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QSAR application for the prediction of compound permeability with in silico descriptors in practical use

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Abstract

The parallel artificial membrane permeation assay (PAMPA) was developed as a model for the prediction of transcellular permeation in the process of drug absorption. In our previous report, it was revealed that PAMPA permeability is governed by log P, pK a, and the hydrogen-bonding ability of compounds. In order to construct a new filtering method for selecting informative compounds from the whole combinatorial library, this study tried to predict PAMPA permeability with in silico descriptors. Log P, pK a, and polar surface areas (PSA) as a hydrogen-bonding descriptor were calculated by commercially available or free-accessible web programs. Five-fold cross-validations and conventional regression analyses were examined with the training set for the entire 81 combinations with nine log P, three pK a and three PSA descriptors. By comparison of statistical indices, four equations were selected and then the model with the best combination of in silico descriptors was determined based on the external validation. The PAMPA prediction equation obtained in this report could be applied for the prediction of both Caco-2 cell permeability and human intestinal absorption of mainly passively-transported drugs.

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References

  1. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Adv Drug Deliv Rev 23:3. doi:10.1016/S0169-409X(96)00423-1

    Article  CAS  Google Scholar 

  2. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD (2002) J Med Chem 45:2615. doi:10.1021/jm020017n

    Article  CAS  Google Scholar 

  3. Lin J, Sahakian DC, de Morais SMF, Xu JJ, Polzer RJ, Winter SM (2003) Curr Top Med Chem 3:1125. doi:10.2174/1568026033452096

