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Ligand Based Design, ADMET and Molecular Docking Studies of Arylpiperazine Derivatives as Potent Anti-Proliferate Agents Against LNCAP Prostate Cancer Cell Lines

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Abstract

Purpose

Prostate cancer is the most common non-cutaneous cancer suffered by men and the second leading cause of cancer-related deaths in men. The efficacy of current therapies have been reported to vary from patient to patient and have been accompanied by varying degrees of side effects. This study aimed to design novel compounds with potent cytotoxic activity and lesser toxicity against prostate cancer.

Method

A Quantitative Structure Activity Relationship (QSAR) model was built to model the cytotoxic activity of arylpiperazine compounds against LNCAP prostate cancer cell line. The built QSAR model was used to design and predict the cytotoxic activity of novel derivatives via the ligand base design. The pharmacokinetic properties of the designed compounds were predicted using the pKCSM tool and their molecular docking interaction with the androgen receptor was investigated using PyRx and Discovery Studio software.

Result

The QSAR model built had statistical parameters; R2 = 0.7588, R2adj = 0.7014, Q 2cv = 0.6394 and R2ext = 0.6099 which met statistical benchmarks for stable and robust model. 29 new arylpiperazine compounds were designed and 20 were observed to be more potent than the template compound. The ADMET properties of nine compounds were observed to be significantly better than that of the template and were comparable to those of standard drugs apalutamide, bicalutamide and abiraterone. Molecular docking studies revealed that the compounds primarily form electrostatic and hydrophobic interactions with the androgen receptor having a binding affinity of – 7.45 to – 8.80 kcal/mol.

Conclusion

This study present compounds which show great potential as excellent leads for the design of novel prostate cancer drugs

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Availability of Data and Materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Cathcart P, Murphy DG, Moon D, Costello AJ, Frydenberg M (2011) Perioperative, functional and oncological outcomes after open and minimally invasive prostate cancer surgery: experience from Australasia. BJU Int. https://doi.org/10.1111/j.1464-410X.2011.10053.x

    Article  PubMed  Google Scholar 

  2. Salinas CA, Tsodikov A, Ishak-Howard M, Cooney KA (2014) Prostate cancer in young men: an important clinical entity. Nat Rev Urol 11(6):317–323. https://doi.org/10.1038/nrurol.2014.91

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. White M, Holman DM, Boehm JE, Peipins LA, Grossman M, Henley SJ (2014) Age and cancer risk: a potentially modifiable relationship. Am J Prev Med 46(301):S7-15. https://doi.org/10.1016/j.amepre.2013.10.029

    Article  PubMed  PubMed Central  Google Scholar 

  4. Rawla P (2019) Epidemiology of prostate cancer. World J Oncol. 10(2):63–89. https://doi.org/10.14740/wjon1191

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Keyes M, Crook J, Morton G, Vigneault E, Usmani N, Morris J (2013) Treatment options for localized prostate cancer. Can Fam Phys 59:1269–1274

    Google Scholar 

  6. DeSantis CE, Miller KD, Sauer AG, Jemal A, Siegel RL (2019) Cancer statistics for African Americans. CA Cancer J Clin. https://doi.org/10.3322/caac.21555

    Article  PubMed  Google Scholar 

  7. Eeles R, Benafif S (2016) Genetic predisposition to prostate cancer. Br Med Bull 120(1):75–89. https://doi.org/10.1093/bmb/ldw039

    Article  CAS  PubMed  Google Scholar 

  8. Mohs RC, Greig NH (2017) Drug discovery and development: Role of basic biological research. Alzheimers Dement (N Y) 3:4. https://doi.org/10.1016/j.trci.2017.10.005

    Article  Google Scholar 

  9. Chen H, Yu Y, Tian X, Wang C, Qian Y, Deng Z, Zhang J, Lv D, Zhang H, Shen J, Yuan M, Zhao S (2018) Synthesis and biological evaluation of arylpiperazine derivatives as potential anti-prostate cancer agents. Bioorg Med Chem Lett. https://doi.org/10.1016/j.bmc.2018.11.029

    Article  PubMed  PubMed Central  Google Scholar 

  10. Chen H, Wang C, Sun T, Zhou Z, Niu J, Tian X, Yuan M (2018) Synthesis, biological evaluation and SAR of naftopidil-based arylpiperazine derivatives. Bioorg Med Chem Lett 28:1534–1539. https://doi.org/10.1016/j.bmcl.2018.03.070

    Article  CAS  PubMed  Google Scholar 

  11. Tripathi RB, Jain J, Siddiqui AW (2018) Design of new peroxisome proliferators gamma activated receptor agonists (PPARγ) via QSAR based modeling. J Appl Pharm Sci Res 1(1):23–26. https://doi.org/10.31069/japsr.v1i01.13059

