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Current Pharmaceutical Design

Editor-in-Chief

ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

Perspective Article

The Block Relevance (BR) Analysis Makes the Choice of Methods for Measuring Lipophilicity and Permeability Safer and Speeds Up Drug Candidate Prioritization

Author(s): Giulia Caron, Maura Vallaro and Giuseppe Ermondi*

Volume 26 , Issue 44 , 2020

Page: [5662 - 5667] Pages: 6

DOI: 10.2174/1381612826666201109111124

Price: $65

Abstract

The Block Relevance (BR) analysis with its recent implementation in MATLAB is a computational tool that allows deconvoluting the balance of intermolecular interactions governing a given drug discoveryrelated phenomenon described by a QSPR/PLS model. Here we discuss a few applications to show how BR analysis can make faster and more efficient the assessment of the drug-likeness of drug candidates. First, we describe how identifying the best chromatographic system provides reliable log Poct surrogates and log P in apolar environments. Then we focus on permeability and show how BR analysis allows to check the universality of passive permeability among cell types and the identification of the PAMPA method that provides the same picture in terms of balance of intermolecular interactions as cell-based systems.

Keywords: BR analysis, lipophilicity, permeability, PAMPA, physicochemical descriptors, QSPR.

[1]
Gleeson MP, Hersey A, Montanari D, Overington J. Probing the links between in vitro potency, ADMET and physicochemical parameters. Nat Rev Drug Discov 2011; 10(3): 197-208.
[http://dx.doi.org/10.1038/nrd3367] [PMID: 21358739]
[2]
Leeson PD, Young RJ. Molecular property design: does everyone get it? ACS Med Chem Lett 2015; 6(7): 722-5.
[http://dx.doi.org/10.1021/acsmedchemlett.5b00157] [PMID: 26191353]
[3]
Varma MVS, Obach RS, Rotter C, et al. Physicochemical space for optimum oral bioavailability: contribution of human intestinal absorption and first-pass elimination. J Med Chem 2010; 53(3): 1098-108.
[http://dx.doi.org/10.1021/jm901371v] [PMID: 20070106]
[4]
Tarbit MH, Berman J. High-throughput approaches for evaluating absorption, distribution, metabolism and excretion properties of lead compounds. Curr Opin Chem Biol 1998; 2(3): 411-6.
[http://dx.doi.org/10.1016/S1367-5931(98)80017-3] [PMID: 9691080]
[5]
Arnott JA, Planey SL. The influence of lipophilicity in drug discovery and design. Expert Opin Drug Discov 2012; 7(10): 863-75.
[http://dx.doi.org/10.1517/17460441.2012.714363] [PMID: 22992175]
[6]
Miller RR, Madeira M, Wood HB, Geissler WM, Raab CE, Martin IJ. Integrating the impact of Lipophilicity on potency and pharmacokinetic parameters enables the use of diverse chemical space during small molecule drug optimization. J Med Chem 2020; 63(21): 12156-70.
[http://dx.doi.org/10.1021/acs.jmedchem.9b01813] [PMID: 32633947]
[7]
Artursson P, Karlsson J. Correlation between oral drug absorption in humans and apparent drug permeability coefficients in human intestinal epithelial (Caco-2) cells. Biochem Biophys Res Commun 1991; 175(3): 880-5.
[http://dx.doi.org/10.1016/0006-291X(91)91647-U] [PMID: 1673839]
[8]
Di L, Artursson P, Avdeef A, et al. The Critical Role of Passive Permeability in Designing Successful Drugs. ChemMedChem 2020; 15(20): 1862-74.
[http://dx.doi.org/10.1002/cmdc.202000419] [PMID: 32743945]
[9]
Wishart DS. Improving early drug discovery through ADME modelling: an overview. Drugs R D 2007; 8(6): 349-62.
[http://dx.doi.org/10.2165/00126839-200708060-00003] [PMID: 17963426]
[10]
Lombardo F, Shalaeva MY, Tupper KA, Gao F, Abraham MH. ElogPoct: a tool for lipophilicity determination in drug discovery. J Med Chem 2000; 43(15): 2922-8.
[http://dx.doi.org/10.1021/jm0000822] [PMID: 10956200]
[11]
Lombardo F, Shalaeva MY, Tupper KA, Gao F. ElogD(oct): a tool for lipophilicity determination in drug discovery. 2. Basic and neutral compounds. J Med Chem 2001; 44(15): 2490-7.
[http://dx.doi.org/10.1021/jm0100990] [PMID: 11448232]
