Skip to main content
PMC home page
The AAPS Journal logoLink to The AAPS Journal
. 2006 Feb 3;8(1):E27–E40. doi: 10.1208/aapsj080104

Recent progress in the computational prediction of aqueous solubility and absorption

Stephen R Johnson 1,✉, Weifan Zheng 2
PMCID: PMC2751421  PMID: 16584131

Abstract

The computational prediction of aqueous solubility and/or human absorption has been the goal of many researchers in recent years. Such anin silico counterpart to the biopharmaceutical classification system (BCS) would have great utility. This review focuses on recent developments in the computational prediction of aqueous solubility, P-glycoprotein transport, and passive absorption. We find that, while great progress has been achieved, models that can reliably affect chemistry and development are still lacking. We briefly discuss aspects of emerging scientific understanding that may lead to breakthroughs in the computational modeling of these properties.

Keywords: Solubility, permeability, P-glycoprotein, BCS, in silico, prediction

Full Text

The Full Text of this article is available as a PDF (248.1 KB).

Contributor Information

Stephen R. Johnson, Phone: 609-252-3003, Email: stephen.johnson@bms.com

Weifan Zheng, Email: weifan_zheng@unc.edu.

References

  • 1.Amidon GL, Lennernaes H, Shah VP, Crison JR. A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm Res. 1995;12:413–420. doi: 10.1023/a:1016212804288. [DOI] [PubMed] [Google Scholar]
  • 2.Center for Drug Evaluation and Research . Guidance for Industry. Rockville, MD: CDER/FDA; 2000. [Google Scholar]
  • 3.Yu LX, Amidon GL, Polli JE, et al. Biopharmaceutics classification system: the scientific basis for biowaiver extensions. Pharm Res. 2002;19:921–925. doi: 10.1023/a:1016473601633. [DOI] [PubMed] [Google Scholar]
  • 4.Valko K. Measurements and predictions of physicochemical properties. High-Throughput ADMETox Estimation. 2002;1–24:A21–A25. [Google Scholar]
  • 5.Lipinski C. Aqueous solubility in discovery, chemistry, and assay changes. Methods Principles Med Chem. 2003;18:215–231. [Google Scholar]
  • 6.Lombardo F, Gifford E, Shalaeva MY. In silico ADME prediction: data, models, facts and myths. Mini Rev Med Chem. 2003;3:861–875. doi: 10.2174/1389557033487629. [DOI] [PubMed] [Google Scholar]
  • 7.Delaney JS. Predicting aqueous solubility from structure. Drug Discov Today. 2005;10:289–295. doi: 10.1016/S1359-6446(04)03365-3. [DOI] [PubMed] [Google Scholar]
  • 8.Jorgensen WL, Duffy EM. Prediction of drug solubility from structure. Adv Drug Deliv Rev. 2002;54:355–366. doi: 10.1016/s0169-409x(02)00008-x. [DOI] [PubMed] [Google Scholar]
  • 9.Eros D, Keri G, Kovesdi I, Szantai-Kis C, Meszaros G, Orfi L. Comparison of predictive ability of water solubility QSPR models generated by MLR, PLS, and ANN methods. Mini Rev Med Chem. 2004;4:167–177. doi: 10.2174/1389557043487466. [DOI] [PubMed] [Google Scholar]
  • 10.Taskinen J, Yliruusi J. Prediction of physicochemical properties based on neural network modelling. Adv Drug Deliv Rev. 2003;55:1163–1183. doi: 10.1016/s0169-409x(03)00117-0. [DOI] [PubMed] [Google Scholar]
  • 11.McFarland JW, Du CM, Avdeef A. Factors influencing the water solubilities of crystalline drugs. Methods Principles Med Chem. 2003;18:232–242. [Google Scholar]
  • 12.Stouch TR, Kenyon JR, Johnson SR, Chen X-Q, Doweyko A, Li Y. In silico ADME/Tox: why models fail. J Comput Aided Mol Des. 2003;17:83–92. doi: 10.1023/a:1025358319677. [DOI] [PubMed] [Google Scholar]
