A General Model for Prediction of Caco-2 Cell Permeability

A General Model for Prediction of Caco-2Cell PermeabilityAnneli Nordqvist a,b ,Jonas Nilsson*c ,Tuulikki Lindmark b ,Alf Eriksson d ,Per Garberg b and Mats Kihle ¬n caDivision of Organic Pharmaceutical Chemistry,Department of Medicinal Chemistry,BMC,Uppsala University,Box 574,SE-75123Uppsala,Sweden b In Vitro Sciences c Stuctural ChemistrydAnalytical Sciences,Biovitrum AB,SE-11276Stockholm,SwedenFull PaperPermeability across the epithelium is one of the major barriers to drug absorption and is one property subject to in silico prediction attempts.Prediction models provide a possibility to address absorption issues,early in drug discovery,for a large number of compounds.The aim of this study was to develop a general comprehensive partial least square projection to latent structures (PLS)-model for prediction of Caco-2cell permeability using theoret-ically calculated descriptors suited for large virtual libra-ries.In order to deal with current issues of data quantity and quality the well-established Caco-2cell model was used to generate accurate permeability data of apparent passive transport for a large set of structurally diverse compounds.PLSstatistics was used to correlate calculated descriptors to log P app .This new prediction model for Caco-2cell permeability has incorporated many different descriptor types to deal with the multivariate nature of permeability.The model is designed to classify discoverycompounds as low,medium or high permeable.A good statistical model was derived (R 2 0.79,Q 2 0.65,n 46)using 70descriptors including lipophilicity,hydrogen bonding,polar surface area,size and charge descriptors and some nonlinear terms.The model has been tested and proved valid on two different external test sets (n 5and n 125respectively).Root mean square error of predic-tion (RMSEP)was 0.45for the small external test set.The model predicted 82%of the compounds in the test sets as members to the correct class,18%were classified wrong.No low permeable compounds were classified as high permeable and only one high permeable compound was classified as low permeable.With this model it has been shown that the in silico prediction models for Caco-2cell permeability has taken a step closer to meet the expect-ations of a high throughput filter tool applied in early phase drug discovery.1IntroductionDuring the last decade,absorption,distribution,metabolism and excretion (ADME)related questions have been introduced earlier in the discovery programs than what has traditionally been the case.One of the reasons is thatADME and pharmacokinetic related issues are major reasons for failure in clinical trials [1].If these issues are addressed early,it is possible that drugs with higher chances of success can be brought forward through the clinical trials.Therefore,various in vitro methods have been introduced to predict in vivo behavior [2].Today,the in vitro methods can no longer match the demand in throughput as the number of compounds that can be generated has increased dramati-cally.A rising need for introducing selection filters prior to design and purchase of large compound libraries has turned the interest towards in silico predictions for ADME related properties.Hence,giving the chemists an opportunity to screen compounds in silico prior to synthesis.The impor-tance of good starting points,i.e.molecules with good ADME properties entering a discovery program,has indeed been emphasized [3],since it is often difficult to improve bad ADME properties of a specific scaffold.Absorption is one of the fundamental ADME properties that is screened for,and the intestinal epithelium (perme-QSAR Comb.Sci.2004,23DOI:10.1002/qsar.200330868¹2004WILEY-VCH Verlag GmbH &Co.KGaA,Weinheim303*To receive all correspondence:E-mail:Jonas.E.Nilsson@;Tel.: 4686973870Key words:PLS,QSPR,ADME prediction,in silico ,passive transportAbbreviations:ADME,Absorption Distribution Metabolism Excretion;BVT,Biovitrum;P app ,Apparent Permeability Coeffi-cient;PCA,Principal Component Analysis;PLS,Partial Least Square Projection to Latent Structures;PSA,Polar Surface area;RMSEP ,Root Mean Square Error of PredictionA General Model for Prediction of Caco-2Cell Permeability&Combinatorial Scienceability)is one of the major barriers to drug absorption.Several in vitro methods are available for permeability screening of which the cell culture model using Caco-2cells is the most widely used [4].Previously derived in silico models to describe Caco-2cell permeability include models based on thermodynamic [5±11],spatial [5,9,12±15],topological [6,16],structural [5,8,9,11,16,17],electronic [18],electro-topological [7,16,17],quantum mechanic [17,19]and 3D-QSAR descriptors [20±22],but many of the models are based only on a few descriptors or one type of descriptors.Polar surface area (PSA)has been used by many authors [5,12±15,17]and it has been shown that the less computer intensive PSA from a single conformation can be used instead of the dynamic PSA approach [17,23].Indications of non-linearity are found in some previous models regarding Caco-2cell permeability [9,11]or in situ absorption in rat gut [24]and non-linear models has been investigated before [5,18,25].In order to gain acceptance among users the model must provide predictions at a reasonable speed as well as guidance on which molecules to synthesize.Interpretability also increases the validity of the model if the descriptors used can be translated into known properties influencing permeability.One of the first correlations using Caco-2cell perme-ability was published in 1991[26]and up until today the size of the datasets,data quality and bias towards drugs with high permeability still limits the use of the derived prediction models [27].Considerable interlaboratory differences [28]and active transport mechanisms [27]further complicates the picture of which data that can be used to develop the models.Only three out of 18previously published Caco-2cell permeability prediction models include a large dataset (around 50compounds)from one source [10,11,22].Due to the limited access of data,good validation sets are an even larger problem.This new Caco-2cell prediction model addresses the problem with data quality.Massbalance,monolayer integrity and the presence of apparent active transport were monitored.The training set of 46compounds is one of the larger published data sets used for model construction.In order to deal with the multivariate nature of permeability many different kinds of previously used descriptors have been evaluated using partial least square projection to latent structures (PLS)statistics.2Materials and Methods2.1Drug Transport Studies 2.1.1ChemicalsUnlabelled substances were purchased from Sigma-Aldrich Co (St Louis,MO,USA).Radiolabelled compounds were obtained from Amersham Pharmacia Biotech (Bucking-hamshire,England)or from Perkin Elmer Life Sciences (Boston,MA,USA).[3H]tranexamic acid was purchased from Moravek Biochemicals (Brea,CA,USA) 2.1.2Cell cultureCaco-2cells were obtained from American Type Culture Collection (Rockville,MD,USA).All tissue culture media and buffers were purchased from Gibco (Paisley,Scotland,UK).Cell culture flasks and filters were obtained from Costar (NY,USA).The cells were cultured according to standard protocols [29].In brief,the cells were maintained in Dulbecco×s modified Eagles medium,containing 10%heat-inactivated fetal calf serum and 1%nonessential amino acids,in 95%relative humidity and 10%CO 2.5*105cells were seeded on polycarbonate filter inserts (Transwell,0.45m m mean pore size,12mm diameter).Passage number 36±46was used for permeability experiments 14±28days post seeding.2.1.3Drug Transport ExperimentsThe compounds to be tested were dissolved in Hank×s balanced salt solution supplemented with 25mM N-[2-hydroxyethyl]piperazine-N '-[2-ethanesulfonic acid](HEPES)buffer (HBSS;pH 7.2)to a final concentration of 0.05±2mM or a specific activity of 0.1±2m Ci/ml.HBSS (pH 7.2)was pipetted to the receiver chamber.The perme-ability experiments were carried out with a Robotic System (Multiprobe 204,Packard)[30]equipped with a shaker (Micromix,DPC)and a heating plate (CO 102,Linkam Scientific).The monolayers were incubated with HBSS in ambient atmosphere at 378C with a stirring rate of 300rpm.To initiate permeability experiment sample solution was added to the donor side at t 0together with [14C]-mannitol to monitor monolayer integrity for unlabeled compounds only.P app for mannitol <0.5*10À6cm/s was considered acceptable.Four samples were withdrawn at regular inter-vals from the receiver chamber making the total time of the experiment 60±120minutes and a sample was withdrawn from the donor side at the last time point.For some high permeable substances the calculation of P app was made with fewer time points,last point(s)excluded,to uphold the ™sink∫condition.Transport experiments were run in apical to basolateral (A/B)as well as basolateral to apical (B/A)direction (n 3±4).HPLC samples were frozen prior to analysis.2.1.4Calculation of Permeability CoefficientsAll experiments were performed under ™sink∫conditions [28].The permeability coefficient was calculated according to Eq.1:P appD Q D t Á11where D Q/D t is the steady state flux (mol/s),A is the area of the filter (cm 2)and C 0is the initial concentration in the donor chamber (mol/ml).All compounds with a ratio,304¹2004WILEY-VCH Verlag GmbH &Co.KGaA,WeinheimQSAR Comb.Sci.2004,23A.Nordqvist et al.&Combinatorial ScienceP app (B/A)divided by P app (A/B),<0.5or >2were consid-ered to be actively transported and were consequently removed from the model building.An exception was made for amiloride (ratio 2.5)to include more low permeable substances.All compounds disturbing the monolayer in-tergity and those showing a poor massbalance were also removed.The massbalance was calculated according to Eq.2:MB %C E ÁVD C S 4 ÁV RP 3n 1C S i ÁV SC 0DÁ1002Where C 0and C E is the initial and end concentration in the donor chamber respectively and C S(i)is the concentration (mol/ml)of each sample,one to four,withdrawn from the receiver chamber,where four is the last measured time point.V D ,V S and V R are the volume of the donor chamber,sample volume and the volume of the receiver chamber (ml).For some of the compounds a passive permeability was obtained after addition of 0.5±1.0mM verapamil,Table 1.Monolayer integrity was intact during the saturation experi-ment time of 60min.2.1.5Analytical MethodsRadioactive samples were analyzed using a liquid scintilla-tion counter (Tri-Carb 2700TR,Packard).Unlabelled substances were analyzed on a reversed phase HPLC with UV or fluorescence detection at a suitable wavelength.Gabapentin was analyzed as an o -phthaldialdehyde deriv-ative [31].The HPLC system was a Hewlett Packard series 1100equipped with a vacuum degasser,a binary pump,an autosampler,an UV detector and a FLD detector.The software used was HP ChemStation for LC (version A.06.01,Hewlett-Packard).Separations were made on a Zorbax SB-C18column (150*4.6mm i.d.).Injection volume was 50m l.Elution was performed at a flow rate of 1.0ml/min and the column was maintained at ambient temperature.The method was mainly isocratic and optimized for eachcompound to give reasonable retention times with water or KH 2PO 4buffer as mobile phase A and acetonitrile or methanol as mobile phase B.2.2Training Set and Test Set SelectionThe data set consisted of 51passively transported molecules,Tables 1and 2.10%(five)of the substances were selected to form an external test set (Table 2),and the remaining 46substances were used as a training set (Table 1).Prior to external test set selection a principal component analysis (PCA)(five components,R 2 0.7)was made with a set of descriptors similar to those used in previously published prediction models [5,6,9,16±18].To ensure a proper range in permeability in the external test set,the whole data set was ordered in ascending order of P app and