The log P Parameter as a Molecular Descriptor in the Computer-aided Drug Design – an Overview
Kujawski Jacek *, Popielarska Hanna, Myka Anna, Drabińska Beata, Bernard Marek K.
Department of Organic Chemistry, Faculty of Pharmacy, Poznan University of Medical Sciences, Grunwaldzka 6 str., 60-780 Poznań
*e-mail: jacekkuj@ump.edu.pl
Received:
(Received: 10 February 2012; accepted: 6 June 2012; published online: 27 August 2012)
DOI: 10.12921/cmst.2012.18.02.81-88
OAI: oai:lib.psnc.pl:413
Abstract:
The computer-aided drug design is an important tool in modern medicinal chemistry. Molecular lipophilicity, usually quantified as log P, is an important molecular characteristic in medicinal chemistry and also in rationalized drug design. The log P coefficient is well-known as one of the principal parameters for the estimation of lipophilicity of chemical compounds and determines their pharmacokinetic properties. This parameter has been measured using known experimental methods, but recently huge progress in determination of log P using computational chemistry methods is observed. The number of methodological publications about lipophilicity predictions has gradually increased over the last ten years, but the number of programs available for an on-line prediction of this important parameter remains limited. This paper presents some of log P prediction methods and very popular programs connected to this topic. The prediction of log P is highly important for the pharmaceutical industry since it limits time-consuming experiments to measure log P required to optimize pharmacodynamic and pharmacokinetic properties of hits and leads. Development of the methods reviewed in this paper concerning log P prediction seems to be a significant tendency in the modern pharmaceutical industry.
Key words:
chemoinformatics, computer program, computer-aided design, pharmaceutical chemistry, predictions and projections
References:
[1] J. Kujawski, M.K. Bernard, A. Janusz, W. Kuźma, Prediction of log P – ALOGPS Application in Medicinal Chemistry Education. J. Chem. Educ. 89, 64-67 (2012).
[2] C.A. Lipinski, F.M. Lombardo, B.W. Dominy, P.J. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev. 46, 3-26 (2001).
[3] F.M. Lombardo, M.Y. Shalaeva, K.A. Tupper, F. Gao, ElogDoct: A Tool for Lipophilicity Determination in Drug Discovery. 2. Basic and Neutral Compounds. J. Med. Chem. 44, 2490-2497 (2001).
[4] S.M. Ulmeanu, H. Jensen, G. Bouchard, P.A. Carrupt, H.H. Girault, Water-Oil Partition Profiling of Ionized Drug Molecules using Cyclic Voltammetry and a 96-Well Microfilter Plate System. Pharm. Res. 20, 1317-1322 (2003).
[5] A.M. Bond, F. Marken, Mechanistic aspects of the electron and ion transport processes across the electrode solid solvent (electrolyte) interface of microcrystalline decamethylferrocene attached mechanically to a graphite electrode. J. Electroanalyt. Chem. 372, 125-135 (1994).
[6] T. Fujita, J. Iwana, C.J. Hansch, A New Substituent Constant, π, Derived from Partition Coefficients. J. Am. Chem. Soc. 86, 5175-5180 (1964).
[7] I.V. Tetko, Computing chemistry on the web. Drug Discovery Today 10, 1497-1500 (2005).
[8] R. Mannhold, van H. Waterbeemd, Substructure and whole molecule approaches for calculating log P. J. Comp-Aided Mol. Des. 15, 337-354 (2001).
[9] R.C. Young, R.C. Mitchell, T.H. Brown, C.R. Ganellin, R. Griffiths, M. Jones, K.K. Rana, D. Saunders, I.R. Smith, N.E. Sore, Development of a new physicochemical model for brain penetration and its application to the design of centrally acting H2 receptor histamine antagonists. J. Med. Chem. 31, 656-671 (1988). [10] A.J. Leo, Calculating log Poct from structures. Chem. Rev. 93, 1281-1306 (1993).
[11] R. Rekker, The Hydrophobic Fragmental Constant. Elsevier, Amsterdam 1997.
[12] R. Rekker, R. Mannhold Calculation of Drug Lipophilicity. VCH, Weinheim 1992.
[13] http://www.biobyte.com/bb/prod/clogp40.html (accessed: Jan 2012).
[14] P. Broto, G. Moreau, C. Vandycke, Molecular structures: perception, autocorrelation descriptor and SAR studies. Eur. J. Med. Chem. 19, 71-78 (1984).
[15] K. Iwase, K. Komatsu, S. Hirono, S. Nakagawa, I. Moriguchi, Estimation of Hydrophobicity Based on The Solvent- Accessible Surface Area of Molecules. Chem. Pharm. Bull. 33, 2114-2121 (1985).
