Generic placeholder image

Current Computer-Aided Drug Design

Author(s): Jun Chang*, Shaoqing Zou, Subo Xu, Yiwen Xiao and Du Zhu*

DOI: 10.2174/1573409919666230417080832

DownloadDownload PDF Flyer Cite As
Screening of Inhibitors against Idiopathic Pulmonary Fibrosis: Few-shot Machine Learning and Molecule Docking based Drug Repurposing

Page: [134 - 144] Pages: 11

  • * (Excluding Mailing and Handling)

Explore Articles
About Journal
For Authors
For Editors
For Reviewers

Abstract

Introduction: Idiopathic pulmonary fibrosis is a chronic progressive disorder and is diagnosed as post-COVID fibrosis. Idiopathic pulmonary fibrosis has no effective treatment because of the low therapeutic effects and side effects of currently available drugs.

Aim: The aim is to screen new inhibitors against idiopathic pulmonary fibrosis from traditional Chinese medicines.

Methods: Few-shot-based machine learning and molecule docking were used to predict the potential activities of candidates and calculate the ligand-receptor interactions. In vitro A549 cell model was taken to verify the effects of the selected leads on idiopathic pulmonary fibrosis.

Results: A logistic regression classifier model with an accuracy of 0.82 was built and, combined with molecule docking, used to predict the activities of candidates. 6 leads were finally screened out and 5 of them were in vitro experimentally verified as effective inhibitors against idiopathic pulmonary fibrosis.

Conclusion: Herbacetin, morusin, swertiamarin, vicenin-2, and vitexin were active inhibitors against idiopathic pulmonary fibrosis. Swertiamarin exhibited the highest anti-idiopathic pulmonary fibrosis effect and should be further in vivo investigated for its activity.

Keywords: Few-shot machine learning, molecule docking, virtual screen, idiopathic pulmonary fibrosis, drug repurposing, traditional chinese medicines.

