Advances in In-Silico based Predictive In-Vivo Profiling of Novel Potent β-Glucuronidase Inhibitors

Author(s): Maria Yousuf*.

Journal Name: Current Cancer Drug Targets

Volume 19 , Issue 11 , 2019

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Abstract:

Background: Intestinal β-glucuronidase enzyme has a significant importance in colorectal carcinogenesis. Specific inhibition of the enzyme helps prevent immune reactivation of the glucuronide- carcinogens, thus protecting the intestine from ROS (Reactive Oxidative Species) mediatedcarcinogenesis.

Objectives: Advancement in In-silico based techniques has provided a broad range of studies to carry out the drug design and development process smoothly using SwissADME and BOILED-Egg tools.

Methods: In our designed case study, we used SwissADME and BOILED-Egg predictive computational tools to estimate the physicochemical, human pharmacokinetics, drug-likeness, medicinal chemistry properties and membrane permeability characteristics of our recently In-vitro evaluated novel β-Glucuronidase inhibitors.

Results: Out of the eleven screened potent inhibitors, compound (8) exhibited excellent bioavailability radar against the six molecular descriptors, good (ADME) Absorption, Distribution, Metabolism and Excretion along with P-glycoprotein, CYP450 isozymes and membranes permeability profile. On the basis of these factual observations, it is to be predicted that compound (8) can achieve in-vivo experimental clearance efficiently, Therefore, in the future, it can be a drug in the market to treat various disorders associated with the overexpression of β-Glucuronidase enzyme such as various types of cancer, particularly hormone-dependent cancer such as (breast, prostate, and colon cancer). Moreover, other compounds (1-7, & 9-11), have also shown good predictive pharmacokinetics, medicinal chemistry, BBB and HIA membranes permeability profiles with slight lead optimization to obtain improved results.

Conclusion: In consequence, in-silico based studies are considered to provide robustness for a rational drug design and development approach to avoid the possibility of failures of drug candidates in the later stages of drug development phases. The results of this study effectively reveal the possible attributes of potent β-Glucuronidase inhibitors, for further experimental evaluation.

Keywords: β-Glucuronidase enzyme, Colorectal carcinogenesis, SwissADME, BOILED-Egg, Pharmacokinetics, Druglikeness, Blood Brain Barrier (BBB), Human intestinal absorption (HIA).

