a b R2

Cefotetan 0.19 0.23 0.96 0.99 0.16 0.76 1.1 0.92 0.13Tiludronate 1.9 9.4 0.62 0.94 0.81 35 0.66 0.86 1.0Captopril 4.1 2.2 1.1 0.99 5.8 3.5 1.2 0.94 0.81Cefazolin 0.18 0.21 0.97 0.96 0.067 1.6 0.82 0.88 0.11Cefoperazone 0.13 0.28 0.83 0.93 0.13 1.5 1.1 0.83 0.14Diclofenac 0.087 0.42 0.63 0.95 0.052 56 0.65 0.95 0.17Fluvastatin 1.7 1.3 1.1 0.85 6.7 134 1.5 0.96 0.42Furosemide 0.18 0.16 1.0 0.97 0.02 7.80 0.67 1.0 0.11Gemfibrozil 1.0 0.33 1.3 0.84 1.6 42 1.5 0.84 1.1Indomethacin 5.3 2.2 1.2 0.96 5.2 611 1.0 0.97 0.93Ketoprofen 0.20 0.34 0.87 1.0 0.029 26 0.70 0.99 0.13Naproxen 0.08 0.14 0.86 0.99 0.12 26 1.1 1.0 0.10Troglitazone 0.61 0.88 0.92 1.0 1.4 122 1.2 1.0 0.71Valproic acid 0.12 0.40 0.71 0.90 0.077 1.8 0.96 1.0 0.15Warfarin 0.17 0.19 0.98 0.94 0.033 28 0.62 0.92 0.11Phenytoin 5.3 1.3 1.3 0.84 3.6 7.2 1.3 0.83 1.4Antipyrine 0.52 0.84 0.89 1.0 0.55 1.2 0.89 1.0 0.87Caffeine 0.71 0.87 0.95 1.0 0.50 1.1 0.88 1.0 0.73Coumarin 15 2.3 1.4 1.0 10 8.0 1.5 1.0 2.2Flunisolide 4.5 3.3 1.1 1.0 4.0 14 1.0 1.0 1.8Isosorbide dinitrate 1.5 3.5 0.80 0.95 0.87 5.6 0.70 0.97 1.9Isosorbide mononitrate 0.51 0.80 0.90 0.98 0.25 1.4 0.67 0.97 0.60Lorazepam 7.6 1.5 1.4 0.93 5.5 16 1.4 0.91 1.3Zidovudine 1.1 1.2 0.99 1.0 0.99 1.6 0.97 1.0 1.8Cyclophosphamide 0.97 0.88 1.0 0.97 1.4 1.2 1.1 0.98 0.70Felodipine 2.5 7.3 0.75 0.80 5.4 989 0.57 0.38 9.7Lamivudine 0.67 1.4 0.82 1.00 0.72 1.5 0.85 1.0 1.3Midazolam 1.2 2.3 0.86 0.94 1.02 89 1.0 1.0 1.1Mifepristone 40 8.3 1.4 0.94 11 440 1.2 0.98 1.5Nifedipine 2.8 0.27 1.6 0.99 0.42 18 0.61 0.97 0.78Indinavir 0.58 1.7 0.75 0.97 0.63 5.7 0.67 0.99 0.82Amiodarone 0.82 23 0.21 0.77 0.90 413 0.26 0.63 66Biperiden 3.8 9.3 0.79 0.88 7.1 95 0.85 0.94 6.2Bupivacaine 0.33 0.93 0.76 1.0 0.85 5.1 1.0 0.93 0.84Chlorpromazine 6.8 19.0 0.76 0.92 4.3 208 0.58 0.50 11Cimetidine 1.3 2.6 0.84 1.00 1.2 3.5 0.81 1.0 1.2Citalopram 6.2 15 0.79 1.00 6.5 41 0.84 0.99 14Diltiazem 20 4.9 1.3 0.92 11 19 1.2 0.85 3.1Erythromycin 0.59 4.2 0.54 0.69 0.61 10 0.59 0.86 0.89Haloperidol 27 11 1.2 0.90 9.5 73 1.00 0.90 18Hydrodolasetron 15 16 0.98 0.93 11 71 0.89 0.92 11Imipramine 5.7 9.3 0.88 0.99 4.9 63 0.88 0.98 13Lidocaine 1.1 1.9 0.87 0.94 0.75 4.8 0.84 0.94 1.1Metoprolol 2.6 5.2 0.83 0.97 2.7 7.0 0.89 0.96 4.2Propafenone 7.1 5.6 1.1 0.98 4.5 57 0.95 0.99 2.2Propranolol 2.4 4.6 0.85 0.95 3.1 19 0.94 0.95 3.6Venlafaxine 2.2 5.0 0.81 1.0 1.7 6.2 0.70 1.0 4.4Verapamil 6.2 4.4 1.1 1.0 5.8 22 1.1 1.0 3.7Disopyramide 1.1 3.9 0.70 0.98 0.93 6.8 0.72 1.0 0.78Imatinib 17 4.8 1.3 1.0 8.8 106 0.96 0.99 3.9Irinotecan 0.77 2.4 0.74 0.96 2.0 14 0.90 0.96 3.8Nicardipine 1.7 1.4 1.0 0.99 0.97 75 0.88 0.99 0.64Nicotine 1.5 3.3 0.82 0.92 1.5 3.3 0.82 0.92 2.6Quinidine 0.96 3.2 0.72 0.58 3.2 26 0.92 0.82 3.0Vinblastine 0.59 5.4 0.48 0.85 1.5 46 0.59 0.64 24Cefpiramide 0.23 0.23 1.0 0.87 0.14 1.7 1.4 0.86 0.11Ciprofloxacin 1.7 3.4 0.83 0.96 1.4 5.4 0.73 1.0 2.3Doxorubicin 42 27 1.1 0.98 41 333 0.98 0.95 25Gabapentin 0.10 0.82 0.51 0.76 0.10 0.82 0.51 0.76 0.82Lamifiban 0.83 0.31 1.2 0.97 0.83 0.33 1.2 0.97 0.29Levofloxacin 0.88 2.6 0.74 1.0 0.72 3.0 0.72 0.99 1.2Methotrexate 1.1 0.55 1.2 0.96 0.82 1.5 1.2 0.97 0.67Morphine 10 4.4 1.2 0.93 11 6.5 1.2 0.92 3.3Naltrexone 14 4.7 1.3 0.99 15 8.1 1.3 0.96 7.6Telmisartan 2.3 2.0 1.0 0.98 0.35 232 0.84 1.0 5.3Topotecan 3.2 0.84 1.3 0.99 5.1 2.9 1.2 0.93 1.8Verlukast 0.13 0.52 0.68 1.0 0.011 152 0.46 0.51 0.11

