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European Journal of Pharmaceutical Sciences 41 (2010) 107–117
Contents lists available at ScienceDirect
European Journal of Pharmaceutical Sciences
journa l homepage: www.e lsev ier .com/ locate /e jps
rediction of the in vitro permeability determined in Caco-2 cells by usingrtificial neural networks
aulo Paixãoa,b, Luís F. Gouveiaa, José A.G. Moraisa,∗
iMed.UL, Faculdade de Farmácia, Universidade de Lisboa, A. Prof. Gama Pinto, 1649-003 Lisboa, PortugalFaculdade de Ciências e Tecnologias da Saúde, Universidade Lusófona de Humanidades e Tecnologias, Lisboa, Portugal
r t i c l e i n f o
rticle history:eceived 10 January 2010eceived in revised form 12 May 2010ccepted 30 May 2010vailable online 8 June 2010
eywords:
a b s t r a c t
Caco-2 cells are currently the most used in vitro tool for prediction of the potential oral absorption ofnew drugs. The existence of computational models based on this data may potentiate the early selectionprocess of new drugs, but the current models are based on a limited number of cases or on a reducedmolecular space. We present an artificial neural network based only on calculated molecular descriptorsfor modelling 296 in vitro Caco-2 apparent permeability (Papp) drug values collected in the literature usingalso a pruning procedure for reducing the descriptors space. Log Papp values were divided into a training
n vitro Caco-2 apparent permeabilityolecular descriptors
n silico predictionrtificial neural networkruning of inputs
group of 192 drugs for network optimization and a testing group of another 59 drugs for early stop andinternal validation resulting in correlations of 0.843 and 0.702 and RMSE of 0.546 and 0.791 for the trainingand testing group, respectively. External validation was made with an additional group of 45 drugs with acorrelation of 0.774 and RMSE of 0.601. The selected molecular descriptors encode information related tothe lipophilicity, electronegativity, size, shape and flexibility characteristics of the molecules, which arerelated to drug absorption. This model may be a valuable tool for prediction and simulation in the drug
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. Introduction
Oral administration of drugs, due to its ease and patient com-liance, is the preferred route and a major goal in the developmentf new drug entities. It is also traditionally one of the reasonsor either discontinuation or prolongation of the developmentime of compounds. These problems lead to a new paradigm, a
ultivariate approach, in compound selection and optimizationVenkatesh and Lipper, 2000). In this context, and as a conse-uence of combinatorial chemistry, initial screening of compounds
n a number of thousands is done typically by using in silicopproaches. In vitro tests are responsible to reduce the number ofompounds from hundreds to dozens and in vivo animal modelso 1–5 finally potential drugs that are included in clinical tri-ls.
Various in silico models already exist to predict drug absorp-ion potential. One of the most known is the “Lipinski rule of 5”
Lipinski et al., 2001), based on the analysis of successful drugs, inhich indications for poor absorbed compounds are based on theumber of H-bond donors and acceptors, molecular weight andlogP. After that initial effort, various models were proposed to∗ Corresponding author. Tel.: +351 21 794 64 44/00/72; fax: +351 21 794 64 70.E-mail address: [email protected] (J.A.G. Morais).
apnbabor
928-0987/$ – see front matter © 2010 Published by Elsevier B.V.oi:10.1016/j.ejps.2010.05.014
ws the in silico estimation of the in vitro Caco-2 apparent permeability.© 2010 Published by Elsevier B.V.
uantitatively predict oral absorption. These were based on theearch to correlate some molecular descriptors to measures ofioavailability by means of multivariate regression tools. Modelsere developed based on the in vivo Human Intestinal Absorp-
ion (Abraham et al., 2002; Butina, 2004; Klopman et al., 2002;orinder and Osterberg, 2001; Zhao et al., 2001), in vivo absorption
ate constants (Linnankoski et al., 2006), in vivo jejunal effectiveermeability (Winiwarter et al., 2003; Winiwarter et al., 1998),
n vitro apparent permeability in artificial membranes (Fujikawat al., 2005; Fujikawa et al., 2007; Nakao et al., 2009; Verma etl., 2007) and in vitro apparent permeability (Papp) in Caco-2 cellsCastillo-Garit et al., 2008; Degim, 2005; Di Fenza et al., 2007;ujiwara et al., 2002; Hou et al., 2004; Nordqvist et al., 2004; Poncet al., 2004; Santos-Filho and Hopfinger, 2008; Yamashita et al.,002a).
In vivo based data, although obviously the target for theead drug, presents some drawbacks. In vivo global bioavail-bility results from different physical−chemical and biologicalrocesses that are difficult to isolate. To be absorbed, a drugeeds first to be in its soluble form. Passive diffusion may
e the principal driving force for drug absorption, but eitherctive transport or efflux mechanisms may condition the finalioavailability. Instability in the gastro-intestinal fluids and metab-lization may also reduce the amount of drug that effectivelyeaches the systemic circulation. For these reasons, calculation of
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08 P. Paixão et al. / European Journal of P
he absorption rate constants, which uses pharmacokinetic datafter intravenous and oral administration, may underestimate thebsorption. Collection of data on Human Intestinal Absorptions also experimentally demanding. It requires the evaluation ofhe fraction of dose, either as parent or metabolites, eliminatedy the faeces and urine which may easily result in underesti-ation of the actual fraction of dose absorbed. Finally, data on
ejunal effective permeability, which isolates the absorption pro-ess but is experimentally complex, is limited to only a smallumber of drugs. Another drawback in these three approaches
s that prediction confirmation is only possible when the drugs administered to humans in the end stages of drug discov-ry, leaving no room for “fine-tuning” during the developmenthase.
In vitro tests, on the contrary, are routinely performed byharmaceutical companies in an early stage of drug develop-ent. For this reason, a large number of Papp values for differentolecules are being produced and are/will be available to build
atabases with a large span of values from high to low bioavail-bilities. Parallel artificial membrane permeation assays (PAMPA)re a very promising tool to predict absorption, but there arearious experimental variables to be considered and in prac-ice it appears to produce the same outcome that a simpleog D7.4 assay in predicting high and low absorption (Galinis-uciani et al., 2007). Caco-2 cells, on the contrary, are a widelyreformed in vitro test with interesting properties when extrap-lating results to bioavailability. It is a cell system, characterizedy easy handling and at the same time resemble morphologicalnd biochemical characteristics of the intestinal cells (Vogel, 2006),hat shows a sigmoid relationship between the fraction absorbedn humans and the Papp across the cells (Artursson and Karlsson,991).
