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Yıl 2022, Cilt: 39 Sayı: 4, 1043 - 1050, 29.10.2022

Suat Utku Keskin Muhammet Volkan Bülbül Semiha Mervenur Kalender Sümeyye Özyaman Arif Mermer

Öz

Kaynakça

  • Kolak A, Kamińska M, Sygit K, Budny A, Surdyka D, Kukiełka-Budny B, et al. Primary and secondary prevention of breast cancer. Ann Agric Environ Med. 2017;24(4):549-53.
  • Anastasiadi Z, Lianos GD, Ignatiadou E, Harissis HV, Mitsis M. Breast cancer in young women: an overview. Updates in surgery. 2017;69(3):313-7.
  • Enger SM, Ross RK, Paganini-Hill A, Bernstein L. Breastfeeding experience and breast cancer risk among postmenopausal women. Cancer Epidemiology and Prevention Biomarkers. 1998;7(5):365-9.
  • Colditz GA, Willett WC, Hunter DJ, Stampfer MJ, Manson JE, Hennekens CH, et al. Family history, age, and risk of breast cancer: prospective data from the Nurses' Health Study. Jama. 1993;270(3):338-43.
  • Cancer CGoHFiB. Familial breast cancer: collaborative reanalysis of individual data from 52 epidemiological studies including 58 209 women with breast cancer and 101 986 women without the disease. The Lancet. 2001;358(9291):1389-99.
  • Pharoah PD, Day NE, Duffy S, Easton DF, Ponder BA. Family history and the risk of breast cancer: a systematic review and meta‐analysis. International journal of cancer. 1997;71(5):800-9.
  • Lu Z-R, Steinmetz NF, Zhu H. New Directions for Drug Delivery in Cancer Therapy. ACS Publications; 2018.
  • Chabner BA, Roberts TG. Chemotherapy and the war on cancer. Nature Reviews Cancer. 2005;5(1):65-72.
  • Fuertes MA, Alonso C, Pérez JM. Biochemical modulation of cisplatin mechanisms of action: enhancement of antitumor activity and circumvention of drug resistance. Chemical reviews. 2003;103(3):645-62.
  • Breslin S, O’Driscoll L. Three-dimensional cell culture: the missing link in drug discovery. Drug discovery today. 2013;18(5-6):240-9.
  • Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal AJCacjfc. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 2018;68(6):394-424.
  • Chen R, Liu X, Jin S, Lin J, Liu J. Machine learning for drug-target interaction prediction. Molecules. 2018;23(9):2208.
  • Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, et al. Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery. 2019;18(6):463-77.
  • Talevi A, Morales JF, Hather G, Podichetty JT, Kim S, Bloomingdale PC, et al. Machine Learning in Drug Discovery and Development Part 1: A Primer. CPT: pharmacometrics & systems pharmacology. 2020;9(3):129-42.
  • Lima AN, Philot EA, Trossini GHG, Scott LPB, Maltarollo VG, Honorio KM. Use of machine learning approaches for novel drug discovery. Expert opinion on drug discovery. 2016;11(3):225-39.
  • Jackson SE, Chester JDJIjoc. Personalised cancer medicine. 2015;137(2):262-6.
  • Mak IW, Evaniew N, Ghert MJAjotr. Lost in translation: animal models and clinical trials in cancer treatment. 2014;6(2):114.
  • Anastasov N, Höfig I, Radulović V, Ströbel S, Salomon M, Lichtenberg J, et al. A 3D-microtissue-based phenotypic screening of radiation resistant tumor cells with synchronized chemotherapeutic treatment. 2015;15(1):1-11.
  • Shoemaker RHJNRC. The NCI60 human tumour cell line anticancer drug screen. 2006;6(10):813-23.
  • Kim MJ, Chi BH, Yoo JJ, Ju YM, Whang YM, Chang IHJPo. Structure establishment of three-dimensional (3D) cell culture printing model for bladder cancer. 2019;14(10):e0223689.
  • Knight E, Przyborski SJJoa. Advances in 3D cell culture technologies enabling tissue‐like structures to be created in vitro. 2015;227(6):746-56.
  • Sokolova V, Rojas-Sánchez L, Białas N, Schulze N, Epple MJAB. Calcium phosphate nanoparticle-mediated transfection in 2D and 3D mono-and co-culture cell models. 2019;84:391-401.
  • Mentese M, Demirbas N, Mermer A, Demirci S, Demirbas A, Ayaz FA. Novel Azole-Functionalited Flouroquinolone Hybrids: Design, Conventional and Microwave Irradiated Synthesis, Evaluation as Antibacterial and Antioxidant Agents, Letters in Drug Design and Discovery. 2018;15:46-64.