accordingly divided into five groups.One molecule was chosen from each of the five groups to be included in the test set.Chemical diversity was ensured with help from the PCA score plots,t 1vs.t 2shown in Figure 1.A second external test set,called Biovitrum (BVT)test set,was formed with P app measured in screening mode by a one-point determination after two hours,one filter in each direction.Thus the BVT test set is only used for classification (low 1.5<medium 12<high (*10À6cm/s))in early screening phase and is not expected to give an absolute value of the permeability coefficient.All actively transported compounds,those with poor massbalance and those disturbing the monolayer integrity were excluded.125molecules in the BVT test set remained after this filtering.2.3Conformational AnalysisCalculations were made on an SGI Origin 2000,with 32processors.A 300-30,000step Monte Carlo Multiple Mini-mum (MCMM)conformational search included in Macro Model (version 7.1,Schrˆdinger)and in Maestro (version 5.0,Schrˆdinger)was used for the conformational analysisQSAR Comb.Sci.2004,23¹2004WILEY-VCH Verlag GmbH &Co.KGaA,Weinheim305&Combinatorial Scienceto find the global energy minimum conformation of all the molecules used in the training set.The force field MM3*or MMFF (Amber for danazol)were used for the energy minimization with simulated water using the truncated Newton conjugate gradient (TNCG),a maximum of 5000iterations and the convergence threshold set to 0.05kJ/ä*mol. 2.4Calculated DescriptorsTo describe a compound thoroughly a number of different descriptors were calculated using the software Cerius 2(version 4.7,Accelrys Inc.)and MolSurf (version 2003-01-07,Qemist AB).Most of the descriptors have previously been used in Caco-2prediction models,see introduction.306¹2004WILEY-VCH Verlag GmbH &Co.KGaA,WeinheimQSAR Comb.Sci.2004,23Table 1.List of compounds in the training set,their observed P app and calculated logP app .No.Structure Name P app a (*10À6cm/s)Observed logP app Calculated logP app 1Acebutolol 1.0Æ0.1À6.00À6.332L-alanine b 3.8Æ1.1À5.42À6.083Amiloride 0.7Æ0.1À6.15À5.734Antipyrine b51Æ9À4.29À4.425Acetylsalicylic acid 1.2Æ0.5À5.92À5.486Atenolol 0.5Æ0.1À6.30À6.157AZT b16Æ6À4.80À4.638Benzylpenicillin 0.6Æ0.2À6.22À5.349Bremazocine d 43Æ2À4.37À4.4310Caffeine b53Æ11À4.28À4.7411Chloramphenicol 25Æ4À4.60À5.2812Cimetidine c2.0Æ0.5À5.70À5.9313Corticosterone 34Æ9À4.47À4.8814Cortisol 24Æ5À4.62À5.1315Cortisone 31Æ8À4.51À4.8716Danazol22Æ2À4.66À4.0917Dexamethasone 36Æ9À4.44À4.4218Diazepam b 48Æ8À4.32À4.1419Digoxin c 13Æ2À4.89À4.6620Diltiazem d 42Æ5À4.38À4.1921L-Dopa b1.5Æ0.8À5.82À6.1522Doxorubicin c 3.1Æ0.7À5.51À5.8223Gabapentin 0.07Æ0.04À7.15À6.0824Ibuprofen 59Æ10À4.23À4.9025Inulin b0.12Æ0.02À6.92À6.8426Ketoprofen 50Æ7À4.30À4.3627Labetalol d 36Æ7À4.44À5.0428Lactic acid b1.1Æ0.4À5.96À5.7229Methyl scopolamine 0.85Æ0.09À6.07À5.7130Methyldopa 0.24Æ0.16À6.70À6.1631Metolazone c 16Æ4À4.80À4.7232Morphine b 10Æ2À5.00À5.0033Nadolol c 0.31Æ0.05À6.51À6.0034Naloxone 48Æ7À4.32À4.6235Nicotine b 35Æ10À4.46À4.8236Ouabain 0.11Æ0.02À6.96À6.7137Phenytoin b 44Æ8À4.36À4.0738Propranolol 44Æ9À4.36À4.5139Ranitidine 1.7Æ0.6À5.77À6.0440Sumatriptan 3.1Æ0.3À5.51À5.1941Timolol d 39Æ6À4.41À4.8142Tiotidine c2.2Æ0.3À5.66À5.7143Tranexamic acid 0.91Æ0.08À6.04À6.2244Urea b6.4Æ1.9À5.19À4.8745Verapamil c 51Æ5À4.29À4.0246Warfarin b40Æ11À4.40À4.20a Average calculated from P app A >B and P app B >A.bData originally published in [37].cPassive transport was measured after adding 0.5±1.0mM verapamil.dPartly measured at non-sink conditions.A.Nordqvist et al.