116
Views
4
CrossRef citations to date
0
Altmetric
Articles

Contribution assessment of multiparameter optimization descriptors in CNS penetration

, , , &
Pages 785-800 | Received 22 Jun 2018, Published online: 02 Oct 2018

References

  • O.A. Raevsky, S.L. Solodova, A.A. Lagunin, and V.V. Poroikov, Computer modeling of blood–brain barrier permeability for physiologically active compounds, Biochem. (Mosc) Suppl. Ser. B Biomed. Chem. 7 (2013), pp. 95–107.
  • 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, and T.J. Wilks, 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 (1988), pp. 656–671.
  • H.D. van de Waterbeemd and M. Kansy, Hydrogen-bonding capacity and brain penetration, Chimia 46 (1992), pp. 299–303.
  • H.D. van de Waterbeemd, G. Camenisch, G. Folkers, and O.A. Raevsky, Estimation of CACO-2 cell permeability using calculated molecular descriptors, Quant. Struct.–Act. Rel. 15 (1996), pp. 480–490.
  • H.D. van de Waterbeemd, G. Camenisch, G. Folkers, J.R. Chretien, and O.A. Raevsky, Estimation of blood–brain barrier crossing of drugs using molecular size and shape, and H-bonding descriptors, J. Drug Target. 6 (1998), pp. 151–165.
  • C.A. Lipinski, F. Lombardo, B.W. Dominy, and P.J. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug Del. Rev. 23 (1997), pp. 3–26.
  • J. Kelder, P.D.J. Grootenhuis, D.M. Bayada, L.P. Delbressine, and J.P. Ploemen, Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs, Pharm. Res. 16 (1999), pp. 1514–1519.
  • M.P. Gleeson, Generation of a set of simple, interpretable ADMET rules of thumb, J. Med. Chem. 51 (2008), pp. 817–834.
  • M.J. Waring, Defining optimum lipophilicity and molecular weight ranges for drug candidates—Molecular weight dependent lower log D limits based on permeability, Bioorg. Med. Chem. Lett. 19 (2009), pp. 2844–2851.
  • O.A. Raevsky, V.Y. Grigorev, D.E. Polianczyk, G.I. Sandakov, S.L. Solodova, A.V. Yarkov, S.O. Bachurin, and J.C. Dearden, Physicochemical property profile for brain permeability: Comparative study by different approaches, J. Drug Target. 24 (2016), pp. 655–662.
  • T.T. Wager, X. Hou, P.R. Verhoest, and A. Villalobos, Moving beyond rules: The development of a central nervous system multiparameter optimization (CNS MPO) approach to enable alignment of druglike properties, ACS Chem. Neurosci. 1 (2010), pp. 435–439.
  • Z. Rankovic, CNS drug design: Balancing physicochemical properties for optimal brain exposure, J. Med. Chem. 58 (2015), pp. 2584–2608.
  • Z. Rankovic, CNS physicochemical property space shaped by a diverse set of molecules with experimentally determined exposure in the mouse brain miniperspective, J. Med. Chem. 60 (2017), pp. 5943–5954.
  • O.A. Raevsky, CNS multiparameter optimization approach: Is it in accordance with Occam’s razor principle? Mol. Inform. 35 (2016), pp. 94–98.
  • O.A. Raevsky, D.E. Polianczyk, A. Mukhametov, and V.Y. Grigorev, Assessment of the classification abilities of the CNS multi-parametric optimization approach by the method of logistic regression, SAR QSAR Environ. Res. 27 (2016), pp. 629–635.
  • J.A. Platts, M.H. Abraham, Y.H. Zhao, A. Hersey, L. Ijaz, and D. Butina, Correlation and prediction of a large blood–brain distribution data set—an LFER study, Eur. J. Med. Chem. 36 (2001), pp. 719–730.
  • O.A. Raevsky, S.V. Trepalin, V.Y. Grigor’ev, S.L. Solodova, A.V. Yarkov, and O.E. Raevskaya, Computer calculation of drug penetration through the blood–brain barrier, Pharm. Chem. J. 48 (2014), pp. 26–28.
