New Design Compounds for Bone Cancer Treatment: Broader Bioactivity of Silicon Modified Methotrexate

Complex diseases such as cancer are mostly described by combining negative effects of multiple biological factors or pathways. Based on that, multi-targeted approach for treating cancer is gaining interest. The aim of this study is to introduce a computational approach and to design new, multi-targeted drug candidates for treatment of bone cancer. In this approach, the FDA approved drugs of bone cancer were evaluated in terms of their molecular pharmaceutical properties and their bioactivity parameters predicted by bioinformatics and cheminformatics softwares. Among them, Methotrexate was chosen as a lead molecule due to its broader spectrum of bioactivity on the most important drug targets reported in literature. The lead molecule was exposed to basic bioisosteric modifications to obtain a better drug compound with improved bioactivity and a stronger drug-likeness profile using the known drug structure. Design compounds produced by a number of bioisosteric modifications performed on the 2D structure of the lead compound were evaluated in terms of both criteria; bioactivity and drug-likeness. Silicone modified compounds M4, M13, M14, and M15 showed a much broader spectrum of biological activity than that of the approved compound Methotrexate. The interesting effect of silicone incorporation makes our compounds promising drug candidates for further pharmaceutical investigation.

New Design Compounds for Bone Cancer Treatment: Broader Bioactivity of Silicon Modified Methotrexate

Complex diseases such as cancer are mostly described by combining negative effects of multiple biological factors or pathways. Based on that, multi-targeted approach for treating cancer is gaining interest. The aim of this study is to introduce a computational approach and to design new, multi-targeted drug candidates for treatment of bone cancer. In this approach, the FDA approved drugs of bone cancer were evaluated in terms of their molecular pharmaceutical properties and their bioactivity parameters predicted by bioinformatics and cheminformatics softwares. Among them, Methotrexate was chosen as a lead molecule due to its broader spectrum of bioactivity on the most important drug targets reported in literature. The lead molecule was exposed to basic bioisosteric modifications to obtain a better drug compound with improved bioactivity and a stronger drug-likeness profile using the known drug structure. Design compounds produced by a number of bioisosteric modifications performed on the 2D structure of the lead compound were evaluated in terms of both criteria; bioactivity and drug-likeness. Silicone modified compounds M4, M13, M14, and M15 showed a much broader spectrum of biological activity than that of the approved compound Methotrexate. The interesting effect of silicone incorporation makes our compounds promising drug candidates for further pharmaceutical investigation.

___

  • Lipinski, C.A., (2001). Avoiding investment in doomed drugs. Curr Drug Discov, 1: p. 17-19.
  • Ghose, A.K., V.N. Viswanadhan, and J.J. Wendoloski, (1999). A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. Journal of combinatorial chemistry, 1(1): p. 55-68.
  • Muegge, I., S.L. Heald, and D. Brittelli, (2001). Simple selection criteria for drug-like chemical matter. Journal of Medicinal Chemistry, 44(12): p. 1841-1846.
  • Schmidt, T., A. Bergner, and T. Schwede, (2014). Modelling three-dimensional protein structures for applications in drug design. Drug discovery today, 19(7): p. 890-897.
  • Geldenhuys, W.J., et al., (2006). Optimizing the use of open-source software applications in drug discovery. Drug Discovery Today. 11(3-4): p. 127-132.
  • Veber, D.F., et al., (2006). Molecular properties that influence the oral bioavailability of drug candidates. Journal of medicinal chemistry. 45(12): p. 2615-2623.
  • Burger, A., (1991). Isosterism and bioisosterism in drug design, in Progress in Drug Research/Fortschritte der Arzneimittelforschung/Progrès des recherches pharmaceutiques. Springer. p. 287-371.
  • Egan, W.J., K.M. Merz, and J.J. Baldwin, (2000). Prediction of drug absorption using multivariate statistics. Journal of Medicinal Chemistry. 43(21): p. 3867-3877.
  • Zheng, H., M. Fridkin, and M. Youdim, (2014). From single target to multitarget/network therapeutics in Alzheimer's therapy. Pharmaceuticals (Basel). 7(2): p. 113-35.
  • Petrelli, A. and S. Giordano, (2008). From single- to multi-target drugs in cancer therapy: when aspecificity becomes an advantage. Curr Med Chem. 15(5): p. 422-32.
  • Li, Y., et al., (2014). Multi-targeted therapy of cancer by niclosamide: A new application for an old drug. Cancer letters. 349(1): p. 8-14.
  • Berquin, I.M., I.J. Edwards, and Y.Q. Chen, (2008). Multi-targeted therapy of cancer by omega-3 fatty acids. Cancer letters. 269(2): p. 363-377.
  • Wishart, D.S., et al., (2018). DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46(D1): p. D1074-D1082.
  • Dogan, E. E. (2021). Computational Bioactivity Analysis and Bioisosteric Investigation of the Approved Breast Cancer Drugs Proposed New Design Drug Compounds: Increased Bioactivity Coming with Silicon and Boron. Letters in Drug Design & Discovery, 18, 1-11.
  • Dogan, E. E. (2021). Computational binding analysis and toxicity evaluation of estrogen receptor with estradiol and the approved SERMs raloxifene, tamoxifen, and toremifene. Medicine, 10, 157-61.
  • Eryilmaz, E., (2019). Multi-targeted anti-leukemic drug design with the incorporation of silicon into Nelarabine: How silicon increases bioactivity. European Journal of Pharmaceutical Sciences. 134: p. 266-273.
  • Eder, J., R. Sedrani, and C. Wiesmann, (2014). The discovery of first-in-class drugs: origins and evolution. Nature Reviews Drug Discovery.. 13(8): p. 577.