&Combinatorial ScienceNoncovalent interactions are important for physicochem-ical properties.These interactions are known to arise from the electron distribution of a molecule and are attributes encoded in the E-State descriptors [32,33].Topological descriptors are related to properties like polarizability,dipole moment and steric effects [34],which in turn are related to permeability.Topological descriptors were there-fore investigated together with descriptors related to PSA,hydrogen bonding and lipophilicity.To account for ioniza-tion of the molecules in a fast way the number of anions and cations at pH 7were included.Squared terms were included to account for possible non-linearity.An SD-file with the lowest energy conformation found during the conforma-tional search was used as inputfile for the compounds in the training set.For the two external test sets a 2D to 3D conversion using Corina (version 2.4,Molecular Networks GmbH)and a following minimization in Maestro (version 5.0,Schrˆdinger)was conducted.All acids and bases were calculated in their neutral form except for methyl scopol-amine,which was calculated as charged species.PLSwas used to establish a prediction model using Simca-P (version 10,Umetrics AB)with unit variance scaling and mean centering.A variable selection was made where variables were excluded alternately,if an increase in Q 2was observed the variable was kept out of the model.3Result and DiscussionToday all prediction models are based on existing drugs,but will be used to predict permeability for future discovery compounds from novel compound classes.Thus,it is important to have a general model and to be able to recognize prediction failures.PLSanalysis was chosen since the ability to detect strong and moderate outliers is very good,it can handle many and correlated descriptors as well as missing values and it offers useful interpretation possi-bilities.Only descriptors that can be calculated with reasonable speed were used,which makes the model suitable for large virtual libraries.Relationships with single physico-chemical descriptors tend to impair when structural diversity is introduced [12,35]and therefore many different descriptor types were used in the present model (Table 3).The PLSanalysis of the training set (n 46)using 70descriptors resulted in a three component model with good statistical qualities (R 2 0.79and Q 2 0.65).PLSmodels with scrambled y-values produced Q 2that were negative or very low.The five compounds in the external test set were predicted with good accuracy,RMSEP 0.45(Figure 2).This model was developed as a tool for in silico screening.82%of the compounds in the test sets were correctly classified.18%were classified wrong (Figure 3),no low permeable compounds were predicted to have a high permeability and only one high permeable compound was predicted to be low permeable.To our knowledge the BVT test set is the largest external test set with compounds measured in the same laboratory on the same Caco-2cells as the training set compounds,a criterion for successful model implementation [36].In the present model descriptors of different types,describing hydrogen bonding properties,lipophilicity,size,PSA and charge,may lead to a more general model and interpretation should therefore be made in wider aspects ofQSAR Comb.Sci.2004,23¹2004WILEY-VCH Verlag GmbH &Co.KGaA,Weinheim307Table 2.List of compounds in the external test set,their observed P app and predicted logP app .No.Structure Name P app a (*10À6cm/s)Observed logP app Predicted logP app 1Famotidine 1.2Æ0.1À5.92À5.582Ketoconazole d 47Æ9À4.33À3.563Mannitol 0.28Æ0.39À6.55À6.474Salicylic acid 17Æ4À4.77À5.215Testosterone d58Æ5À4.24À4.58a Average calculated from P app A >B and P app B >A.dPartly measured at non-sinkconditions.&Combinatorial ScienceA.Nordqvist et al. &Combinatorial ScienceTable3.Descriptors considered in the Caco-2cell PLSmodel.No.Descriptor Type Source1SdCH2Electrotopological state index[38]Cerius2a2SsssCH Electrotopological state index[38]Cerius2a3StsC Electrotopological state index[38]Cerius2a4SssssC Electrotopological state index[38]Cerius2a5SsNH2Electrotopological state index[38]Cerius2a6SssNH Electrotopological state index[38]Cerius2a7SdsN Electrotopological state index[38]Cerius2a8SaaN Electrotopological state index[38]Cerius2a9SdO Electrotopological state index[38]Cerius2a10SssO Electrotopological state index[38]Cerius2a11SaaO Electrotopological state