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Yıl 2023, Cilt: 27 Sayı: Current Research Topics In Pharmacy: In silico Approaches for Drug Design and Discovery, 9 - 12, 28.06.2025

Ceren Sucularlı , Birsen Tozkoparan , Sevim Peri Aytaç

https://doi.org/10.29228/jrp.455

Öz

Kaynakça

  • [1] Ferrero E, Dunham I, Sanseau P. In silico prediction of novel therapeutic targets using gene-disease association data. J Transl Med. 2017;15(1):182. [CrossRef]
  • [2] Yamanishi Y, Kotera M, Kanehisa M, Goto S. Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics. 2010;26(12):i246-54. [CrossRef]
  • [3] Wooller SK, Benstead-Hume G, Chen X, Ali Y, Pearl FMG. Bioinformatics in translational drug discovery. Biosci Rep. 2017;37(4):BSR20160180. [CrossRef]
  • [4] Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des. 2011;7(2):146-157. [CrossRef]
  • [5] Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001;46(1-3):3-26. [CrossRef]
  • [6] Aytac PS, Durmaz I, Houston DR, Cetin-Atalay R, Tozkoparan B. Novel triazolothiadiazines act as potent anticancer agents in liver cancer cells through Akt and ASK-1 proteins. Bioorg Med Chem. 2016;24(4):858-872. [CrossRef]
  • [7] Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717. [CrossRef]
  • [8] Sucularlı C, Tozkoparan B, Aytac PS. In silico activity and target prediction analyses of three triazolothiadiazine derivatives. Acta Medica. 2022;53(3):251-260. [CrossRef]
  • [9] Filimonov DA, Gloriozova TA, Rudik AV, Druzhilovskii DS, Pogodin PV, Popoikov VV. Prediction of the biological activity spectra of organic compounds using the Pass Online Web Resource. Chem Heterocycl Compds. 2014;50:444–457. [CrossRef]
  • [10] Gfeller D, Grosdidier A, Wirth M, Daina A, Michielin O, Zoete V. SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res. 2014;42(Web Server issue):W32-38. [CrossRef]
  • [11] Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J. Binding DB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 2016;44(D1):D1045-1053. [CrossRef]
  • [12] Grosdidier A, Zoete V, Michielin O. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res. 2011;39(Web Server issue):W270-277. [CrossRef]
  • [13] Grosdidier A, Zoete V, Michielin O. Fast docking using the CHARMM force field with EADock DSS. J Comput Chem. 2011;32(10):2149-2159. [CrossRef]
  • [14] Hansen RT, 3rd, Conti M, Zhang HT. Mice deficient in phosphodiesterase-4A display anxiogenic-like behavior. Psychopharmacology (Berl). 2014;231(15):2941-2954. [CrossRef]
  • [15] Teng F, Xu Z, Chen J, Zheng G, Zheng G, Lv H, Wang Y, Wang L, Cheng X. DUSP1 induces apatinib resistance by activating the MAPK pathway in gastric cancer. Oncol Rep. 2018;40(3):1203-1222. [CrossRef]
  • [16] Thirunavukkarasu C, Wang LF, Harvey SA, Watkins SC, Chaillet JR, Prelich J, Starzl TE, Gandhi CR. Augmenter of liver regeneration: an important intracellular survival factor for hepatocytes. J Hepatol. 2008;48(4):578-588. [CrossRef]
  • [17] Polimeno L, Pesetti B, De Santis F, Resta L, Rossi R, De Palma A, Girardi B, Amoruso A, Francavilla A. Decreased expression of the augmenter of liver regeneration results in increased apoptosis and oxidative damage in human-derived glioma cells. Cell Death Dis. 2012;3(4):e289. [CrossRef]

COMPUTATIONAL IDENTIFICATION OF NOVEL TARGETS FOR DRUG CANDIDATE COMPOUNDS

Yıl 2023, Cilt: 27 Sayı: Current Research Topics In Pharmacy: In silico Approaches for Drug Design and Discovery, 9 - 12, 28.06.2025