[12]
Berben P, Bauer-Brandl A, Brandl M, et al. Drug permeability profiling using cell-free permeation tools: Overview and applications. Eur J Pharm Sci 2018; 119: 219-33.
[http://dx.doi.org/10.1016/j.ejps.2018.04.016] [PMID: 29660464]
[13]
Smith D, Artursson P, Avdeef A, et al. Passive lipoidal diffusion and carrier-mediated cell uptake are both important mechanisms of membrane permeation in drug disposition. Mol Pharm 2014; 11(6): 1727-38.
[http://dx.doi.org/10.1021/mp400713v] [PMID: 24724562]
[14]
Kerns EH, Di L. Pharmaceutical profiling in drug discovery. Drug Discov Today 2003; 8(7): 316-23.
[http://dx.doi.org/10.1016/S1359-6446(03)02649-7] [PMID: 12654544]
[15]
Kerns EH, Di L. Physicochemical profiling: overview of the screens. Drug Discov Today Technol 2004; 1(4): 343-8.
[http://dx.doi.org/10.1016/j.ddtec.2004.08.011] [PMID: 24981613]
[16]
Guimarães CRW, Mathiowetz AM, Shalaeva M, Goetz G, Liras S. Use of 3D properties to characterize beyond rule-of-5 property space for passive permeation. J Chem Inf Model 2012; 52(4): 882-90.
[http://dx.doi.org/10.1021/ci300010y] [PMID: 22394163]
[17]
Abraham MH, Acree WE Jr, Leo AJ, Hoekman D, Cavanaugh JE. Water-solvent partition coefficients and Delta Log P values as predictors for blood-brain distribution; application of the Akaike information criterion. J Pharm Sci 2010; 99(5): 2492-501.
[http://dx.doi.org/10.1002/jps.22010] [PMID: 19967782]
[18]
Abraham MH. Scales of solute hydrogen-bonding: their construction and application to physicochemical and biochemicacal processes. Chem Soc Rev 1993; 22: 73-83.
[http://dx.doi.org/10.1039/cs9932200073]
[19]
Abraham MH, Chadha HS, Leitao RAE, Mitchell RC. Determination of solute lipophilicity, as log P(octanol) and log P(alkane) using poly(styrene-divinylbenzene) and immobilised artificial membrane stationary phases in reversed-phase high-performance liquid chromatography. J Chromatogr A 1997; 766: 35-47.
[http://dx.doi.org/10.1016/S0021-9673(96)00977-6]
[20]
Katritzky AR, Kuanar M, Slavov S, et al. Quantitative correlation of physical and chemical properties with chemical structure: utility for prediction. Chem Rev 2010; 110(10): 5714-89.
[http://dx.doi.org/10.1021/cr900238d] [PMID: 20731377]
[21]
Katritzky AR, Petrukhin R, Tatham D, et al. Interpretation of quantitative structure-property and -activity relationships. J Chem Inf Comput Sci 2001; 41(3): 679-85.
[http://dx.doi.org/10.1021/ci000134w] [PMID: 11410046]
[22]
Gramatica P, Cassani S, Roy PP, Kovarich S, Yap CW, Papa E. QSAR modeling is not “Push a button and find a correlation”: A case study of toxicity of (Benzo-)triazoles on Algae. Mol Inform 2012; 31(11-12): 817-35.
[http://dx.doi.org/10.1002/minf.201200075] [PMID: 27476736]
[23]
Goodford PJ. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 1985; 28(7): 849-57.
[http://dx.doi.org/10.1021/jm00145a002] [PMID: 3892003]
[24]
Sciabola S, Stanton RV, Mills JE, et al. High-throughput virtual screening of proteins using GRID molecular interaction fields. J Chem Inf Model 2010; 50(1): 155-69.
[http://dx.doi.org/10.1021/ci9003317] [PMID: 19919042]
[25]
Cruciani G, Crivori P, Carrupt PA, Testa B. Molecular fields in quantitative structure-permeation relationships: The VolSurf approach. J Mol Struct THEOCHEM 2000; 503(1-2): 17-30.
[http://dx.doi.org/10.1016/S0166-1280(99)00360-7]
[26]
Oprea TI. On the information content of 2D and 3D descriptors for QSAR. J Braz Chem Soc 2002; 13(6): 811-5.
[http://dx.doi.org/10.1590/S0103-50532002000600013]
[27]
Mehmood T, Liland KH, Snipen L, Sæbø S. A review of variable selection methods in Partial Least Squares Regression. Chemom Intell Lab Syst 2012; 118: 62-9.
[http://dx.doi.org/10.1016/j.chemolab.2012.07.010]
[28]
Wold S, Sjöström M, Eriksson L. PLS-regression: A basic tool of chemometrics. Chemom Intell Lab Syst 2001; 58(2): 109-30.
[http://dx.doi.org/10.1016/S0169-7439(01)00155-1]
[29]
Biancolillo A, Liland KH, Måge I, Næs T, Bro R. Variable selection in multi-block regression. Chemom Intell Lab Syst 2016; 156: 89-101.
