Generic placeholder image

Letters in Drug Design & Discovery

Editor-in-Chief

ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

Research Article

QSAR Studies on a Series of Pyrazole Azabicyclo [3.2.1] Octane Sulfonamides N-acylethanolamine-hydrolyzing Acid Amidase Inhibitors

Author(s): Shengnan Ren, Liyang Sun, Hongzong Si, Zhuang Yu and Huan Wang*

Volume 21, Issue 9, 2024

Published on: 10 May, 2023

Page: [1481 - 1492] Pages: 12

DOI: 10.2174/1570180820666230418093238

Price: $65

Abstract

Background: Inflammation is a common and intractable disease for humans. Current antiinflammatory drugs have a lot of side effects, which cause irreversible damage to the body.

Objective: We predict the activity of the N-acylethanolamine-hydrolyzing acid amidase (NAAA) inhibitor to find more effective compounds.

Methods: we established a quantitative structure-activity relationship (QSAR) model by gene expression programming to predict the IC50 values of natural compounds. The NAAA inhibitor, as a cysteine enzyme, plays an important role in the therapy of pain, anti-inflammatory effects and application of other diseases. A total of 36 NAAA inhibitors were optimized by the heuristic method in the CODESSA program to build a linear model. The 27 compounds and 9 compounds were in train and test sets. On this basis, we selected three descriptors and used them to build nonlinear models in gene expression programming.

Results: The best model in the gene expression programming method was found, the square of correlation coefficients of R2 and mean square error for the training set were 0.79 and 0.14, testing set was 0.78 and 0.20, respectively.

Conclusion: From this method, the activity of molecules could be predicted, and the best method was found. Therefore, this model has a stronger predictive ability to develop NAAA inhibitors.

