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A QSAR and similarity search based on 1,2-benzisothiazol-3-ones to identify potential new inhibitors of caspase-3

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Abstract

Among the neurodegenerative diseases responsible for cases of dementia, Alzheimer’s disease (AD) is responsible for 60–80% of cases. Caspase-3 is an enzyme essential in the synaptic degeneration caused by this disease, as it acts on the cleavage of tau and APP proteins, which leads to the formation of β-amyloid plaques in the brain. Therefore, the inhibition of caspase-3 may be an effective treatment method. In this work, QSAR studies based on descriptors derived from SMILES, similarity search, toxicity, and ADME studies were performed, including an evaluation of the applicability domain based on 71 1,2-benzisothiazol-3-ones capable of inhibiting caspase-3. Among the three hits identified, H2 proved to be the most interesting hit to be explored. Therefore, this hit was selected for future biological assay studies to confirm its activity. It may indicate new and original caspase inhibitors with drug-like properties suitable for AD therapy.

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References

  1. Sereniki A, Vital MABF (2008) Rev Psiquiatr Rio Gd Sul 30:(suppl 1). https://doi.org/10.1590/S0101-81082008000200002

  2. Smith MDAC (1999) Rev Bras Neurol Psiquiatr 20(Suppl 2):3–7

    Article  Google Scholar 

  3. Costa AS, Martins JPA, de Melo EB (2022) Struct Chem 33:1691–1706. https://doi.org/10.1007/s11224-022-02008-9

  4. Forlenza OV (2005) Arch Clin Psychiatry (S Paulo) 32:137–148. https://doi.org/10.1590/S0101-60832005000300006

    Article  Google Scholar 

  5. Mansur LL, Carthery MT, Caramelli P, Nitrini R (2005) Psicol Reflex Crit 18:300–307. https://doi.org/10.1590/S0102-79722005000300002

    Article  Google Scholar 

  6. Porter AG, Jänicke RU (1999) Cell Death Differ 6:99–104. https://doi.org/10.1038/sj.cdd.4400476

    Article  CAS  PubMed  Google Scholar 

  7. Troy CM, Akpan N, Jean YY (2011) Prog Mol Biol Transl Sci 99:265–305. https://doi.org/10.1016/B978-0-12-385504-6.00007-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Su JH, Zhao M, Anderson AJ, Srinivasan A, Cotman CW (2001) Brain Res 898:350–357. https://doi.org/10.1016/S0006-8993(01)02018-2

    Article  CAS  PubMed  Google Scholar 

  9. Roy K, Kar S, Das RN (2015) Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. 1st ed. Academic Press

  10. Ferreira MMC (2002) J Braz Chem Soc 13:742–753. https://doi.org/10.1590/S0103-50532002000600004

    Article  CAS  Google Scholar 

  11. Liu D, Tian Z, Yan Z, Wu L, Ma Y, Wang Q et al (2013) Bioorg Med Chem 21:2960–2967. https://doi.org/10.1016/j.bmc.2013.03.075

    Article  CAS  PubMed  Google Scholar 

  12. Guo Z, Yan Z, Zhou X, Wang Q, Lu M, Liu W et al (2015) Med Chem Res 24:1814–1829. https://doi.org/10.1007/s00044-014-1259-7

    Article  CAS  Google Scholar 

  13. Wermuth CG (2011) The practice of medicinal chemistry. Academic Press

    Google Scholar 

  14. Stumpfe D, Hu Y, Dimova D, Bajorath J (2014) J Med Chem 57:18–28. https://doi.org/10.1021/jm401120g

    Article  CAS  PubMed  Google Scholar 

  15. Todeschini R, Consonni V (2008) Handbook of molecular descriptors. John Wiley & Sons

    Google Scholar 

  16. Puzyn T, Leszczynski J, Cronin MTD (2010) Recent advances in QSAR studies methods and applications. Springer

    Book  Google Scholar 

  17. Martins JPA, Ferreira MMC (2013) Quim Nova 36:554–560. https://doi.org/10.1590/S0100-40422013000400013

    Article  CAS  Google Scholar 

  18. Teófilo RF, Martins JP, Ferreira MMC (2009) J Chemom 23:32–48. https://doi.org/10.1002/cem.1192

    Article  CAS  Google Scholar 

  19. Veerasamy R, Rajak H, Jain A, Sivadasan S, Varghese CP, Agrawal RK (2011) Int J Drug Des Discov 3:511–519

    Google Scholar 

  20. Calabrò M, Rinaldi C, Santoro G, Crisafulli C (2021) AIMS Neurosci 8:86–132. https://doi.org/10.3934/Neuroscience.2021005