[1]
Ahmad, S.; Hughes, M.A.; Lane, K.T.; Redinbo, M.R.; Yeh, L.A.; Scott, J.E. High throughput assay for discovery of bacterial β-glucuronidase inhibitors. Curr. Chem. Genomics, 2011, 5, 13-20.
[http://dx.doi.org/10.2174/1875397301105010013] [PMID: 21643506]
[2]
Di, Li; Feng, Bo; Theunis, C. Scott Obach A Perspective on the Prediction of Drug Pharmacokinetics and Disposition in Drug Research and Development Drug Metab Dispos, 2013. 41, p. 1975- 1993.
[3]
Hongmao, S.; Henrike, V.; Menghang, X.; Christopher, P. Austin, and ruili huang predictive models for cytochrome p450 isozymes based on quantitative high throughput screening data. Chem Inf Model., 2011, 51(10), 2474-2481.
[4]
Yousuf, M.; Shaikh, N.N. Zaheer ul-Haq & M.I Choudhary, Bioinformatics: A rational Combine approach used for in-silico identification and in-vitro evaluation of potent β-Glucuronidase Inhibitors. PLoS One, 2018. (December)
[http://dx.doi.org/10.1371/journal.pone.0200502] [PMID: 30517092]
[5]
Wong, A.W.; He, S.; Grubb, J.H.J.; Sly, W.S.; Withers, S.G. Identification of Glu-540 as the catalytic nucleophile of human beta-glucuronidase using electrospray mass spectrometry. J. Biol. Chem., 1998, 273(51), 34057-34062.
[http://dx.doi.org/10.1074/jbc.273.51.34057] [PMID: 9852062]
[6]
Walaszek, Z.; Szemraj, J.; Narog, M.; Adams, A.K.; Kilgore, J.; Sherman, U.; Hanausek, M. Metabolism, uptake, and excretion of a D-glucaric acid salt and its potential use in cancer prevention. Cancer Detect. Prev., 1997, 21(2), 178-190.
[PMID: 9101079]
[7]
Antoine Daina1, Olivier Michielin1,2,3 & Vincent Zoete1 SwissADME: a free web tool to Evaluate pharmacokinetics, druglikeness and medicinal chemistry friendliness of small molecules. Sci. Rep., 2017, 7, 42717.
[http://dx.doi.org/10.1038/srep42717] [PMID: 28256516]
[8]
Daina, A.; Zoete, V. BOILED-Egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem, 2016, 11(11), 1117-1121.
[http://dx.doi.org/10.1002/cmdc.201600182] [PMID: 27218427]
[9]
Waring, M.J.; Arrowsmith, J.; Leach, A.R.; Leeson, P.D.; Mandrell, S.; Owen, R.M.; Pairaudeau, G.; Pennie, W.D.; Pickett, S.D.; Wang, J.; Wallace, O.; Weir, A. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat. Rev. Drug Discov., 2015, 14(7), 475-486.
[http://dx.doi.org/10.1038/nrd4609] [PMID: 26091267]
[10]
Bansal, T.; Akhtar, N.; Jaggi, M.; Khar, R.K.; Talegaonkar, S. Novel formulation approaches for optimising delivery of anticancer drugs based on P-glycoprotein modulation. Drug Discov. Today, 2009, 14(21-22), 1067-1074.
[http://dx.doi.org/10.1016/j.drudis.2009.07.010] [PMID: 19647803]
[11]
Aszalos, A. Drug-drug interactions affected by the transporter protein, P-glycoprotein (ABCB1, MDR1) I. Preclinical aspects. Drug Discov. Today, 2007, 12(19-20), 833-837.
[http://dx.doi.org/10.1016/j.drudis.2007.07.022] [PMID: 17933684]
[12]
Di, L. The role of drug metabolizing enzymes in clearance. Expert Opin. Drug Metab. Toxicol., 2014, 10(3), 379-393.
[http://dx.doi.org/10.1517/17425255.2014.876006] [PMID: 24392841]
[13]
Hollenberg, P.F. Characteristics and common properties of inhibitors, inducers, and activators of CYP enzymes. Drug Metab. Rev., 2002, 34(1-2), 17-35.
[http://dx.doi.org/10.1081/DMR-120001387] [PMID: 11996009]
[14]
Huang, S-M.; Strong, J.M.; Zhang, L.; Reynolds, K.S.; Nallani, S.; Temple, R.; Abraham, S.; Habet, S.A.; Baweja, R.K.; Burckart, G.J.; Chung, S.; Colangelo, P.; Frucht, D.; Green, M.D.; Hepp, P.; Karnaukhova, E.; Ko, H.S.; Lee, J.I.; Marroum, P.J.; Norden, J.M.; Qiu, W.; Rahman, A.; Sobel, S.; Stifano, T.; Thummel, K.; Wei, X.X.; Yasuda, S.; Zheng, J.H.; Zhao, H.; Lesko, L.J. New era in drug interaction evaluation: US Food and Drug Administration update on CYP enzymes, transporters, and the guidance process. J. Clin. Pharmacol., 2008, 48(6), 662-670.
[http://dx.doi.org/10.1177/0091270007312153] [PMID: 18378963]
[15]
Kirchmair, J.; Göller, A.H.; Lang, D.; Kunze, J.; Testa, B.; Wilson, I.D.; Glen, R.C.; Schneider, G. Predicting drug metabolism: experiment and/or computation? Nat. Rev. Drug Discov., 2015, 14(6), 387-404.