  • 13.Bergstrom CA, Wassvik CM, Norinder U, Luthman K, Artursson P. Global and local computational models for aqueous solubility prediction of drug-like molecules. J Chem Inf Comput Sci. 2004;44:1477–1488. doi: 10.1021/ci049909h. [DOI] [PubMed] [Google Scholar]
  • 14.Cheng A, Merz KM. Prediction of aqueous solubility of a diverse set of compounds using quantitative structure-property relationships. J Med Chem. 2003;46:3572–3580. doi: 10.1021/jm020266b. [DOI] [PubMed] [Google Scholar]
  • 15.Bergstrom CA. In silico predictions of drug solubility and permeability: two rate-limiting barriers to oral drug absorption. Basic Clin Pharmacol Toxicol. 2005;96:156–161. doi: 10.1111/j.1742-7843.2005.pto960303.x. [DOI] [PubMed] [Google Scholar]
  • 16.Bergstrom CAS, Norinder U, Luthman K, Artursson P. Experimental and computational screening models for prediction of aqueous drug solubility. Pharm Res. 2002;19:182–188. doi: 10.1023/a:1014224900524. [DOI] [PubMed] [Google Scholar]
  • 17.Chen X-Q, Cho SJ, Li Y, Venkatesh S. Prediction of aqueous solubility of organic compounds using a quantitative structure-property relationship. J Pharm Sci. 2002;91:1838–1852. doi: 10.1002/jps.10178. [DOI] [PubMed] [Google Scholar]
  • 18.Lobell M, Sivarajah V. In silico prediction of aqueous solubility, human plasma protein binding and volume of distribution of compounds from calculated pKa and AlogP98 values. Mol Divers. 2003;7:69–87. doi: 10.1023/b:modi.0000006562.93049.36. [DOI] [PubMed] [Google Scholar]
  • 19.Votano JR, Parham M, Hall LH, Kier LB, Hall LM. Prediction of aqueous solubility based on large datasets using several QSPR models utilizing topological structure representation. Chem Biodivers. 2004;1:1829–1841. doi: 10.1002/cbdv.200490137. [DOI] [PubMed] [Google Scholar]
  • 20.Hilal SH, Karickhoff SW, Carreira LA. Prediction of the solubility, activity coefficient and liquid/liquid partition coefficient of organic compounds. QSAR Combinator Sci. 2004;23:709–720. [Google Scholar]
  • 21.Nohair M, Zakarya D. Prediction of solubility of aliphatic alcohols using the restricted components of autocorrelation method (RCAM) J Mol Model (Online) 2003;9:365–371. doi: 10.1007/s00894-003-0137-x. [DOI] [PubMed] [Google Scholar]
  • 22.Yan A, Gasteiger J. Prediction of aqueous solubility of organic compounds by topological descriptors. QSAR Combinator Sci. 2003;22:821–829. [Google Scholar]
  • 23.Hou TJ, Xia K, Zhang W, Xu XJ. ADME evaluation in drug discovery, IV: prediction of aqueous solubility based on atom contribution approach. J Chem Inf Comput Sci. 2004;44:266–275. doi: 10.1021/ci034184n. [DOI] [PubMed] [Google Scholar]
  • 24.Xia X, Maliski E, Cheetham J, Poppe L. Solubility prediction by recursive partitioning. Pharm Res. 2003;20:1634–1640. doi: 10.1023/a:1026195503465. [DOI] [PubMed] [Google Scholar]
  • 25.Yan A, Gasteiger J. Prediction of aqueous solubility of organic compounds based on a 3D structure representation. J Chem Inf Comput Sci. 2003;43:429–434. doi: 10.1021/ci025590u. [DOI] [PubMed] [Google Scholar]
  • 26.Yin C, Liu X, Guo W, Lin T, Wang X, Wang L. Prediction and application in QSPR of aqueous solubility of sulfur-containing aromatic esters using GA-based MLR with quantum descriptors. Water Res. 2002;36:2975–2982. doi: 10.1016/s0043-1354(01)00532-2. [DOI] [PubMed] [Google Scholar]