A number of statistical multivariate methods for developing inilico models are currently used. Artificial neural networks (ANN),owever, have shown superior performance to the linear multi-ariate class of methods in various QSAR models (Fujiwara et al.,002; Paixao et al., 2009; Sutherland et al., 2004; Votano et al.,004). To the best of our knowledge, only three studies were pre-iously reported to predict in vitro apparent permeability (Papp)n Caco-2 cells using ANN and molecular descriptors. Of these,wo studies (Degim, 2005; Fujiwara et al., 2002) were based onsmall number of drugs, did not make any particulate approach tovoid overfitting, nor made a convenient search of optimal molec-lar descriptors to predict the in vitro Caco-2 permeability. Thether study (Di Fenza et al., 2007) resolved some of these prob-ems, but was based on proprietary analogue drugs. Since theroposed model was built with the use of molecules contained invery similar chemical space, it was proved to be less competent
n predicting Log Papp values in a more chemical diverse externalataset.
With the purpose to overcome these limitations, we presentbackpropagation ANN model using early stop to predict in
itro Caco-2 Log Papp based on 296 structurally different drugsnd drug-like molecules collected in the literature. This largeataset would provide a broad molecular space, ideal to enlargehe applicability of the proposed model. Various molecularescriptors, encoding different molecular characteristics were
nitially used. Reduction of the number of possible molecularescriptors was made by a simple pruning procedure, allow-
ng to point out some possible factors influencing the drugs
bsorption process. Use of early stop during the whole mod-lling procedure also ensured that optimal prediction capabilitiesere maintained in the final model. This model, and proposedethodology, may be a valuable tool in early drug develop-ent.lbwta
ceutical Sciences 41 (2010) 107–117
. Materials and methods
.1. Data collection
In vitro Log Papp values (Table 1 ) for 296 different drugs werebtained from published studies of drug absorption in Caco-2 cells.xamples of drugs absorbed by passive diffusion using either thearacellular or transcellular route, and cases of active transport byifferent transporters were included. Examples of drugs sufferingfflux mechanisms were also included. In all cases when non-linearelations between Papp and apical concentrations were described,app values were collected in the saturation zone. Experimentalalues were obtained in pH values ranging from 6.8 to 7.4, withow to median passage numbers (28–46) and typically at a cell agelose to 21 days. Due to a large interlaboratory variability it is fre-uent to test various “model” drugs in order to validate the resultsbtained. In our case, we used the study with the larger numberf drugs tested as a “reference” (Yazdanian et al., 1998), and thether studies were normalized using the common drugs with thereference”. Using this procedure the overall interlaboratory vari-bility, judged by the experimental RMSE, was reduced from 0.427o 0.367. After normalization, median values were determined forach drug across laboratories. Finally, drugs were randomly dividedetween a training group (n = 192) used to train the ANN and aesting group (n = 59) used also in the training process but for theearly stop” process and as an internal validation dataset. A vali-ation group (n = 45) used to externally validate the in silico modelnd to establish the model ability to predict the in vitro Papp valuesas also created.
.2. In silico calculation of the molecular descriptors
The following methodology was used for the in sil-co descriptors: SMILES notation of each molecule wasbtained using the on-line PubChem Compound databasehttp://www.ncbi.nlm.nih.gov). Ionization descriptors (pKa(acid);Ka(base), and lipophilicity (Log P)) were obtained using then-line ALOGPS 2.1 program (Tetko and Bruneau, 2004). For drugsithout an acid ionisable group, a value of 15 was attributed toKa(acid). For drugs without a basic ionisable group, a value of −1as attributed to pKa(base). The remaining descriptors, related
o size, compactness, lipophilicity and others, were obtainedrom the on-line E-Dragon 1.0 software using CORINA to converthe SMILES notation to the 3D representation of the moleculeTetko et al., 2005). A total of 246 molecular descriptors werealculated, consisting of 8 molecular properties, 48 constitutionalescriptors, 73 topological descriptors, 26 geometrical descriptors,7 information indices and 44 WHIM descriptors.
.3. ANN model building
The Artificial Neural Network (ANN) non-linear regression waserformed using the backpropagation neural modelling systemnet for Windows v.2000 build 751 (Vesta Services inc., USA)nd an in-house developed Microsoft Excel® VBA routine for pro-ess automation. Papp values were log transformed and randomlyivided between a training (TR group with 65% of the total data),testing (TE group with 20% of the total data) and an external val-
dation group (VAL group with 15% of the total data). In order tollow the calculation of the relative relevance of the used molecu-
ar descriptors, all networks were built using normalized variablesoth in the input and output, and a sigmoid transfer functionas used in all connections. Early stopping of training is one ofhe methods that aim to prevent overfitting in ANN (Cataltepe etl., 1999). In early stopping, it is assumed that the generalization
P. Paixão et al. / European Journal of Pharmaceutical Sciences 41 (2010) 107–117 109
Table 1In vitro Log Papp (cm/s) Caco-2 values collected in the literature.
