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Molecular Diversity

, Volume 18, Issue 3, pp 599–610 | Cite as

Analysis of structure-Caco-2 permeability relationships using a property landscape approach

  • Yareli Rojas-Aguirre
  • José L. Medina-FrancoEmail author
Full-Length Paper

Abstract

Understanding the relationship between the chemical structure of bioactive compounds and Caco-2 permeability is of major importance in modern drug discovery. The purpose of this work was to characterize systematically the Caco-2 permeability landscape of a benchmark dataset of 100 molecules using a novel approach based on the emerging concept of property landscape modeling. Pairwise comparisons of the Caco-2 permeability and chemical structures were calculated for all possible combinations in the dataset. To compare the chemical structures, two distinct manners to represent the molecules were employed, namely, continuous properties previously used to derive QSPR models and molecular fingerprints with different designs. We introduce the concept of “permeability cliffs” discussing cases of compounds with high molecular similarity but large permeability difference. All permeability cliffs were regarded as shallow cliffs, since no extreme difference in Caco-2 permeability (less than two log units) was identified in the dataset. A clear dependence of Caco-2 permeability landscape with molecular representation was observed. The current approach can be further extended to model other ADME relevant landscapes.

Keywords

ADME Caco-2 permeability landscape Permeability cliffs Property landscape Structure-property relationships  

Abbreviations

ADME

Absorption, distribution, metabolism, and excretion

CPD

Caco-2 permeability difference

log D

Experimental distribution coefficient

MACCS

Molecular ACCess System

GpiDAPH3

Graph \(\pi \)-donor-acceptor-polar-hydrophobe triangle

HCPSA

High-charged polar surface area

MOE

Molecular Operating Environment

fNrotb

Fraction of rotatable bonds

QSAR

Quantitative-structure-activity relationship

rgyr

Radius of gyration

SAS

Structure-activity similarity

SPR

Structure-property relationships

SPS

Structure-property similarity

TGT

Typed graph triangle.

Notes

Acknowledgments

The authors thank Jacob Waddell for writing scripts used to generate the SPS maps.

Supplementary material

11030_2014_9514_MOESM1_ESM.pdf (193 kb)
Supplementary material 1 (PDF 192 kb)
11030_2014_9514_MOESM2_ESM.doc (48 kb)
Supplementary material 2 (DOC 48 kb)
11030_2014_9514_MOESM3_ESM.doc (28 kb)
Supplementary material 3 (DOC 28 kb)
11030_2014_9514_MOESM4_ESM.xlsx (28 kb)
Supplementary material 4 (XLSX 27 kb)
11030_2014_9514_MOESM5_ESM.doc (48 kb)
Supplementary material 5 (DOC 48 kb)
11030_2014_9514_MOESM6_ESM.doc (29 kb)
Supplementary material 6 (DOC 29 kb)