  • 27.Klamt A, Eckert F, Hornig M, Beck ME, Burger T. Prediction of aqueous solubility of drugs and pesticides with COSMO-RS. J Comput Chem. 2002;23:275–281. doi: 10.1002/jcc.1168. [DOI] [PubMed] [Google Scholar]
  • 28.Bergstrom CA, Wassvik CM, Norinder U, Luthman K, Artursson P. Global and local computational models for aqueous solubility prediction of drug-like molecules. J Chem Inf Comput Sci. 2004;44:1477–1488. doi: 10.1021/ci049909h. [DOI] [PubMed] [Google Scholar]
  • 29.Yan A, Gasteiger J, Krug M, Anzali S. Linear and nonlinear functions on modeling of aqueous solubility of organic compounds by two structure representation methods. J Comput Aided Mol Des. 2004;18:75–87. doi: 10.1023/b:jcam.0000030031.81235.05. [DOI] [PubMed] [Google Scholar]
  • 30.Manallack DT, Tehan BG, Gancia E, et al. A consensus neural network-based technique for discriminating soluble and poorly soluble compounds. J Chem Inf Comput Sci. 2003;43:674–679. doi: 10.1021/ci0202741. [DOI] [PubMed] [Google Scholar]
  • 31.Ma W, Zhang X, Luan F, et al. Support vector machine and the heuristic method to predict the solubility of hydrocarbons in electrolyte. J Phys Chem A. 2005;109:3485–3492. doi: 10.1021/jp0501446. [DOI] [PubMed] [Google Scholar]
  • 32.Tetko IV, Bruneau P. Application of ALOGPS to predict 1-octanol/water distribution coefficients, logP, and logD, of AstraZeneca in-house database. J Pharm Sci. 2004;93:3103–3110. doi: 10.1002/jps.20217. [DOI] [PubMed] [Google Scholar]
  • 33.Thompson JD, Cramer CJ, Truhlar DG. Predicting aqueous solubilities from aqueous free energies of solvation and experimental or calculated vapor pressures of pure substances. J Chem Phys. 2003;119:1661–1670. [Google Scholar]
  • 34.Wegner JK, Zell A. Prediction of aqueous solubility and partition coefficient optimized by a genetic algorithm based descriptor selection method. J Chem Inf Comput Sci. 2003;43:1077–1084. doi: 10.1021/ci034006u. [DOI] [PubMed] [Google Scholar]
  • 35.Engkvist O, Wrede P. High-throughput, in silico prediction of aqueous solubility based on one- and two-dimensional descriptors. J Chem Inf Comput Sci. 2002;42:1247–1249. doi: 10.1021/ci0202685. [DOI] [PubMed] [Google Scholar]
  • 36.Raevsky OA, Raevskaja OE, Schaper K-J. Analysis of water solubility data on the basis of HYBOT descriptors, part 3: solubility of solid neutral chemicals and drugs. QSAR Combinator Sci. 2004;23:327–343. [Google Scholar]
  • 37.Schaper K-J, Kunz B, Raevsky OA. Analysis of water solubility data on the basis of HYBOT descriptors, part 2: solubility of liquid chemicals and drugs. QSAR Combinator Sci. 2003;22:943–958. [Google Scholar]
  • 38.Huuskonen J, Salo M, Taskinen J. Aqueous solubility prediction of drugs based on molecular topology and neural network modeling. J Chem Inf Comput Sci. 1998;38:450–456. doi: 10.1021/ci970100x. [DOI] [PubMed] [Google Scholar]
  • 39.Jorgensen WL, Duffy EM. Prediction of drug solubility from Monte Carlo simulations. Bioorg Med Chem Lett. 2000;10:1155–1158. doi: 10.1016/s0960-894x(00)00172-4. [DOI] [PubMed] [Google Scholar]
  • 40.Tantishaiyakul V. Prediction of aqueous solubility of organic salts of diclofenac using PLS and molecular modeling. Int J Pharm. 2004;275:133–139. doi: 10.1016/j.ijpharm.2004.01.028. [DOI] [PubMed] [Google Scholar]
  • 41.Tantishaiyakul V. Prediction of the aqueous solubility of benzylamine salts using QSPR model. J Pharm Biomed Anal. 2005;37:411–415. doi: 10.1016/j.jpba.2004.11.005. [DOI] [PubMed] [Google Scholar]