a See Table 2 for source of in vivo data.

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plasma protein binding was minimal or moderate. For example, con-sidering propranolol, the ratio between the species with the highestand lowest Vss was 2.4-fold, whereas the ratio between the specieswith the highest and lowest Vss,u was 2.3-fold (Fig. 1, A and B).Likewise, for lorazepam, the ratio between the species with thehighest and lowest Vss was 7-fold, whereas the ratio between thespecies with the highest and lowest Vss,u was 8.6-fold. Occasionally,minimal changes in extent of species difference also occurred whenspecies differences in plasma protein binding were large. In these cases,there was often a change in the rank order of species, in order of increasingvalues for Vss or Vss,u. For example, considering imatinib, the ratio betweenthe species with the highest and lowest Vss was 2.9-fold, whereas the ratiobetween the species with the highest and lowest Vss,u was 3.2-fold, essentiallyunchanged. However, the rank order of species, in order of increasing Vss,was rat�human�monkey�dog, whereas the rank order of species, in orderof increasing Vss,u, was human�dog�rat�monkey.

However, several compounds did show meaningfully reduced spe-cies difference with Vss,u compared with Vss. For example, consider-ing midazolam, the ratio between the species with the highest andlowest Vss was 2.6-fold, whereas the ratio between the species withthe highest and lowest Vss,u was 1.2-fold (Fig. 1, C and D). Othercompounds showing a similar reduction in species difference includedtiludronate (15–5-fold), valproic acid (4.4–2-fold), mifepristone (22–7.2-fold), nifedipine (9.2–5-fold), and quinidine (11–4.8-fold).

In addition, several compounds showed meaningfully increasedspecies difference with Vss,u compared with Vss. For example,considering felodipine, the ratio between the species with thehighest and lowest Vss was 5.5-fold, whereas the ratio between thespecies with the highest and lowest Vss,u was 23-fold (Fig. 1, E andF). Other compounds showing a similar increase in species differ-ence included cefoperazone (3– 6.1-fold), gemfibrozil (9.8 –17-

fold), warfarin (2.5–5.2-fold), chlorpromazine (3.6 –15-fold), cef-piramide (4.5–12-fold), temisartan (3.4 –12-fold), and verlukast(7.4 –14-fold).

Prediction of Human Vss by Allometric Scaling. Human Vss waspredicted by allometric scaling of Vss or Vss,u from three preclinicalspecies (rat, dog, and monkey) versus body weight. The results ofallometric scaling are shown in Table 4 and Fig. 2. In addition, theprediction accuracy was assessed by determining the percentage ofcompounds falling into 1.5-, 2-, and 3-fold prediction error (Table 5),

TABLE 5

Prediction accuracy for the prediction of human Vss by interspecies scaling andSimcyp methods

Predicted by

Percentage of All Compounds Predictedwithin

1.5-Fold 2-Fold 3-Fold

Vss,u from Rat 36 55 79Dog 37 54 81Monkey 37 64 81Average 40 72 87

futa from Rat 39 55 82

Dog 43 63 84Monkey 43 67 81Average 55 75 90

futb from Rat 36 54 84

Dog 48 69 85Monkey 40 66 84Average 58 78 90

Allometry of Vss 30 60 73Vss,u 28 55 75

Simcyp Method 1 19 40 67Method 2 15 43 64

a Equation 2.b Equation 3.