Investigation, design and synthesis of new anticancer agents with anticancer effect potential on MCF-7 Breast Cancer Cells by Machine Learning Method

Yıl 2022, Cilt: 39 Sayı: 4, 1043 - 1050, 29.10.2022

Suat Utku Keskin Muhammet Volkan Bülbül Semiha Mervenur Kalender Sümeyye Özyaman Arif Mermer

Öz

Cancer is one of the diseases with a high mortality rate, which occurs when cells multiply uncontrollably, acquire an invasive character and metastasize. Breast cancer is one of the cancer types with an increasing incidence worldwide. Chemotherapy is a method used in the treatment of cancer diseases, and the chemotherapeutic drugs used inhibit the growth and proliferation of cancer cells due to their cytotoxic properties. Today, machine learning techniques offer significant advantages by helping several steps of the drug discovery process, reducing the time spent in the laboratory, the use of consumables and chemical materials, and the maximum time predicted for the discovery of a drug with traditional methods. In our study, it was aimed to determine the 3 Schiff base derivatives with the most active cytotoxic effect on breast cancer cells from the large data set using machine learning. In our study, 7 Schiff base derivatives were determined from a large data set containing 98 compounds, and the 3 most active compounds with cytotoxic properties on breast cancer cells and their IC50 values were determined by machine learning method. In the future, it is thought that compound 1 can be used as an alternative to pharmacological applications to be used in preclinical studies as a therapeutic agent, supported by in vitro and in vivo applications, in order to be used in cancer treatments.

Anahtar Kelimeler

Machine Learning Drug Discovery Breast Cancer MCF-7

Kaynakça

  • Kolak A, Kamińska M, Sygit K, Budny A, Surdyka D, Kukiełka-Budny B, et al. Primary and secondary prevention of breast cancer. Ann Agric Environ Med. 2017;24(4):549-53.
  • Anastasiadi Z, Lianos GD, Ignatiadou E, Harissis HV, Mitsis M. Breast cancer in young women: an overview. Updates in surgery. 2017;69(3):313-7.
  • Enger SM, Ross RK, Paganini-Hill A, Bernstein L. Breastfeeding experience and breast cancer risk among postmenopausal women. Cancer Epidemiology and Prevention Biomarkers. 1998;7(5):365-9.
  • Colditz GA, Willett WC, Hunter DJ, Stampfer MJ, Manson JE, Hennekens CH, et al. Family history, age, and risk of breast cancer: prospective data from the Nurses' Health Study. Jama. 1993;270(3):338-43.
  • Cancer CGoHFiB. Familial breast cancer: collaborative reanalysis of individual data from 52 epidemiological studies including 58 209 women with breast cancer and 101 986 women without the disease. The Lancet. 2001;358(9291):1389-99.
  • Pharoah PD, Day NE, Duffy S, Easton DF, Ponder BA. Family history and the risk of breast cancer: a systematic review and meta‐analysis. International journal of cancer. 1997;71(5):800-9.
  • Lu Z-R, Steinmetz NF, Zhu H. New Directions for Drug Delivery in Cancer Therapy. ACS Publications; 2018.
  • Chabner BA, Roberts TG. Chemotherapy and the war on cancer. Nature Reviews Cancer. 2005;5(1):65-72.
  • Fuertes MA, Alonso C, Pérez JM. Biochemical modulation of cisplatin mechanisms of action: enhancement of antitumor activity and circumvention of drug resistance. Chemical reviews. 2003;103(3):645-62.
  • Breslin S, O’Driscoll L. Three-dimensional cell culture: the missing link in drug discovery. Drug discovery today. 2013;18(5-6):240-9.
  • Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal AJCacjfc. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 2018;68(6):394-424.
  • Chen R, Liu X, Jin S, Lin J, Liu J. Machine learning for drug-target interaction prediction. Molecules. 2018;23(9):2208.
  • Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, et al. Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery. 2019;18(6):463-77.
  • Talevi A, Morales JF, Hather G, Podichetty JT, Kim S, Bloomingdale PC, et al. Machine Learning in Drug Discovery and Development Part 1: A Primer. CPT: pharmacometrics & systems pharmacology. 2020;9(3):129-42.
  • Lima AN, Philot EA, Trossini GHG, Scott LPB, Maltarollo VG, Honorio KM. Use of machine learning approaches for novel drug discovery. Expert opinion on drug discovery. 2016;11(3):225-39.
  • Jackson SE, Chester JDJIjoc. Personalised cancer medicine. 2015;137(2):262-6.
  • Mak IW, Evaniew N, Ghert MJAjotr. Lost in translation: animal models and clinical trials in cancer treatment. 2014;6(2):114.
  • Anastasov N, Höfig I, Radulović V, Ströbel S, Salomon M, Lichtenberg J, et al. A 3D-microtissue-based phenotypic screening of radiation resistant tumor cells with synchronized chemotherapeutic treatment. 2015;15(1):1-11.
  • Shoemaker RHJNRC. The NCI60 human tumour cell line anticancer drug screen. 2006;6(10):813-23.
  • Kim MJ, Chi BH, Yoo JJ, Ju YM, Whang YM, Chang IHJPo. Structure establishment of three-dimensional (3D) cell culture printing model for bladder cancer. 2019;14(10):e0223689.
  • Knight E, Przyborski SJJoa. Advances in 3D cell culture technologies enabling tissue‐like structures to be created in vitro. 2015;227(6):746-56.
  • Sokolova V, Rojas-Sánchez L, Białas N, Schulze N, Epple MJAB. Calcium phosphate nanoparticle-mediated transfection in 2D and 3D mono-and co-culture cell models. 2019;84:391-401.
  • Mentese M, Demirbas N, Mermer A, Demirci S, Demirbas A, Ayaz FA. Novel Azole-Functionalited Flouroquinolone Hybrids: Design, Conventional and Microwave Irradiated Synthesis, Evaluation as Antibacterial and Antioxidant Agents, Letters in Drug Design and Discovery. 2018;15:46-64.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sağlık Kurumları Yönetimi
Bölüm Clinical Research
Yazarlar