  • 42.Parshad H, Frydenvang K, Liljefors T, Larsen CS. Correlation of aqueous solubility of salts of benzylamine with experimentally and theoretically derived parameters: a multivariate data analysis approach. Int J Pharm. 2002;237:193–207. doi: 10.1016/s0378-5173(02)00042-x. [DOI] [PubMed] [Google Scholar]
  • 43.Ikeda H, Chiba K, Kanou A, Hirayama N. Prediction of solubility of drugs by conductor-like screening model for real solvents. Chem Pharm Bull (Tokyo) 2005;53:253–255. doi: 10.1248/cpb.53.253. [DOI] [PubMed] [Google Scholar]
  • 44.Hendriksen BA, Felix MV, Bolger MB. The composite solubility versus pH profile and its role in intestinal absorption prediction. AAPS PharmSci. 2003;5:E4–E4. doi: 10.1208/ps050104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Horter D, Dressman JB. Influence of physicochemical properties on dissolution of drugs in the gastrointestinal tract. Adv Drug Deliv Rev. 2001;46:75–87. doi: 10.1016/s0169-409x(00)00130-7. [DOI] [PubMed] [Google Scholar]
  • 46.Agoram B, Woltosz WS, Bolger MB. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv Drug Deliv Rev. 2001;50:S41–S67. doi: 10.1016/s0169-409x(01)00179-x. [DOI] [PubMed] [Google Scholar]
  • 47.Rinaki E, Valsami G, Macheras P. Quantitative biopharmaceutics classification system: the central role of dose/solubility ratio. Pharm Res. 2003;20:1917–1925. doi: 10.1023/b:pham.0000008037.57884.11. [DOI] [PubMed] [Google Scholar]
  • 48.Bergstrom CAS, Luthman K, Artursson P. Accuracy of calculated pH-dependent aqueous drug solubility. Eur J Pharm Sci. 2004;22:387–398. doi: 10.1016/j.ejps.2004.04.006. [DOI] [PubMed] [Google Scholar]
  • 49.Setschenow JZ. Uber Die Konstitution Der Salzlosungenauf Grund Ihres Verhaltens Zu Kohlensaure. Z Physik Chem. 1889;4:117–125. [Google Scholar]
  • 50.Ni N, Yalkowsky SH. Prediction of Setschenow constants. Int J Pharm. 2003;254:167–172. doi: 10.1016/s0378-5173(03)00008-5. [DOI] [PubMed] [Google Scholar]
  • 51.Li Y, Hu Q, Zhong C. Topological modeling of the Setschenow constant. Ind Eng Chem Res. 2004;43:4465–4468. [Google Scholar]
  • 52.Gould PL. Salt selection for basic drugs. Int J Pharm. 1986;33:201–217. [Google Scholar]
  • 53.Reid RC, Pransnitz JM, Poling BE. The Properties of Gases and Liquids. New York, NY: McGraw Hill; 1984. [Google Scholar]
  • 54.Hribar B, Southall NT, Vlachy V, Dill KA. How ions affect the structure of water. J Am Chem Soc. 2002;124:12302–12311. doi: 10.1021/ja026014h. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Abraham MH, Le J. The correlation and prediction of the solubility of compounds in water using an amended solvation energy relationship. J Pharm Sci. 1999;88:868–880. doi: 10.1021/js9901007. [DOI] [PubMed] [Google Scholar]
  • 56.Huang L-F, Tong W-Q. Impact of solid state properties on developability assessment of drug candidates. Adv Drug Deliv Rev. 2004;56:321–334. doi: 10.1016/j.addr.2003.10.007. [DOI] [PubMed] [Google Scholar]
  • 57.Pudipeddi M, Serajuddin ATM. Trends in solubility of polymorphs. J Pharm Sci. 2005;94:929–939. doi: 10.1002/jps.20302. [DOI] [PubMed] [Google Scholar]
  • 58.Nielsen AB, Frydenvang K, Liljefors T, Buur A, Larsen C. Assessment of the combined approach of N-alkylation and salt formation to enhance aqueous solubility of tertiary amines using bupivacaine as a model drug. Eur J Pharm Sci. 2005;24:85–93. doi: 10.1016/j.ejps.2004.09.012. [DOI] [PubMed] [Google Scholar]
