DOI: https://doi.org/10.14710/jksa.26.3.109-117

Quantitative Structure-Activity Relationship of 3-Thiocyanate-1H-Indoles Derived Compounds as Antileukemia by AM1, PM3, and RM1 Methods

1Department of Chemistry, Faculty of Mathematics and Natural Sciences, Jenderal Soedirman University, Jl. dr. Soeparno No 61, Karangwangkal, Purwokerto 53122, Indonesia

2Al Irsyad Al Islamiyyah Integrated Islamic Senior High School, Jl. Raya Kebumen-Baturraden KM. 10, Banyumas, Central Java 53151, Indonesia, Indonesia

3Al Irsyad Al Islamiyyah Integrated Islamic Senior High School, Jl. Raya Kebumen-Baturraden KM. 10, Banyumas, Central Java 53151, Indonesia

4 Department of Pharmacy, Faculty of Health Sciences, Jenderal Soedirman University, Jl. dr. Soeparno, Karangwangkal, Purwokerto 53122, Indonesia

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Received: 4 Apr 2023; Revised: 4 May 2023; Accepted: 22 May 2023; Published: 31 May 2023.
Open Access Copyright 2023 Jurnal Kimia Sains dan Aplikasi under http://creativecommons.org/licenses/by-sa/4.0.

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Abstract

Cancer is a disease with fatal consequences; thus, searching for innovative compounds with anticancer properties remains an active pursuit. One of the highly promising candidates is a compound derived from 3-thiocyanato-1H-indoles. However, the number of derivative compounds is currently limited. A quantitative structure and activity relationship (QSAR) study was conducted on derivate compounds 3-thiocyanato-1H-indoles to establish equations that predict the anticancer activity of more effective derivatives. This study aims to compare the effectiveness of the AM1 (Austin Model 1), PM3 (Parameterized Model 3), and RM1 (Recife Model 1) semiempirical methods, which are new techniques implemented in the Hyperchem version 8.0. Twenty experimental data were used, 16 derivatives of 3-thiocyanate-1H-indoles as regression compounds (fitting) and four derivates as test compounds. QSAR analysis was performed based on multiple linear regression calculations on 3-thiocyanate-1H-indoles derivative compounds by plotting IC50 (µM) as the dependent variable and descriptors as the independent variable. The best QSAR equation was obtained from the AM1 semiempirical calculation method with the following equation: IC50 = -1.705 + 0.511(Delta) + 0.346(Dipol) + 18.287(qC9) – 0.645(Log P) + 13.952(qC6), with n =20; r =0.814; r2 =0.662; The standard error (SE) =1.044; Fcount/Ftable =1.851; PRESS =15.219.

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Keywords: anticancer; QSAR; 3-thiocyanato-1H-indoles; semi-empirical