FIG. 2. A and B, prediction of human Vss byallometric scaling of preclinical Vss (A) or Vss,u

(B). C and D, relationship between the allomet-ric exponent b and the fold error in human Vss

prediction (predicted Vss/observed Vss) with al-lometry of Vss (C) or Vss,u (D). Diamonds, acidswith pKa �7; circles, acids with pKa �7, neu-trals and bases with pKa �7; squares, baseswith pKa �7; triangles, zwitterions. Solid anddashed lines represent unity and 3-fold error,respectively.

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TABLE 6

Predicted human Vss (l/kg) by interspecies scaling and Simcyp

DrugPredicted by Vss,u from Predicted by fut

a from Predicted by futb from Predicted by Simcyp

ObvervedHumanc

Rat Dog Monkey Average Rat Dog Monkey Average Rat Dog Monkey Average Method 1 Method 2

Cefotetan 0.09 0.08 0.25 0.14 0.12 0.11 0.23 0.13 0.15 0.13 0.17 0.15 0.35 0.20 0.13Tiludronate 5.0 1.0 3.5 3.2 5.0 1.0 3.5 2.0 5.3 0.91 2.8 1.8 0.45 0.20 1.0Captopril 1.7 2.3 6.7 3.5 1.6 2.3 6.7 2.5 1.7 1.9 5.2 2.2 1.3 0.26 0.81Cefazolin 0.17 0.06 0.20 0.14 0.18 0.09 0.18 0.13 0.21 0.13 0.13 0.14 0.28 0.21 0.11Cefoperazone 0.07 0.05 0.30 0.14 0.11 0.08 0.24 0.11 0.16 0.13 0.14 0.14 0.27 0.21 0.14Diclofenac 0.40 0.13 0.10 0.21 0.43 0.15 0.11 0.16 0.48 0.16 0.13 0.16 1.9 0.21 0.17Fluvastatin 0.44 1.6 3.7 1.9 0.46 1.6 3.7 0.95 0.53 1.2 2.9 0.94 3.6 0.25 0.42Furosemide 0.13 0.04 0.05 0.07 0.14 0.07 0.07 0.08 0.17 0.12 0.11 0.10 0.30 0.21 0.11Gemfibrozil 0.14 2.0 0.11 0.74 0.17 2.0 0.13 0.19 0.21 1.6 0.13 0.17 1.5 0.22 1.1Indomethacin 4.3 6.5 3.1 4.7 4.2 6.5 3.1 4.2 4.4 5.5 2.4 3.6 1.8 0.23 0.93Ketoprofen 0.15 0.05 0.07 0.09 0.18 0.08 0.10 0.10 0.23 0.13 0.14 0.14 0.34 0.23 0.13Naproxen 0.07 0.09 0.10 0.09 0.10 0.10 0.10 0.10 0.15 0.10 0.10 0.10 0.29 0.20 0.10Troglitazone 0.60 1.2 0.84 0.86 0.62 1.1 0.83 0.81 0.7 0.92 0.66 0.74 3.0 1.1 0.71Valproic acid 0.09 0.07 0.10 0.09 0.13 0.11 0.11 0.12 0.18 0.14 0.13 0.14 0.60 0.20 0.15Warfarin 0.26 0.05 0.13 0.15 0.27 0.09 0.12 0.12 0.29 0.13 0.11 0.11 0.38 0.20 0.11Phenytoin 0.50 0.70 5.6 2.3 0.51 0.71 5.7 0.83 0.54 0.62 4.5 0.77 0.37 0.31 1.4Antipyrine 1.0 0.68 0.73 0.81 1.0 0.68 0.73 0.78 0.98 0.54 0.57 0.64 0.50 0.44 0.87Caffeine 1.0 0.66 0.68 0.79 1.0 0.66 0.69 0.76 0.98 0.55 0.55 0.63 0.48 0.42 0.73Coumarin 0.67 3.8 3.1 2.5 0.69 3.8 3.1 1.4 0.74 3.2 2.5 1.4 0.45 0.31 2.2Flunisolide 3.2 3.7 3.6 3.5 3.2 3.8 3.6 3.5 3.4 3.2 2.8 3.1 0.97 0.87 1.8Isosorbide dinitrate 4.9 1.9 1.5 2.8 4.8 1.9 1.5 2.1 5.0 1.6 1.2 1.8 0.59 0.51 1.9Isosorbide mononitrate 1.7 0.58 0.47 0.90 1.6 0.59 0.47 0.67 1.6 0.49 0.37 0.52 0.47 0.41 0.60Lorazepam 0.63 5.4 0.69 2.2 0.65 5.5 0.71 0.94 0.70 4.6 0.60 0.87 0.72 0.57 1.3Zidovudine 1.2 0.96 1.2 1.1 1.2 0.97 1.2 1.1 1.1 0.81 0.93 0.94 0.48 0.42 1.8Cyclophosphamide 0.85 0.91 1.6 1.1 0.85 0.89 1.6 1.0 0.80 0.71 1.2 0.85 0.58 0.54 0.70Felodipine 82 44 3.6 43 80 44 3.5 9.4 85 37 2.8 7.3 25 87 9.7Lamivudine 1.7 0.94 