Ceren Sucularlı , Birsen Tozkoparan , Sevim Peri Aytaç

https://doi.org/10.29228/jrp.455

Öz

Computational applications have been used in several steps in research of identifying new drug candidates, such as target discovery and prediction of drug-target interactions, and facilitate drug discovery and development [1, 2]. Interaction of proteins with druglike molecules, due to folding and physical properties of protein and structure of druglike molecules, with high affinity is named as druggability. Computational methods have also been used in prediction of druggability [3]. Molecular docking has been used to model the interaction of drug-like molecules and protein, therefore, this tool become an important method in drug discovery [4]. The druglikeness of compounds can be assessed by Lipinski's rule of five (RO5), that poor absorption is more probable when a compound has more than 5 H-bond donors, 10 H-bond acceptors, molecular weight higher than 500 and the calculated Log P is greater than 5 [5]. In a previous study, some new triazolothiadiazine derivatives have been synthesized, characterized and their antiproliferative effects on liver cancer cells have been investigated [6]. Three triazolothiadiazine derivatives 1h, 3c and 3h have been selected in our study to identify potential action mechanisms and targets and to evaluate their likeliness as new drug candidates. Druglikeness of these compounds were assessed according to Lipinski’s rule of five by using SwissADME [7]. According to our results, 1h, 3c and 3h, had molecular weight ranged in 406-449, H-bond donors 0, H-bond acceptors between 4-5 and therefore fulfilled the required criteria. Among three compounds, 1h had consensus Log P at 4.85, 3c had 5.87 and 3h had 5.25 [8]. Biological activity prediction of compounds was performed by using PASS online version 2.0 [9]. According to our results, three compounds might have various biological activities, such as being inhibitors of several phosphodiesterases (PDEs) and Dual specificity phosphatase 1 (DUSP1) inhibitor activity. Potential targets of 1h, 3c and 3h have been investigated using Swiss Target Prediction and BindingDB databases [10, 11]. According to Swiss Target Prediction results, muscleblind-like proteins, FAD-linked sulfhydryl oxidase ALR, and several phosphodiesterases might be targets [8]. BindingDB predicted targets for 1h and 3h. Cholinesterases were predicted to be target for 1h and 3h while PDE4A, Carbonic anhydrases and Steroidogenic factor-1 were predicted as targets for only 1h [8]. In order to further investigate the interaction of these compounds with predicted targets, we selected three targets, PDE4A, ALR and DUSP1, which were emerged in both or either in activity and target prediction results, and performed molecular docking analysis using SwissDock [12, 13]. According to our results, 1h, 3c and 3h might interact with selected proteins [8]. Our results anticipated new activities and targets for the 1h, 3c and 3h. PDE4A, DUSP1 and ALR could be important targets for these compounds, since PDE4A has been suggested as a therapeutic target for anxiety and central nervous system disorders [14]. DUSP1, as an oncogene, involves in several cellular processes, such as cell proliferation, differentiation, cell cycle arrest and apoptosis, by its involvement in MAPK signaling [15]. ALR is another important target, inhibition of ALR caused apoptosis in rat hepatocytes and human derived glioma cells [16, 17].

Anahtar Kelimeler

target prediction activity prediction molecular docking

Kaynakça

  • [1] Ferrero E, Dunham I, Sanseau P. In silico prediction of novel therapeutic targets using gene-disease association data. J Transl Med. 2017;15(1):182. [CrossRef]
  • [2] Yamanishi Y, Kotera M, Kanehisa M, Goto S. Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics. 2010;26(12):i246-54. [CrossRef]
  • [3] Wooller SK, Benstead-Hume G, Chen X, Ali Y, Pearl FMG. Bioinformatics in translational drug discovery. Biosci Rep. 2017;37(4):BSR20160180. [CrossRef]
  • [4] Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des. 2011;7(2):146-157. [CrossRef]
  • [5] Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001;46(1-3):3-26. [CrossRef]
  • [6] Aytac PS, Durmaz I, Houston DR, Cetin-Atalay R, Tozkoparan B. Novel triazolothiadiazines act as potent anticancer agents in liver cancer cells through Akt and ASK-1 proteins. Bioorg Med Chem. 2016;24(4):858-872. [CrossRef]
  • [7] Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717. [CrossRef]
  • [8] Sucularlı C, Tozkoparan B, Aytac PS. In silico activity and target prediction analyses of three triazolothiadiazine derivatives. Acta Medica. 2022;53(3):251-260. [CrossRef]
  • [9] Filimonov DA, Gloriozova TA, Rudik AV, Druzhilovskii DS, Pogodin PV, Popoikov VV. Prediction of the biological activity spectra of organic compounds using the Pass Online Web Resource. Chem Heterocycl Compds. 2014;50:444–457. [CrossRef]
  • [10] Gfeller D, Grosdidier A, Wirth M, Daina A, Michielin O, Zoete V. SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res. 2014;42(Web Server issue):W32-38. [CrossRef]
  • [11] Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J. Binding DB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 2016;44(D1):D1045-1053. [CrossRef]
  • [12] Grosdidier A, Zoete V, Michielin O. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res. 2011;39(Web Server issue):W270-277. [CrossRef]
  • [13] Grosdidier A, Zoete V, Michielin O. Fast docking using the CHARMM force field with EADock DSS. J Comput Chem. 2011;32(10):2149-2159. [CrossRef]
  • [14] Hansen RT, 3rd, Conti M, Zhang HT. Mice deficient in phosphodiesterase-4A display anxiogenic-like behavior. Psychopharmacology (Berl). 2014;231(15):2941-2954. [CrossRef]
  • [15] Teng F, Xu Z, Chen J, Zheng G, Zheng G, Lv H, Wang Y, Wang L, Cheng X. DUSP1 induces apatinib resistance by activating the MAPK pathway in gastric cancer. Oncol Rep. 2018;40(3):1203-1222. [CrossRef]
  • [16] Thirunavukkarasu C, Wang LF, Harvey SA, Watkins SC, Chaillet JR, Prelich J, Starzl TE, Gandhi CR. Augmenter of liver regeneration: an important intracellular survival factor for hepatocytes. J Hepatol. 2008;48(4):578-588. [CrossRef]
  • [17] Polimeno L, Pesetti B, De Santis F, Resta L, Rossi R, De Palma A, Girardi B, Amoruso A, Francavilla A. Decreased expression of the augmenter of liver regeneration results in increased apoptosis and oxidative damage in human-derived glioma cells. Cell Death Dis. 2012;3(4):e289. [CrossRef]
Toplam 17 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Eczacılık ve İlaç Bilimleri (Diğer)
Bölüm Commentary
Yazarlar