  • 59.Romero AJ, Rhodes CT. Stereochemical aspects of the molecular pharmaceutics of ibuprofen. J Pharm Pharmacol. 1993;45:258–262. doi: 10.1111/j.2042-7158.1993.tb05549.x. [DOI] [PubMed] [Google Scholar]
  • 60.Sanghvi T, Jain N, Yang G, Yalkowsky SH. Estimation of aqueous solubility by the general solubility equation (GSE) the easy way. QSAR Combinator Sci. 2003;22:258–262. [Google Scholar]
  • 61.Bergstrom CA, Norinder U, Luthman K, Artursson P. Molecular descriptors influencing melting point and their role in classification of solid drugs. J Chem Inf Comput Sci. 2003;43:1177–1185. doi: 10.1021/ci020280x. [DOI] [PubMed] [Google Scholar]
  • 62.Jain A, Yang G, Yalkowsky SH. Estimation of melting points of organic compounds. Ind Eng Chem Res. 2004;43:7618–7621. [Google Scholar]
  • 63.Johnson JLH, Yalkowsky SH. Two new parameters for predicting the entropy of melting: eccentricity (e) and spirality (m) Ind Eng Chem Res. 2005;44:7559–7566. [Google Scholar]
  • 64.Karthikeyan M, Glen RC, Bender A. General melting point prediction based on a diverse compound data set and artificial neural networks. J Chem Inf Model. 2005;45:581–590. doi: 10.1021/ci0500132. [DOI] [PubMed] [Google Scholar]
  • 65.Thomas E, Rubino J. Solubility, melting point and salting-out relationships in a group of secondary amine hydrochloride salts. Int J Pharm. 1996;130:179–185. [Google Scholar]
  • 66.Katritzky AR, Jain R, Lomaka A, Petrukhin R, Maran U, Karelson M. Perspective on the relationship between melting points and chemical structure. Cryst Growth Des. 2001;1:261–265. [Google Scholar]
  • 67.Raevsky OA, Schaper K-J, Seydel JK. H-bond contribution to octanol-water partition coefficients of polar compounds. Quant Struct-Activ Relat. 1995;14:433–436. [Google Scholar]
  • 68.Raevsky OA. Hydrogen bond strength estimation by means of the HYBOT program package. In: van de Waterbeemd H, Testa B, Folkers G, editors. Computer-Assisted Lead Finding and Optimization. Basel, Switzerland: Verlag Helvetica Chimica Acta; 1997. pp. 369–378. [Google Scholar]
  • 69.Raevsky OA, Grigor'ev VG. Quantitative description of lipophilicity of organic chemicals on the basis of polarizability and H-bond acceptor factors. Chem-Pharm Z (Rus) 1999;33:46–49. [Google Scholar]
  • 70.Raevsky OA, Schaper KJ, van de Waterbeemd H, McFarland JW. Hydrogen bond contributions to properties and activities of chemicals and drugs. In: Gundertofte K, Jorgensen FS, editors. Molecular Modeling and Prediction of Bioactivity. New York, NY: Springer; 2000. pp. 221–227. [Google Scholar]
  • 71.Ran Y, He Y, Yang G, Johnson JLH, Yalkowsky SH. Estimation of aqueous solubility of organic compounds by using the general solubility equation. Chemosphere. 2002;48:487–509. doi: 10.1016/s0045-6535(02)00118-2. [DOI] [PubMed] [Google Scholar]
  • 72.Ouvrard C, Mitchell JBO. Can we predict lattice energy from molecular structure? Acta Crystallogr B. 2003;59:676–685. doi: 10.1107/s0108768103019025. [DOI] [PubMed] [Google Scholar]
  • 73.Copley RCB, Deprez LS, Lewis TC, Price SL. Computational prediction and X-ray determination of the crystal structures of 3-oxauracil and 5-hydroxyuracil—an informal blind test. CrystEngComm. 2005;7:421–428. [Google Scholar]
  • 74.Dey A, Kirchner MT, Vangala VR, Desiraju GR, Mondal R, Howard JAK. Crystal structure prediction of aminols: advantages of a supramolecular synthon approach with experimental structures. J Am Chem Soc. 2005;127:10545–10559. doi: 10.1021/ja042738c. [DOI] [PubMed] [Google Scholar]