[http://dx.doi.org/10.1016/j.chemolab.2016.05.016]
[30]
Westerhuis JA, Smilde AK. Deflation in multiblock PLS. J Chemometr 2001; 15(5): 485-93.
[http://dx.doi.org/10.1002/cem.652]
[31]
Ermondi G, Caron G. MLR, PLSR-BR Analysis and MBPLSR to Interpret Multivariate QSPR Models. The Case of a Micellar Liquid Chromatography Descriptor (log KWSDS). Mol Inform 2019; 38(8-9)e1800144
[http://dx.doi.org/10.1002/minf.201800144] [PMID: 30768770]
[32]
Giusepe Ermondi GC, Ermondi G, Caron G, Giuseppe Ermondi GC. Block relevance (BR) analysis and polarity descriptors in property-based drug design. ADMET DMPK 2018; 6(3): 215-24.
[http://dx.doi.org/10.5599/admet.532]
[33]
Valkó K. Application of high-performance liquid chromatography based measurements of lipophilicity to model biological distribution. J Chromatogr A 2004; 1037(1-2): 299-310.
[http://dx.doi.org/10.1016/j.chroma.2003.10.084] [PMID: 15214672]
[34]
Valkó KL. Lipophilicity and biomimetic properties measured by HPLC to support drug discovery. J Pharm Biomed Anal 2016; 130: 35-54.
[http://dx.doi.org/10.1016/j.jpba.2016.04.009] [PMID: 27084527]
[35]
Ermondi G, Vallaro M, Goetz G, Shalaeva M, Caron G. Updating the portfolio of physicochemical descriptors related to permeability in the beyond the rule of 5 chemical space. Eur J Pharm Sci 2020; 146105274
[http://dx.doi.org/10.1016/j.ejps.2020.105274] [PMID: 32088315]
[36]
Ermondi G, Vallaro M, Goetz G, Shalaeva M, Caron G. Experimental lipophilicity for beyond Rule of 5 compounds. Futur Drug Discov 2019; 1(1): 1-12.
[http://dx.doi.org/10.4155/fdd-2019-0002]
[37]
Caron G, Vallaro M, Ermondi G, et al. A fast chromatographic method for estimating lipophilicity and ionization in nonpolar membrane-like environment. Mol Pharm 2016; 13(3): 1100-10.
[http://dx.doi.org/10.1021/acs.molpharmaceut.5b00910] [PMID: 26767433]
[38]
Shalaeva M, Caron G, Abramov YA, et al. Integrating intramolecular hydrogen bonding (IMHB) considerations in drug discovery using ΔlogP as a tool. J Med Chem 2013; 56(12): 4870-9.
[http://dx.doi.org/10.1021/jm301850m] [PMID: 23710574]
[39]
Irvine JD, Takahashi L, Lockhart K, et al. MDCK (Madin-Darby canine kidney) cells: A tool for membrane permeability screening. J Pharm Sci 1999; 88(1): 28-33.
[http://dx.doi.org/10.1021/js9803205] [PMID: 9874698]
[40]
Broccatelli F, Salphati L, Plise E, et al. Predicting passive permeability of drug-like molecules from chemical structure: where are we? Mol Pharm 2016; 13(12): 4199-208.
[http://dx.doi.org/10.1021/acs.molpharmaceut.6b00836] [PMID: 27806577]
[41]
Goetz GHGH, Shalaeva M, Caron G, Ermondi G, Philippe L. Relationship between passive permeability and molecular polarity using block relevance analysis. Mol Pharm 2017; 14(2): 386-93.
[http://dx.doi.org/10.1021/acs.molpharmaceut.6b00724] [PMID: 28035823]
[42]
Faller B. Artificial membrane assays to assess permeability. Curr Drug Metab 2008; 9(9): 886-92.
[http://dx.doi.org/10.2174/138920008786485227] [PMID: 18991585]
[43]
Chen X, Murawski A, Patel K, Crespi CL, Balimane PV. A novel design of artificial membrane for improving the PAMPA model. Pharm Res 2008; 25(7): 1511-20.
[http://dx.doi.org/10.1007/s11095-007-9517-8] [PMID: 18185985]
[44]
Caudana F, Ermondi G, Vallaro M, Shalaeva M, Caron G. Permeability prediction for zwitterions via chromatographic indexes and classification into ‘certain’ and ‘uncertain’. Future Med Chem 2019; 11(13): 1553-63.
[http://dx.doi.org/10.4155/fmc-2019-0071] [PMID: 31240942]
[45]
Potter T, Ermondi G, Newbury G, Caron G. Relating Caco-2 permeability to molecular properties using block relevance analysis. MedChemComm 2015; 6(4): 626-9.
[http://dx.doi.org/10.1039/C4MD00470A]
[46]
Thomas S, Brightman F, Gill H, Lee S, Pufong B. Simulation modelling of human intestinal absorption using Caco-2 permeability and kinetic solubility data for early drug discovery. J Pharm Sci 2008; 97(10): 4557-74.
[http://dx.doi.org/10.1002/jps.21305] [PMID: 18300298]

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