Dataset Drug Observed Predicted Residue
Tr (2E)-N-I-E-D −3.59 −4.18 0.59Tr (2E)-N-M-E-D −4.02 −4.13 0.12Tr (2E,4E)-N-I-D −5.24 −4.18 −1.07Tr (2E,4E,8Z)-N-I-T −4.46 −4.11 −0.35Tr (2E,4Z)-N-I-D-D −3.32 −4.05 0.73Tr (2E,4Z)-N-m-D-D −3.92 −4.02 0.10Tr (2E,4Z,8Z)-N-I-T-Y −3.80 −4.00 0.20Tr (2E,4Z,8Z,10Z)-N-I-T −3.67 −4.06 0.40Tr (2E,7Z)-N-I-D-D −4.84 −4.18 −0.66Tr (2E,9Z)-N-I-D-D −4.96 −4.32 −0.64Tr (2E,9Z)-N-M-D-D −4.21 −4.29 0.08Tr 5-Aminolevulinic acid −5.34 −5.36 0.02Tr Acetaminophen −4.44 −4.83 0.39Tr Alanine −5.63 −5.39 −0.24Tr Alfa-Methyldopa −6.63 −6.42 −0.22Tr Alfentanil −4.26 −4.55 0.29Tr Amoxicillin −6.31 −6.25 −0.06Tr Ampicillin −5.70 −6.06 0.36Tr Antipyrine −4.47 −4.77 0.30Tr Artemisinin −4.52 −4.19 −0.33Tr Artesunate −5.40 −5.71 0.31Tr Atenolol −6.34 −5.80 −0.54Tr Azithromycin −6.37 −6.46 0.09Tr Benzoic acid −4.15 −3.72 −0.43Tr Betaxolol ester −4.58 −5.47 0.89Tr Bosentan −6.19 −6.21 0.02Tr Bremazocine −4.82 −4.78 −0.03Tr Bromocriptine −5.91 −5.72 −0.18Tr Budesonide −4.89 −4.79 −0.10Tr Camptothecin −4.11 −4.52 0.41Tr Carbamazepine −4.37 −4.70 0.32Tr Catechin −6.82 −5.98 −0.84Tr Ceftriaxone −6.65 −6.72 0.07Tr Cefuroxime −6.79 −6.96 0.17Tr Cephalexin −6.42 −6.33 −0.09Tr Cephradine −5.69 −6.39 0.70Tr Chlorpromazine −4.70 −4.38 −0.32Tr Cimetidine −5.90 −5.43 −0.46Tr Ciprofloxacin −5.90 −5.24 −0.66Tr Clozapine −4.51 −4.59 0.08Tr Coumarin −4.25 −4.14 −0.11Tr Cromolina −6.89 −6.88 0.00Tr Cymarin −5.70 −5.74 0.04Tr Desipramine −4.97 −5.25 0.28Tr Dexamethasone −4.91 −5.24 0.33Tr d-glucose −4.67 −5.82 1.15Tr Diazepam −4.45 −4.75 0.30Tr Diclofenac −4.75 −5.81 1.06Tr Diltiazem −4.53 −4.76 0.23Tr DMP 581 −5.48 −5.70 0.22Tr DMP 811 −7.82 −6.03 −1.79Tr DMXAA-G −6.52 −6.23 −0.29Tr Dopamine −5.03 −5.15 0.12Tr DuP 167 −5.11 −5.53 0.42Tr DuP 532 −8.20 −6.74 −1.46Tr DuP 996 −4.62 −4.34 −0.28Tr Elarofiban −6.21 −6.08 −0.13Tr Enalapril −6.21 −6.60 0.39Tr Enalaprilat −6.59 −6.60 0.01Tr Ephedrine −4.97 −4.69 −0.28Tr Epicatechin −6.82 −6.15 −0.67Tr Epicatechin-3-Gallate −6.84 −6.60 −0.24Tr Epinephrine −6.23 −5.51 −0.71Tr Erythritol −6.16 −6.15 0.00Tr Erythromycin −5.78 −6.59 0.80Tr Estradiol −4.69 −4.27 −0.42Tr Etoposide −5.81 −6.24 0.43Tr EXP3174 −6.74 −6.70 −0.05Tr Felodipine −4.64 −4.77 0.13Tr Flavone −3.33 −4.37 1.03Tr Formoterol −5.63 −5.80 0.17Tr foscarnet −7.47 −6.72 −0.75Tr Furosemide −6.62 −6.78 0.16Tr Glycine −4.36 −5.32 0.95
Table 1 (Continued. )
Dataset Drug Observed Predicted Residue
Tr Glycine-Valine acyclovir −5.28 −5.66 0.38Tr Gly-Pro −5.12 −5.59 0.47Tr GlySar −5.62 −5.72 0.09Tr Griseofulvin −4.36 −4.51 0.15Tr Guanoxan −4.87 −5.60 0.73Tr H216/44 −6.88 −6.91 0.03Tr H244/45 −5.77 −5.54 −0.23Tr H95/71 −6.00 −5.58 −0.42Tr Harmaline −6.07 −6.18 0.11Tr Harmane −6.13 −6.43 0.29Tr Harmine −6.13 −6.15 0.02Tr Harmol −6.37 −5.80 −0.56Tr Hydralazine −5.17 −4.55 −0.63Tr Hydrochlorothiazide −6.06 −5.79 −0.27Tr Ibuprofen −4.58 −5.15 0.57Tr Imipramine −5.17 −5.21 0.04Tr Indomethacin −4.89 −4.41 −0.48Tr Inuline −6.25 −5.92 −0.33Tr Ketoconazole −4.93 −4.59 −0.33Tr Ketoprofen −4.48 −5.24 0.76Tr Labetalol −4.82 −6.12 1.30Tr Lactic acid −6.19 −5.18 −1.01Tr lactulose −6.81 −6.70 −0.11Tr Lamotrigine −4.39 −4.82 0.43Tr Lef553 −7.76 −6.96 −0.80Tr Lisinopril −7.39 −7.11 −0.27Tr l-phenylalanine −5.00 −5.04 0.03Tr LY122772 −3.87 −4.16 0.30Tr LY341904 −4.33 −4.51 0.18Tr LY341908 −4.14 −4.50 0.37Tr LY353462 −4.17 −4.66 0.48Tr LY354030 −4.06 −4.60 0.54Tr LY354400 −4.46 −4.56 0.10Tr LY357822 −4.21 −4.41 0.20Tr LY362546 −4.19 −4.45 0.25Tr LY366347 −4.14 −4.42 0.28Tr LY366349 −4.48 −4.59 0.11Tr LY366659 −4.20 −4.53 0.33Tr LY366799 −4.54 −4.55 0.02Tr LY366856 −4.33 −4.55 0.22Tr LY368177 −5.15 −4.41 −0.74Tr Mannitol −6.48 −6.90 0.41Tr Metformin −6.20 −5.60 −0.60Tr Methanol −4.40 −4.55 0.14Tr Methotrexate −6.10 −6.65 0.56Tr Methylprednisolone −4.93 −4.61 −0.32Tr Methylscopolamine −6.23 −5.44 −0.79Tr Metoprolol −4.60 −5.35 0.75Tr Mibefradil −5.04 −4.62 −0.42Tr Naloxone −4.67 −4.91 0.24Tr Naringenin −4.41 −5.34 0.92Tr Naringin −6.82 −6.22 −0.60Tr N-desmethylclozapine −4.68 −4.72 0.04Tr Netivudine −6.84 −6.18 −0.66Tr Nevirapine −4.52 −4.67 0.15Tr Nicotine −4.71 −4.61 −0.10Tr Nitrendipine −4.93 −4.90 −0.04Tr Nordazepan −4.20 −4.57 0.38Tr Norfloxacin −6.70 −5.24 −1.46Tr Octyl gallate −6.82 −5.90 −0.92Tr Olsalazine −7.80 −6.80 −1.00Tr ondansetron −4.34 −5.00 0.66Tr Ouabain −7.23 −7.92 0.69Tr Oxacillin −5.58 −5.87 0.30Tr Oxprenolol −4.76 −5.20 0.44Tr Oxprenolol ester −4.57 −5.14 0.57Tr PEG 282 −5.84 −6.14 0.30Tr PEG 326 −6.35 −6.24 −0.11Tr PEG 370 −6.68 −6.35 −0.33Tr PEG900 −6.26 −6.38 0.12Tr Penicillin V −7.51 −6.23 −1.28Tr pindolol −4.71 −5.47 0.76Tr Piroxicam −4.33 −5.36 1.03Tr Pnu200603 −6.47 −5.54 −0.93Tr Pravastatin −5.84 −6.63 0.79Tr Prazosin −5.26 −4.95 −0.31
110 P. Paixão et al. / European Journal of Pharmaceutical Sciences 41 (2010) 107–117
Table 1 (Continued. )
Dataset Drug Observed Predicted Residue