  • 75.Price SL. Quantifying intermolecular interactions and their use in computational crystal structure prediction. CrystEngComm. 2004;6:344–353. [Google Scholar]
  • 76.Price SL. The computational prediction of pharmaceutical crystal structures and polymorphism. Adv Drug Deliv Rev. 2004;56:301–319. doi: 10.1016/j.addr.2003.10.006. [DOI] [PubMed] [Google Scholar]
  • 77.Datta S, Grant DJ. Crystal structures of drugs: advances in determination, prediction and engineering. Nat Rev Drug Discov. 2004;3:42–57. doi: 10.1038/nrd1280. [DOI] [PubMed] [Google Scholar]
  • 78.Gavezzotti A. A molecular dynamics test of the different stability of crystal polymorphs under thermal strain. J Am Chem Soc. 2000;122:10724–10725. [Google Scholar]
  • 79.Gavezzotti A. The chemistry of intermolecular bonding: organic crystals, their structures and transformations. Synlett. 2002;2002:0201–0214. [Google Scholar]
  • 80.Gavezzotti A. Quantitative ranking of crystal packing modes by systematic calculations on potential energies and vibrational amplitudes of molecular dimers. J Chem Theory Computat. 2005;1:834–840. doi: 10.1021/ct050091w. [DOI] [PubMed] [Google Scholar]
  • 81.Pajeva I, Wiese M. Molecular modeling of phenothiazines and related drugs as multidrug resistance modifiers: a comparative molecular field analysis study. J Med Chem. 1998;41:1815–1826. doi: 10.1021/jm970786k. [DOI] [PubMed] [Google Scholar]
  • 82.Pajeva IK, Wiese M. Pharmacophore model of drugs involved in P-glycoprotein multidrug resistance: explanation of structural variety (hypothesis) J Med Chem. 2002;45:5671–5686. doi: 10.1021/jm020941h. [DOI] [PubMed] [Google Scholar]
  • 83.Ekins S, Kim RB, Leake BF, et al. Application of three-dimensional quantitative structure-activity relationships of P-glycoprotein inhibitors and substrates. Mol Pharmacol. 2002;61:974–981. doi: 10.1124/mol.61.5.974. [DOI] [PubMed] [Google Scholar]
  • 84.Penzotti JE, Lamb ML, Evensen E, Grootenhuis PD. A computational ensemble pharmacophore model for identifying substrates of P-glycoprotein. J Med Chem. 2002;45:1737–1740. doi: 10.1021/jm0255062. [DOI] [PubMed] [Google Scholar]
  • 85.Xue Y, Yap CW, Sun LZ, Cao ZW, Wang JF, Chen YZ. Prediction of P-glycoprotein substrates by a support vector machine approach. J Chem Inf Comput Sci. 2004;44:1497–1505. doi: 10.1021/ci049971e. [DOI] [PubMed] [Google Scholar]
  • 86.Wang YH, Li Y, Yang SL, Yang L. An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network. J Comput Aided Mol Des. 2005;19:137–147. doi: 10.1007/s10822-005-3321-5. [DOI] [PubMed] [Google Scholar]
  • 87.Wang YH, Li Y, Yang SL, Yang L. Classification of substrates and inhibitors of P-glycoprotein using unsupervised machine learning approach. J Chem Inf Model. 2005;45:750–757. doi: 10.1021/ci050041k. [DOI] [PubMed] [Google Scholar]
  • 88.Gombar VK, Polli JW, Humphreys JE, Wring SA, Serabjit-Singh CS. Predicting P-glycoprotein substrates by a quantitative structure-activity relationship model. J Pharm Sci. 2004;93:957–968. doi: 10.1002/jps.20035. [DOI] [PubMed] [Google Scholar]
  • 89.Cianchetta G, Singleton RW, Zhang M, et al. A pharmacophore hypothesis for P-glycoprotein substrate recognition using GRIND-based 3D-QSAR. J Med Chem. 2005;48:2927–2935. doi: 10.1021/jm0491851. [DOI] [PubMed] [Google Scholar]
  • 90.Seelig A. A general pattern for substrate recognition by P-glycoprotein. Eur J Biochem. 1998;251:252–261. doi: 10.1046/j.1432-1327.1998.2510252.x. [DOI] [PubMed] [Google Scholar]
