Skip to main content
SpringerLink
Log in

A new approach for simultaneous calculation of pIC50 and logP through QSAR/QSPR modeling on anthracycline derivatives: a comparable study

  • Original Paper
  • Published:
Journal of the Iranian Chemical Society Aims and scope Submit manuscript

Abstract

In an overview, the relationships between activity, property, and chemical structure of the anthracycline derivatives, as topoisomerase II enzyme inhibitors, were studied in pairs. These analogs were investigated in three categories with known half-maximal inhibitory concentration (IC50), known octanol/water partition coefficient (logP), and both IC50 and logP unknown values. The QSAR and QSPR models were developed on analogs of the first and second groups, respectively. LogP was related to calculated pIC50 ((pIC50)calc) surprisingly (R2 = 0.929, Q2LOO = 0.910, RMSE = 0.235, F = 157.511). External verification was performed through comparing calculated logP (logPcalc) with three programs using 77 analogs out of the training and test sets. In application domain of the model, the suggested model had the highest correlation with ClogP method (R = 0.744) and this was better than the correlation between milogP–ClogP (R = 0.724), AlogP–ClogP (R = 0.662), and AlogP–milogP (R = 0.691). Furthermore, in the case of analogs possessing a long carbon chain substitution (nc ≥ 3), estimation methods had a significant error in calculation logP, while our suggested model provides more accurate results. The proposed algorithm can be useful in primary screening among anthracycline derivatives to select potent drug candidates.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. V. Prachayasittikul, R. Pingaew, A. Worachartcheewan, C. Nantasenamat, S. Prachayasittikul, S. Ruchirawat, V. Prachayasittikul, Eur. J. Med. Chem. 84, 247–263 (2014). https://doi.org/10.1016/j.ejmech.2014.07.024

    Article  CAS  PubMed  Google Scholar 

  2. G. Minotti, P. Menna, E. Salvatorelli, G. Cairo, L. Gianni, Pharmacol. Rev. 56, 185–229 (2004). https://doi.org/10.1124/pr.56.2.6

    Article  CAS  PubMed  Google Scholar 

  3. J. Marinello, M. Delcuratolo, G. Capranico, Int. J. Mol. Sci. 19, 3480–3497 (2018). https://doi.org/10.3390/ijms19113480

    Article  CAS  PubMed Central  Google Scholar 

  4. C. Monneret, Eur. J. Med. Chem. 36, 483–493 (2001). https://doi.org/10.1016/S0223-5234(01)01244-2

    Article  CAS  PubMed  Google Scholar 

  5. E. Salvatorelli, S. Guarnieri, P. Menna, G. Liberi, A.M. Calafiore, M.A. Mariggio, A. Mordente, L. Gianni, G. Minotti, J. Biol. Chem. 281, 10990–11001 (2006). https://doi.org/10.1074/jbc.M508343200

    Article  CAS  PubMed  Google Scholar 

  6. E.M. Acton, G.L. Tong, C.W. Mosher, R.L. Wolgemuth, J. Med. Chem. 27, 638–645 (1984). https://doi.org/10.1021/jm00371a014

    Article  CAS  PubMed  Google Scholar 

  7. M.F. Harris, J.L. Logan, J. Chem. Educ. 91, 915–918 (2014). https://doi.org/10.1021/ed400655b

    Article  CAS  Google Scholar 

  8. A. Paneth, A. Hawryl, T. Plech, M. Hawryl, R. Swieboda, D. Janowska, M. Wujec, P. Paneth, Molecules 22, 952–963 (2017). https://doi.org/10.3390/molecules22060952

    Article  CAS  PubMed Central  Google Scholar 

  9. E. Friche, E.J.F. Demant, M. Sehested, N.I. Nissen, Br. J. Cancer. 67, 226–231 (1993). https://doi.org/10.1038/bjc.1993.44

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. A. Bahmani, S. Saaidpour, A. Rostami, Sci. Rep. 7, 1–12 (2017). https://doi.org/10.1038/s41598-017-05964-z

    Article  CAS  Google Scholar 

  11. N. Komeshima, T. Tsuruo, H. Umezawa, J. Antibiot. (Tokyo). 41, 548–553 (1988). https://doi.org/10.7164/antibiotics.41.548

    Article  CAS  Google Scholar 

  12. P.P. Constantinides, L. Ghosaini, N. Inouchi, S. Kitamura, R. Seshadri, M. Israel, A.C. Sartorelli, J.M. Sturtevant, Chem. Phys. Lipids. 51, 105–118 (1989). https://doi.org/10.1016/0009-3084(89)90044-3

    Article  CAS  PubMed  Google Scholar 

  13. E. Friche, P.B. Jensen, H. Roed, T. Skovsgaard, N.I. Nissen, Biochem. Pharmacol. 39, 1721–1726 (1990). https://doi.org/10.1016/0006-2952(90)90117-4