Tr Prednisolone −4.72 −4.71 −0.01Tr Progesterone −4.64 −4.19 −0.45Tr Propofol −4.77 −4.37 −0.40Tr Propranolol ester −4.54 −4.97 0.43Tr Propylthiouracil −4.46 −4.55 0.09Tr Proscillaridin −6.41 −5.83 −0.58Tr Quercetin −6.82 −6.35 −0.47Tr Quinidine −4.94 −4.44 −0.49Tr Rapamycin −4.96 −4.62 −0.34Tr Roxithromycin −6.91 −6.87 −0.05Tr Saquinavir −6.48 −6.36 −0.12Tr SB209670 −5.23 −4.19 −1.04Tr Scopolamine −4.93 −5.03 0.10Tr SDZ-RAD −4.63 −4.99 0.36Tr Serotonin −4.86 −5.17 0.30Tr Sulfadiazine −4.75 −5.50 0.74Tr Sulfamethoxazole −4.88 −5.78 0.90Tr Sulfanilamide −5.26 −5.23 −0.02Tr Sulpiride −6.65 −5.69 −0.96Tr Sumatriptan −5.80 −4.91 −0.89Tr Talinolol −6.11 −5.46 −0.65Tr TAPP −7.73 −6.51 −1.22Tr TArPP −6.12 −7.34 1.22Tr Tartaric acid −6.65 −6.75 0.10Tr Taurocholic acid −4.75 −5.28 0.53Tr Telithromycin −6.65 −6.38 −0.27Tr Telmisartan −4.82 −5.05 0.23Tr Terbutaline −6.16 −5.38 −0.79Tr Tetracycline −5.70 −6.62 0.92Tr Timolol −4.92 −4.94 0.02Tr Tiotidine −5.88 −5.95 0.07Tr Topiramate −4.54 −3.94 −0.59Tr Tranexamic acid −6.28 −5.55 −0.72Tr Trimethoprim −4.50 −4.70 0.19Tr Urea −5.34 −5.71 0.37Tr Valacyclovir −5.20 −5.18 −0.01Tr Valproic acid −4.60 −4.61 0.01Tr Vinblastine −5.48 −5.64 0.16Tr XM970 −5.30 −5.92 0.62Tr Zidovudine −5.06 −5.35 0.29Tr Ziprasidone −5.23 −5.08 −0.15Tr Zomepirac −5.61 −5.66 0.05
Te (2E,4Z)-N-I-D-D −3.57 −4.00 0.43Te Acyclovir −6.07 −5.51 −0.57Te Alprenolol −4.57 −5.01 0.44Te Alprenolol ester −4.52 −4.98 0.45Te Amiloride −6.46 −6.52 0.06Te Amisulpride −5.66 −5.62 −0.03Te Betaxolol −4.91 −5.38 0.46Te Caffeine −4.48 −4.52 0.04Te Caftaric acid −5.41 −7.60 2.19Te Cefazolin −6.23 −6.66 0.43Te Cefcapene −6.94 −6.89 −0.04Te Cefcapene Pivoxil −5.11 −5.65 0.54Te Cephalexin acetylated −6.63 −6.28 −0.36Te Chlorothiazide −6.72 −5.37 −1.35Te Cichoric acid −5.13 −7.66 2.54Te Cinnamic acid −3.64 −3.80 0.16Te Corticosterone −4.50 −4.43 −0.07Te Creatinine −5.95 −4.75 −1.20Te Digoxin −5.58 −7.63 2.05Te DMXAA −4.60 −5.06 0.46Te Echinacoside −6.65 −6.60 −0.05Te Famotidine −6.16 −6.68 0.52Te fleroxacin −4.98 −5.17 0.19Te Fluconazole −4.82 −4.93 0.10Te Gabapentin −8.16 −5.90 −2.25Te Harmalol −6.37 −5.80 −0.56Te Isoxicam −5.61 −5.71 0.10Te lidocaine −4.36 −4.53 0.17Te LY153186 −4.36 −4.50 0.14Te LY355081 −4.26 −4.41 0.15
Table 1 (Continued. )
Dataset Drug Observed Predicted Residue
Te LY357132 −4.20 −4.35 0.15Te LY362683 −4.43 −4.55 0.12Te LY366572 −4.24 −4.51 0.27Te LY366853 −5.30 −4.72 −0.58Te LY368766 −4.07 −4.29 0.23Te Metaprotenol −6.42 −5.45 −0.97Te Morphine −5.45 −4.74 −0.71Te Naproxen −4.66 −4.74 0.08Te Paclitaxel −7.30 −7.18 −0.12Te Phenytoin −4.49 −5.15 0.66Te Pirenzepine −6.36 −4.94 −1.42Te Pivampicillin −4.49 −5.36 0.86Te Propranolol −4.63 −5.09 0.46Te Raffinose −7.62 −7.61 0.00Te Ranitidine −6.31 −5.58 −0.73Te Remikiren −6.34 −5.97 −0.38Te SB217242 −4.46 −4.24 −0.21Te Sildenafil −4.51 −4.12 −0.39Te Sotalol −5.76 −5.35 −0.41Te Sucrose −5.77 −7.29 1.52Te Sulfasalazine −6.89 −6.91 0.03Te Tenidap −4.57 −4.20 −0.37Te Testosterone −4.43 −4.28 −0.15Te Tiacrilast −5.07 −4.93 −0.14Te Timolol ester −4.67 −4.71 0.04Te Trovafloxacin −4.81 −5.27 0.45Te Uracil −5.37 −4.58 −0.79Te Verapamil −4.81 −4.70 −0.11Te Warfarin −4.63 −5.50 0.87
Va Acebutolol −6.10 −5.71 −0.38Va Acebutolol ester −4.68 −5.46 0.77Va Acetylsalicylic Acid −5.62 −4.81 −0.81Va Acrivastine −6.35 −6.03 −0.32Va Aminopyrine −4.44 −5.00 0.56Va Benzyl penicillin −6.08 −6.04 −0.04Va Bupropion −4.24 −4.54 0.30Va Chloramphenicol −4.96 −5.35 0.39Va Clonidine −4.58 −4.82 0.24Va Cortisona −4.69 −4.29 −0.40Va Danazol −4.84 −4.39 −0.45Va Dexamethasone-�-d-glucoronide −6.38 −5.84 −0.54Va Dexamethasone-b-d-glucoside −6.83 −7.76 0.93Va DMP 728 −6.58 −6.84 0.26Va Doxorubicin −6.48 −6.25 −0.24Va Doxycycline −4.95 −6.60 1.65Va E3174 −7.30 −6.61 −0.69Va Fexofenadine −6.51 −6.25 −0.26Va Fluparoxan −4.10 −4.90 0.80Va Ganciclovir −6.37 −5.70 −0.68Va Glipizide −5.97 −5.68 −0.28Va Guanabenz −4.97 −4.89 −0.08Va Hydrocortisone −4.82 −4.63 −0.19Va l-DOPA −6.05 −6.25 0.20Va Loracarbef −7.34 −6.34 −1.00Va Losartan −6.05 −6.11 0.06Va LY366092 −4.03 −4.42 0.39Va LY366094 −5.22 −4.64 −0.58Va LY368227 −4.36 −4.51 0.15Va LY368228 −4.37 −4.56 0.20Va Meloxicam −4.71 −5.59 0.88Va Methyl gallate −5.39 −5.61 0.21Va Metolazone −5.21 −5.30 0.09Va Nadolol −6.14 −5.89 −0.25Va Oxazepam −4.22 −4.74 0.52Va PEG 194 −5.28 −6.02 0.74Va PEG 400 −6.98 −6.43 −0.54Va Phencyclidine −4.61 −5.04 0.43Va Practolol −6.02 −5.56 −0.45Va Propyl gallate −6.82 −5.78 −1.04Va Salicylic acid −4.82 −4.18 −0.64Va Sulfapyridine −5.00 −5.35 0.35
P. Paixão et al. / European Journal of Pharma
Table 1 (Continued. )
Dataset Drug Observed Predicted Residue
Va Sulfisoxazole −4.92 −5.84 0.92Va Theophylline −4.61 −4.20 −0.41Va Tolbutamide −4.28 −5.47 1.19