1.1 1.3 1.7 0.93 1.1 1.2 1.7 0.75 0.89 0.96 0.52 0.50 1.3Midazolam 0.96 1.1 0.89 0.98 0.98 1.1 0.93 0.99 1.1 0.91 0.78 0.90 2.4 2.5 1.1Mifepristone 4.3 5.9 11 7.0 4.3 6.0 11 6.1 4.5 5.1 8.6 5.6 37 1029 1.5Nifedipine 3.9 1.1 0.92 2.0 3.0 1.1 0.89 1.2 1.8 0.92 0.67 0.94 9.7 16 0.78Indinavir 4.0 1.1 1.7 2.2 3.9 1.1 1.7 1.7 4.0 0.86 1.3 1.3 16 37 0.82Amiodarone 62 5.3 4.1 24 61 5.4 4.1 6.7 65 4.5 3.3 5.5 38 2127 66Biperiden 18 14 6.5 13 18 14 6.5 11 19 12 5.1 8.9 4.7 4.5 6.2Bupivacaine 0.90 1.3 0.46 0.90 0.91 1.3 0.47 0.74 0.97 0.96 0.40 0.63 3.4 1.9 0.84Chlorpromazine 56 26 3.7 29 55 26 3.7 9.2 59 22 3.0 7.4 8.7 12 11Cimetidine 3.5 1.9 1.7 2.4 3.5 1.9 1.8 2.2 3.6 1.6 1.4 1.8 0.53 1.2 1.2Citalopram 17 9.9 8.7 12 16 10 8.8 11 17 8.4 6.9 9.3 3.7 6.6 14Diltiazem 3.8 17 2.0 7.4 3.7 17 2.0 3.7 3.9 14 1.6 3.1 11 14 3.1Erythromycin 6.6 2.0 1.1 3.2 6.5 2.0 1.1 1.9 6.9 1.7 0.84 1.5 0.82 2.1 0.89Haloperidol 11 17 4.8 11 11 17 4.8 8.3 11 14 3.8 7.0 6.2 9.1 18Hydrodolasetron 19 8.7 26 18 18 8.8 26 14 19 7.3 20 13 3.7 2.0 11Imipramine 9.3 4.9 9.0 7.7 9.2 5.0 9.0 7.1 9.7 4.2 7.1 6.2 4.6 6.0 13Lidocaine 2.0 1.5 0.72 1.4 2.0 1.5 0.73 1.2 2.1 1.3 0.60 0.99 1.2 2.0 1.1Metoprolol 5.4 4.6 2.4 4.2 5.4 4.6 2.4 3.7 5.6 3.9 1.9 3.1 0.82 2.5 4.2Propafenone 6.3 5.6 4.5 5.5 6 5.6 4.5 5.4 7 4.7 3.6 4.6 2.4 1.2 2.2Propranolol 4.0 2.4 5.6 4.0 3.9 2.4 5.6 3.5 4.2 2.0 4.4 3.1 2.0 1.6 3.6Venlafaxine 9.6 3.3 3.5 5.5 9.4 3.3 3.5 4.3 9.8 2.8 2.7 3.6 2.8 1.1 4.4Verapamil 3.4 5.2 4.0 4.2 3.4 5.2 4.0 4.1 3.6 4.4 3.2 3.6 4.6 3.2 3.7Disopyramide 4.6 1.7 1.9 2.7 4.5 1.7 1.9 2.2 4.8 1.4 1.5 1.9 1.5 2.4 0.78Imatinib 10 7.8 12 10 10 7.8 12 9.8 11 6.6 9.7 8.6 1.7 6.2 3.9Irinotecan 3.7 3.3 1.8 2.9 3.7 3.2 1.8 2.6 3.9 2.5 1.4 2.2 3.9 6.2 3.8Nicardipine 2.0 1.5 1.1 1.5 1.9 1.5 1.1 1.4 2.0 1.2 0.89 1.2 18 38 0.64Nicotine 4.7 3.3 1.5 3.2 4.7 3.3 1.5 2.5 4.8 2.8 1.2 2.0 0.66 0.92 2.6Quinidine 5.9 7.7 1.6 5.1 5.9 7.7 1.5 3.1 6.2 6.4 1.1 2.4 1.6 3.6 3.0Vinblastine 17 7.1 1.7 8.7 17 7.0 1.7 3.7 18 5.7 1.3 2.9 38 1625 24Cefpiramide 0.02 0.03 0.18 0.07 0.06 0.07 0.14 0.07 0.11 0.12 0.09 0.13 0.26 0.66 0.11Ciprofloxacin 6.5 2.5 2.6 3.8 6.4 2.5 2.6 3.2 6.6 2.1 2.0 2.6 0.93 0.43 2.3Doxorubicin 49 62 26 45 48 62 26 40 51 52 20 34 0.39 13 25Gabapentin 1.4 0.16 0.68 0.76 1.4 0.15 0.68 0.31 1.4 0.10 0.53 -0.22 0.73 0.76 0.82Lamifiban 0.25 0.79 0.27 0.44 0.25 0.79 0.27 0.33 0.15 0.63 0.22 0.40 0.52 0.90 0.29Levofloxacin 3.4 1.1 1.7 2.1 3.4 1.1 1.7 1.7 3.5 0.94 1.3 1.4 1.3 4.2 1.2Methotrexate 0.20 0.36 0.70 0.42 0.23 0.37 0.72 0.35 0.25 0.33 0.60 0.33 0.33 0.24 0.67Morphine 2.6 3.8 12 6.3 2.6 3.8 12 4.1 2.7 3.2 9.7 3.8 0.52 1.4 3.3Naltrexone 3.6 14 4.1 7.2 3.5 14 4.1 5.0 3.7 12 3.2 4.5 1.7 4.8 7.6Telmisartan 0.86 0.45 0.58 0.63 0.88 0.49 0.61 0.62 0.97 0.47 0.53 0.58 4.3 0.40 5.3Topotecan 1.8 5.9 1.5 3.1 1.7 5.8 1.5 2.1 1.6 4.6 1.2 1.7 0.49 1.2 1.8Verlukast 0.19 0.01 0.12 0.11 0.22 0.05 0.14 0.07 0.28 0.11 0.17 0.12 0.31 0.23 0.11