Ceren Sucularlı

Birsen Tozkoparan 0000-0001-7337-8360

Sevim Peri Aytaç 0000-0002-9985-3382

Yayımlanma Tarihi 28 Haziran 2025
Yayımlandığı Sayı Yıl 2023 Cilt: 27 Sayı: Current Research Topics In Pharmacy: In silico Approaches for Drug Design and Discovery

Kaynak Göster

APA Sucularlı, C., Tozkoparan, B., & Aytaç, S. P. (2025). COMPUTATIONAL IDENTIFICATION OF NOVEL TARGETS FOR DRUG CANDIDATE COMPOUNDS. Journal of Research in Pharmacy, 27(Current Research Topics In Pharmacy: In silico Approaches for Drug Design and Discovery), 9-12. https://doi.org/10.29228/jrp.455
AMA Sucularlı C, Tozkoparan B, Aytaç SP. COMPUTATIONAL IDENTIFICATION OF NOVEL TARGETS FOR DRUG CANDIDATE COMPOUNDS. J. Res. Pharm. Temmuz 2025;27(Current Research Topics In Pharmacy: In silico Approaches for Drug Design and Discovery):9-12. doi:10.29228/jrp.455
Chicago Sucularlı, Ceren, Birsen Tozkoparan, ve Sevim Peri Aytaç. “COMPUTATIONAL IDENTIFICATION OF NOVEL TARGETS FOR DRUG CANDIDATE COMPOUNDS”. Journal of Research in Pharmacy 27, sy. Current Research Topics In Pharmacy: In silico Approaches for Drug Design and Discovery (Temmuz 2025): 9-12. https://doi.org/10.29228/jrp.455.
EndNote Sucularlı C, Tozkoparan B, Aytaç SP (01 Temmuz 2025) COMPUTATIONAL IDENTIFICATION OF NOVEL TARGETS FOR DRUG CANDIDATE COMPOUNDS. Journal of Research in Pharmacy 27 Current Research Topics In Pharmacy: In silico Approaches for Drug Design and Discovery 9–12.
IEEE C. Sucularlı, B. Tozkoparan, ve S. P. Aytaç, “COMPUTATIONAL IDENTIFICATION OF NOVEL TARGETS FOR DRUG CANDIDATE COMPOUNDS”, J. Res. Pharm., c. 27, sy. Current Research Topics In Pharmacy: In silico Approaches for Drug Design and Discovery, ss. 9–12, 2025, doi: 10.29228/jrp.455.
ISNAD Sucularlı, Ceren vd. “COMPUTATIONAL IDENTIFICATION OF NOVEL TARGETS FOR DRUG CANDIDATE COMPOUNDS”. Journal of Research in Pharmacy 27/Current Research Topics In Pharmacy: In silico Approaches for Drug Design and Discovery (Temmuz 2025), 9-12. https://doi.org/10.29228/jrp.455.
JAMA Sucularlı C, Tozkoparan B, Aytaç SP. COMPUTATIONAL IDENTIFICATION OF NOVEL TARGETS FOR DRUG CANDIDATE COMPOUNDS. J. Res. Pharm. 2025;27:9–12.
MLA Sucularlı, Ceren vd. “COMPUTATIONAL IDENTIFICATION OF NOVEL TARGETS FOR DRUG CANDIDATE COMPOUNDS”. Journal of Research in Pharmacy, c. 27, sy. Current Research Topics In Pharmacy: In silico Approaches for Drug Design and Discovery, 2025, ss. 9-12, doi:10.29228/jrp.455.
Vancouver Sucularlı C, Tozkoparan B, Aytaç SP. COMPUTATIONAL IDENTIFICATION OF NOVEL TARGETS FOR DRUG CANDIDATE COMPOUNDS. J. Res. Pharm. 2025;27(Current Research Topics In Pharmacy: In silico Approaches for Drug Design and Discovery):9-12.