References

  1. 1.
    Lombardino JG, Lowe JA (2004) The role of the medicinal chemist in drug discovery—then and now. Nat Rev Drug Discov 3:853–862. doi: 10.1038/nrd1523 PubMedCrossRefGoogle Scholar
  2. 2.
    Keseru GM, Makara GM (2009) The influence of lead discovery strategies on the properties of drug candidates. Nat Rev Drug Discov 8:203–212. doi: 10.1038/nrd2796 PubMedCrossRefGoogle Scholar
  3. 3.
    Hann MM, Keserü GM (2012) Finding the sweet spot: The role of nature and nurture in medicinal chemistry. Nat Rev Drug Discov 11:355–365. doi: 10.1038/nrd3701 PubMedCrossRefGoogle Scholar
  4. 4.
    van de Waterbeemd H, Gifford E (2003) ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov 2:192–204. doi: 10.1038/nrd1032 PubMedCrossRefGoogle Scholar
  5. 5.
    Moroy G, Martiny VY, Vayer P, Villoutreix BO, Miteva MA (2012) Toward in silico structure-based ADMET prediction in drug discovery. Drug Discov Today 17:44–55. doi: 10.1016/j.drudis.2011.10.023 PubMedCrossRefGoogle Scholar
  6. 6.
    Pavurala N, Achenie LEK (2013) A mechanistic approach for modeling oral drug delivery. Comput Chem Eng 57:196–206. doi: 10.1016/j.compchemeng.2013.06.002 CrossRefGoogle Scholar
  7. 7.
    Kerns EH, Di L (2003) Pharmaceutical profiling in drug discovery. Drug Discov Today 8:316–323. doi: 10.1016/S1359-6446(03)02649-7 PubMedCrossRefGoogle Scholar
  8. 8.
    Kobayashi M, Sada N, Sugawara M, Iseki K, Miyazaki K (2001) Development of a new system for prediction of drug absorption that takes into account drug dissolution and pH change in the gastro-intestinal tract. Int J Pharm 221:87–94. doi: 10.1016/S0378-5173(01)00663-9 PubMedCrossRefGoogle Scholar
  9. 9.
    Hidalgo IJ, Raub TJ, Borchardt RT (1989) Characterization of the human colon carcinoma cell line (Caco-2) as a model system for intestinal epithelial permeability. Gastroenterology 96:736–749PubMedGoogle Scholar
  10. 10.
    Sugano K, Kansy M, Artursson P, Avdeef A, Bendels S, Di L et al (2010) Coexistence of passive and carrier-mediated processes in drug transport. Nat Rev Drug Discov 9:597–614. doi: 10.1038/nrd3187 PubMedCrossRefGoogle Scholar
  11. 11.
    Artursson P, Karlsson J (1991) Correlation between oral drug absorption in humans and apparent drug permeability coefficients in human intestinal epithelial (Caco-2) cells. Biochem Biophys Res Commun 175:880–885. doi: 10.1016/0006-291X(91)91647-U PubMedCrossRefGoogle Scholar
  12. 12.
    Artursson P, Palm K, Luthman K (2001) Caco-2 monolayers in experimental and theoretical predictions of drug transport. Adv Drug Delivery Rev 46:27–43. doi: 10.1016/S0169-409X(00)00128-9 CrossRefGoogle Scholar
  13. 13.
    Camenisch G, Alsenz J, van de Waterbeemd H, Folkers G (1998) Estimation of permeability by passive diffusion through Caco-2 cell monolayers using the drugs’ lipophilicity and molecular weight. Eur J Pharm Sci 6:313–319. doi: 10.1016/S0928-0987(97)10019-7 CrossRefGoogle Scholar
  14. 14.
    Chan ECY, Tan WL, Ho PC, Fang LJ (2005) Modeling Caco-2 permeability of drugs using immobilized artificial membrane chromatography and physicochemical descriptors. J Chromatogr A 1072:159–168. doi: 10.1016/j.chroma.2005.03.006 PubMedCrossRefGoogle Scholar
  15. 15.
    Medina-Franco JL (2013) Activity cliffs: Facts or artifacts? Chem Biol Drug Des 81:553–556. doi: 10.1111/cbdd.12115 PubMedCrossRefGoogle Scholar
  16. 16.