index[38]Cerius2a12SsF Electrotopological state index[38]Cerius2a13Wiener Topological Cerius2a14Kappa-1Topological Cerius2a15Kappa-2Topological Cerius2a16Kappa-1-AM Topological Cerius2a17Kappa-2-AM Topological Cerius2a18Density Topological Cerius2a19PMI-mag Topological Cerius2a20AlogP c Lipophilicity Cerius2a21AlogP98Lipophilicity Cerius2a22Jurs-PPSA-1Polar surface area[39]Cerius2a23Jurs-PPSA-2Polar surface area[39]Cerius2a24Jurs-PNSA-2Polar surface area[39]Cerius2a25Jurs-PPSA-3Polar surface area[39]Cerius2a26Jurs-PNSA-3Polar surface area[39]Cerius2a27Jurs-DPSA-3Polar surface area[39]Cerius2a28Jurs-FPSA-2Polar surface area[39]Cerius2a29Jurs-FNSA-2Polar surface area[39]Cerius2a30Jurs-FNSA-3Polar surface area[39]Cerius2a31Jurs-WNSA-2Polar surface area[39]Cerius2a32Jurs-WNSA-3Polar surface area[39]Cerius2a33Jurs-RNCG c Polar surface area[39]Cerius2a34Jurs-RPCSPolar surface area[39]Cerius2a35Jurs-TPSA c Polar surface area[39]Cerius2a36Jurs-TASA Polar surface area[39]Cerius2a37Jurs-RPSA c Polar surface area[39]Cerius2a38Jurs-RASA c Polar surface area[39]Cerius2a39Largest polar region c Polar surface area Molsurf b40Polar surface c Polar surface area Molsurf b41MR Size Cerius2a42Shadow-XZfrac c Size Cerius2a43Shadow-nu c Size Cerius2a44Shadow-Zlength c Size Cerius2a45Hbond donor c Hydrogen bond donating Cerius2a46a(H)c Hydrogen bond donating Molsurf b47Hbond acceptor c Hydrogen bond accepting Cerius2a48b(H)c Hydrogen bond accepting Molsurf b49HOMO Electronic Cerius2a50Apol Electronic Cerius2a51Polarity Electronic Molsurf b52Nuclephilicty olephins c Electronic Molsurf b53Electrophilicity olephins Electronic Molsurf b54#anions at pH7Electronic Molsurf b55#cations at pH7Electronic Molsurf b56P app Apparent permeability Determined in housea Cerius2(version4.7,Accelrys Inc.)b Molsurf(version2003-01-07,Qemist AB)c Squared terms also included308¹2004WILEY-VCH Verlag GmbH&Co.KGaA,Weinheim QSAR Comb.Sci.2004,23the PLScomponents rather than trying to simplify the relationship to include only a few descriptors.The majority of the descriptors related to hydrogen bonds and PSA are captured in the first component and are negatively corre-lated to logP app ,Figure 4and 5.Hydrophobic surface area and logP are strongly positively correlated with logP app accounted for in all components,component 1and 2shown in Figure 5.The two calculated logP values,no.20and 21,arenot perfectly correlated,indicating that they both contribute with information to stabilize the model.The E-State descriptors SsNH 2and SssNH,no.5and 6respectively,are connected to groups with hydrogen bond donor ability,thus negatively correlated to permeability,whereas E-State descriptors related to lipophilic atom types such as ÀF,ÀCH <and CH 2are positively correlated to logP app .The topological descriptors related to shape and size,no.13±19,are captured by the second component and all positively correlated to logP app .Two large negative contributions to permeability is the number of cations and anions captured inQSAR Comb.Sci.2004,23¹2004WILEY-VCH Verlag GmbH &Co.KGaA,Weinheim309Figure 5.Loading plot w*c[1]vs.w*c[2].Variables numbered according to Table 3.A General Model for Prediction of Caco-2Cell Permeability&Combinatorial Sciencethe second and third component (Figure 5),thus it is important to account for ionization in permeability model-ling.These two descriptors together with Hbond donors,squared Shadow-Zlength,AlogP98and AlogP make the single largest contributions (Figure 4).Preliminary results (unpublished)indicate that a wider range of descriptors introduce a more general applicability as compared to when only a subset of the descriptors are utilized.Simple models derived with this dataset and only combinations of MW,TPSA,AlogP ,Hbond donors and acceptors had a Q 2below 0.5,result not shown.General descriptors are also empha-sized in a recent paper analyzing failures of AMDE/Tox models [36].4ConclusionsThis new Caco-2cell prediction model has good statistical properties (R 2 0.79and