    Article  CAS  PubMed  Google Scholar 

  14. R. Tamaian, V. Niculescu, M. Anghel, Bull. Univ. Agric. Sci. Vet. Med. 67, 329–336 (2010). https://doi.org/10.15835/buasvmcn-vm:67:1:5974

    Article  CAS  Google Scholar 

  15. K. Studzian, K. Kik, M. Lukawska, I. Oszczapowicz, M. Strek, L. Szmigiero, Investig. New Drugs. 33, 1032–1039 (2015). https://doi.org/10.1007/s10637-015-0276-9

    Article  CAS  Google Scholar 

  16. C. Marbeuf-Gueye, H.J. Broxterman, F. Dubru, W. Priebe, A. Garnier-Suillerot, Mol. Pharmacol. 53, 141–147 (1998). https://doi.org/10.1124/mol.53.1.141

    Article  CAS  PubMed  Google Scholar 

  17. B.S. Chhikara, D. Mandal, K. Parang, J. Med. Chem. 55, 1500–1510 (2012). https://doi.org/10.1021/jm201653u

    Article  CAS  PubMed  Google Scholar 

  18. P. Shaul, M. Frenkel, E.B. Goldstein, L. Mittelman, A. Grunwald, Y. Ebenstein, I. Tsarfaty, M. Fridman, A.C.S. Med, Chem. Lett. 4, 323–328 (2013). https://doi.org/10.1021/ml3002852

    Article  CAS  Google Scholar 

  19. B.S. Chhikara, N. St Jean, D. Mandal, A. Kumar, K. Parang, Eur. J. Med. Chem. 46, 2037–2042 (2011). https://doi.org/10.1016/j.ejmech.2011.02.056

    Article  CAS  PubMed  Google Scholar 

  20. M. Denel-Bobrowska, M. Łukawska, B. Bukowska, A. Gajek, I. Oszczapowicz, A. Marczak, Toxicol. In Vitro 46, 323–334 (2018). https://doi.org/10.1016/j.tiv.2017.10.021

    Article  CAS  PubMed  Google Scholar 

  21. T. Yamaoka, M. Hanada, S. Ichii, S. Morisada, T. Noguchi, Y. Yanagi, J Cancer Res. 89, 1067–1073 (1998). https://doi.org/10.1111/j.1349-7006.1998.tb00498.x

    Article  CAS  Google Scholar 

  22. F. Sadeghi, A. Afkhami, T. Madrakian, R. Ghavami, Chem. Pap. 75, 523–538 (2021). https://doi.org/10.1007/s11696-020-01321-z

    Article  CAS  Google Scholar 

  23. N. Sabnis, M. Nair, M. Israel, W.J. McConathy, A.G. Lacko, Int. J. Nanomed. 7, 975–983 (2012). https://doi.org/10.2147/IJN.S28029

    Article  CAS  Google Scholar 

  24. S.S. Matikonda, D.L. Orsi, V. Staudacher, I.A. Jenkins, F. Fiedler, J. Chen, A.B. Gamble, Chem. Sci. 6, 1212–1218 (2015). https://doi.org/10.1039/c4sc02574a

    Article  CAS  PubMed  Google Scholar 

  25. D.H. Altreuter, J.S. Dordick, D.S. Clark, J. Am. Chem. Soc. 124, 1871–1876 (2002). https://doi.org/10.1021/ja015977y

    Article  CAS  PubMed  Google Scholar 

  26. Y.S. Rho, G. Kim, W.J. Kim, S. Park, D. Jin Yoo, H. Soo Kang, S.R. Chung, Bull. Korean Chem. Soc. 22, 587–592 (2001)

    CAS  Google Scholar 

  27. R. Akasov, M. Drozdova, D. Zaytseva-Zotova, M. Leko, P. Chelushkin, A. Marc, I. Chevalot, S. Burov, N. Klyachko, T. Vandamme, E. Markvicheva, Adv. Pharm. Bull. 7, 593–601 (2017). https://doi.org/10.15171/apb.2017.071

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. F. Sztaricskai, A. Sum, E. Roth, I.F. Pelyvás, S. Sándor, G. Batta, P. Herczegh, J. Reményi, Z. Miklán, F. Hudecz, J. Antibiot. (Tokyo) 58, 704–714 (2005). https://doi.org/10.1038/ja.2005.96

    Article  CAS  Google Scholar 

  29. Y. Matsuzawa, T. Oki, T. Takeuchi, H. Umezawa, J. Antibiot. (Tokyo) 34, 1596–1607 (1981). https://doi.org/10.7164/antibiotics.34.1596

    Article  CAS  Google Scholar 

  30. E.M. Acton, G.L. Tong, D.L. Taylor, D.G. Streeter, J.A. Filppi, R.L. Wolgemuth, J. Med. Chem. 29, 2074–2079 (1986). https://doi.org/10.1021/jm00160a047