Drugs were randomly divided between a training (TR), testing (TE) and externalvalidation (VA) group. Mean values after normalization with common drugs with thework from Yazdanian et al. (1998) are presented in the observed column. Predictedvalues and the residuals between observed and predicted values, using the 12-6-3-3-1 network are also presented. Data values were obtained from references (Altet al., 2004; Anand et al., 2003; Artursson et al., 1993; Behrens and Kissel, 2003;Bhardwaj et al., 2005; Caldwell et al., 1998; Cavet et al., 1997; Crowe, 2002; Croweand Lemaire, 1998; Da Violante et al., 2004; Ekins et al., 2001; Ertl et al., 2000;Fujiwara et al., 2002; Fukada et al., 2002; Furfine et al., 2004; Hartter et al., 2003;He et al., 2004; Hilgendorf et al., 2000; Hu and Borchardt, 1990; Hunter et al., 1993;Irvine et al., 1999; Karlsson et al., 1999; Kerns et al., 2004; Khan et al., 2004; Lallooet al., 2004; Lee et al., 2005; Marino et al., 2005; Markowska et al., 2001; Martel etal., 2003; Masungi et al., 2004; Matthias et al., 2004; Neuhoff et al., 2003; Nishimuraet al., 2004; Nordqvist et al., 2004; Oka et al., 2002; Pachot et al., 2003; Pade andStavchansky, 1998; Polli and Ginski, 1998; Saitoh et al., 2004; Soldner et al., 2000;Stenberg et al., 2001; Sun et al., 2003; Tammela et al., 2004; Tannergren, 2004;Tronde, 2002; Vaidyanathan and Walle, 2003; Watson et al., 2001; Wong et al.,21R
eamtsaDonAt
cdrdadrorpdepganlmwbitsmctTtt
dgbo
anara
vdp
2
dtIsPlwn
3
mstpnstiwowelttvapcfsiisiwofcBwas pursued using a brute force approach. In order to maintain
002; Yang and Wang, 2003; Zhou et al., 2004; Camenisch et al., 1998; Gan et al.,993; Hou et al., 2004; Laitinen et al., 2003; Miret et al., 2004; Raoof et al., 1996;ibadeneira et al., 1996; Yazdanian et al., 1998).
rror decreases in an early period of training, reaches a minimumnd then increases as training goes on, while the training erroronotonically decreases. Therefore, it is considered better to stop
raining at an adequate time (Amari et al., 1997), However, the realituation is a lot more complex with generalization curves almostlways having more than one local minimum (Prechelt, 1998).ue to this fact, each network was run for an excessive numberf iterations, being kept the iteration that resulted in the lowestormalized data mean square error (RMS) of the testing group.dditionally, each network was started 15 times with random ini-
ial values to avoid training convergence to local minima.Network optimization was performed in a two-step pro-
ess. The first step consisted in the reduction of the molecularescriptors space. This was initially done by removing highly cor-elated (r > 0.90) descriptors, allowing the removal of molecularescriptors with information contained within another descriptor,nd repetitive descriptors (90% or more equal values) removingescriptors with non-discriminatory characteristics. In order toemove molecular descriptors that were uncorrelated with theutput variable, the remaining descriptors were tested against aandom input variable, considered as a negative control. To accom-lish this, the inputs were randomly divided in four groups ofata in order to have sufficient discrimination between the rel-vant and non-relevant descriptors and at the same time a highrobability of interdependent inputs being present in the sameroup. These groups of inputs were then tested in an ANN withnetwork architecture of two hidden layers with three hidden
eurons in the first layer and two hidden neurons in the secondayer (n-3-2-1 network). These ANN were optimized for each of the
olecular descriptor groups, and the relevance of each descriptorithin each group of inputs was tested against the percent contri-
ution for the final output of the random input variable, the onlynputs being kept were the ones with relevance above this nega-ive control. Next, a pruning procedure was undertaken, using theame network architecture and including all the remaining relevantolecular descriptors. After optimization of this ANN, the percent
ontribution for the final output was calculated to all descrip-
ors and these were organised by quartiles of relative importance.hree new networks were built and optimized, with the descrip-ors grouped based on the quartiles of relevance (Q1, Q2 and Q3),he quartile group with the lowest test RMS being kept. Finally, theawnb
ceutical Sciences 41 (2010) 107–117 111
escriptor presenting the worst percent contribution value in thisroup was removed and a new network with n-1 descriptors wasuild. This procedure was repeated until a significant degradationf the RMS with a descriptor removal was observed.
The second step consisted of the optimization of the networkrchitecture for the most relevant molecular descriptors. Severaletworks were built varying the number of hidden layers (1–3)nd the number of hidden neurons (1–10) but maintaining theatio between the number of patterns to the number of connectionsbove 1.
External validation was made by comparing the ANN predictedalues to the observed in vitro values with the drugs in the vali-ation group, not previously used in the training and optimizationrocess.