Suat Utku Keskin 0000-0002-2277-3800

Muhammet Volkan Bülbül 0000-0003-1526-2065

Semiha Mervenur Kalender 0000-0002-0885-3417

Sümeyye Özyaman 0000-0002-6077-7423

Arif Mermer 0000-0002-4789-7180

Yayımlanma Tarihi 29 Ekim 2022
Gönderilme Tarihi 28 Temmuz 2022
Kabul Tarihi 28 Ağustos 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 39 Sayı: 4

Kaynak Göster

APA Keskin, S. U., Bülbül, M. V., Kalender, S. M., Özyaman, S., vd. (2022). Investigation, design and synthesis of new anticancer agents with anticancer effect potential on MCF-7 Breast Cancer Cells by Machine Learning Method. Journal of Experimental and Clinical Medicine, 39(4), 1043-1050.
AMA Keskin SU, Bülbül MV, Kalender SM, Özyaman S, Mermer A. Investigation, design and synthesis of new anticancer agents with anticancer effect potential on MCF-7 Breast Cancer Cells by Machine Learning Method. J. Exp. Clin. Med. Ekim 2022;39(4):1043-1050.
Chicago Keskin, Suat Utku, Muhammet Volkan Bülbül, Semiha Mervenur Kalender, Sümeyye Özyaman, ve Arif Mermer. “Investigation, Design and Synthesis of New Anticancer Agents With Anticancer Effect Potential on MCF-7 Breast Cancer Cells by Machine Learning Method”. Journal of Experimental and Clinical Medicine 39, sy. 4 (Ekim 2022): 1043-50.
EndNote Keskin SU, Bülbül MV, Kalender SM, Özyaman S, Mermer A (01 Ekim 2022) Investigation, design and synthesis of new anticancer agents with anticancer effect potential on MCF-7 Breast Cancer Cells by Machine Learning Method. Journal of Experimental and Clinical Medicine 39 4 1043–1050.
IEEE S. U. Keskin, M. V. Bülbül, S. M. Kalender, S. Özyaman, ve A. Mermer, “Investigation, design and synthesis of new anticancer agents with anticancer effect potential on MCF-7 Breast Cancer Cells by Machine Learning Method”, J. Exp. Clin. Med., c. 39, sy. 4, ss. 1043–1050, 2022.
ISNAD Keskin, Suat Utku vd. “Investigation, Design and Synthesis of New Anticancer Agents With Anticancer Effect Potential on MCF-7 Breast Cancer Cells by Machine Learning Method”. Journal of Experimental and Clinical Medicine 39/4 (Ekim 2022), 1043-1050.
JAMA Keskin SU, Bülbül MV, Kalender SM, Özyaman S, Mermer A. Investigation, design and synthesis of new anticancer agents with anticancer effect potential on MCF-7 Breast Cancer Cells by Machine Learning Method. J. Exp. Clin. Med. 2022;39:1043–1050.
MLA Keskin, Suat Utku vd. “Investigation, Design and Synthesis of New Anticancer Agents With Anticancer Effect Potential on MCF-7 Breast Cancer Cells by Machine Learning Method”. Journal of Experimental and Clinical Medicine, c. 39, sy. 4, 2022, ss. 1043-50.
Vancouver Keskin SU, Bülbül MV, Kalender SM, Özyaman S, Mermer A. Investigation, design and synthesis of new anticancer agents with anticancer effect potential on MCF-7 Breast Cancer Cells by Machine Learning Method. J. Exp. Clin. Med. 2022;39(4):1043-50.
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