[16] W.J. Dunn III, The role of solvent-accessible surface area in determining partition coefficients. J. Med. Chem. 30, 1121-1126 (1987).
[17] D.J. Abraham, A.J. Leo, Extension of the fragment method to calculate amino acid zwitterion and side chain partition coefficients. Proteins 2, 130-52 (1987).
[18] G.E. Kellogg, D.J. Abraham, Hydrophobicity: is LogPo/w more than the sum of its parts? Eur. J. Med. Chem. 35, 651-61 (2000).
[19] G.E. Kellogg, G.S. Joshi, D.J. Abraham, New tools for modeling and understanding hydrophobicity and hydrophobic interactions. Med. Chem. Res. 1, 444-53 (1992).
[20] SRC KOWWIN, http://www.syrres.com/search.aspx? searchtext=KOWWIN&folderid=0&searchfor=all&orderby =id&orderdirection=ascending (accessed: Jan 2012).
[21] Molinsipartion, http://www.molinspiration.com (accessed: Jan 2012).
[22] Virtual log P, http://nova.colombo58.unimi.it/vlogp.htm (accessed: Jan 2012).
[23] R. Wang, Calculating partition coefficient by atom-additive method. Persp. Drug Discov. Des. 19, 47-66 (2000).
[24] Virtual Computational Chemistry Laboratory (VCCLAB), http://www.vcclab.org/ (accessed: Jan 2012).
[25] R. Mannhold, G.I. Poda, C. Ostermann, I.V. Tetko, Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds. J. Pharm. Sci . 98, 861-93 (2009).
[26] Advanced Chemical Development (ACD) as ACD/log P freeware, www.acdlabs.com/download/ (accessed: Jan 2012).
[27] A. Geronikaki, D. Druzhilovsky, A. Zakharov, V. Poroikov, Computer-aided prediction for medicinal chemistry via the Internet. SAR and QSAR Eniromen. Res. 19, 1-2, 27-38 (2008).
[28] I.V. Tetko, J. Gasteiger, R. Todeschini, A. Mauri, D. Livingstone, P. Ertl, V.A. Palyulin, E.V. Radchenko, N.S. Zefirov, A.S. Makarenko, V.Y. Tanchuk, V.V. Prokopenko, Virtual Computational Chemistry Laboratory – Design and Description. J. Com.p-Aided Mol. Des. 19, 453-63 (2005).
[29] I.V. Tetko, Associative Neural Network, CogPrints archive code: cog00001441, http://www.vcclab.org/lab/alogps/lib rary.html (accessed: Jan 2012).
[30] I.V. Tetko, Introduction to associative neural networks. J. Chem. Inf. Comp. Sci. 42, 717-28 (2002).
[31] Syracuse Research Corporation. Physical/Chemical Property Database (PHYSPROP); SRC, Environmental Science Center: Syracuse, NY.
[32] I.V. Tetko, V.Y. Tanchuk, A.E. Villa, Prediction of n-Octanol/ Water Partition Coefficients from PHYSPROP Database Using Artificial Neural Networks and E-State Indices. J. Chem. Inf. Comp. Sci. 41, 1407-1421 (2001).
[33] F.L. Stahura, J.W. Godden, J. Bajorath, Differential Shannon Entropy Analysis Identifies Molecular Property Descriptors that Predict Aqueous Solubility of Synthetic Compounds with High Accuracy in Binary QSAR Calculations. J. Chem. Inf. Comp. Sci. 42, 550-558 (2002).
[34] OpenBabel, http://openbabel.sourceforge.net (accessed: Jan 2012).
[35] P. Ertl, P. Selzer, J. Műhlbacher, Web-based cheminformatics tools deployed via corporate Intranets. Drug Discovery Today: Biosilico 2, 5, 201-207 (2004).
[36] L.K. Schnackenberg, R.D. Beger, Whole-Molecule Calculation of Log P Based on Molar Volume, Hydrogen Bonds, and Simulated 13C NMR Spectra. J. Chem. Inf. Model. 45, 360-365 (2005).
[37] M. Stefaniak, A. Niestrój, J. Klupsch, J. Śliwok, A. Pyka. Use of RP-TLC to determine the log P Values of Isomers of Organic Compounds. Chromatographia 62, 87-89 (2005).
[38] I.V. Tetko, Can we estimate the accuracy of ADME-Tox predictions? Drug Discovery Today 11, 700-706 (2006).
[39] H. Sun, A Universal Molecular Descriptor System for Prediction of LogP, LogS, LogBB, and Absorption. J. Chem. Inf. Comput. Sci. 44, 748-757 (2004).
[40] H. Mo, K.M. Balko, D.A. Colby, A practical deuteriumfree NMR method for the rapid determination of 1-octanol/ water partition coefficient of pharmaceutical agents. Bioorg. Med. Chem. Lett. 20, 6712-6715 (2010).