    Article  CAS  Google Scholar 

  12. Arthur DE, Uzairu A, Mamza P, Abechi SE, Shallangwa GA (2018) Insilico Modelling of Quantitative Structure-Activity Relationship of Pgi50 Anticancer Compounds on k-562 Cell Line. Cogent Chem 4:1432520

    Article  Google Scholar 

  13. Becke AD (1993) Becke’s three parameter hybrid method using the LYP correlation functional. J Chem Phys 98:5648–5652

    Article  CAS  Google Scholar 

  14. Abdullahi M, Uzairu A, Shallangwa GA, Mamza P, Arthur DE, Ibrahim MT (2019) An Insilico modelling study on some C14-urea-Tetrandrine derivatives as potent anti-cancer against prostate (PC3) cell line. J King Saud Univ Sci. https://doi.org/10.1016/j.jksus.2019.01.008

    Article  Google Scholar 

  15. Ogadimma AI, Adamu U (2016) Quantitative Structure Activity Relationship Analysis of Selected Chalcone Derivatives as Mycobacterium tuberculosis Inhibitors. Open Access Libr J 3:1–13. https://doi.org/10.4236/oalib.1102432

    Article  Google Scholar 

  16. Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137–148

    Article  Google Scholar 

  17. Ibrahim MT, Uzairu A, Shallangwa GA, Ibrahim A (2018) In-silico studies of some oxadiazoles derivatives as anti-diabetic compounds. J King Saud Univ Sci. https://doi.org/10.1016/j.jksus.2018.06.006

    Article  Google Scholar 

  18. Schneider A, Hommel G, Blettner M (2010) Linear regression analysis. Dtsch Ärztebl Int. 107(44):776–782. https://doi.org/10.3238/arztebl.2010.0776

    Article  PubMed  PubMed Central  Google Scholar 

  19. Veerasamy R, Rajak H, Jain A, Sivadasan S, Varghese CP, Agrawal RK (2011) Validation of QSAR models-strategies and importance. Int J Drug Des Discov 3:511–519

    Google Scholar 

  20. Tropsha A (2010) Best practices for QSAR model development, validation and exploitation. Mol Inf 29(6–7):476–488. https://doi.org/10.1002/minf.201000061

    Article  CAS  Google Scholar 

  21. Minovski N, Župerl Š, Drgan V, Novič M (2013) Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum Euclidean distance space analysis: a case study. Anal Chim Acta 759:28–42

    Article  CAS  Google Scholar 

  22. Daoud JL (2017) Multicollinearity and regression analysis. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/949/1/012009

    Article  Google Scholar 

  23. Edache EI, Arthur DE, Abdulfatai U (2017) Quantitative structure-activity relationship analysis of the anti-tyrosine activity of some tetraketone and benzyl-benzoate derivatives based on genetic algorithm-multiple linear regression. J Chem Mater Res 6(1):2–12

    CAS  Google Scholar 

  24. Netzeva TI, Worth A, Aldenberg T, Benigni R, Cronin MT et al (2005) Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. ATLA 33(2):155–173

    CAS  PubMed  Google Scholar 

  25. Lee C, Huang H, Juan H (2011) Reviewing ligand-based rational drug design: the search for an ATP synthase inhibitor. Int J Mol Sci 12(8):5304–5318. https://doi.org/10.3390/ijms12085304

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Lipinski CA (2004) Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol. https://doi.org/10.1016/j.ddtec.2004.11.007

    Article  PubMed  Google Scholar 

  27. Pollastri MP (2010) Overview on the rule of five. Curr Protoc Pharmacol 49(9):1–9. https://doi.org/10.1002/0471141755.ph0912s49

    Article  Google Scholar 

  28. Pires ED, Blundell LT, Ascher DB (2015) pkCSM: predicting small-molecule pharmacokinetic properties using graph-based signatures. J Med Chem 58(9):4066–4072. https://doi.org/10.1021/acs.jmedchem.5b00104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Castanares MA, Copeland BT, Chowdhury WS, Liu MM, Rodriguez R, Pomper MG, Lupold SE, Foss CA (2015) Characterization of a novel metastatic prostate cancer cell line of LNCap Origin. Prostate 76:215–225. https://doi.org/10.1002/pros.23115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem. https://doi.org/10.1002/jcc.21334

    Article  PubMed  PubMed Central  Google Scholar 

  31. Nekoei M, Salimi M, Dolatabadi M, Majid M (2011) A quantitative structure–activity relationship study of tetrabutylphosphonium bromide analogs as muscarinic acetylcholine receptors agonists. J Serb Chem Soc 76(8):1117–1127. https://doi.org/10.2298/JSC101122102S