.4. Statistical analysis
Correlation between the predicted and observed values wasetermined by means of the Pearson correlation coefficient (r) forhe training, testing and validation groups for the Log(Papp) data.n order to assess the precision and bias of the network, root meanquared error (RMSE) and mean error (ME) were also estimated.ercentage of correct values within a 10-fold error (difference ofog predicted to log observed values outside the interval −1 to 1)
as also determined in order to assess the qualitative ability of theetwork.
. Results
Optimization of the ANN model was made as described underethods. Regarding the reduction of the molecular descriptors
pace, the removal of correlated and non-discriminatory descrip-ors reduced the number of descriptors to 79. The next tworocedures were performed using an ANN with an architecture of-3-2-1, n being the number of molecular descriptors. This ANNtructure was chosen as a compromise between simplicity, in ordero avoid memorisation, and complexity to allow an adequate learn-ng ability of the network. To escape local minima each network
as run multiple times with random initial values. To avoid mem-risation of data and loss of predictive ability, the learning processas early stopped based on the degradation of test RMS error. In the
nd of each descriptor set optimization step, the network with theower average test RMS error was kept, and the relative contribu-ion of each input on the Log(Papp) prediction was established usinghe “input node interrogator” option in QNet. In this option, indi-idual input contribution is determined by cycling each input forll training patterns and computing the effect on the network’s out-ut response. By comparison with a random input descriptor it wasoncluded that out of the 79 descriptors randomly distributed inour groups, 22 were considered non-relevant and were removed,ince their input contribution was equal or inferior to the randomnput. Finally, by the pruning procedure described under methods,t was observed that the first quartile with 14 descriptors pre-ented the lower training and testing RMS error. The subsequentndividual input removal resulted that 12 molecular descriptors
ere needed to characterize the output response, as a degradationf either the training and testing RMS error was observed whenurther removals were undertaken (Fig. 1). The final model wasonstructed with the 12 molecular descriptors presented in Table 2.ased on these descriptors, a network architecture optimization
practical computational time, the network architectural spaceas swept between 1 and 3 hidden layers. The number of hiddeneurons by layer was variable taking into consideration the ratioetween the number of patterns to the number of connections.
112 P. Paixão et al. / European Journal of Pharmaceutical Sciences 41 (2010) 107–117
Table 2Summary of the 12 molecular descriptors used in the ANN model.
Molecular descriptor Description Molecular descriptor Description
Log P Octanol:water partition coefficient Te T total size index weighted by atomic Sanderson electronegativitiesnR09 Number of nine-membered rings Jhetv Balaban-type index from van der Waals weighted distance matrixHy Hydrophilic Index FDI Folding Degree IndexMAXDP Maximal electrotopological positive variation SPH Spherosity
Rotable Bond Fractionid) pKa of the strongest acidic group
D deschini and Consonni (2000).
TtR
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Fig. 1. Plot of the performance of the pruning procedure in reducing the molec-ular descriptors number both in the training (TR) and in the testing (TE) groupsnormalized mean square error (RMS).
Fig. 2. Plot of the in vitro observed vs. in silico predicted Log Papp (cm/s) values fortlgt
Cso
TPSA(tot) Topological Polar Surface Area for O, N and S RBFnO Number of oxygen atoms in the molecule pKa(ac
etailed information of the presented molecular descriptors may be obtained in To
his ratio was maintained above 1 in order to reduce the ability ofhe network to memorise the data and avoid overfitting (So andichards, 1992; Turner et al., 2004).
At the end of the optimization process the networks with theest statistical characteristics in terms of the train correlation, testorrelation and test 1 − Log value tolerance were kept and theirerformance is presented in Table 3. The three networks show iden-ical performances, with similar values for precision and bias in theredicted values for the TR and TE groups of data. The network 12--4-1 presented the best behaviour in terms of the TR group of dataut is the less competent in terms of the TE group of data. This isn indication on the dangers of using the training statistics for net-ork selection, even when early stopping is made. On the contrary,etwork 12-6-3-3-1, although the most complex of the three archi-ectures considered, presented the lowest difference between thetatistics of the TR and TE groups of data, indicating that overfit-ing was not significant and that probably this network has betterrediction characteristics.
The ability of the selected networks to predict the Log Papp ofew drugs was tested in the 45 drugs presented in the validationroup and not previously used in the training process. As can beeen (Table 3 and Fig. 2) the network 12-6-3-3-1 presented the bestredictions with 91% of drugs well predicted within the ±1−Logolerance value. It also presented the lower RMSE of the three net-orks considered and an insignificant bias in the three groups ofata, confirming the better prediction abilities of the 12-6-3-3-1etwork.
. Discussion
.1. Model data
Drug absorption in the GIT is a complex process. Various phys-cal, chemical and biological processes are known to be involvedn the overall oral bioavailability of drugs that may condition the
ovement of compounds from the intestinal lumen to the bloodirculation. Permeation in the epithelium lining is, however, onef the major factors governing the drug bioavailability, and the initro Caco-2 cell system has been shown to be a suitable modelor studies on intestinal drug absorption (Artursson and Karlsson,991; Rubas et al., 1993; Yee, 1997).
We collected a large database of drug and drug-like Papp Caco-2alues described in the literature. Examples of drugs absorbed byll mechanisms of membrane movement were included. However,hen concentration-dependent Papp values were described, dataas collected in the saturation zone in order to primarily consider
he passive diffusion process of drug absorption. Various studiesave enlightened the fact that variability within and between lab-ratories may condition the creation of large databases of Papp
alues from Caco-2 cells (Sun et al., 2002; Volpe, 2007). Reasonsor this variability may be related to differences in the experimen-al conditions and with heterogeneity of the cell lines. The effect ofH in the permeability of drugs, as expected by the Brodie the-ry (Jollow and Brodie, 1972), was experimentally described in
(ttdK
he 12-6-3-3-1 ANN model. Solid line represents the line of unity and the dashedines the ±1 Log tolerance value. Closed circles are indicative of drugs in the trainingroup and open circles represent drugs in the testing group. Grey marks are relativeo the external validation group.
aco-2 cells by Neuhoff et al. (Neuhoff et al., 2003). It is also demon-trated that ideal passage numbers would be inside the intervalf 28–65 with significant differences in TEER in higher passages
Briske-Anderson et al., 1997). Cell culture time also influenceshe morphology, formation of tight junctions and membrane pro-eins expression, and the complete differentiation of Caco-2 cells isescribed to be achieved only after 21 days in culture (Behrens andissel, 2003). Some authors have previously dealt with the issue
P. Paixão et al. / European Journal of Pharmaceutical Sciences 41 (2010) 107–117 113
Table 3Statistical evaluation of the performance of the best three ANN to predict Log Papp values based on the molecular descriptors for the training, testing and external validationdatasets.