a Equation 2.b Equation 3.c See Table 2 for source of in vivo data.

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where fold error is defined as predicted Vss/observed Vss. With allo-metric scaling of Vss, 30, 60, and 73% of all compounds fell within1.5-, 2-, and 3-fold error, respectively. With allometric scaling ofVss,u, 28, 55, and 75% of all compounds fell within 1.5-, 2-, and 3-folderror, respectively. Incorporating plasma protein binding informationdid not seem to improve the overall chance of prediction success usingallometric scaling. The allometric exponents b ranged from 0.21 to 1.4and from 0.26 to 1.5 for scaling of Vss and Vss,u, respectively. Withallometric scaling of both Vss and Vss,u, there was a clear relationshipbetween the fold error in prediction and the value of the best fit for theallometric exponent b from the three preclinical species (Fig. 2, C andD). Exponents of �1 tended to result in an underprediction of humanVss, whereas exponents of �1 tended to result in an overprediction ofhuman Vss.

Prediction of Human Vss by Interspecies Scaling of Vss,u or fut.Human Vss was predicted using the Vss,u equivalency approach,assuming human Vss,u will be equivalent to the Vss,u from eachindividual preclinical species (i.e., single-species scaling from rats,dogs, or monkeys) or equivalent to the average Vss,u across thethree preclinical species (multispecies scaling). The results ofinterspecies scaling of Vss,u are shown in Table 6 and Fig. 3. Inaddition, the prediction accuracy was assessed by determining thepercentage of compounds falling within 1.5-, 2-, and 3-fold pre-diction error (Table 5). Considering all compounds, no singlespecies appeared to predict human Vss better than any other. Usingthe Vss,u equivalency approach, 36 to 37%, 54 to 64%, and 79 to81% of all compounds fell within 1.5-, 2-, and 3-fold error,respectively, depending on the species. Using the average Vss,u

across three preclinical species showed some improvement com-pared with single-species scaling, with 40, 72, and 87% of allcompounds falling within 1.5-, 2-, and 3-fold error, respectively.

Human Vss was also predicted using the fut equivalency approach,assuming human fut will be equivalent to the fut from each individualpreclinical species (i.e., single-species scaling from rats, dogs, ormonkeys) or equivalent to the average fut across the three preclinicalspecies (multispecies scaling). The results of interspecies scaling of fut

are shown in Table 5 and Figs. 4 and 5. In addition, the predictionaccuracy was assessed by determining the percentage of compoundsfalling into 1.5-, 2-, and 3-fold prediction error (Table 6). The fut wascalculated using either eq. 2 or 3. Considering all compounds, nosingle species appeared to predict human Vss better than any other.Using the fut equivalency approach, where fut is calculated by eq. 2, 39to 43%, 54 to 66%, and 81 to 84% of all compounds fell within 1.5-,2-, and 3-fold error, respectively, depending on the species. Using theaverage fut (eq. 2) across three preclinical species showed someimprovement compared with single-species scaling, with 55, 75, and90% of all compounds falling within 1.5-, 2-, and 3-fold error,respectively. Substantially similar results were observed when fut wascalculated using eq. 3 (Tables 5 and 6; Fig. 5). Using the average fut

(eq. 3) across three preclinical species also showed some improve-ment compared with single-species scaling, with 58, 78, and 90% ofall compounds falling within 1.5-, 2-, and 3-fold error, respectively.Finally, interspecies scaling using the average Vss,u or fut (regardlessof calculation method) seemed to provide a better chance of fallingwithin 1.5-, 2-, or 3-fold error compared with allometric scalingversus body weight when predicting human Vss.