    Palm K, Luthman K, Ungell A-L, Strandlund G, Artursson P (1996) Correlation of drug absorption with molecular surface properties. J Pharm Sci 85:32–39. doi: 10.1021/js950285r PubMedCrossRefGoogle Scholar
  17. 17.
    Hou T, Zhang W, Xia K, Qiao X, Xu X (2004) ADME evaluation in drug discovery. 5. Correlation of Caco-2 permeation with simple molecular properties. J Chem Inf Comput Sci 44:1585–1600. doi: 10.1021/ci049884m PubMedCrossRefGoogle Scholar
  18. 18.
    Castillo-Garit JA, Marrero-Ponce Y, Torrens F, García-Domenech R (2008) Estimation of ADME properties in drug discovery: Predicting Caco-2 cell permeability using atom-based stochastic and non-stochastic linear indices. J Pharm Sci 97:1946–1976. doi: 10.1002/jps.21122 PubMedCrossRefGoogle Scholar
  19. 19.
    Paixão P, Gouveia LF, Morais JAG (2010) Prediction of the in vitro permeability determined in Caco-2 cells by using artificial neural networks. Eur J Pharm Sci 41:107–117. doi: 10.1016/j.ejps.2010.05.014 PubMedCrossRefGoogle Scholar
  20. 20.
    Maggiora GM (2006) On outliers and activity cliffs-why QSAR often disappoints. J Chem Inf Model 46:1535. doi: 10.1021/ci060117s PubMedCrossRefGoogle Scholar
  21. 21.
    Wassermann AM, Wawer M, Bajorath J (2010) Activity landscape representations for structure-activity relationship analysis. J Med Chem 53:8209–8223. doi: 10.1021/jm100933w PubMedCrossRefGoogle Scholar
  22. 22.
    Medina-Franco JL, Waddell J (2012) Towards the bioassay activity landscape modeling in compound databases. J Mex Chem Soc 56:163–168Google Scholar
  23. 23.
    Medina-Franco JL (2012) Scanning structure-activity relationships with structure-activity similarity and related maps: From consensus activity cliffs to selectivity switches. J Chem Inf Model 52:2485–2493. doi: 10.1021/ci300362x PubMedCrossRefGoogle Scholar
  24. 24.
    Shanmugasundaram V, Maggiora GM (2001) Characterizing property and activity landscapes using an information-theoretic approach. CINF-032, Chicago, IL, USA. American Chemical Society, WashingtonGoogle Scholar
  25. 25.
    Yongye AB, Medina-Franco JL (2013) Systematic characterization of structure-activity relationships and ADMET compliance: A case study. Drug Discov Today 18:732–739. doi: 10.1016/j.drudis.2013.04.002 PubMedCrossRefGoogle Scholar
  26. 26.
    Pérez-Villanueva J, Santos R, Hernández-Campos A, Giulianotti MA, Castillo R, Medina-Franco JL (2010) Towards a systematic characterization of the antiprotozoal activity landscape of benzimidazole derivatives. Bioorg Med Chem 18:7380–7391. doi: 10.1016/j.bmc.2010.09.019 Google Scholar
  27. 27.
    Yongye A, Byler K, Santos R, Martínez-Mayorga K, Maggiora GM, Medina-Franco JL (2011) Consensus models of activity landscapes with multiple chemical, conformer and property representations. J Chem Inf Model 51:1259–1270. doi: 10.1021/ci200081k Google Scholar
  28. 28.
    Medina-Franco JL, Yongye AB, Pérez-Villanueva J, Houghten RA, Martínez-Mayorga K (2011) Multitarget structure-activity relationships characterized by activity-difference maps and consensus similarity measure. J Chem Inf Model 51:2427–2439. doi: 10.1021/ci200281v PubMedCrossRefGoogle Scholar
  29. 29.
    Willett P, Barnard JM, Downs GM (1998) Chemical similarity searching. J Chem Inf Comput Sci 38:983–996. doi: 10.1021/ci9800211 CrossRefGoogle Scholar
  30. 30.
    Askjaer S, Langgard M (2008) Combining pharmacophore fingerprints and PLS-discriminant analysis for virtual screening and SAR elucidation. J Chem Inf Model 48:476–488. doi: 10.1021/ci700356w PubMedCrossRefGoogle Scholar