Graphical Abstract

[1]
Chovatiya, R.; Medzhitov, R. Stress, inflammation, and defense of homeostasis. Mol. Cell, 2014, 54(2), 281-288.
[http://dx.doi.org/10.1016/j.molcel.2014.03.030] [PMID: 24766892]
[2]
Piomelli, D.; Sasso, O. Peripheral gating of pain signals by endogenous lipid mediators. Nat. Neurosci., 2014, 17(2), 164-174.
[http://dx.doi.org/10.1038/nn.3612] [PMID: 24473264]
[3]
Daskalaki, M.G.; Tsatsanis, C.; Kampranis, S.C. Histone methylation and acetylation in macrophages as a mechanism for regulation of inflammatory responses. J. Cell. Physiol., 2018, 233(9), 6495-6507.
[http://dx.doi.org/10.1002/jcp.26497] [PMID: 29574768]
[4]
Serhan, C.N.; Levy, B.D. Resolvins in inflammation: Emergence of the pro-resolving superfamily of mediators. J. Clin. Invest., 2018, 128(7), 2657-2669.
[http://dx.doi.org/10.1172/JCI97943] [PMID: 29757195]
[5]
Vassileva, V.; Piquette-Miller, M. Inflammation: Extinguishing the fires within. Clin. Pharmacol. Ther., 2010, 87(4), 375-379.
[http://dx.doi.org/10.1038/clpt.2010.10] [PMID: 20305667]
[6]
Haley, R.M.; von Recum, H.A. Localized and targeted delivery of NSAIDs for treatment of inflammation: A review. Exp. Biol. Med. , 2019, 244(6), 433-444.
[http://dx.doi.org/10.1177/1535370218787770] [PMID: 29996674]
[7]
Fotio, Y.; Jung, K.M.; Palese, F.; Obenaus, A.; Tagne, A.M.; Lin, L.; Rashid, T.I.; Pacheco, R.; Jullienne, A.; Ramirez, J.; Mor, M.; Spadoni, G.; Jang, C.; Hohmann, A.G.; Piomelli, D. NAAA-regulated lipid signaling governs the transition from acute to chronic pain. Sci. Adv., 2021, 7(43), eabi8834.
[http://dx.doi.org/10.1126/sciadv.abi8834] [PMID: 34678057]
[8]
Berkes, E.A. Anaphylactic and anaphylactoid reactions to aspirin and other NSAIDs. Clin. Rev. Allergy Immunol., 2003, 24(2), 137-148.
[http://dx.doi.org/10.1385/CRIAI:24:2:137] [PMID: 12668894]
[9]
Bandiera, T.; Ponzano, S.; Piomelli, D. Advances in the discovery of N-acylethanolamine acid amidase inhibitors. Pharmacol. Res., 2014, 86, 11-17.
[http://dx.doi.org/10.1016/j.phrs.2014.04.011] [PMID: 24798679]
[10]
Piomelli, D.; Scalvini, L.; Fotio, Y.; Lodola, A.; Spadoni, G.; Tarzia, G.; Mor, M. N -Acylethanolamine Acid Amidase (NAAA): Structure, Function, and Inhibition. J. Med. Chem., 2020, 63(14), 7475-7490.
[http://dx.doi.org/10.1021/acs.jmedchem.0c00191] [PMID: 32191459]
[11]
Fiasella, A.; Nuzzi, A.; Summa, M.; Armirotti, A.; Tarozzo, G.; Tarzia, G.; Mor, M.; Bertozzi, F.; Bandiera, T.; Piomelli, D. 3-Aminoazetidin-2-one derivatives as N-acylethanolamine acid amidase (NAAA) inhibitors suitable for systemic administration. ChemMedChem, 2014, 9(7), 1602-1614.
[http://dx.doi.org/10.1002/cmdc.201300546] [PMID: 24828120]
[12]
Jin, W.; Yang, L.; Yi, Z.; Fang, H.; Chen, W.; Hong, Z.; Zhang, Y.; Zhang, G.; Li, L. Anti-inflammatory effects of fucoxanthinol in LPS-induced RAW264.7 cells through the NAAA-PEA pathway. Mar. Drugs, 2020, 18(4), 222.
[http://dx.doi.org/10.3390/md18040222] [PMID: 32326173]
[13]
Christofides, A.; Konstantinidou, E.; Jani, C.; Boussiotis, V.A. The role of peroxisome proliferator-activated receptors (PPAR) in immune responses. Metabolism, 2021, 114, 154338.
[http://dx.doi.org/10.1016/j.metabol.2020.154338] [PMID: 32791172]
[14]
Li, Y.; Chen, Q.; Yang, L.; Li, Y.; Zhang, Y.; Qiu, Y.; Ren, J.; Lu, C. Identification of highly potent N -acylethanolamine acid amidase (NAAA) inhibitors: Optimization of the terminal phenyl moiety of oxazolidone derivatives. Eur. J. Med. Chem., 2017, 139, 214-221.
[http://dx.doi.org/10.1016/j.ejmech.2017.08.004] [PMID: 28802121]
[15]
Roy, K.; Chakraborty, P.; Mitra, I.; Ojha, P.K.; Kar, S.; Das, R.N. Some case studies on application of “ rm2 ” metrics for judging quality of quantitative structure-activity relationship predictions: Emphasis on scaling of response data. J. Comput. Chem., 2013, 34(12), 1071-1082.
[http://dx.doi.org/10.1002/jcc.23231] [PMID: 23299630]
[16]
Verma, J.; Khedkar, V.; Coutinho, E. 3D-QSAR in drug design-a review. Curr. Top. Med. Chem., 2010, 10(1), 95-115.