[41] E.L. Willighagen, H.M.G.W. Denissen, R. Wehrens, L.M.C. Buydens. On the Use of 1H and 13C 1D NMR Spectra as QSPR Descriptors. J. Chem. Inf. Model. 46, 487-494 (2006).
The computer-aided drug design is an important tool in modern medicinal chemistry. Molecular lipophilicity, usually quantified as log P, is an important molecular characteristic in medicinal chemistry and also in rationalized drug design. The log P coefficient is well-known as one of the principal parameters for the estimation of lipophilicity of chemical compounds and determines their pharmacokinetic properties. This parameter has been measured using known experimental methods, but recently huge progress in determination of log P using computational chemistry methods is observed. The number of methodological publications about lipophilicity predictions has gradually increased over the last ten years, but the number of programs available for an on-line prediction of this important parameter remains limited. This paper presents some of log P prediction methods and very popular programs connected to this topic. The prediction of log P is highly important for the pharmaceutical industry since it limits time-consuming experiments to measure log P required to optimize pharmacodynamic and pharmacokinetic properties of hits and leads. Development of the methods reviewed in this paper concerning log P prediction seems to be a significant tendency in the modern pharmaceutical industry.
Key words:
chemoinformatics, computer program, computer-aided design, pharmaceutical chemistry, predictions and projections
References:
[1] J. Kujawski, M.K. Bernard, A. Janusz, W. Kuźma, Prediction of log P – ALOGPS Application in Medicinal Chemistry Education. J. Chem. Educ. 89, 64-67 (2012).
[2] C.A. Lipinski, F.M. Lombardo, B.W. Dominy, P.J. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev. 46, 3-26 (2001).
[3] F.M. Lombardo, M.Y. Shalaeva, K.A. Tupper, F. Gao, ElogDoct: A Tool for Lipophilicity Determination in Drug Discovery. 2. Basic and Neutral Compounds. J. Med. Chem. 44, 2490-2497 (2001).
[4] S.M. Ulmeanu, H. Jensen, G. Bouchard, P.A. Carrupt, H.H. Girault, Water-Oil Partition Profiling of Ionized Drug Molecules using Cyclic Voltammetry and a 96-Well Microfilter Plate System. Pharm. Res. 20, 1317-1322 (2003).
[5] A.M. Bond, F. Marken, Mechanistic aspects of the electron and ion transport processes across the electrode solid solvent (electrolyte) interface of microcrystalline decamethylferrocene attached mechanically to a graphite electrode. J. Electroanalyt. Chem. 372, 125-135 (1994).
[6] T. Fujita, J. Iwana, C.J. Hansch, A New Substituent Constant, π, Derived from Partition Coefficients. J. Am. Chem. Soc. 86, 5175-5180 (1964).
[7] I.V. Tetko, Computing chemistry on the web. Drug Discovery Today 10, 1497-1500 (2005).
[8] R. Mannhold, van H. Waterbeemd, Substructure and whole molecule approaches for calculating log P. J. Comp-Aided Mol. Des. 15, 337-354 (2001).
[9] R.C. Young, R.C. Mitchell, T.H. Brown, C.R. Ganellin, R. Griffiths, M. Jones, K.K. Rana, D. Saunders, I.R. Smith, N.E. Sore, Development of a new physicochemical model for brain penetration and its application to the design of centrally acting H2 receptor histamine antagonists. J. Med. Chem. 31, 656-671 (1988). [10] A.J. Leo, Calculating log Poct from structures. Chem. Rev. 93, 1281-1306 (1993).
[11] R. Rekker, The Hydrophobic Fragmental Constant. Elsevier, Amsterdam 1997.
[12] R. Rekker, R. Mannhold Calculation of Drug Lipophilicity. VCH, Weinheim 1992.
[13] http://www.biobyte.com/bb/prod/clogp40.html (accessed: Jan 2012).
[14] P. Broto, G. Moreau, C. Vandycke, Molecular structures: perception, autocorrelation descriptor and SAR studies. Eur. J. Med. Chem. 19, 71-78 (1984).
[15] K. Iwase, K. Komatsu, S. Hirono, S. Nakagawa, I. Moriguchi, Estimation of Hydrophobicity Based on The Solvent- Accessible Surface Area of Molecules. Chem. Pharm. Bull. 33, 2114-2121 (1985).
[16] W.J. Dunn III, The role of solvent-accessible surface area in determining partition coefficients. J. Med. Chem. 30, 1121-1126 (1987).
[17] D.J. Abraham, A.J. Leo, Extension of the fragment method to calculate amino acid zwitterion and side chain partition coefficients. Proteins 2, 130-52 (1987).