    Article  CAS  PubMed  Google Scholar 

  31. http://www.hmdb.ca/metabolites/HMDB0014832

  32. http://www.molbase.com/en/synthesis_1242592-26-2-moldata-3188641.html#

  33. ChemIDplus Database. (accessed Oct 7, 2020) https://chem.nlm.nih.gov/chemidplus/. Accessed 07 Oct 2020

  34. HyperChem Software Version 8.0, Hypercube, Inc. (2007) http://www.hyper.com

  35. DRAGON Software Version 5.5, R. Todeschini, V. Consonni, A. Mauri, M. Pavan, TALETE SRL: Milano, Italy, (2007) http://www.talete.mi.it

  36. O. Devinyak, D. Havrylyuk, R. Lesyk, J. Mol. Graph. Model. 54, 194–203 (2014). https://doi.org/10.1016/j.jmgm.2014.10.006

    Article  CAS  PubMed  Google Scholar 

  37. G. Moreau, P. Broto, A new molecular descriptor. Nouv. J. Chim. 4, 757–764 (1980)

    CAS  Google Scholar 

  38. E. Estrada, J. Chem. Inf. Comput. Sci. 37, 320–328 (1997). https://doi.org/10.1021/ci960113v

    Article  CAS  Google Scholar 

  39. R.D. Snee, Technometrics 19, 415–428 (1977). https://doi.org/10.1080/00401706.1977.10489581

    Article  Google Scholar 

  40. J. Xu, L. Pei, R.Z. Zhu, Proced. Comput. Sci. 131, 937–945 (2018). https://doi.org/10.1016/j.procs.2018.04.230

    Article  Google Scholar 

  41. R 2016 version 16; math work. Inc. Natick, MA, USA

  42. G.R. Famini, C.A. Penski, L.Y. Wilson, J. Phys. Org. Chem. 5, 395–408 (1992). https://doi.org/10.1002/poc.610050704

    Article  CAS  Google Scholar 

  43. Z. Rasouli, Z. Hassanzadeh, R. Ghavami, Talanta 160, 86–98 (2016). https://doi.org/10.1016/j.talanta.2016.06.065

    Article  CAS  PubMed  Google Scholar 

  44. B. Walczak, D.L. Massart, Anal. Chim. Acta. 331, 177–185 (1996). https://doi.org/10.1016/0003-2670(96)00202-4

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  47. http://www.openmolecules.org/datawarrior/ (Data warrior v05.02.01)

  48. https://www.molinspiration.com/cgi-bin/properties

  49. http://146.107.217.178/web/alogps/ (ALOGPS 2.1 program)

  50. V. Consonni, R. Todeschini, H. Lodhi, Chemoinformatics and advanced machine learning perspectives: complex computational methods and collaborative techniques, in Ch. 5 Structure–activity relationships by autocorrelation descriptors and genetic algorithms. ed. by H. Lodhi, Y. Yamanishi (IGI Global, New York, 2011), pp. 60–93

    Google Scholar 

  51. M. Khoshneviszadeh, N. Edraki, R. Miri, A. Foroumadi, B. Hemmateenejad, Chem. Biol. Drug Des. 79, 442–458 (2012). https://doi.org/10.1111/j.1747-0285.2011.01284.x

    Article  CAS  PubMed  Google Scholar 

  52. J. Caballero, A. Tundidor-Camba, M. Fernandez, QSAR Comb. Sci. 26, 27–40 (2007). https://doi.org/10.1002/qsar.200610001

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Chemistry, Bu-Ali Sina University, Hamedan, 65178-38695, Iran

    Fereydoun Sadeghi, Abbas Afkhami & Tayyebeh Madrakian

  2. D-8 International University, Hamedan, Iran

    Abbas Afkhami

  3. Chemometrics Laboratory, Department of Chemistry, Faculty of Science, University of Kurdistan, P. O. Box 416, Sanandaj, 66177-15175, Iran

    Raouf Ghavami

Authors
  1. Fereydoun Sadeghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abbas Afkhami
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tayyebeh Madrakian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Raouf Ghavami
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

FS did modeling work, FS, AA, and TM wrote the manuscript, FS and RG contributed equally to prepare figures, tables, and supplementary information, and all authors reviewed the manuscript.

Corresponding author

Correspondence to Abbas Afkhami.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 183 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sadeghi, F., Afkhami, A., Madrakian, T. et al. A new approach for simultaneous calculation of pIC50 and logP through QSAR/QSPR modeling on anthracycline derivatives: a comparable study. J IRAN CHEM SOC 18, 2785–2800 (2021). https://doi.org/10.1007/s13738-021-02233-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13738-021-02233-9

Keywords

Search

Navigation