Graphical Abstract

[1]
Wilson, M.S.; Wynn, T.A. Pulmonary fibrosis: Pathogenesis, etiology and regulation. Mucosal Immunol., 2009, 2(2), 103-121.
[http://dx.doi.org/10.1038/mi.2008.85] [PMID: 19129758]
[2]
Bazdyrev, E.; Rusina, P.; Panova, M.; Novikov, F.; Grishagin, I.; Nebolsin, V. Lung fibrosis after COVID-19: Treatment prospects. Pharmaceuticals, 2021, 14(8), 807-821.
[http://dx.doi.org/10.3390/ph14080807] [PMID: 34451904]
[3]
Murray, L.A.; Rubinowitz, A.; Herzog, E.L. Interstitial lung disease. Curr. Opin. Rheumatol., 2012, 24(6), 656-662.
[http://dx.doi.org/10.1097/BOR.0b013e3283588de4] [PMID: 22955020]
[4]
Glass, D.S.; Grossfeld, D.; Renna, H.A.; Agarwala, P.; Spiegler, P.; DeLeon, J.; Reiss, A.B. Idiopathic pulmonary fibrosis: Current and future treatment. Clin. Respir. J., 2022, 16(2), 84-96.
[http://dx.doi.org/10.1111/crj.13466] [PMID: 35001525]
[5]
Jin, H. Imrecoxib inhibits paraquat-induced pulmonary fibrosis through the NF-κB/snail signaling pathway. Comput. Math. Methods Med., 2020, 2020, 1-9.
[http://dx.doi.org/10.1155/2020/6374014] [PMID: 33123215]
[6]
Ekins, S.; Gerlach, J.; Zorn, K.M.; Antonio, B.M.; Lin, Z.; Gerlach, A. Repurposing approved drugs as inhibitors of Kv7.1 and Nav1.8 to Treat Pitt Hopkins syndrome. Pharm. Pharm. Res., 2019, 36(9), 137.
[http://dx.doi.org/10.1007/s11095-019-2671-y] [PMID: 31332533]
[7]
Dudley, J.T.; Deshpande, T.; Butte, A.J. Exploiting drug-disease relationships for computational drug repositioning. Brief. Bioinform., 2011, 12(4), 303-311.
[http://dx.doi.org/10.1093/bib/bbr013] [PMID: 21690101]
[8]
Baker, N.C.; Ekins, S.; Williams, A.J.; Tropsha, A. A bibliometric review of drug repurposing. Drug Discov. Today, 2018, 23(3), 661-672.
[http://dx.doi.org/10.1016/j.drudis.2018.01.018] [PMID: 29330123]
[9]
Ru, J.; Li, P.; Wang, J.; Zhou, W.; Li, B.; Huang, C.; Li, P.; Guo, Z.; Tao, W.; Yang, Y.; Xu, X.; Li, Y.; Wang, Y.; Yang, L. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform., 2014, 6(1), 13.
[http://dx.doi.org/10.1186/1758-2946-6-13] [PMID: 24735618]
[10]
Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461.
[PMID: 19499576]
[11]
O’Boyle, N.M.; Banck, M.; James, C.A.; Morley, C.; Vandermeersch, T.; Hutchison, G.R. Open babel: An open chemical toolbox. J. Cheminform., 2011, 3(1), 33.
[http://dx.doi.org/10.1186/1758-2946-3-33] [PMID: 21982300]
[12]
Kim, D.J.; Lee, M.H.; Liu, K.; Lim, D.Y.; Roh, E.; Chen, H.; Kim, S.H.; Shim, J.H.; Kim, M.O.; Li, W.; Ma, F.; Fredimoses, M.; Bode, A.M.; Dong, Z. Herbacetin suppresses cutaneous squamous cell carcinoma and melanoma cell growth by targeting AKT and ODC. Carcinogenesis, 2017, 38(11), 1136-1146.
[http://dx.doi.org/10.1093/carcin/bgx082] [PMID: 29029040]
[13]
Li, L.; Sapkota, M.; Kim, S.; Soh, Y. Herbacetin inhibits inducible nitric oxide synthase via JNK and nuclear factor-κB in LPS-stimulated RAW264.7 cells. Eur. J. Pharmacol., 2015, 765, 115-123.
[http://dx.doi.org/10.1016/j.ejphar.2015.08.032] [PMID: 26297979]
[14]
Jin, S.; Ha, H.; Shin, H.K.; Seo, C.S. Anti-allergic and anti-inflammatory effects of kuwanon G and morusin on MC/9 mast cells and Ha-CaT keratinocytes. Molecules, 2019, 24(2), 265.
[http://dx.doi.org/10.3390/molecules24020265] [PMID: 30642008]
[15]
Chen, C.; Wang, J.; Chen, J.; Zhou, L.; Wang, H.; Chen, J.; Xu, Z.; Zhu, S.; Liu, W.; Yu, R.; Lu, J.; Luo, H.; Chen, M.; Chen, W. Morusin alleviates mycoplasma pneumonia via the inhibition of Wnt/β-catenin and NF-κB signaling. Biosci. Rep., 2019, 39(6), BSR20190190.
[http://dx.doi.org/10.1042/BSR20190190] [PMID: 31171712]
[16]
Martins, B.A.; Sande, D.; Solares, M.D.; Takahashi, J.A. Antioxidant role of morusin and mulberrofuran B in ethanol extract of Morus alba roots. Nat. Prod. Res., 2021, 35(24), 5993-5996.
[http://dx.doi.org/10.1080/14786419.2020.1810036] [PMID: 32840147]
[17]
Jaishree, V.; Badami, S. Antioxidant and hepatoprotective effect of swertiamarin from Enicostemma axillare against d-galactosamine induced acute liver damage in rats. J. Ethnopharmacol., 2010, 130(1), 103-106.
[http://dx.doi.org/10.1016/j.jep.2010.04.019] [PMID: 20420896]
[18]
Vaijanathappa, J.; Badami, S. Antiedematogenic and free radical scavenging activity of swertiamarin isolated from Enicostemma axillare. Planta Med., 2009, 75(1), 12-17.