  • S. Gupta, N. Basant, and K.P. Singh, Qualitative and quantitative structure–activity relationship modelling for predicting blood–brain barrier permeability of structurally diverse chemicals, SAR QSAR Environ. Res. 26 (2015), pp. 95–124.
  • S. Varadharajan, S. Winiwarter, L. Carlsson, O. Engkvist, A. Anantha, T. Kogej, M. Friden, J. Stalring, and H. Chen, Exploring in silico prediction of the unbound brain-to-plasma drug concentration ratio: Model validation, renewal, and interpretation, J. Pharm. Sci. 104 (2015), pp. 1197–1206.
  • Y.Y. Zhang, H. Liu, S.G. Summerfield, C.N. Luscombe, and J. Sahi, Integrating in silico and in vitro approaches to predict drug accessibility to the central nervous system, Mol. Pharm. 13 (2016), pp. 1540–1550.
  • H.D. van de Waterbeemd, Physicochemical properties in drug profiling, in Molecular Drug Properties. Measurement and Prediction, R. Mannhold, ed., Wiley-VCH, Weinheim, 2008, pp. 25–52.
  • O.A. Raevsky, H-bonding parametrization in quantitative structure–activity relationships and drug design, in Molecular Drug Properties. Measurement and Prediction, R. Mannhold, ed., Wiley-VCH, Weinheim, 2008, pp. 127–154.
  • D.J. Livingstone, Data Analysis for Chemists: Applications to QSAR and Chemical Product Design, Oxford University Press, Oxford, 1995.
  • L. Eriksson, E. Johansson, N. Kettaneh-Wold, and S. Wold, Multi- and Megavariate Data Analysis: Principles And Applications, Umetrics Academy, Umea, 2001.
  • L.C. Yee and Y.C. Wei, Current modeling methods used in QSAR/QSPR, in Statistical Modelling of Molecular Descriptors in QSAR/QSPR, M. Dehmer, D. Bonchev, and K. Varmuza, eds., Wiley-VCH, Weinheim, 2012, pp. 1–32.
  • SPSS Statistics for Windows, Version 17.0. SPSS Inc., Chicago, USA, 2008; software available at http://www.ibm.com/analytics/us/en/technology/spss/
  • F. Murtagh and A. Heck, Astrophysics and Space Science Library. Volume 131: Multivariate Data Analysis, Kluwer Academic Publishers, Dordrecht, 1987.
  • L. Breiman and L. Cutler, RF5new; software available at http://www.stat.berkeley.edu/~breiman/RandomForests/cc_examples/prog.f.
  • J. Colmenares, flssvm; software available at https://github.com/jbcolme/fortran-lssvm
  • O. Obrezanova, G. Csanyi, J.M.R. Gola, and M.D. Segall, Gaussian processes: A method for automatic QSAR modeling of ADME properties, J. Chem. Inf. Model. 47 (2007), pp. 1847–1857.
  • E. C. Harrington, The desirability function, Ind. Qual. Control 21 (1965), pp. 494–498.
  • O.A. Raevsky, K.-J. Schaper, and J.K. Seydel, H-bond contribution to octanol–water partition coefficient of polar compounds, Quant. Struct.–Act. Relat. 14 (1995), pp. 433–436.
  • T.X. Xiang and B.D. Anderson, The relationship between permeant size and permeability in lipid bilayer membranes, J. Membr. Biol. 140 (1994), pp. 111–122.
  • E. Kerns and L. Di, Drug-like Properties: Concepts, Structure Design, and Methods: From ADME to Toxicity Optimization, Elsevier, San Diego, 2008.
  • G.M. Maggiora, On outliers and activity cliffs. Why QSAR often disappoints, J. Chem. Inf. Model. 46 (2006), pp. 1535–1535.
  • A. Mukhametov and O.A. Raevsky, On the mechanism of substrate/nonsubstrate recognition by P-glycoprotein, J. Mol. Graph. Model. 71 (2017), pp. 227–232.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.