    Article  Google Scholar 

  4. Kansy M, Senner F, Gubernator K (1998) J Med Chem 41:1007. doi:10.1021/jm970530e

    Article  CAS  Google Scholar 

  5. Sugano K, Hamada H, Machida M, Ushio H (2001) J Biomol Screen 6:189. doi:10.1177/108705710100600309

    Article  CAS  Google Scholar 

  6. Sugano K, Takata N, Machida M, Saitoh K, Terada K (2002) Int J Pharm 241:241. doi:10.1016/S0378-5173(02)00240-5

    Article  CAS  Google Scholar 

  7. Sugano K, Nabuchi Y, Machida M, Aso Y (2003) Int J Pharm 257:245. doi:10.1016/S0378-5173(03)00161-3

    Article  CAS  Google Scholar 

  8. Avdeef A (2003) Absorption and drug development. Wiley-Interscience, New Jersey

    Book  Google Scholar 

  9. Fujikawa M, Ano R, Nakao K, Shimizu R, Akamatsu M (2005) Bioorg Med Chem 13:4721. doi:10.1016/j.bmc.2005.04.076

    Article  CAS  Google Scholar 

  10. Fujikawa M, Nakao K, Shimizu R, Akamatsu M (2007) Bioorg Med Chem 15:3756. doi:10.1016/j.bmc.2007.03.040

    Article  CAS  Google Scholar 

  11. Ano R, Kimura Y, Shima M, Matsuno R, Ueno T, Akamatsu M (2004) Bioorg Med Chem 12:257. doi:10.1016/j.bmc.2003.10.002

    Article  CAS  Google Scholar 

  12. MacLogP 4.0; Biobyte Corp., Claremont

  13. CQSAR 4.31; Biobyte Corp., Claremont

  14. SYBYL Molecular Modeling Software; Tripos Associates Inc., St Louis

  15. Leonard JT, Roy K (2006) QSAR Comb Sci 25:235. doi:10.1002/qsar.200510161

    Article  CAS  Google Scholar 

  16. Mannhold R, Dross K (1996) Quant Struct Act Relat 15:403. doi:10.1002/qsar.19960150506

    Article  CAS  Google Scholar 

  17. Leo AJ, Hoekman D (2000) Perspect Drug Discov Des 18:19 (17. CLOGP ver.4)

    Article  CAS  Google Scholar 

  18. Meylan WM, Howard PH (1995) J Pharm Sci 84:83

    Article  CAS  Google Scholar 

  19. Petrauskas AA, Kolovanov EA (2000) Perspect Drug Discov Des 19:99 (ACD/LogP ver. 10)

    Article  CAS  Google Scholar 

  20. miLogP ver. 2.2.; Molinspiration Cheminformatics, Slovak Republic

  21. Japertas P, Didziapetris R, Petrauskas A (2002) Quant Struct Act Relat 21:23

    Article  CAS  Google Scholar 

  22. Ghose AK, Viswanadhan VN, Wendoloski JJ (1998) J Phys Chem A 102:3762

    Article  CAS  Google Scholar 

  23. Wang R, Fu Y, Lai L (1997) J Chem Inf Comput Sci 37:615 (XLOGP ver.1)

    CAS  Google Scholar 

  24. Tetko IV, Tanchuk VY (2002) J Chem Inf Comput Sci 42:1136 (ALOGPs ver. 2.1)

    CAS  Google Scholar 

  25. Tetko IV, Gasteiger J, Todeschini R, Mauri A, Livingstone D, Ertl P, Palyulin VA, Radchenko EV, Zefirov NS, Makarenko AS, Tanchuk VY, Prokopenko VV (2005) J Comput Aided Mol Des 19:453. doi:10.1007/s10822-005-8694-y

    Article  CAS  Google Scholar 

  26. Tetko IV (2005) Drug Discov Today 10:1497

    Article  Google Scholar 

  27. Meloun M, Bordovska S (2007) Anal Bioanal Chem 389:1267 (ACD/pKa ver.9.04)

    Article  CAS  Google Scholar 

  28. AB/pKa in Virtual Computational Chemistry Laboratory (VCCLAB, http://www.vcclab.org, 2005)

  29. Csizmadia F, Szegezdi J, Darvas F (1993) In: Wermuth CG (ed) Trends in QSAR and Molecular Modeling 92. ESCOM, Leiden (Marvin_pKa ver.4.1), p 507

  30. Ertl P, Rohde B, Selzer P (2000) J Med Chem 43:3714. doi:10.1021/jm000942e

    Article  CAS  Google Scholar 

  31. Hehre WJ (2003) A guide to molecular mechanics and quantum chemical calculations. Wavefunction Inc., Irvine, p 72

    Google Scholar 

  32. Stenberg P, Norinder U, Luthman K, Artursson P (2001) J Med Chem 44:1927. doi:10.1021/jm001101a

    Article  CAS  Google Scholar 

  33. Yazdanian M, Glynn SL, Wright JL, Hawi A (1998) Phram Res 15:1490. doi:10.1023/A:1011930411574

    Article  CAS  Google Scholar 

  34. Morita N, Kusuhara H, Sekine T, Endou S, Sugiyama Y (2001) J Pharmacol Exp Ther 298:1179

    CAS  Google Scholar 

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Acknowledgments

The authors are grateful to Dr. Hiroshi Ohmizu in Tanabe Seiyaku, Co., Ltd for his stimulating discussions and continuing encouragement. The authors would like to thank Mr. Masaaki Asao in Tanabe Seiyaku, Co., Ltd for his skillful collaboration of statistical analyses. The authors also appreciate to the reviewer for his/her helpful advices and fruitful suggestions.

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Authors and Affiliations

  1. Tanabe Seiyaku, Co. Ltd, Osaka, 532-8505, Japan

    Kazuya Nakao & Ryo Shimizu

  2. Division of Environmental Science and Technology, Graduate School of Agriculture, Kyoto University, Kyoto, 606-8502, Japan

    Masaaki Fujikawa & Miki Akamatsu

Authors
  1. Kazuya Nakao
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  2. Masaaki Fujikawa
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  3. Ryo Shimizu
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  4. Miki Akamatsu
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Corresponding author

Correspondence to Miki Akamatsu.

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Nakao, K., Fujikawa, M., Shimizu, R. et al. QSAR application for the prediction of compound permeability with in silico descriptors in practical use. J Comput Aided Mol Des 23, 309–319 (2009). https://doi.org/10.1007/s10822-009-9261-8

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  • DOI: https://doi.org/10.1007/s10822-009-9261-8

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