Olatunde A, Nigam M, Singh RK, Panwar AS, Lasisi A, Alhumaydhi FA, Jyoti kumar V, et al. Cancer and diabetes: the interlinking metabolic pathways and repurposing actions of antidiabetic drugs. Cancer Cell Int. 2021;21(1):499. https://doi.org/10.1186/s12935-021-02202-5.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. https://doi.org/10.3322/caac.21660.

Article  CAS  PubMed  Google Scholar 

Fu L, Jin W, Zhang J, Zhu L, Lu J, Zhen Y, et al. Repurposing non-oncology small-molecule drugs to improve cancer therapy: current situation and future directions. Acta Pharm Sin B. 2022;12(2):532–57. https://doi.org/10.1016/j.apsb.2021.09.006.

Article  CAS  PubMed  Google Scholar 

Prasad V, Mailankody S. Research and development spending to bring a single cancer drug to market and revenues after approval. JAMA Intern Med. 2017;177(11):1569–75. https://doi.org/10.1001/jamainternmed.2017.3601.

Article  PubMed  PubMed Central  Google Scholar 

Kaushik I, Ramachandran S, Prasad S, Srivastava SK. Drug rechanneling: a novel paradigm for cancer treatment. Semin Cancer Biol. 2021;68:279–90. https://doi.org/10.1016/j.semcancer.2020.03.011.

Article  CAS  PubMed  Google Scholar 

Aggarwal S, Verma SS, Aggarwal S, Gupta SC. Drug repurposing for breast cancer therapy: Old weapon for new battle. Semin Cancer Biol. 2021;68:8–20. https://doi.org/10.1016/j.semcancer.2019.09.012.

Article  CAS  PubMed  Google Scholar 

Basso J, Miranda A, Sousa J, Pais A, Vitorino C. Repurposing drugs for glioblastoma: from bench to bedside. Cancer Lett. 2018;428:173–83. https://doi.org/10.1016/j.canlet.2018.04.039.

Article  CAS  PubMed  Google Scholar 

Abaza Y, Kantarjian H, Garcia-Manero G, Estey E, Borthakur G, Jabbour E, et al. Long-term outcome of acute promyelocytic leukemia treated with all-trans-retinoic acid, arsenic trioxide, and gemtuzumab. Blood. 2017;129(10):1275–83. https://doi.org/10.1182/blood-2016-09-736686.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Dimopoulos MA, Kastritis E. Thalidomide for myeloma: still here? Lancet Haematol. 2018;5(10):e439–40. https://doi.org/10.1016/S2352-3026(18)30154-6.

Article  PubMed  Google Scholar 

Liu Q, Tong D, Liu G, Gao J, Wang LA, Xu J, et al. Metformin inhibits prostate cancer progression by targeting tumor-associated inflammatory infiltration. Clin Cancer Res. 2018;24(22):5622–34. https://doi.org/10.1158/1078-0432.CCR-18-0420.

Article  CAS  PubMed  Google Scholar 

Raju TN. The Nobel chronicles. 1988: James Whyte Black, (b 1924), Gertrude Elion (1918-99), and George H Hitchings (1905-98). The Lancet. 2000;355(9208):1022. https://doi.org/10.1016/s0140-6736(05)74775-9.

Article  CAS  Google Scholar 

Kresge N, Simoni RD, Hill RL. The rational design of nucleic acid inhibitors to treat leukemia: the work of George H. Hitchings. J Biol Chem. 2008;283(18):e10–11.

Article  CAS  Google Scholar 

Overington JP, Al-Lazikani B, Hopkins AL. How many drug targets are there? Nat Rev Drug Discov. 2006;5(12):993–6. https://doi.org/10.1038/nrd2199.

Article  CAS  PubMed  Google Scholar 

Zhong L, Li Y, Xiong L, Wang W, Wu M, Yuan T, et al. Small molecules in targeted cancer therapy: advances, challenges, and future perspectives. Signal Transduct Target Ther. 2021;6(1):201. https://doi.org/10.1038/s41392-021-00572-w.

Article  PubMed  PubMed Central  Google Scholar 

Gonzalez-Fierro A, Dueñas-González A. Drug repurposing for cancer therapy, easier said than done. Semin Cancer Biol. 2021;68:123–31. https://doi.org/10.1016/j.semcancer.2019.12.012.

Article  PubMed  Google Scholar 

Medema MH, Fischbach MA. Computational approaches to natural product discovery. Nat Chem Biol. 2015;11(9):639–48. https://doi.org/10.1038/nchembio.1884.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Baltz RH. Natural product drug discovery in the genomic era: realities, conjectures, misconceptions, and opportunities. J Ind Microbiol Biotechnol. 2019;46(3–4):281–99. https://doi.org/10.1007/s10295-018-2115-4.