    Article  CAS  Google Scholar 

  32. Liu Y, Winkler DA, Epa VC, Zhang B, Yan B (2014) Probing enzyme-nanoparticle interactions using combinatorial gold nanoparticle libraries. Nano Res. https://doi.org/10.1007/s12274-014-0618-5

    Article  PubMed  PubMed Central  Google Scholar 

  33. Guan D, Fan K, Spence I, Matthews S (2018) Combining machine learning models of in vitro and in vivo bioassays improves rat carcinogenicity prediction. Regul Toxicol Pharmacol 94:8–15. https://doi.org/10.1016/j.yrtph.2018.01.008

    Article  CAS  PubMed  Google Scholar 

  34. Vermula VR, Lagishetty V, Lingala S (2010) Solubility enhancement techniques. Int J Pharm Sci Rev Res 5(1):41–51

    Google Scholar 

  35. Savjani KT, Anuradha KG, Savjani JK (2012) Drug solubility: Importance and Enhancement Techniques. ISRN Pharm. https://doi.org/10.5402/2012/195727

    Article  PubMed  PubMed Central  Google Scholar 

  36. Choi YH, Yu A (2014) ABC transporters in multidrug resistance and pharmacokinetics, and strategies for drug development. Curr Pharm Des 20(5):793–807

    Article  CAS  Google Scholar 

  37. Ahmed AT (2015) Pharmacokinetics of drugs following IV bolus, IV infusion, and oral administration. In: Ahmed AT (Ed.) Basic pharmacokinetic concepts and some clinical applications. https://doi.org/10.5772/61573

  38. Smith DA, Beaumont K, Maurer TS, Di L (2015) Volume of distribution in drug design: miniperspective. J Med Chem 58(15):5691–5698. https://doi.org/10.1021/acs.jmedchem.5b00201

    Article  CAS  PubMed  Google Scholar 

  39. Bohnert T, Gan L-S (2013) Plasma protein binding: From discovery to development. J Pharm Sci 102(9):2953–2994. https://doi.org/10.1002/jps.23614

    Article  CAS  PubMed  Google Scholar 

  40. Upadhyay RK (2014) Drug delivery systems, CNS protection, and the blood brain barrier. Biomed Res Int. https://doi.org/10.1155/2014/869269

    Article  PubMed  PubMed Central  Google Scholar 

  41. Bibi Z (2008) Role of cytochrome P450 in drug interactions. Nutr Metab. https://doi.org/10.1186/1743-7075-5-27

    Article  Google Scholar 

  42. Xu L, Das B, Prakash C (2012) CYP450 enzymes in drug discovery and development: an overview. Encycl Drug Metab Interact. https://doi.org/10.1002/9780470921920.edm117

    Article  Google Scholar 

  43. Yin J, Wang J (2016) Renal drug transporters and their significance in drug–drug interactions. Acta Pharm Sin B 6(5):363–373. https://doi.org/10.1016/j.apsb.2016.07.013

    Article  PubMed  PubMed Central  Google Scholar 

  44. Rodríguez E, Piccini C, Sosa V, Zunino P (2012) The use of the ames test as a tool for addressing problem-based learning in the microbiology lab. J Microbiol Biol Educ 13(2):175–177. https://doi.org/10.1128/jmbe.v13i2.421

    Article  PubMed  PubMed Central  Google Scholar 

  45. Strenberg CN (2019) Enzalutamide, an oral androgen receptor inhibitor for treatment of castration-resistant prostate cancer. Future Oncol. https://doi.org/10.2217/fon-2018-0940

    Article  Google Scholar 

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Chemistry, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria

    Fabian A. Ikwu, Gideon A. Shallangwa & Paul A. Mamza

Authors
  1. Fabian A. Ikwu
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  2. Gideon A. Shallangwa
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  3. Paul A. Mamza
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Contributions

FAI.: Conceptualization, Methodology, Investigation, Data curation, Writing–Original Draft Preparation. GAS: Conceptualization, Writing–Review and Editing, Supervision. PAMA.: Conceptualization, Writing–Review and Editing, Supervision.

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Correspondence to Fabian A. Ikwu.

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Ikwu, F.A., Shallangwa, G.A. & Mamza, P.A. Ligand Based Design, ADMET and Molecular Docking Studies of Arylpiperazine Derivatives as Potent Anti-Proliferate Agents Against LNCAP Prostate Cancer Cell Lines. Chemistry Africa 4, 71–84 (2021). https://doi.org/10.1007/s42250-020-00210-y

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