Predicting Human Vss Using Mechanistic Tissue CompositionEquations in Simcyp. For comparison, human Vss was also predictedusing methods 1 and 2 as available in Simcyp version 8.0 population-based pharmacokinetic simulator (Simcyp Ltd.). Physicochemicalproperties used for these methods were calculated using ACD LabsChemistry version 12 and are shown in Table 2. Plasma protein

FIG. 3. Prediction of human Vss by the Vss,u

equivalency approach using Vss,u from rat (A),dog (B), monkey (C), or average of three spe-cies (D). Diamonds, acids with pKa �7; circles,acids with pKa �7, neutrals and bases with pKa�7; squares, bases with pKa �7; triangles,zwitterions. Solid and dashed lines representunity and 3-fold error, respectively.

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binding and blood-to-plasma concentration ratio values used for thesemethods are also provided in Table 2. The results of the predictions ofhuman Vss by methods 1 and 2 are shown in Table 6 and Fig. 6. Inaddition, the prediction accuracy was assessed by determining thepercentage of compounds falling within 1.5-, 2-, and 3-fold predictionerror (Table 5). Considering all compounds, method 1 resulted in 19,40, and 67% of all compounds falling within 1.5-, 2-, and 3-fold error,respectively. Correspondingly, method 2 resulted in 15, 43, and 64%of all compounds falling within 1.5-, 2-, and 3-fold error, respectively.Some compounds showed considerable error in prediction (�4-folderror) using the tissue composition equations, particularly usingmethod 2, including diclofenac, fluvastatin, troglitazone, mifepris-tone, nifedipine, indinavir, amiodarone, nicardipine, and vinblastine.Most of these compounds had a clogD at pH 7.4 of �3.5. Therelationship between fold prediction error from method 1 or 2 andclogD at pH 7.4 is shown in Fig. 6, C and D. The relationship betweenprediction error and clogD at pH 7.4 was less pronounced withmethod 1. However, method 1 also tended to overpredict the Vss forseveral compounds with a clogP of �4.0 or higher (figure with clogPnot shown).

Discussion

The aims of the present study were to examine the assumptionsregarding equivalency of Vss,u and fut across species and to examineside by side several common approaches for predicting human Vss

from preclinical data. To do this, observed rat, dog, monkey, andhuman Vss values were compiled from the literature, and plasmaprotein binding was determined across species, for a wide range of 67reference compounds.

Despite this common assumption, Vss,u or fut equivalency does notnecessarily seem to be the norm. Only 39% of the compounds tested

exhibited moderate to minimal species difference in Vss,u (�3-foldratio between the species with the highest and lowest value). Further-more, only 24 to 40% of the compounds exhibited moderate tominimal species difference in fut, depending on how fut is calculated,whereas 70% of the compounds showed moderate to minimal speciesdifference in fup. This seems to indicate that species similarity in fup

is more likely to be observed than species similarity in fut (as calcu-lated by the Oie-Tozer-style equations), despite early evidence thatbinding to tissues (as determined in vitro) could be quite similaracross species (Fitchl and Schulmann, 1986). There are several pos-sibilities for these findings. One possibility is that some reportedvalues for Vss may not be accurate because of limited blood samplingor insufficiencies in the analytical method used. Another possibility isthat plasma (or tissue) protein binding as determined in vitro might, insome cases, not accurately reflect the in vivo situation. In cases ofinaccurate determination of Vss or protein binding, apparent speciesdifferences in calculated Vss,u or fut would be an artifact of limitationsin the experimental methodology, rather than true species differencesin distribution.

A third possible source of discrepancy is that fut calculated by theOie-Tozer-style equations is practically quite different than fut deter-mined in vitro. When determined in vitro using tissue homogenates,fut represents simply the fraction of drug unbound to tissue material,including tissue solids, and soluble proteins in the intracellular andextracellular spaces. In this case, as with plasma protein bindingmeasurements, the theoretical maximum for free fraction is 1. How-ever, fut calculated from observed Vss and plasma protein binding is aderived term resembling an average fut across all tissue tissues and,therefore, might not represent the binding to any specific tissue. Inaddition, calculated fut might capture any number of other mecha-nisms affecting distribution that are unrelated to tissue binding. For

FIG. 4. Prediction of human Vss by the fut

equivalency approach using rat (A), dog (B),monkey (C), or average (D) fut as calculated byeq. 2. Diamonds, acids with pKa �7; circles,acids with pKa �7, neutrals and bases with pKa�7; squares, bases with pKa �7; triangles,zwitterions. Solid and dashed lines representunity and 3-fold error, respectively.

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example, several compounds exhibited calculated fut values of �1 inat least one species. Additionally, using eq. 3, negative values for fut

were also observed (Table 3) for some compounds, including cefo-tetan, cefazolin, furosemide, naproxen, cefpiramide, gabapentin, andlamifiban. These compounds also exhibited Vss values that wereamong the lowest in the dataset. Waters and Lombardo (2010) alsoreported similar anomalies in calculated fut. Such anomalies certainlyillustrate a limitation with this particular model for low Vss drugs.However, they might not only reflect a lack of binding to tissueproteins but also a lack of penetration (i.e., permeability) into tissuecompartments. For drugs that do not penetrate into intracellular tissue spaces,calculated fut could be forced above 1 by the fraction of intracellulartissue volume. In the original derivation of eq. 3 (Oie and Tozer,1979), the authors suggested that for drugs restricted to the extracel-lular fluid, Vr should be set to zero, making tissue binding irrelevant.However, toward the application of interspecies scaling, it might bedifficult to judge whether or not a drug is restricted to the extracellularfluid based on Vss and fup alone.