Network TR TE VAL
Corr Tol RMSE ME Corr Tol RMSE ME Corr Tol RMSE ME
12-3-3-1 0.893 95.8 0.458 0.000 0.709 81.4 0.766 −0.064 0.717 88.9 0.679 0.06078.086.4
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12-6-4-1 0.919 97.9 0.400 0.003 0.67512-6-3-3-1 0.843 92.2 0.546 0.001 0.702
orr, correlation coefficient; Tol, % of correct values within ±1 Log value; RMSE, roo
f variability between different laboratories in their QSAR works.amashita et al (Yamashita et al., 2002a) proposed the “latent mem-rane permeability” concept, assuming that all Caco-2 permeabilityatasets share a hidden, common relationship between their mem-rane permeability and physicochemical properties. In practice,he authors made an iterative normalization between the origi-al data sources in order to reduce variability and concluded forheir applicability in QSAR analysis. Fujiwara et al. (Fujiwara et al.,002) made an analysis of variance for the residual sum of squaresetween before and after taking each sub-dataset in their QSARodel, and concluded that none of the sub-dataset was signifi-
antly different from the rest of the entire dataset. The authors didot indicate any particular rationale in their data collection. In ourase, by including drugs from studies with similar experimentalrotocols, as described under methods, part of the described vari-bility is expected to be reduced. Additionally, normalization byommon drugs of the different sources of data from the data sourceith the largest number of molecules, the experimental RMSE was
educed from 0.427 to 0.367.
.2. Model building and validation
We used a multilayer feedforward backpropagation neuralodelling system in order to build a QSAR model to relate molec-
lar descriptors to Papp values as this type of modelling approachas shown its superiority over other multivariate methods in QSARhen non-linear relations exist (Sutherland et al., 2004; Winkler,
004). However, like other regression methods, neural networksre prone to overfitting and validation problems. To overcome this,
training group of data with 192 drugs was used to provide infor-ation to the network, and a testing group with 59 drugs to indicatehere overfitting begins, finalising the training process. In order toalidate the model, we used an external validation dataset with 45rugs. As a primary approach to overcome overfitting, early stop
2PrbH
able 4ummary of several previously published in silico methods to predict Caco-2 Log Papp.
Reference Method Group
Yamashita et al. GA-PLS TRVA
Hou et al. MLR TRVA
Guangli and Yiyu SVM TRVA
Castillo-Garit et al. MLR TRVA
Fujiwara et al. ANN TRVA
Degim ANN TRVA
Di Fenza et al. GA-ANN TR 1VATR 2VA
OO, leave-one-out; NR, not reported.a RMSE.
0.809 −0.042 0.763 88.9 0.636 −0.0680.791 −0.058 0.774 91.1 0.601 −0.043
n squared error; ME, mean error.
as used in order to reduce the effective size of each parameter.arly stop has shown to be an effective procedure to decrease theeneralization error when the number of training cases is not muchigger than the number of modified parameters (Amari et al., 1997).he results obtained seem to confirm that overfitting did not occur,s the statistics of the training and testing group of data for the threeest ANN architectures are not different (Table 3). We also tried toinimise overfitting by reducing the number of dimensions of the
arameter space, using a sensitivity-based pruning procedure. Asan be seen in Fig. 1 and as described by others (Tetko et al., 1996),educing the number of descriptors resulted in a better predictionbility of the ANN as judged by the RMS of the testing group of data.owever, by removing more than 12 descriptors, an evident loss ofoth training and prediction ability was observed.
Although the data in the testing group was not used to train theetwork, if used to test the predictive performance of the method,
ike a “leave-one-out” approach, a weak form of validation woulde obtained and ideally an external group of data should be usedEkins, 2003). Evaluation of the external validation group statisticsor the ANN 12-6-3-3-1 (Table 3) clearly shows that this modelontains good prediction properties with a RMSE value less than theouble of the experimental error (0.367 vs. 0.601), an acceptableerformance for a QSAR method (Dearden and Worth, 2007).
.3. Comparison with other approaches
Since the initial efforts of Palm et al. (Palm et al., 1996), variousuthors have proposed in silico models to predict the in vitro Caco-2ermeability of drugs (Table 4). Yamashita et al. (Yamashita et al.,
002b) used a genetic-algorithm-based partial least squares (GA-LS) based on Log Papp values of 73 drugs resulting in a model with= 0.886 and standard deviation (SE) = 0.389. Validation was madey a “leave-some-out” procedure with an r = 0.825 and SE = 0.474.ou et al. (Hou et al., 2004) used multiple linear regression (MLR)n Correlation (r) Precision (SE)
73 0.89 0.39LOO 0.83 0.4777 0.85 0.4123 0.78 0.4977 0.88 0.36a
23 0.85 0.52a
75 0.85 0.4319 0.71 0.5187 0.79 0.44a
LOO NR 0.51a
50 0.98 0.197 0.86 0.46106 0.72 NR50 0.40 NR101 0.75 NR50 0.61 NR
114 P. Paixão et al. / European Journal of Pharmaceutical Sciences 41 (2010) 107–117
F PSA(to
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ig. 3. ANN interaction trends between the (A) Log P and Hy, (B) FDI and SPH, (C) Tescriptors constant as median values.
ith 100 diverse drugs resulting in an r = 0.85 and SE = 0.41 in theraining group of 77 drugs and r = 0.78 and SE = 0.49 in the val-dation group of 23 drugs. Guangli and Yiyu (Guangli and Yiyu,006) with the same dataset but using a support vector machineSVM) method obtained an r = 0.88 and RMSE = 0.361 for the train-ng and r = 0.85 and RMSE = 0.518 for the validation group of data.lose inspection of the data revealed, however, that the validationata was overpredicted with a mean error of 0.11, especially athe lower end of permeability values. Again with the same datasetut using atom-based stochastic and non-stochastic linear indicess molecular descriptors, Castillo-Garit et al. (Castillo-Garit et al.,008) used MLR resulting in an r = 0.85 and SE = 0.43 in the trainingroup of data, after the removal of two outliers. Validation group,ith only 19 drugs, presented an r = 0.71 and SE = 0.51. Fujiwara et
l. (Fujiwara et al., 2002) used an ANN to correlate five molecularescriptors to Log Papp values in 87 drugs. They reported a correla-ion coefficient of 0.79 for the whole dataset and a RMSE of 0.435nd 0.507 for the entire dataset and for the leave-one-out cross-alidation. The authors also concluded for superior results withheir ANN model when comparing with a multiple linear regres-ion analysis in the same dataset. In another study, Degim (Degim,
005) also used an ANN to correlate four molecular descriptors toog Papp values in 50 drugs. The author reported an r = 0.976 andSE = 0.188 in the training group. ANN performance in a valida-ion group of seven drugs was not so effective with an r = 0.856 andSE = 0.457, that together with the training SE below the typical
siai(
t) and nO and (D) Te and pKa(acid), in the Log Papp prediction, maintaining all other
xperimental error are indicative of a possible situation of over-tting in the model. Finally, Di Fenza et al. (Di Fenza et al., 2007)sed an ANN coupled with a genetic algorithm (GA-ANN) searcho optimize and correlate Volsurf descriptors to Papp values in tworoprietary set of 106 NK-2 receptors antagonists (TR 1) and 101ompounds with anti-tumour activity (TR 2). Authors reported an= 0.75 in a “leave-14%-out” test for their best model, but due tostrong structure similarity in the training data, the model onlyrovided an r = 0.61 in an external dataset.