[http://dx.doi.org/10.1055/s-0028-1088333] [PMID: 19006050]
[19]
Wu, X.; Gu, Y.; Li, L. The anti-hyperplasia, anti-oxidative and anti-inflammatory properties of Qing Ye Dan and swertiamarin in testosterone-induced benign prostatic hyperplasia in rats. Toxicol. Lett., 2017, 265, 9-16.
[http://dx.doi.org/10.1016/j.toxlet.2016.11.011] [PMID: 27866977]
[20]
Seo, K.H.; Park, M.J.; Ra, J.E.; Han, S.I.; Nam, M.H.; Kim, J.H.; Lee, J.H.; Seo, W.D. Saponarin from barley sprouts inhibits NF-κB and MAPK on LPS-induced RAW 264.7 cells. Food Funct., 2014, 5(11), 3005-3013.
[http://dx.doi.org/10.1039/C4FO00612G] [PMID: 25238253]
[21]
Min, S.Y.; Park, C.H.; Yu, H.W.; Park, Y.J. Anti-inflammatory and anti-allergic fffects of saponarin and its impact on signaling pathways of RAW 264.7, RBL-2H3, and HaCaT cells. Int. J. Mol. Sci., 2021, 22(16), 8431.
[http://dx.doi.org/10.3390/ijms22168431] [PMID: 34445132]
[22]
Kamiyama, M.; Shibamoto, T. Flavonoids with potent antioxidant activity found in young green barley leaves. J. Agric. Food Chem., 2012, 60(25), 6260-6267.
[http://dx.doi.org/10.1021/jf301700j] [PMID: 22681491]
[23]
Vitcheva, V.; Simeonova, R.; Krasteva, I.; Yotova, M.; Nikolov, S.; Mitcheva, M. Hepatoprotective effects of saponarin, isolated from Gypsophila trichotoma Wend. on cocaine-induced oxidative stress in rats. Redox Rep., 2011, 16(2), 56-61.
[http://dx.doi.org/10.1179/174329211X12989133691530] [PMID: 21722413]
[24]
Duan, X.; Wu, T.; Liu, T.; Yang, H.; Ding, X.; Chen, Y.; Mu, Y. Vicenin-2 ameliorates oxidative damage and photoaging via modulation of MAPKs and MMPs signaling in UVB radiation exposed human skin cells. J. Photochem. Photobiol. B, 2019, 190, 76-85.
[http://dx.doi.org/10.1016/j.jphotobiol.2018.11.018] [PMID: 30502588]
[25]
Yang, D.; Zhang, X.; Zhang, W.; Thamaraiselvan, R. Vicenin-2 inhibits Wnt/β-catenin signaling and induces apoptosis in HT-29 human colon cancer cell line. Drug Des. Devel. Ther., 2018, 12, 1303-1310.
[http://dx.doi.org/10.2147/DDDT.S149307] [PMID: 29849451]
[26]
Yin, Y.; Ye, L.; Niu, Z.; Fang, W. Anti-inflammatory effects of Vicenin-2 on dextran sulfate sodium-induced colitis in mice. Drug Dev. Res., 2019, 80(5), 546-555.
[http://dx.doi.org/10.1002/ddr.21529] [PMID: 30972795]
[27]
Rosa, S.I.G.; Rios-Santos, F.; Balogun, S.O.; Martins, D.T.O. Vitexin reduces neutrophil migration to inflammatory focus by down-regulating pro-inflammatory mediators via inhibition of p38, ERK1/2 and JNK pathway. Phytomedicine, 2016, 23(1), 9-17.
[http://dx.doi.org/10.1016/j.phymed.2015.11.003] [PMID: 26902402]
[28]
Chen, Y.; Yang, J.; Huang, Z.; Yin, B.; Umar, T.; Yang, C.; Zhang, X.; Jing, H.; Guo, S.; Guo, M.; Deng, G.; Qiu, C. Vitexin Mitigates Staphylococcus aureus-induced mastitis via regulation of ROS/ER stress/NF-kappa B/MAPK pathway. Oxid. Med. Cell. Longev., 2022, 2022, 1-20.
[http://dx.doi.org/10.1155/2022/7977433] [PMID: 35795861]
[29]
Li, S.; Lv, H.; Chen, Y.; Song, H.; Zhang, Y.; Wang, S.; Luo, L.; Guan, X. N-trimethyl chitosan coated targeting nanoparticles improve the oral bioavailability and antioxidant activity of vitexin. Carbohydr. Polym., 2022, 286, 119273.
[http://dx.doi.org/10.1016/j.carbpol.2022.119273] [PMID: 35337500]
[30]
Vamathevan, J.; Clark, D.; Czodrowski, P.; Dunham, I.; Ferran, E.; Lee, G.; Li, B.; Madabhushi, A.; Shah, P.; Spitzer, M.; Zhao, S. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov., 2019, 18(6), 463-477.
[http://dx.doi.org/10.1038/s41573-019-0024-5] [PMID: 30976107]
[31]
Yaseen, B.T.; Kurnaz, S. Drug-target interaction prediction using artificial intelligence. Appl. Nanosci., 2021, 195, 2.
[32]
Xue, L.; Godden, J.W.; Bajorath, J. Mini-fingerprints for virtual screening: Design principles and generation of novel prototypes based on information theory. SAR QSAR Environ. Res., 2003, 14(1), 27-40.
[http://dx.doi.org/10.1080/1062936021000058764] [PMID: 12688414]
[33]
Matsuyama, Y.; Ishida, T. Stacking multiple molecular fingerprints for improving ligand-based virtual screening. Cham; Springer International Publishing: Berlin, 2018.
[34]
Kim, H.; Nam, H. hERG-Att: Self-attention-based deep neural network for predicting hERG blockers. Comput. Biol. Chem., 2020, 87, 107286.
[http://dx.doi.org/10.1016/j.compbiolchem.2020.107286] [PMID: 32531518]