As a derived term, calculated fut could also reflect active uptake orefflux, concentrative uptake due to pH gradients, and distribution tocompartments that are unrelated to partitioning into tissues, such asenterohepatic recycling, or intestinal or renal secretion and reabsorp-tion, depending on the properties of the compound of interest. Anyone or combination of these mechanisms could potentially be a sourcefor species differences in apparent Vss,u or fut. The impact of thesemechanisms on the calculation of Vss,u or fut would be difficult todetermine without substantially more information about each com-pound. Another consideration is that the physiological volumes rec-ommended for use with eq. 3 (Obach et al., 1997) only account fortotal body water and do not account for the volume attributed to tissuesolids, where many high Vss drugs could primarily reside, leading to

inaccurate estimation of fut. Finally, Waters and Lombardo (2010)suggested that an assumed value for Re/i of 1.4 might not be appro-priate for all drugs. For these reasons, although the calculation of fut

is mechanistic in appearance, it is difficult to derive any mechanisticinsight by simply calculating fut using Oie-Tozer-style equations.These equations should still be considered as empirical when used inthe application of interspecies scaling.

Despite the observation that species similarities in Vss,u or fut (ascalculated by the Oie-Tozer-style equations) are not necessarily thenorm, predictions of human Vss by interspecies scaling can still beuseful and reasonably accurate. All interspecies scaling methods eval-uated, including allometric scaling, and Vss,u or fut equivalency, bysingle-species scaling or averaging across species, can result in rea-sonably accurate predictions. However, some methods seemed toresult in a better chance of obtaining the most accurate prediction thanothers. In particular, the Vss,u or fut equivalency approaches (usingaverage Vss,u or fut across three preclinical species) seemed to mean-ingfully outperform allometric scaling of Vss or Vss,u. Allometricscaling of Vss or Vss,u resulted in the most accurate prediction only15% of the time, whereas the Vss,u or fut equivalency approach resultedin the most accurate prediction 67% of the time. In other cases,prediction accuracies were similar. The lesser performance of allo-metric scaling could be due to the extrapolation using a log-logrelationship. Any species differences (or errors) in Vss estimation,particularly in animals with the lowest and highest body weights, willpropagate exponentially when extrapolating human Vss using theallometric power function. There was a strong relationship betweenthe fold error in prediction and the value of the best fit for theallometric exponent b from the three preclinical species (Fig. 2, C andD). Given the observed relationship, predictions resulting from expo-

FIG. 5. Prediction of human Vss by the fut

equivalency approach using rat (A), dog (B),monkey (C), or average (D) fut as calculated byeq. 3. Diamonds, acids with pKa �7; circles,acids with pKa �7, neutrals and bases with pKa�7; squares, bases with pKa �7; triangles,zwitterions. Solid and dashed lines representunity and 3-fold error, respectively.

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nents outside the range of between �0.8 and �1.2 should be consid-ered cautiously because of the risk of exponential error propagation.

With the Vss,u or fut equivalency approaches, single-species scalingfrom rats, dogs, or monkeys resulted in a similar chance of predictionsuccess as allometric scaling (Table 5), in agreement with someprevious reports. However, in the present study, no single species(rats, dogs, or monkeys) seemed to perform significantly better overallthan any other (Table 5). This conclusion is somewhat different thanother reports that have found rats, dogs, or monkeys to be the bestspecies for predicting human Vss, depending on the study (Ward andSmith, 2004; Hosea et al., 2009; Sui et al., 2010). Although statisticalanalysis may periodically suggest that one species could be better thananother at predicting human Vss, depending on the dataset, there is noknown physiological basis for this to be so. That single-speciesscaling has performed similarly to allometric scaling has led some tosuggest that scaling from a single preclinical species (i.e., rat) issufficient and that considering data from additional preclinical species(such as dog or monkey) provides little benefit (Hosea et al., 2009).Certainly, accurate predictions can be obtained from a single species,and such an approach might be useful at the early stages of drugdiscovery. However, further analysis shows that, when predictinghuman Vss, the Vss,u or fut equivalency approaches using the averageVss,u or fut across three preclinical species (i.e., multispecies scaling)provides considerable improvement in extent of prediction accuracycompared with single-species scaling (Table 5). The pharmacokineticsscientist would be best advised to use all available preclinical data (asresources allow) to arrive at predicted human pharmacokinetic param-eters for purposes of estimating the potential therapeutic dose andsafety margins before the first dose to humans.