  31. 31.
    Medina-Franco JL, Martínez-Mayorga K, Bender A, Marín RM, Giulianotti MA, Pinilla C et al (2009) Characterization of activity landscapes using 2D and 3D similarity methods: Consensus activity cliffs. J Chem Inf Model 49:477–491. doi: 10.1021/ci800379q PubMedCrossRefGoogle Scholar
  32. 32.
    López-Vallejo F, Giulianotti MA, Houghten RA, Medina-Franco JL (2012) Expanding the medicinally relevant chemical space with compound libraries. Drug Discov Today 17:718–726. doi: 10.1016/j.drudis.2012.04.001 PubMedCrossRefGoogle Scholar
  33. 33.
    Martínez-Mayorga K, Peppard TL, Yongye AB, Santos R, Giulianotti M, Medina-Franco JL (2011) Characterization of a comprehensive flavor database. J Chemometr 25:550–560. doi: 10.1002/cem.1399 CrossRefGoogle Scholar
  34. 34.
    Rubas W, Jezyk N, Grass G (1993) Comparison of the permeability characteristics of a human colonic epithelial (Caco-2) cell line to colon of rabbit, monkey, and dog intestine and human drug absorption. Pharm Res 10:113–118. doi: 10.1023/A:1018937416447 PubMedCrossRefGoogle Scholar
  35. 35.
    Fischer W, Metzner L, Hoffmann K, Neubert RH, Brandsch M (2006) Substrate specificity and mechanism of the intestinal clonidine uptake by Caco-2 cells. Pharm Res 23:131–137. doi: 10.1007/s11095-005-8925-x PubMedCrossRefGoogle Scholar
  36. 36.
    Artursson P, Palm K, Luthman K (1996) Caco-2 monolayers in experimental and theoretical predictions of drug transport. Adv Drug Delivery Rev 22:67–84. doi: 10.1016/S0169-409X(96)00415-2 CrossRefGoogle Scholar
  37. 37.
    Koljonen M, Hakala KS, Ahtola-Sätilä T, Laitinen L, Kostiainen R, Kotiaho T et al (2006) Evaluation of cocktail approach to standardise Caco-2 permeability experiments. Eur J Pharm Biopharm 64:379–387. doi: 10.1016/j.ejpb.2006.06.006 PubMedCrossRefGoogle Scholar
  38. 38.
    Pade V, Stavchansky S (1997) Estimation of the relative contribution of the transcellular and paracellular pathway to the transport of passively absorbed drugs in the Caco-2 cell culture model. Pharm Res 14:1210–1215. doi: 10.1023/A:1012111008617 PubMedCrossRefGoogle Scholar
  39. 39.
    Hovgaard L, Brøndsted H, Buur A, Bundgaard H (1995) Drug delivery studies in Caco-2 monolayers. Synthesis, hydrolysis, and transport of o-cyclopropane carboxylic acid ester prodrugs of various \(\beta \)-blocking agents. Pharm Res 12:387–392. doi:  10.1023/A:1016204602471 PubMedCrossRefGoogle Scholar
  40. 40.
    Santos R, Giulianotti MA, Houghten RA, Medina-Franco JL (2013) Conditional probabilistic analysis for prediction of the activity landscape and relative compound activities. J Chem Inf Model 53:2613–2625. doi: 10.1021/ci400243e PubMedCrossRefGoogle Scholar
  41. 41.
    Guha R, Van Drie JH (2008) Assessing how well a modeling protocol captures a structure-activity landscape. J Chem Inf Model 48:1716–1728. doi: 10.1021/ci8001414 PubMedCrossRefGoogle Scholar
  42. 42.
    Cruz-Monteagudo M, Medina-Franco JL, Pérez-Castillo Y, Cordeiro MNDS, Borges F (2014) Activity cliffs in drug discovery: Dr. Jekyll or Mr. Hyde? Drug Discov Today. doi: 10.1016/j.drudis.2014.02.003

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yareli Rojas-Aguirre
    • 1
    • 2
  • José L. Medina-Franco
    • 1
    • 3
    Email author
  1. 1.Instituto de QuímicaUniversidad Nacional Autónoma de MéxicoMexico CityMexico
  2. 2.University of MichiganAnn ArborUSA
  3. 3.Mayo ClinicScottsdaleUSA

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