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Section: Research Articles
Language : EN
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  1. Kementerian Kesehatan Republik Indonesia, Profil Kesehatan Indonesia Tahun 2019, Kementerian Kesehatan Republik Indonesia, Jakarta, 2020,
  2. M. Fahruddin Ghozali, Ade Eviyanti, Sistem Pakar Diagnosa Dini Penyakit Leukimia Dengan Metode Certainty Factor, Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 1, 3, (2016), 135-146 https://doi.org/10.22219/kinetik.v1i3.122
  3. Mukhlis Ramadhan, Sistem Pakar Dalam Mengidentifikasi Penyakit Kanker Pada Anak Sejak Dini dan Cara Penanggulangannya, Jurnal SAINTIKOM, 10, 2, (2011), 125-135
  4. Richa Monika, Dhingra Neelima, 3-Thiocyanato-1H-indoles as potential anticancer agents: Two dimensional quantitative structure activity relationship study, International Journal of Pharmaceutical Chemistry and Analysis, 3, 4, (2016), 198-204
  5. Syarifah Nugraheni, Ponco Iswanto, Iqmal Tahir, Hubungan Kuantitatif Struktur dan Aktivitas Antikanker Senyawa Turunan Estradiol Hasil Perhitungan Metode Semiempiris AM1, Prosiding Seminar Nasional Kimia XV ISSN NO, 2004
  6. Asril Burhan, Andi Nur Aisyah, Akbar Awaluddin, Zulham Zulham, Burhanuddin Taebe, Abdul Gafur, Efek antioksidan dan antikanker ekstrak batang murbei (Morus alba L.) secara in vitro, Kartika: Jurnal Ilmiah Farmasi, 7, 1, (2019), 17-21
  7. Petko Alov, Ivanka Tsakovska, Ilza Pajeva, Computational studies of free radical-scavenging properties of phenolic compounds, Current Topics in Medicinal Chemistry, 15, 2, (2015), 85-104 http://dx.doi.org/10.2174/1568026615666141209143702
  8. Tao Huang, Guohui Sun, Lijiao Zhao, Na Zhang, Rugang Zhong, Yongzhen Peng, Quantitative structure-activity relationship (QSAR) studies on the toxic effects of nitroaromatic compounds (NACs): A systematic review, International Journal of Molecular Sciences, 22, 16, (2021), 8557 https://doi.org/10.3390/ijms22168557
  9. Amit Kumar Halder, M Natália Dias Soeiro Cordeiro, Development of multi-target chemometric models for the inhibition of class I PI3K enzyme isoforms: A case study using QSAR-Co tool, International Journal of Molecular Sciences, 20, 17, (2019), 4191 https://doi.org/10.3390/ijms20174191
  10. Ponco Iswanto, Isnaeni Tatik Rosdiyana, Iqmal Tahir, Hubungan kuantitatif struktur dan aktivitas antikanker senyawa turunan estradiol hasil perhitungan metode semiempiris PM3, Berkala MIPA, 17, 1, (2007), 12-20
  11. Eva Vaulina Y. D., Mochammad Chasani, Mokhammad Abdulghani, Hubungan kuantitatif struktur-aktivitas (HKSA) antikanker senyawa turunan kalanon dengan metode semi empiris PM3 (Parameterized Model 3), Molekul, 7, 2, (2012), 130-142 http://dx.doi.org/10.20884/1.jm.2012.7.2.115
  12. Panagiotis C. Petris, Paul Becherer, Johannes G. E. M Fraaije, Alkane/water partition coefficient calculation based on the modified AM1 method and internal hydrogen bonding sampling using COSMO-RS, Journal of Chemical Information and Modeling, 61, 7, (2021), 3453-3462 https://doi.org/10.1021/acs.jcim.0c01478
  13. Xiaoliang Pan, Pengfei Li, Junming Ho, Jingzhi Pu, Ye Mei, Yihan Shao, Accelerated computation of free energy profile at ab initio quantum mechanical/molecular mechanical accuracy via a semi-empirical reference potential. II. Recalibrating semi-empirical parameters with force matching, Physical Chemistry Chemical Physics, 21, 37, (2019), 20595-20605 https://doi.org/10.1039/C9CP02593F
  14. Elton A. S. de Castro, Daniel A. B. de Oliveira, Sergio A. S. Farias, Ricardo Gargano, João B. L. Martins, Structure and electronic properties of azadirachtin, Journal of Molecular Modeling, 20, (2014), 2084 https://doi.org/10.1007/s00894-014-2084-0
  15. Nathalia B. D. Lima, Gerd B. Rocha, Ricardo O. Freire, Alfredo M. Simas, RM1 semiempirical model: chemistry, pharmaceutical research, molecular biology and materials science, Journal of the Brazilian Chemical Society, 30, 4, (2019), 683-716 https://doi.org/10.21577/0103-5053.20180239
  16. Qi Guan, Chunming Han, Daiying Zuo, Zengqiang Li, Qian Zhang, Yanpeng Zhai, Xuewei Jiang, Kai Bao, Yingliang Wu, Weige Zhang, Synthesis and evaluation of benzimidazole carbamates bearing indole moieties for antiproliferative and antitubulin activities, European Journal of Medicinal Chemistry, 87, (2014), 306-315 https://doi.org/10.1016/j.ejmech.2014.09.071