  • 91.Ekins S, Kim RB, Leake BF, et al. Three-dimensional quantitative structure-activity relationships of inhibitors of P-glycoprotein. Mol Pharmacol. 2002;61:964–973. doi: 10.1124/mol.61.5.964. [DOI] [PubMed] [Google Scholar]
  • 92.Balimane PV, Chong S. Cell culture-based models for intestinal permeability: a critique. Drug Discov Today. 2005;10:335–343. doi: 10.1016/S1359-6446(04)03354-9. [DOI] [PubMed] [Google Scholar]
  • 93.Bergstrom CA. In silico predictions of drug solubility and permeability: two rate-limiting barriers to oral drug absorption. Basic Clin Pharmacol Toxicol. 2005;96:156–161. doi: 10.1111/j.1742-7843.2005.pto960303.x. [DOI] [PubMed] [Google Scholar]
  • 94.Malkia A, Murtomaki L, Urtti A, Kontturi K. Drug permeation in biomembranes: in vitro and in silico prediction and influence of physicochemical properties. Eur J Pharm Sci. 2004;23:13–47. doi: 10.1016/j.ejps.2004.05.009. [DOI] [PubMed] [Google Scholar]
  • 95.Kulkarni A, Han Y, Hopfinger AJ. Predicting Caco-2 cell permeation coefficients of organic molecules using membrane-interaction QSAR analysis. J Chem Inf Comput Sci. 2002;42:331–342. doi: 10.1021/ci010108d. [DOI] [PubMed] [Google Scholar]
  • 96.Yamashita F, Wanchana S, Hashida M. Quantitative structure/property relationship analysis of Caco-2 permeability using a genetic algorithm-based partial least squares method. J Pharm Sci. 2002;91:2230–2239. doi: 10.1002/jps.10214. [DOI] [PubMed] [Google Scholar]
  • 97.Yamashita F, Fujiwara S, Hashida M. The “latent membrane permeability” concept: QSPR analysis of inter/intralaboratory variable Caco-2 permeability. J Chem Inf Comput Sci. 2002;42:408–413. doi: 10.1021/ci010317y. [DOI] [PubMed] [Google Scholar]
  • 98.Fujiwara S, Yamashita F, Hashida M. Prediction of Caco-2 cell permeability using a combination of MO-calculation and neural network. Int J Pharm. 2002;237:95–105. doi: 10.1016/s0378-5173(02)00045-5. [DOI] [PubMed] [Google Scholar]
  • 99.Marrero Ponce Y, Cabrera Perez MA, Romero Zaldivar V, Gonzalez Diaz H, Torrens F. A new topological descriptors based model for predicting intestinal epithelial transport of drugs in Caco-2 cell culture. J Pharm Pharm Sci. 2004;7:186–199. [PubMed] [Google Scholar]
  • 100.Hou TJ, Zhang W, Xia K, Qiao XB, Xu XJ. ADME evaluation in drug discovery, V: correlation of Caco-2 permeation with simple molecular properties. J Chem Inf Comput Sci. 2004;44:1585–1600. doi: 10.1021/ci049884m. [DOI] [PubMed] [Google Scholar]
  • 101.Fujikawa M, Ano R, Nakao K, Shimizu R, Akamatsu M. Relationships between structure and high-throughput screening permeability of diverse drugs with artificial membranes: application to prediction of Caco-2 cell permeability. Bioorg Med Chem. 2005;13:4721–4732. doi: 10.1016/j.bmc.2005.04.076. [DOI] [PubMed] [Google Scholar]
  • 102.Refsgaard HH, Jensen BF, Brockhoff PB, Padkjaer SB, Guldbrandt M, Christensen MS. In silico prediction of membrane permeability from calculated molecular parameters. J Med Chem. 2005;48:805–811. doi: 10.1021/jm049661n. [DOI] [PubMed] [Google Scholar]
  • 103.Artursson P, Palm K, Luthman K. Caco-2 monolayers in experimental and theoretical predictions of drug transport. Adv Drug Deliv Rev. 1996;22:67–84. doi: 10.1016/s0169-409x(00)00128-9. [DOI] [PubMed] [Google Scholar]
  • 104.Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 1997;23:3–25. doi: 10.1016/s0169-409x(00)00129-0. [DOI] [PubMed] [Google Scholar]