    Article  PubMed  Google Scholar 

  21. Tropsha A (2010) Mol Inform 29:476–488. https://doi.org/10.1002/minf.201000061

    Article  CAS  PubMed  Google Scholar 

  22. Gramatica P (2007) QSAR Comb Sci 26:694–701. https://doi.org/10.1002/qsar.200610151

    Article  CAS  Google Scholar 

  23. Tropsha A, Golbraikh A (2010) Predictive quantitative structure–activity relationships modeling: development and validation of QSAR models. Handbook of chemoinformatics algorithms. Chapman  Hall/CRC p. 223–244

  24. Shi LM, Fang H, Tong W, Wu J, Perkins R, Blair RM et al (2001) J Chem Inf Comput Sci 41:186–195. https://doi.org/10.1021/ci000066d

    Article  CAS  PubMed  Google Scholar 

  25. Roy K, Ambure P (2016) Chemom Intell Lab Syst 159:108–126. https://doi.org/10.1016/j.chemolab.2016.10.009

    Article  CAS  Google Scholar 

  26. Rodrigues RP, Mantoani SP, de Almeida JR, Pinsetta FR, Semighini EP, da Silva VB et al (2012) Rev Virtual Quim 4:739–776. https://doi.org/10.5935/1984-6835.20120055

    Article  CAS  Google Scholar 

  27. Zoete V, Daina A, Bovigny C, Michielin O (2016) J Chem Inf Model 56:1399–1404. https://doi.org/10.1021/acs.jcim.6b00174

    Article  CAS  PubMed  Google Scholar 

  28. Benfenati E, Manganaro A, Gini GC (2013) VEGA-QSAR. AI inside a platform for predictive toxicology. Italy: Politecnico di Mano. p. 21–28

  29. Sahigara F, Mansouri K, Ballabio D, Mauri A, Consonni V, Todeschini R (2012) Molecules 17:4791–4810. https://doi.org/10.3390/molecules17054791

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Minovski N, Župerl Š, Drgan V, Novič M (2013) Anal Chim Acta 759:28–42. https://doi.org/10.1016/j.aca.2012.11.002

    Article  CAS  PubMed  Google Scholar 

  31. Daina A, Zoete V (2016) ChemMedChem 11:1117–1121. https://doi.org/10.1002/cmdc.201600182

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Daina A, Michielin O, Zoete V (2017) Sci Rep 7:42717. https://doi.org/10.1038/srep42717

    Article  PubMed  PubMed Central  Google Scholar 

  33. Toropov AA, Benfenati E (2007) Curr Drug Discov Technol 4:77–116. https://doi.org/10.2174/157016307781483432

    Article  CAS  PubMed  Google Scholar 

  34. Kiralj R, Ferreira MMC (2009) J Braz Chem Soc 20:770–787. https://doi.org/10.1590/S0103-50532009000400021

    Article  CAS  Google Scholar 

  35. Golbraikh A, Tropsha A (2002) J Mol Graph Model 20:269–276. https://doi.org/10.1016/S1093-3263(01)00123-1

    Article  CAS  PubMed  Google Scholar 

  36. Africa JGG, Arturo HCP, Bernardo LJM, Ching JKAR, de la Cruz OCE, Hernandez JBE et al (2022) Philipp J Sci 151:35–158. https://doi.org/10.56899/151.01.04

  37. Manikandan P, Nagini S (2017) Curr Drug Targets 19:38–54. https://doi.org/10.2174/1389450118666170125144557

    Article  CAS  Google Scholar 

  38. Orrling KM, Jansen C, Vu XL, Balmer V, Bregy P, Shanmugham A et al (2012) J Med Chem 55:8745–8756. https://doi.org/10.1021/jm301059b

    Article  CAS  PubMed  Google Scholar 

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Funding

The authors wish to thank Fundação Araucária (grant 2010/7354), PROAP/CAPES, and CNPq (grant 311048/2018–8).

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Authors and Affiliations

  1. Department of Chemistry, Western Paraná State University (UNIOESTE), 645 Faculdade St, Toledo, Paraná, 85903-000, Brazil

    Paula Beatriz Jesus Santos & Eduardo Borges de Melo

  2. Theoretical Medicinal and Environmental Chemistry Laboratory (LQMAT), Department of Pharmacy, UNIOESTE, 2069 Universitária St., Cascavel, Paraná, 85819-110, Brazil

    Paula Beatriz Jesus Santos & Eduardo Borges de Melo

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  1. Paula Beatriz Jesus Santos
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All authors contributed equally to the study.

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Correspondence to Eduardo Borges de Melo.

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Santos, P.B.J., de Melo, E.B. A QSAR and similarity search based on 1,2-benzisothiazol-3-ones to identify potential new inhibitors of caspase-3. Struct Chem (2024). https://doi.org/10.1007/s11224-024-02280-x

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