Article  CAS  PubMed  Google Scholar 

Hur M, Campbell AA, Almeida-de-Macedo M, Li L, Ransom N, Jose A, et al. A global approach to analysis and interpretation of metabolic data for plant natural product discovery. Nat Prod Rep. 2013;30(4):565–83. https://doi.org/10.1039/c3np20111b.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Biermann F, Wenski SL, Helfrich EJN. Navigating and expanding the roadmap of natural product genome mining tools. Beilstein J Org Chem. 2022;18:1656–71. https://doi.org/10.3762/bjoc.18.178.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cui W, Aouidate A, Wang S, Yu Q, Li Y, Yuan S. Discovering anti-cancer drugs via computational methods. Front Pharmacol. 2020;11:733. https://doi.org/10.3389/fphar.2020.00733.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Prada-Gracia D, Huerta-Yépez S, Moreno-Vargas LM. Application of computational methods for anticancer drug discovery, design, and optimization. Bol Med Hosp Infant Mex. 2016;73(6):411–23. https://doi.org/10.1016/j.bmhimx.2016.10.006.

Article  PubMed  PubMed Central  Google Scholar 

Baldi A. Computational approaches for drug design and discovery: an overview. Syst Rev Pharm. 2010;1(1):99–105. https://doi.org/10.4103/0975-8453.59519.

Article  CAS  Google Scholar 

Sahu A, Prabhash K, Noronha V, Joshi A, Desai S. Crizotinib: a comprehensive review. South Asian J Cancer. 2013;2(2):91–97. https://doi.org/10.4103/2278-330X.110506.

Article  PubMed  PubMed Central  Google Scholar 

Selassie C, Verma RP. History of quantitative structure-activity relationships. Burger’s Med Chem Drug Discov. 2003;1:1–48. https://doi.org/10.1002/0471266949.bmc001.pub2.

Article  Google Scholar 

Mendenhall J, Meiler J. Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout. J Comput Aided Mol Des. 2016;30(2):177–89. https://doi.org/10.1007/s10822-016-9895-2.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Esposito EX, Hopfinger AJ, Madura JD. Methods for applying the quantitative structure-activity relationship paradigm. Methods Mol Biol. 2004;275:131–214. https://doi.org/10.1385/1-59259-802-1:131.

Article  CAS  PubMed  Google Scholar 

Morris GM, Lim-Wilby M. Molecular docking. Methods Mol Biol. 2008;443:365–82. https://doi.org/10.1007/978-1-59745-177-2_19.

Article  CAS  PubMed  Google Scholar 

Fan J, Fu A, Zhang L. Progress in molecular docking. Quant Biol. 2019;7:83–9. https://doi.org/10.1007/s40484-019-0172-y.

Article  CAS  Google Scholar 

Yuriev E, Ramsland PA. Latest developments in molecular docking: 2010-2011 in review. J Mol Recognit. 2013;26(5):215–39. https://doi.org/10.1002/jmr.2266.

Article  CAS  PubMed  Google Scholar 

Dias R, de Azevedo Jr WF. Molecular docking algorithms. Curr Drug Targets. 2008;9(12):1040–7. https://doi.org/10.2174/138945008786949432.

Article  CAS  PubMed  Google Scholar 

Bhattarai D, Singh S, Jang Y, Hyeon Han S, Lee K, Choi Y. An insight into drug repositioning for the development of novel anti-cancer drugs. Curr Top Med Chem. 2016;16(19):2156–68. https://doi.org/10.2174/1568026616666160216153618.

Article  CAS  PubMed  Google Scholar 

Zhang Z, Zhou L, Xie N, Nice EC, Zhang T, Cui Y, et al. Overcoming cancer therapeutic bottleneck by drug repurposing. Signal Transduct Target Ther. 2020;5(1):113. https://doi.org/10.1038/s41392-020-00213-8.

Article  PubMed  PubMed Central  Google Scholar 

Du JH, Zhang HD, Ma ZJ, Ji KM. Artesunate induces oncosis-like cell death in vitro and has antitumor activity against pancreatic cancer xenografts in vivo. Cancer Chemother Pharmacol. 2010;65(5):895–902. https://doi.org/10.1007/s00280-009-1095-5.

Article  CAS  PubMed  Google Scholar 

Zhou X, Sun WJ, Wang WM, Chen K, Zheng JH, Lu MD, et al. Artesunate inhibits the growth of gastric cancer cells through the mechanism of promoting oncosis both in vitro and in vivo. Anticancer Drugs. 2013;24(9):920–7. https://doi.org/10.1097/CAD.0b013e328364a109.