For comparison, Vss was also calculated according to the tissuecomposition equations proposed by Poulin and Theil (2002) and

Rodgers and Rowland (2007). These tissue composition equations areconsidered to be mechanistic alternatives to interspecies scaling andfacilitate the use of physiologically based pharmacokinetic models inthe absence of in vivo tissue distribution data. Prediction of Vss usingthe tissue composition equations did not necessarily provide a betterchance of lowest error in prediction than interspecies scaling methods.Methods 1 and 2 resulted in 67 and 64% of all predictions fallingwithin 3-fold error, respectively, versus 87 to 90% for Vss,u or fut

equivalency using average Vss,u or fut across three preclinical species.Method 1 or 2 provided the most accurate prediction only 7% of thetime, whereas the value was 61% for Vss,u or fut equivalency.

There are a number of possible reasons for the lesser performanceof the tissue composition equations. First, inaccuracies in calculationor measurement of physicochemical properties could result in inac-curacies in prediction of tissue partitioning, as discussed by Rodgersand Rowland (2007). Second, partitioning into octanol (or vegetableoil) might not always adequately represent the partitioning of a druginto all classes of neutral lipids found in animal tissues (i.e., triglyc-erides, diglycerides, monoglycerides, cholesterol, lipid components ofphospholipids, etc.). Evidence for this lies in that human Vss wassubstantially overpredicted for several lipophilic neutral and basiccompounds, particularly using method 2 (Fig. 6, C and D). This effectwas most pronounced for neutral and basic compounds when clogD atpH 7.4 was greater than �3.5. The theory behind these modelssuggests that predicted Kp, and therefore Vss, will continue to increaseas log P increases. This was illustrated by a simulation conducted byRodgers and Rowland (2007), where Vss,u increased exponentiallywhen log P exceeded 3. However, initial validations of these methodscontained relatively few drugs of high lipophilicity. In contrast, somecomparisons of observed adipose Kp versus log P have indicated thatKp might not increase exponentially with log P but might plateau

FIG. 6. A and B, prediction of human Vss usingSimcyp method 1 (A), or method 2 (B). C andD, relationship between lipophilicity and thefold error in human Vss prediction (predictedVss/observed Vss) with Simcyp method 1 (C) ormethod 2 (D). Diamonds, acids with pKa �7;circles, acids with pKa �7, neutrals and baseswith pKa �7; squares, bases with pKa �7;triangles, zwitterions. Solid and dashed linesrepresent unity and 3-fold error, respectively.

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instead (Haddad et al., 2000). This discrepancy would lead to anoverprediction of Vss for highly lipophilic compounds using the tissuecomposition equations, as observed with the present dataset. Giventhe current findings, Vss predictions for highly lipophilic compoundsusing method 2 should be interpreted cautiously. The Vss,u or fut

equivalency approaches were likely to give a more accurate predictionfor this class of compounds, possibly because partitioning into humantissues by lipophilic compounds is best represented by actual in vivodata from preclinical species. Third, not all tissue phospholipids mightbehave exactly as commercial lecithin (see Materials and Methods).Fourth, partitioning into water might not always accurately reflectpartitioning into hydrophilic tissue components (i.e., salt-bufferedintracellular and extracellular fluids, hydrophilic components of phos-pholipids, hydrophilic protein matrices, etc.). Fifth, the possibility ofspecific binding to numerous tissue macromolecules is neglected.Finally, as with calculation of fut using the Oie-Tozer-style equations,the tissue composition equations still neglect other mechanisms thatcan contribute to distribution such as lack of permeability, activeuptake or efflux, enterohepatic recycling, or intestinal or renal secre-tion and reabsorption.

Although the mechanistic tissue compositions did not necessarilyprovide the most accurate Vss prediction, some important benefits ofthe approach should be acknowledged. Whereas refinement of themethods should be investigated, they currently provide the best op-portunity for a mechanistic understanding of distribution, short ofperforming a tissue distribution study in vivo. In addition, predictionsare possible using only in vitro and physicochemical property data asinputs, whereas data from preclinical species are not required, pro-viding a valuable alternative to interspecies scaling.

In summary, the assumptions regarding equivalency of Vss,u and fut

across species were examined, and several common approaches forpredicting human Vss from preclinical data were evaluated. Speciessimilarity in Vss,u or fut does not seem to be the norm among rats, dogs,monkeys, or humans. Despite this, interspecies scaling from rats,dogs, and monkeys is useful and can provide reasonably accuratepredictions of human Vss, although some interspecies scaling ap-proaches were better than others. For example, the performance of thecommon Vss,u or fut equivalency approaches using average Vss,u or fut

across three preclinical species was substantially superior to allomet-ric scaling techniques. In addition, considering data from severalpreclinical species, using the equivalency approach, provided im-proved prediction success compared with scaling from any singlespecies. Although the mechanistic tissue composition equations avail-able in the Simcyp population-based pharmacokinetic simulator didnot necessarily provide the most accurate predictions, and the equa-tions used likely need refinement, they still provide the best oppor-tunity for a mechanistic understanding and prediction of human Vss.

Authorship Contributions

Participated in research design: Berry and Zhao.Conducted experiments: Berry and Li.Performed data analysis: Berry.Wrote or contributed to the writing of the manuscript: Berry and Zhao.

References

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Address correspondence to: Loren M. Berry, Amgen, Inc., 360 Binney St.,

Cambridge, MA 02142. E-mail: loren.berry@amgen.com

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