Direct comparison between our proposed approach with theserevious models is difficult as an independent external validationroup of data would be required. However, our model presentsimilar statistical performance to the formers with a significantlyarger dataset of drugs. Moreover, the fact that an external valida-ion was made in our ANN model is an additional indication that itears good predictive abilities.
.4. Model interpretation
When considering the relevant molecular descriptors (Table 2),hey can be grouped in terms of lipophilicity, electronegativity,
ize, shape and flexibility characteristics of the molecules, and aren agreement with the expected physical forces involved in drugbsorption (Stenberg et al., 2002). Log P is a measure of lipophilic-ty and has been used in various studies regarding drug absorptionEgan et al., 2000; Lipinski et al., 2001; Winiwarter et al., 1998).
harma
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P. Paixão et al. / European Journal of P
R09 is the number of cyclic structures containing nine carbontoms in a molecule, and besides its simple structural character-zation, it may also be a measure of either size or lipophilicity.y is a simple empirical index related to hydrophilicity of com-ounds based on count descriptors (Todeschini and Consonni,000). MAXDP, which represents the maximum positive intrin-ic state difference and can be related to the electrophilicity ofhe molecule (Gramatica et al., 2000), is directly related with thelectronegativity and water solubility of the molecule. TPSA(tot) isefined as the part of the surface area of the molecule associatedith O, N, S and the H bonded to any of these atoms, and is related to
he hydrogen bonding ability of the molecule. nO is a count descrip-or that indicates the number of O atoms in a molecule. SolubilityMartini et al., 1999), PSA and simple atom counts related to the-bonding capacity were extensively used to predict drug absorp-
ion in various studies (Hou et al., 2004; Kelder et al., 1999; Palmt al., 1997; Winiwarter et al., 2003; Winiwarter et al., 1998). Te isWHIM descriptor and Jhetv is a Balaban type index both relatedith the molecular size. FDI and SPH are both shape descriptors
ndicating, respectively, if a molecule is folded or linear and flatr spherical. RBF indicates the number of bonds which allow freeotation around themselves weighted by the total number of bonds,nd is a measure of molecular flexibility (Todeschini and Consonni,000). Both size and flexibility have also been correlated withrug absorption previously (Hou et al., 2004; Lipinski et al., 2001).Ka(acid) is the pKa of the strongest acid group in the molecule.
Interpretation of the individual descriptors in an ANN, due tohe “black-box” nature of this approach, is frequently described asifficult. One possible approach to overcome this issue is to findow input trends interact with output predictions. This was doney varying some selected inputs at a time, considering all the othersonstant with the median value. Fig. 3A) presents the relation-hip between Log P and Hy. Log P relates positively with Log Papp
s various authors have shown (Egan et al., 2000; Hou et al., 2004;orinder and Osterberg, 2001), but at values for Log P > 5 the effect
eaches a plateau, which is in accordance with the “Lipinsky’s rulef five” (Lipinski et al., 2001). Hy relates negatively with Log Papp,s the more hydrophilic the molecule, the lower the permeabil-ty value. Fig. 3B shows the relationship between the FDI and SPHescriptors and may be an indication of the molecular behaviour inhe paracellular route of absorption. As seen, when neither foldedFDI close to 1) nor spherical (SPH close to 0), the molecule presentslower Log Papp value. Fig. 3C presents the relationship between
PSA(tot) and nO and, as visible, Log Papp values decrease with bothhe increase in TPSA(tot) and nO. It has been suggested that PSA inome way describes the desolvation of a compound as it movesrom an aqueous to a lipid environment (van de Waterbeemd etl., 1998) and it has been described that drugs with less than 10%bsorption had PSA values above 140 Å (Palm et al., 1997). nO is aimple descriptor that may also be related to the H-bonding abilityf the molecule, and its increased size, like the increase of the sumf H-bond donors and acceptors, relates negatively with Log Papp
Winiwarter et al., 1998). Fig. 3D shows the relationship betweenhe Te and pKa(acid) descriptors. Te is a molecular descriptor closelyelated to the molecular weight and its increase, as reported earlyFagerholm et al., 1999; Lipinski et al., 2001), resulted in a lowerog Papp value. pKa(acid) indicates that acids with pKa values beloware generally badly absorbed. This may not be completely relatedith the “pH’s partitioning hypotheses” as the pKa(basic) did not
eem to be an important descriptor for Log Papp. Martin (Martin,
005) also reported a difference in absorption between anionsnd the remaining classes of molecules that resulted in differentbsorption rules. One possible explanation for this effect may beelated to the electrostatic repulsion between a negatively chargedolecule and the also negatively charge enterocyte.C
ceutical Sciences 41 (2010) 107–117 115
. Conclusions
In conclusion, we presented an ANN methodology based on aruning procedure to reduce the descriptors space, and using anarly stop approach that produced a robust and logical model withood predictive abilities. Comparison of our model with the previ-usly reported ANN models also stresses its characteristics. It wasuild with more drugs than the method of Fujiwara et al. (Fujiwarat al., 2002), with an external validation with similar statistics of theuthors own “leave-one-out” cross-validation. Additionally, it wasot overfitted as the model of Degim (Degim, 2005) appears to bend it provides a larger chemical space than the model developedy Di Fenza et al. (Di Fenza et al., 2007). The presented methodologylso provides some advantages over these same reports. Early stoprovided an efficient training procedure to reduce the overfittingroblem of the ANN. Pruning of the molecular inputs, in order toeduce the molecular space, resulted in a low computational inten-ive approach still capable to select molecular descriptors that arenown to be related with the drug absorption process. Overall, thispproach may be a useful prediction tool in the initial screening ofompounds.
cknowledgment
This work was partially supported by project numberFRH/BD/28545/2006 from Fundacão para a Ciência e a Tecnologia.
ppendix A. Supplementary data
Supplementary data associated with this article can be found,n the online version, at doi:10.1016/j.ejps.2010.05.014.
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