[http://dx.doi.org/10.2174/156802610790232260] [PMID: 19929826]
[17]
Kumar, V.; Ojha, P.K.; Saha, A.; Roy, K. Exploring 2D-QSAR for prediction of beta-secretase 1 (BACE1) inhibitory activity against Alzheimer’s disease. SAR QSAR Environ. Res., 2020, 31(2), 87-133.
[http://dx.doi.org/10.1080/1062936X.2019.1695226] [PMID: 31865778]
[18]
Zhu, H. From QSAR to QSIIR: Searching for enhanced computational toxicology models. Methods Mol. Biol., 2013, 930, 53-65.
[http://dx.doi.org/10.1007/978-1-62703-059-5_3] [PMID: 23086837]
[19]
Petrosino, S.; Ahmad, A.; Marcolongo, G.; Esposito, E.; Allarà, M.; Verde, R.; Cuzzocrea, S.; Di Marzo, V. Diacerein is a potent and selective inhibitor of palmitoylethanolamide inactivation with analgesic activity in a rat model of acute inflammatory pain. Pharmacol. Res., 2015, 91, 9-14.
[http://dx.doi.org/10.1016/j.phrs.2014.10.008] [PMID: 25447594]
[20]
Di Fruscia, P.; Carbone, A.; Bottegoni, G.; Berti, F.; Giacomina, F.; Ponzano, S.; Pagliuca, C.; Fiasella, A.; Pizzirani, D.; Ortega, J.A.; Nuzzi, A.; Tarozzo, G.; Mengatto, L.; Giampà, R.; Penna, I.; Russo, D.; Romeo, E.; Summa, M.; Bertorelli, R.; Armirotti, A.; Bertozzi, S.M.; Reggiani, A.; Bandiera, T.; Bertozzi, F. Discovery and SAR evolution of pyrazole azabicyclo[3.2.1]octane sulfonamides as a novel class of non-covalent N -acylethanolamine-hydrolyzing acid amidase (NAAA) inhibitors for oral administration. J. Med. Chem., 2021, 64(18), 13327-13355.
[http://dx.doi.org/10.1021/acs.jmedchem.1c00575] [PMID: 34469137]
[21]
Jing, O.; Liu, X.; Zhao, Y.; Liu, Y.; Si, H. Zhai, Honglin studies on the pIC50 of 4,5-diarylisoxazole as HSP90 inhibitors. Lett. Drug Des. Discov., 2020, 17(4), 467-478.
[22]
Si, Y.; Xu, X.; Hu, Y.; Si, H.; Zhai, H. Novel quantitative structure–activity relationship model to predict activities of natural products against COVID‐19. Chem. Biol. Drug Des., 2021, 97(4), 978-983.
[http://dx.doi.org/10.1111/cbdd.13822] [PMID: 33386649]
[23]
Danishuddin; Khan, A.U. Descriptors and their selection methods in QSAR analysis: paradigm for drug design. Drug Discov. Today, 2016, 21(8), 1291-1302.
[http://dx.doi.org/10.1016/j.drudis.2016.06.013] [PMID: 27326911]
[24]
Gómez-Jiménez, G.; Gonzalez-Ponce, K.; Castillo-Pazos, D.J.; Madariaga-Mazon, A.; Barroso-Flores, J.; Cortes-Guzman, F.; Martinez-Mayorga, K. The OECD principles for (Q)SAR models in the context of knowledge discovery in databases (KDD). Adv. Protein Chem. Struct. Biol., 2018, 113, 85-117.
[http://dx.doi.org/10.1016/bs.apcsb.2018.04.001] [PMID: 30149907]
[25]
De, P.; Kar, S.; Ambure, P.; Roy, K. Prediction reliability of QSAR models: An overview of various validation tools. Arch. Toxicol., 2022, 96(5), 1279-1295.
[http://dx.doi.org/10.1007/s00204-022-03252-y] [PMID: 35267067]
[26]
Shukla, A.; Sharma, P.; Prakash, O.; Singh, M.; Kalani, K.; Khan, F.; Bawankule, D.U.; Luqman, S.; Srivastava, S.K. QSAR and docking studies on capsazepine derivatives for immunomodulatory and anti-inflammatory activity. PLoS One, 2014, 9(7), e100797.
[http://dx.doi.org/10.1371/journal.pone.0100797] [PMID: 25003344]
[27]
Kanan, T.; Kanan, D.; Al Shardoub, E.J.; Durdagi, S. Transcription factor NF-κB as target for SARS-CoV-2 drug discovery efforts using inflammation-based QSAR screening model. J. Mol. Graph. Model., 2021, 108, 107968.
[http://dx.doi.org/10.1016/j.jmgm.2021.107968] [PMID: 34311260]
[28]
Ghidini, A.; Scalvini, L.; Palese, F.; Lodola, A.; Mor, M.; Piomelli, D. Different roles for the acyl chain and the amine leaving group in the substrate selectivity of N -Acylethanolamine acid amidase. J. Enzyme Inhib. Med. Chem., 2021, 36(1), 1410-1422.
[http://dx.doi.org/10.1080/14756366.2021.1912035] [PMID: 34256657]
[29]
Gorelik, A.; Gebai, A.; Illes, K.; Piomelli, D.; Nagar, B. Molecular mechanism of activation of the immunoregulatory amidase NAAA. Proc. Natl. Acad. Sci. USA, 2018, 115(43), E10032-E10040.
[http://dx.doi.org/10.1073/pnas.1811759115] [PMID: 30301806]

© 2024 Bentham Science Publishers | Privacy Policy