[http://dx.doi.org/10.1038/nrd4581] [PMID: 25907346]
[16]
Veith, H.; Southall, N.; Huang, R.; James, T.; Fayne, D.; Artemenko, N.; Shen, M.; Inglese, J.; Austin, C.P.; Lloyd, D.G.; Auld, D.S. Comprehensive characterization of cytochrome P450 isozyme selectivity across chemical libraries. Nat. Biotechnol., 2009, 27(11), 1050-1055.
[http://dx.doi.org/10.1038/nbt.1581] [PMID: 19855396]
[17]
Potts, R.O.; Guy, R.H. Predicting skin permeability. Pharm. Res., 1992, 9(5), 663-669.
[http://dx.doi.org/10.1023/A:1015810312465] [PMID: 1608900]
[18]
Ghose, A.K.; Viswanadhan, V.N.; Wendoloski, J.J. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J. Comb. Chem., 1999, 1(1), 55-68.
[http://dx.doi.org/10.1021/cc9800071] [PMID: 10746014]
[19]
Veber, D.F.; Johnson, S.R.; Cheng, H.Y.; Smith, B.R.; Ward, K.W.; Kopple, K.D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem., 2002, 45(12), 2615-2623.
[http://dx.doi.org/10.1021/jm020017n] [PMID: 12036371]
[20]
Egan, W.J.; Merz, K.M., Jr; Baldwin, J.J. Prediction of drug absorption using multivariate statistics. J. Med. Chem., 2000, 43(21), 3867-3877.
[http://dx.doi.org/10.1021/jm000292e] [PMID: 11052792]
[21]
Muegge, I.; Heald, S.L.; Brittelli, D. Simple selection criteria for drug-like chemical matter. J. Med. Chem., 2001, 44(12), 1841-1846.
[http://dx.doi.org/10.1021/jm015507e] [PMID: 11384230]
[22]
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 2001, 46(1-3), 3-26.
[http://dx.doi.org/10.1016/S0169-409X(00)00129-0] [PMID: 11259830]
[23]
Kubinyi, H. Drug research: myths, hype and reality. Nat. Rev. Drug Discov., 2003, 2(8), 665-668.
[http://dx.doi.org/10.1038/nrd1156] [PMID: 12904816]
[24]
Pliska, V. Testa, B.; van de Waterbeemd, H. In: Lipophilicity in Drug Action and Toxicology 1–6; Wiley-VCH Verlag GmbH. 1996
[http://dx.doi.org/10.1002/9783527614998]
[25]
Arnott, J.A.; Planey, S.L. The influence of lipophilicity in drug discovery and design. Expert Opin. Drug Discov., 2012, 7(10), 863-875.
[http://dx.doi.org/10.1517/17460441.2012.714363] [PMID: 22992175]
[26]
Mannhold, R.; Poda, G.I.; Ostermann, C.; Tetko, I.V. Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds. J. Pharm. Sci., 2009, 98(3), 861-893.
[http://dx.doi.org/10.1002/jps.21494] [PMID: 18683876]
[27]
Cheng, T.; Zhao, Y.; Li, X.; Lin, F.; Xu, Y.; Zhang, X.; Li, Y.; Wang, R.; Lai, L. Computation of octanol-water partition coefficients by guiding an additive model with knowledge. J. Chem. Inf. Model., 2007, 47(6), 2140-2148.
[http://dx.doi.org/10.1021/ci700257y] [PMID: 17985865]
[28]
Wildman, S.A.; Crippen, G.M. Prediction of physicochemical parameters by atomic contributions. J. Chem. Inf. Model., 1999, 39, 868-873.
[29]
Moriguchi, I.; Shuichi, H.; Liu, Q.; Nakagome, I.; Matsushita, Y. Simple method of calculating octanol/water partition coefficient. Chem. Pharm. Bull. (Tokyo), 1992, 40, 127-130.
[http://dx.doi.org/10.1248/cpb.40.127]
[30]
Moriguchi, I.; Shuichi, H.; Nakagome, I.; Hirano, H. Comparison of reliability of log P values for Drugs calculated by several methods. Chem. Pharm. Bull. (Tokyo), 1994, 42, 976-978.
[http://dx.doi.org/10.1248/cpb.42.976]
[31]
Ali, J.; Camilleri, P.; Brown, M.B.; Hutt, A.J.; Kirton, S.B. Revisiting the general solubility equation: in silico prediction of aqueous solubility incorporating the effect of topographical polar surface area. J. Chem. Inf. Model., 2012, 52(2), 420-428.
[http://dx.doi.org/10.1021/ci200387c] [PMID: 22196228]
[32]
John, S. Delaney, ESOL: Estimating aqueous solubility directly from molecular structure. J. Chem. Inf. Comput. Sci., 2004, 44, 1000-1005.
[http://dx.doi.org/10.1021/ci034243x]
[33]
Shih, H.P.; Zhang, X.; Aronov, A.M. Drug discovery effectiveness from the standpoint of therapeutic mechanisms and indications. Nat. Rev. Drug Discov., 2018, 17(1), 19-33.


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VOLUME: 19
ISSUE: 11
Year: 2019
Page: [906 - 918]
Pages: 13
DOI: 10.2174/1568009619666190320102238
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