  • 105.Willmann S, Schmitt W, Keldenich J, Lippert J, Dressman JB. A physiological model for the estimation of the fraction dose absorbed in humans. J Med Chem. 2004;47:4022–4031. doi: 10.1021/jm030999b. [DOI] [PubMed] [Google Scholar]
  • 106.Usansky HH, Sinko PJ. Estimating human drug oral absorption kinetics from Caco-2 permeability using an absorption-disposition model: model development and evaluation and derivation of analytical solutions for k(a) and F(a) J Pharmacol Exp Ther. 2005;314:391–399. doi: 10.1124/jpet.104.076182. [DOI] [PubMed] [Google Scholar]
  • 107.Obata K, Sugano K, Saitoh R, et al. Prediction of oral drug absorption in humans by theoretical passive absorption model. Int J Pharm. 2005;293:183–192. doi: 10.1016/j.ijpharm.2005.01.005. [DOI] [PubMed] [Google Scholar]
  • 108.Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45:2615–2623. doi: 10.1021/jm020017n. [DOI] [PubMed] [Google Scholar]
  • 109.Lu JJ, Crimin K, Goodwin JT, et al. Influence of molecular flexibility and polar surface area metrics on oral bioavailability in the rat. J Med Chem. 2004;47:6104–6107. doi: 10.1021/jm0306529. [DOI] [PubMed] [Google Scholar]
  • 110.Vieth M, Siegel MG, Higgs RE, et al. Characteristic physical properties and structural fragments of marketed oral drugs. J Med Chem. 2004;47:224–232. doi: 10.1021/jm030267j. [DOI] [PubMed] [Google Scholar]
  • 111.Martin YC. A bioavailability score. J Med Chem. 2005;48:3164–3170. doi: 10.1021/jm0492002. [DOI] [PubMed] [Google Scholar]
  • 112.Liu HX, Hu RJ, Zhang RS, et al. The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine. J Comput Aided Mol Des. 2005;19:33–46. doi: 10.1007/s10822-005-0095-8. [DOI] [PubMed] [Google Scholar]
  • 113.Bai JP, Utis A, Crippen G, et al. Use of classification regression tree in predicting oral absorption in humans. J Chem Inf Comput Sci. 2004;44:2061–2069. doi: 10.1021/ci040023n. [DOI] [PubMed] [Google Scholar]
  • 114.Turner JV, Maddalena DJ, Agatonovic-Kustrin S. Bioavailability prediction based on molecular structure for a diverse series of drugs. Pharm Res. 2004;21:68–82. doi: 10.1023/b:pham.0000012154.09631.26. [DOI] [PubMed] [Google Scholar]
  • 115.Zmuidinavicius D, Didziapetris R, Japertas P, Avdeef A, Petrauskas A. Classification structure-activity relations (C-SAR) in prediction of human intestinal absorption. J Pharm Sci. 2003;92:621–633. doi: 10.1002/jps.10321. [DOI] [PubMed] [Google Scholar]
  • 116.Klopman G, Stefan LR, Saiakhov RD. ADME evaluation, II: a computer model for the prediction of intestinal absorption in humans. Eur J Pharm Sci. 2002;17:253–263. doi: 10.1016/s0928-0987(02)00219-1. [DOI] [PubMed] [Google Scholar]
  • 117.Yoshida F, Topliss JG. QSAR model for drug human oral bioavailability. J Med Chem. 2000;43:2575–2585. doi: 10.1021/jm0000564. [DOI] [PubMed] [Google Scholar]
  • 118.Andrews CW, Bennett L, Yu LX. Predicting human oral bioavailability of a compound: development of a novel quantitative structure-bioavailability relationship. Pharm Res. 2000;17:639–644. doi: 10.1023/a:1007556711109. [DOI] [PubMed] [Google Scholar]
  • 119.Palm K, Stenberg P, Luthman K, Artursson P. Polar molecular surface properties predict the intestinal absorption of drugs in humans. Pharm Res. 1997;14:568–571. doi: 10.1023/a:1012188625088. [DOI] [PubMed] [Google Scholar]

Articles from The AAPS Journal are provided here courtesy of American Association of Pharmaceutical Scientists

RESOURCES