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Research Article

Quantitative structure–property relationship of distribution coefficients of organic compounds

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Pages 585-596 | Received 06 May 2020, Accepted 10 Jun 2020, Published online: 02 Jul 2020

References

  • M. Remko, A. Boháč, and L. Kováčiková, Molecular structure, pKa, lipophilicity, solubility, absorption, polar surface area, and blood brain barrier penetration of some antiangiogenic agents, J. Struct. Chem. 22 (2011), pp. 635–648. doi:10.1007/s11224-011-9741-z.
  • J.A. Arnott and S.L. Planey, The influence of lipophilicity in drug discovery and design, Expert. Opin. Drug Discov. 7 (2012), pp. 863–875. doi:10.1517/17460441.2012.714363.
  • D.J. Livingstone, Theoretical property predictions, Curr. Top. Med. Chem. 3 (2003), pp. 1171–1192. doi:10.2174/1568026033452078.
  • C. Liang and H. Lian, Recent advances in lipophilicity measurement by reversed-phase high-performance liquid chromatography, Trends. Anal. Chem. 68 (2015), pp. 28–36. doi:10.1016/j.trac.2015.02.009.
  • K. Mansouri, N.F. Cariello, A. Korotcov, V. Tkachenko, C.M. Grulke, C.S. Sprankle, D. Allen, W.M. Casey, C. Kleinstreuer, and A.J. Williams, QSAR models for pKa prediction using multiple machine learning approaches, J. Cheminform. 11 (2019), pp. 60. doi:10.1186/s13321-019-0384-1.
  • B. Testa, P. Crivori, M. Reist, and P.A. Carrupt, The influence of lipophilicity on the pharmacokinetic behavior of drugs: Concepts and examples, Perspect. Drug Discov. Des. 19 (2000), pp. 179–211. doi:10.1023/A:1008741731244.
  • M. Kah and C.D. Brown, Log D: Lipophilicity for ionisable compounds, Chemosphere 72 (2008), pp. 1401–1408. doi:10.1016/j.chemosphere.2008.04.074.
  • Z. Chen and S.G. Weber, High-throughput method for lipophilicity measurement, Anal. Chem. 79 (2007), pp. 1043–1049. doi:10.1021/ac061649a.
  • J. De Bruijn, F. Busser, W. Seinen, and J. Hermens, Determination of octanol/water partition coefficients for hydrophobic organic chemicals with the “slowstirring” method, Environ. Toxicol. Chem. 8 (1989), pp. 499–512. doi:10.1002/etc.5620080607.
  • X. Gao, C.H. Yu, K.Y. Tam, and S.C. Tsang, New magnetic nano-absorbent for the determination of n-octanol/water partition coefficients, J. Pharm. Biomed. Anal. 38 (2005), pp. 197–203. doi:10.1016/j.jpba.2004.12.029.
  • C. Barzanti, R. Evans, J. Fouquet, L. Gouzin, N.M. Howarth, G. Kean, E. Levet, D. Wang, E. Wayemberg, A.A. Yeboah, and A. Kraft, Potentiometric determination of octanol-water and liposome-water partition coefficients (log P) of ionizable organic compounds, Tetrahedron Lett. 48 (2007), pp. 3337–3341. doi:10.1016/j.tetlet.2007.03.085.
  • N.A. Marine, S.A. Klein, and J.D. Posner, Partition coefficient measurements in picoliter drops using a segmented flow microfluidic device, Anal. Chem. 81 (2009), pp. 1471–1476. doi:10.1021/ac801673w.
  • Y. Dohta, T. Yamashita, S. Horiike, T. Nakamura, and T. Fukami, A system for logD screening of 96-well plates using a water-plug aspiration/injection method combined with high-performance liquid chromatography-mass spectrometry, Anal. Chem. 79 (2007), pp. 8312–8315. doi:10.1021/ac0709798.
  • A. Tsantili-Kakoulidou, I. Panderi, F. Csizmadia, and F. Darvas, Prediction of distribution coefficient from structure. 2. Validation of Prolog D, an expert system, J. Pharm. Sci. 86 (1997), pp. 1173–1179. doi:10.1021/js9601804.
  • O.A. Raevsky, S.V. Trepalin, H.P. Trepalina, V.A. Gerasimenko, and O.E. Raevskaja, SLIPPER-2001 - Software for predicting molecular properties on the basis of physicochemical descriptors and structural similarity, J. Chem. Inf. Comput. Sci. 42 (2001), pp. 540–549. doi:10.1021/ci010097o.
  • R. Scherrer, Biolipid pKa values and the lipophilicity of ampholytes and ion pairs, in Pharmacokinetic Optimization in Drug Research, B. Testa, H. van de Waterbeemd, G. Folkers, and R. Guy, eds., Publ. Wily-VCH, Germany, 2001, pp. 351–338.
  • I.V. Tetko and V.Y. Tanchuk, Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program, J. Chem. Inf. Mod. 42 (2002), pp. 1136–1145.
  • Molecular Operating Environment (MOE), 10th ed, Chemical Computing Group Inc., 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2011.
  • ACD/Labs ACD/LogD, 2020. Available at http://www.acdlabs.com/products/percepta/predictors/logd/.
  • ChemAxon, 2020. Available at https://chemaxon.com/products/chemicalize.
  • Schrödinger Release 2020-2: QikProp, Schrödinger LLC, New York, NY, 2020.