[18] G.E. Kellogg, D.J. Abraham, Hydrophobicity: is LogPo/w more than the sum of its parts? Eur. J. Med. Chem. 35, 651-61 (2000).
[19] G.E. Kellogg, G.S. Joshi, D.J. Abraham, New tools for modeling and understanding hydrophobicity and hydrophobic interactions. Med. Chem. Res. 1, 444-53 (1992).
[20] SRC KOWWIN, http://www.syrres.com/search.aspx? searchtext=KOWWIN&folderid=0&searchfor=all&orderby =id&orderdirection=ascending (accessed: Jan 2012).
[21] Molinsipartion, http://www.molinspiration.com (accessed: Jan 2012).
[22] Virtual log P, http://nova.colombo58.unimi.it/vlogp.htm (accessed: Jan 2012).
[23] R. Wang, Calculating partition coefficient by atom-additive method. Persp. Drug Discov. Des. 19, 47-66 (2000).
[24] Virtual Computational Chemistry Laboratory (VCCLAB), http://www.vcclab.org/ (accessed: Jan 2012).
[25] R. Mannhold, G.I. Poda, C. Ostermann, I.V. Tetko, Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds. J. Pharm. Sci . 98, 861-93 (2009).
[26] Advanced Chemical Development (ACD) as ACD/log P freeware, www.acdlabs.com/download/ (accessed: Jan 2012).
[27] A. Geronikaki, D. Druzhilovsky, A. Zakharov, V. Poroikov, Computer-aided prediction for medicinal chemistry via the Internet. SAR and QSAR Eniromen. Res. 19, 1-2, 27-38 (2008).
[28] I.V. Tetko, J. Gasteiger, R. Todeschini, A. Mauri, D. Livingstone, P. Ertl, V.A. Palyulin, E.V. Radchenko, N.S. Zefirov, A.S. Makarenko, V.Y. Tanchuk, V.V. Prokopenko, Virtual Computational Chemistry Laboratory – Design and Description. J. Com.p-Aided Mol. Des. 19, 453-63 (2005).
[29] I.V. Tetko, Associative Neural Network, CogPrints archive code: cog00001441, http://www.vcclab.org/lab/alogps/lib rary.html (accessed: Jan 2012).
[30] I.V. Tetko, Introduction to associative neural networks. J. Chem. Inf. Comp. Sci. 42, 717-28 (2002).
[31] Syracuse Research Corporation. Physical/Chemical Property Database (PHYSPROP); SRC, Environmental Science Center: Syracuse, NY.
[32] I.V. Tetko, V.Y. Tanchuk, A.E. Villa, Prediction of n-Octanol/ Water Partition Coefficients from PHYSPROP Database Using Artificial Neural Networks and E-State Indices. J. Chem. Inf. Comp. Sci. 41, 1407-1421 (2001).
[33] F.L. Stahura, J.W. Godden, J. Bajorath, Differential Shannon Entropy Analysis Identifies Molecular Property Descriptors that Predict Aqueous Solubility of Synthetic Compounds with High Accuracy in Binary QSAR Calculations. J. Chem. Inf. Comp. Sci. 42, 550-558 (2002).
[34] OpenBabel, http://openbabel.sourceforge.net (accessed: Jan 2012).
[35] P. Ertl, P. Selzer, J. Műhlbacher, Web-based cheminformatics tools deployed via corporate Intranets. Drug Discovery Today: Biosilico 2, 5, 201-207 (2004).
[36] L.K. Schnackenberg, R.D. Beger, Whole-Molecule Calculation of Log P Based on Molar Volume, Hydrogen Bonds, and Simulated 13C NMR Spectra. J. Chem. Inf. Model. 45, 360-365 (2005).
[37] M. Stefaniak, A. Niestrój, J. Klupsch, J. Śliwok, A. Pyka. Use of RP-TLC to determine the log P Values of Isomers of Organic Compounds. Chromatographia 62, 87-89 (2005).
[38] I.V. Tetko, Can we estimate the accuracy of ADME-Tox predictions? Drug Discovery Today 11, 700-706 (2006).
[39] H. Sun, A Universal Molecular Descriptor System for Prediction of LogP, LogS, LogBB, and Absorption. J. Chem. Inf. Comput. Sci. 44, 748-757 (2004).
[40] H. Mo, K.M. Balko, D.A. Colby, A practical deuteriumfree NMR method for the rapid determination of 1-octanol/ water partition coefficient of pharmaceutical agents. Bioorg. Med. Chem. Lett. 20, 6712-6715 (2010).
[41] E.L. Willighagen, H.M.G.W. Denissen, R. Wehrens, L.M.C. Buydens. On the Use of 1H and 13C 1D NMR Spectra as QSPR Descriptors. J. Chem. Inf. Model. 46, 487-494 (2006).