  17. Martyn Inman, Christopher J. Moody, Indole synthesis–something old, something new, Chemical Science, 4, 1, (2013), 29-41 https://doi.org/10.1039/C2SC21185H
  18. Robert J. Capon, Colin Skene, Edward Hsiang-Te Liu, Ernest Lacey, Jennifer H. Gill, Kirstin Heiland, Thomas Friedel, The isolation and synthesis of novel nematocidal dithiocyanates from an Australian marine sponge, Oceanapia sp, The Journal of Organic Chemistry, 66, 23, (2001), 7765-7769 https://doi.org/10.1021/jo0106750
  19. Saw Simeon, Nathjanan Jongkon, Construction of quantitative structure activity relationship (QSAR) Models to predict potency of structurally diversed janus kinase 2 inhibitors, Molecules, 24, 23, (2019), 4393 https://doi.org/10.3390/molecules24234393
  20. Samina Kausar, Andre O. Falcao, Analysis and comparison of vector space and metric space representations in QSAR modeling, Molecules, 24, 9, (2019), 1698 https://doi.org/10.3390/molecules24091698
  21. Hypercube, in, Hypercube Inc., Florida, USA, 2007,
  22. José Diogo L. Dutra, Manoel A. M. Filho, Gerd B. Rocha, Alfredo M. Simas, Ricardo O. Freire, RM1 semiempirical quantum chemistry: Parameters for trivalent Lanthanum, Cerium and Praseodymium, PLoS ONE, 10, 7, (2015), e0124372 https://doi.org/10.1371/journal.pone.0124372
  23. Ponco Iswanto, Maylani Permata Saputri, Vaulina Y. D. Eva, Sari Paramita, Quantitative Structure Activity Relationship Analysis of 1, 3, 4-Thiadiazole Derivatives as Anti-Inflamatory using Parameterized Model 3 Method, Journal of Physics: Conference Series, 2020 https://doi.org/10.1088/1742-6596/1494/1/012031
  24. IBM, in, USA, 2016,
  25. Eva Vaulina, Ponco Iswanto, Model QSAR Senyawa Fluorokuinolon Baru Sebagai Zat Antibakteri Salmonella thypimurium, Molekul, 1, 1, (2006), 10-18 http://dx.doi.org/10.20884/1.jm.2006.1.1.17
  26. Ponco Iswanto, Eva Vaulina Yulistia Delsy, Ely Setiawan, Fiandy Aminullah Putra, Quantitative Structure-Property Relationship Analysis Against Critical Micelle Concentration of Sulfonate-Based Surfactant Based on Semiempirical Zindo/1 Calculation, Molekul, 14, 2, (2019), 78-83 http://dx.doi.org/10.20884/1.jm.2019.14.2.467
  27. Sari Paramita, Maylani Permata, Eva Vaulina Y.D., Nasrokhah Nasrokhah, Ponco Iswanto, Pemilihan Metode Perhitungan Kimia Komputasi Semi-Empiris Untuk Pengembangan 1, 3, 4-Thiadiazole, Indonesian Journal of Chemical Research, 8, 1, (2020), 51-56 https://doi.org/10.30598/10.30598//ijcr.2020.8-pon
  28. Adina Zharifah, Eysa Kusumowardani, Akbar Saputro, Devvi Sarwinda, Efficient dynamic molecular simulation using QSAR model to know inhibition activity in breast cancer medicine, AIP Conference Proceedings, 2017 https://doi.org/10.1063/1.4991192
  29. Piotr Kawczak, Leszek Bober, Tomasz Bączek, The comparison of semiempirical and ab initio molecular modeling methods in activity and property evaluation of selected antimicrobial sulfonamides, Medicinal Chemistry Research, 28, (2019), 778-787 https://doi.org/10.1007/s00044-019-02334-4
  30. Harno Dwi Pranowo, Abdul Kadir Rukmana Hetadi, Pengantar Kimia Komputasi, Universitas Gadjah Mada, Yogyakarta, 2011,
  31. Nursalam Hamzah, Nur Syamsi Dhuha, Hasma Nur Putrianti, Hubungan Kuantitatif Skruktur Aktivitas Senyawa Turunan 1-Benzene Acyl-2-(Methylindol-3-Yl)-Bensimidazole Sebagai Inhibitor Pertumbuhan MCF-7, Jurnal farmasi, UIN Alauddin Makassar, Makassar, 2017
  32. Soekardjo Siswandono, Prinsip-prinsip rancangan obat, Universitas Airlangga, Surabaya, 1995,
  33. Emmy Yuanita, Ni Komang Tri Dharmayani, Maria Ulfa, Jufrizal Syahri, Quantitative structure–activity relationship (QSAR) and molecular docking of xanthone derivatives as anti-tuberculosis agents, Journal of Clinical Tuberculosis and Other Mycobacterial Diseases, 21, (2020), 100203 https://doi.org/10.1016/j.jctube.2020.100203
  34. Renske P. J. Hoondert, Rik Oldenkamp, Dick de Zwart, Dik van de Meent, Leo Posthuma, QSAR‐based estimation of species sensitivity distribution parameters: An exploratory investigation, Environmental Toxicology and Chemistry, 38, 12, (2019), 2764-2770 https://doi.org/10.1002/etc.4601

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