  • I.V. Tetko and G.I. Poda, Application of ALOGPS 2.1 to predict log D distribution coefficient for pfizer proprietary compounds, J. Med. Chem. 47 (2004), pp. 5601–5604. doi:10.1021/jm049509l.
  • E.P. Andreeva and O.A. Raevsky, Lipophilicity of organic compounds calculated using structural similarity and molecular physicochemical descriptors, Pharm. Chem. J. 43 (2009), pp. 258–262. doi:10.1007/s11094-009-0280-5.
  • R.R. Rekker, The Hydrophobic Fragment Constant, Elsevier, Amsterdam, 1976.
  • A.J. Leo, P.Y.C. Jow, C. Silipo, and C. Hansch, Calculation of hydrophobic constant (log P) from pi and f constants, J. Med. Chem. 18 (1975), pp. 865–868. doi:10.1021/jm00243a001.
  • S.A. Wildman and G.M. Crippen, Prediction of physicochemical parameters by atomic contributions, J. Chem. Inf. Comput. Sci. 39 (1999), pp. 868–873. doi:10.1021/ci990307l.
  • X. Yu, Y. Wang, H. Yang, and X. Huang, Prediction of the binding affinity of aptamers against the influenza virus, SAR QSAR Environ. Res. 30 (2019), pp. 51–62. doi:10.1080/1062936X.2018.1558416.
  • X. Yu, H. Yang, and X. Huang, Novel method for structure−activity relationship of aptamer sequences for human prostate cancer, ACS Omega 3 (2018), pp. 10002–10007. doi:10.1021/acsomega.8b01464.
  • G. Vistoli, A. Pedretti, and B. Testa, Partition coefficient and molecular flexibility: The concept of lipophilicity space, Chem. Biodiv. 6 (2009), pp. 1152–1169. doi:10.1002/cbdv.200900072.
  • P. Comba, B. Martin, A. Sanyal, and H. Stephan, The computation of lipophilicities of 64Cu PET systems based on a novel approach for fluctuating charges, Dalton. Trans. 42 (2013), pp. 11066–11073. doi:10.1039/c3dt51049b.
  • I.A. Sima, M.V. Diudea, and C. Sârbu, Prediction of lipophilicity of catecholamine related compounds based on the hypermolecule concept, Rev. Roum. Chim. 60 (2015), pp. 665–676.
  • J.-B. Wang, D.-S. Cao, M.-F. Zhu, Y.-H. Yun, N. Xiao, and Y.-Z. Liang, In silico evaluation of logD7.4 and comparison with other prediction methods, J. Chemom. 29 (2015), pp. 389–398. doi:10.1002/cem.2718.
  • R. Todeschini, V. Consonni, A. Mauri, and M. Pavan, DRAGON Software for the Calculation of Molecular Descriptors, Revision 6.0 For Windows, Talete Srl, Milan, Italy, 2012.
  • M. Daszykowski, S. Serneels, K. Kaczmarek, P.V. Espen, C. Croux, and B. Walczak, TOMCAT: A MATLAB toolbox for multivariate calibration techniques, Chemom. Intellig. Lab. Syst. 85 (2007), pp. 269–277. doi:10.1016/j.chemolab.2006.03.006.
  • L.C. Lee, C.-Y. Liong, and A.A. Jemain, Iterative random vs. Kennard-Stone sampling for IR spectrum-based classification task using PLS2-DA, AIP Conf. Proc. 1940 (2018), pp. 020116. doi:10.1063/1.5028031.
  • X. Yu, J. Deng, J. Chen, and H. Yang, Prediction of 13C NMR chemical shifts of quinolone derivatives based on DFT calculations, J. Struct. Chem. 60 (2019), pp. 772–779. doi:10.1134/S0022476619050093.
  • F.R. Burden and D.A. Winkler, Relevance vector machines: Sparse classification methods for QSAR, J. Chem. Inf. Model. 55 (2015), pp. 1529–1534. doi:10.1021/acs.jcim.5b00261.
  • J. Shadmanesh, A.P. Jadid, Z. Azari, M. Niazi, and M.S. Aghbolagh, QSAR study of active human glucagon receptor antagonists by SW-MLR and SW-SVM methods, Med. Chem. Res. 23 (2014), pp. 2639–2650. doi:10.1007/s00044-013-0851-6.
  • Y. Yu, Extrapolation for aeroengine gas path faults with SVM bases on genetic algorithm, Sound Vib. 53 (2019), pp. 237–243.
  • J.P. Doucet, E. Papa, A. Doucet-Panaye, and J. Devillers, QSAR models for predicting the toxicity of piperidine derivatives against Aedes aegypti, SAR QSAR Environ. Res. 28 (2017), pp. 451–470. doi:10.1080/1062936X.2017.1328855.
  • C.C. Chang and C.J. Lin, LIBSVM: A library for support vector machines, ACM Trans. Intell. Syst. Technol. 2 (2011), pp. 27. doi:10.1145/1961189.1961199.
  • X. Yu and L. Dang, Complete sets of descriptors for the prediction of 13C NMR chemical shifts of quinoline derivatives, J. Chemom. 33 (2019), pp. e3107. doi:10.1002/cem.3107.
  • I. Moriguchi, S. Hirono, I. Nakagome, and H. Hirano, Comparison of reliability of log P values for drugs calculated by several methods, Chem. Pharm. Bull. 42 (1994), pp. 976–978. doi:10.1248/cpb.42.976.
  • A.J. Leo, CLOGP, Version 3.63, Daylight Chemical Information Systems, Irvine, CA, 1991.
  • A.K. Ghose, V.N. Viswanadhan, and J.J. Wendoloski, Prediction of hydrophobic (lipophilic) properties of small organic molecules using fragmental methods: An analysis of ALOGP and CLOGP methods, J. Phys. Chem. A 102 (1998), pp. 3762–3772. doi:10.1021/jp980230o.

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