Q 2 0.65,RMSEP 0.45)and provides a general tool to predict Caco-2cell permeability of large,structurally diverse virtual libraries.It has been tested on two external test sets and has proven its usefulness in classification of discovery compounds.82%of the com-pounds in the test sets were correctly classified and no low permeable compounds were classified as high permeable and only one high permeable compound was classified as low permeable.By the use of many different descriptor types this model deals with the multivariate nature of permeability and provides a general interpretation into known properties such as lipophilicity,hydrogen bonding,PSA,size and charge affecting epithelial cell permeability.AcknowledgementsWe would like to thank professor Michael Sjˆstrˆm at UmeaUniversity for valuable discussions and input to this manu-script.We would like to thank Ulf Martens,Maria Mastej and Helena Flatow for skilful experimental assistance.References[1]T.Kennedy,Drug Discov.Today 1997,2,436±444.[2]A.P .Li,Drug Discov.Today 2001,6,357±366.[3]C.A.Lipinski,F.Lombardo,B.W.Dominy,P .J.Feeney,Adv.Drug Deliv.Rev.2001,46,3±26.[4]P .Stenberg,K.Luthman,P .Artursson,J.Control.Release 2000,65,231±243.[5]T.I.Oprea,J.Gottfries,J.Mol.Graph.1999,17,261±274.[6]A.Kulkarni,Y.Han,A.J.Hopfinger,put.Sci.2002,42,331±342.[7]U.Norinder,T.÷sterberg,J.Pharm.Sci.2001,90,1076±1085.[8]T.÷sterberg,U.Norinder,put.Sci.2000,40,1408±1411.[9]H.van de Waterbeemd,G.Camenisch,G.Folkers,O.A.Raevsky,Quant.Struct.-Act.Relat.1996,15,480±490.[10]M.Yazdanian,S.L.Glynn,J.L.Wright,A.Hawi,Pharm.Res.1998,15,1490±1494.[11]S.Ren,S.E.J.Lien,Prog.Drug Res.2000,54,1±23.[12]K.Palm,K.Luthman,A.L.Ungell,G.Strandlund,F.Beigi,P .Lundahl,P .Artursson,J.Med.Chem.1998,41,5382±5392.[13]K.Palm,K.Luthman, A.L.Ungell,G.Strandlund,P .Artursson,J.Pharm.Sci.1996,85,32±39.[14]L.H.Krarup,I.T.Christensen,L.Hovgaard,S.Frokjaer,Pharm.Res.1998,15,972±978.[15]C.A.Bergstrˆm,M.Strafford,zorova,A.Avdeef,K.Luthman,P .Artursson,J.Med.Chem.2003,46,558±570.[16]F.Yamashita,S.Wanchana,M.Hashida,J.Pharm.Sci.2002,91,2230±2239.[17]P .Stenberg,U.Norinder,K.Luthman,P .Artursson,J.Med.Chem.2001,44,1927±1937.[18]S.Fujiwara,F.Yamashita,M.Hashida,Int.J.Pharm.2002,237,95±105.[19]U.Norinder,T.÷sterberg,P .Artursson,Pharm.Res.1997,14,1786±1791.[20]G.Bravi,J.H.Wikel,Quant.Struct.-Act.Relat.2000,19,39±49.[21]G.Cruciani,P .Crivori,P .A.Carrupt,B.Testa,Theochem-J.Mol.Struct.2000,503,17±30.[22]G.Cruciani,M.Pastor,W.Guba,Eur.J.Pharm.Sci.2000,11,S29±S39.[23]D.E.Clark,J.Pharm.Sci.1999,88,807±814.[24]V .Merino,J.Freixas,M.Delvalbermejo,T.M.Garrigues,J.Moreno,J.M.Pladelfina,J.Pharm.Sci.1995,84,777±782.[25]M.D.Wessel,P .C.Jurs,J.W.Tolan,S.M.Muskal,J.Chem.put.Sci.1998,38,726±735.[26]P .Artursson,J.Karlsson,mun.1991,175,880±885.[27]W.J.Egan,uri,Adv.Drug Deliv.Rev.2002,54,273±289.[28]P .Artursson,K.Palm,K.Luthman,Adv.Drug Deliv.Rev.1996,22,67±84.[29]P .Artursson,J.Karlsson,G.Ocklind,N.Schipper,Studyingtransport processes in absorptive epithelia,in:A.J.Shaw,(Ed.),Epithelial Cell Culture.A Practical Approach ,IRL Press,1996,pp.111±133.[30]P .Garberg,P .Eriksson,N.Schipper,B.Sjˆstrˆm,Pharm.Res.1999,16,441±445.[31]P .Lindroth,K.Mopper,Anal.Chem.1979,51,1667±1674.[32]K.Rose,L.H.Hall,L.B.Kier,put.Sci.2002,42,651±666.[33]L.H.Hall,L.B.Kier,put.Sci.1995,35,1039±1045.[34]M.Charton,B.I.Charton,put.Aided Mol.Des.2003,17,211±221.[35]K.Palm,P .Stenberg,K.Luthman,P .Artursson,Pharm.Res.1997,14,568±571.[36]T.R.Stouch,J.R.,Kenyon,S.R.,Johnson,X.-Q.Chem,A.Doweyko,Y.Li,put.Aided Mol.Des.2003,17,83±92.[37]P .Garberg,N.Borg,R.Cecchelli,R.D.Hurst,T.Lindmark,A.Mabondzo,J.E.Nilsson,T.J.Raub,D.Stanimirovic,T.Terasaki,J.-O.÷berg,T.÷sterberg,Toxicol .in Vitro ,accepted.[38]L.B.Kier,L.H.Hall,Molecular structure description ,Aca-demic Press,San Diego 1999,pp.245.[39]D.T.Stanton,P .C.Jurs,Anal.Chem.1990,62,2323±2329.Received on December 23,2003;Accepted on May 19,2004310¹2004WILEY-VCH Verlag GmbH &Co.KGaA,Weinheim QSAR Comb.Sci.2004,23A.Nordqvist et al.&Combinatorial Science。