In silico studies for the effect of fluorination of Hydroxypyridin-4-ones on their toxicity profile in comparison to Deferiprone
Main Article Content
Abstract
The vast advancement in pharmaceutical analysis technology, and the evolutionary rise in molecular Biology following the milestone coding of the human genome and the splendid utilisation of computational chemistry and bioinformatics, have called for urgent employment of knowledge, tools and skills to optimise therapies and clinical management. Oral iron chelators have long been in the market but nevertheless require a quality booster to parallel the overall upgrade in all life aspects let alone clinical advances, drugs, and therapeutics.
The implementation for such development lies in the hands of medicinal chemists and experts in drug design and discovery. Using bioinformatic approaches, this study aims to predict an ideal structural configuration capable of producing a more effective compound than the long-used oral iron chelator, Deferiprone.
In pursuit of such an aspiration, a range of fluorinated hydroxypyridinones (the backbone structure for Deferiprone) have been investigated. Fluorination of drugs in general, has recently been shown to bring about many desired features such as metabolic stability. ADMET lab 2.0 along with few other software that are used in molecular docking; have been brought into play to unfold an ideal, more potent fluorinated hydroxypyridinone than Deferiprone particularly with regards to the toxicity profile.
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
1- The Role of Deferiprone in Iron Chelation. Robert C. Hider, and A. Victor Hoffbrand, N Engl J Med 2018; 379: 2140-2150. DOI: https://doi.org/10.1056/NEJMra1800219
2- British Medical Association, Royal Pharmaceutical Society. British National Formulary (BNF) [Internet]. London: BMJ Group and Pharmaceutical Press; 2023 Mar [cited 2024 Aug 31]. Available from: https://bnf.nice.org.uk/
3- Masera N, Tavecchia L, Longoni DV, Maglia O, Biondi A, Masera G. Agranulocytosis due to deferiprone: a case report with cytomorphological and functional bone marrow examination. Blood Transfus. 2011 Oct;9 (4):462-5.
4- Tricta F, Uetrecht J, Galanello R, Connelly J, Rozova A, Spino M, Palmblad J. Deferiprone-induced agranulocytosis: 20 years of clinical observations. Am J Hematol. 2016 Oct;91(10):1026-31. DOI: https://doi.org/10.1002/ajh.24479
5- Kontoghiorghes GJ. The Vital Role Played by Deferiprone in the Transition of Thalassaemia from a Fatal to a Chronic Disease and Challenges in Its Repurposing for Use in Non-Iron-Loaded Diseases. Pharmaceuticals (Basel). 2023 Jul 18;16 (7):1016. DOI: https://doi.org/10.3390/ph16071016
6- Electronic Medicines Compendium (EMC). SmPC [Internet]. London: Datapharm; [updated 2025 Feb 06; cited 2025 Feb15]. Available from: https://www.medicines.org.uk/emc/product/10958/smpc
7- Zhou P, Zou J, Tian F, Shang Z. Fluorine bonding-how does it work in protein-ligand interactions. J Chem Inf Model. 2009 Oct;49(10):2344-55. DOI: https://doi.org/10.1021/ci9002393
8- Brunton LL, Hilal-Dandan R, Knollmann BC, editors. Goodman & Gilman’s: The Pharmacological Basis of Therapeutics. 14th ed. New York: McGraw-Hill; 2023. Chapter 2, Pharmacokinetics.
9- S. SINGH, A. 0. EPEMOLU, P. S. DOBBIN, G. S. TILBROOK, B. L. ELLIS, L. A. DAMANI, AND R. C. HIDER, 1992 by The American Society for Pharmacology and Experimental Therapeutics Vol. 20, No. 2.
10- Yongmin Ma, Sourav Roy, Xiaole Kong, Yulin Chen, Dingyong Liu, and Robert C Hider. J Med. Chem. 2012, 55, 2185−2195 DOI: https://doi.org/10.1021/jm201475u
11- Cormac D Murphy & Graham Sandford. Recent advances in fluorination techniques and their anticipated impact on drug metabolism and toxicity, y, Expert Opinion on Drug Metabolism & Toxicology, 11:4, 589-599. DOI: https://doi.org/10.1517/17425255.2015.1020295
12- O’Hagan, D. Understanding Organofluorine Chemistry. An Introduction to the C–F Bond. Chem. Soc. Rev. 2008, DOI: https://doi.org/10.1039/B711844A
13- B. Kevin Park & Neil R. Kitteringham (1994) Effects of Fluorine Substitution on Drug Metabolism: Pharmacological and Toxicological Implications, Drug Metabolism Reviews, 26:3, 605-643. DOI: https://doi.org/10.3109/03602539408998319
14- Filler R, Kobayashi Y, American Chemical Society, Nihon Kagakkai. Biomedicinal Aspects of Fluorine Chemistry. Elsevier Biomedical Press ; Sole distributors for the U.S.A. and Canada, Elsevier Science Pub. Co.; 1982.
15- ADMETlab 2.0: a comprehensive online ADMET prediction platform [Internet]. [cited 2024 Aug 31]. Available from: https://admetmesh.scbdd.com/
16- Xiong, G., et al., ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res., 2021. 49(W1):W5-W14. DOI: https://doi.org/10.1093/nar/gkab255
17- Biomodel - JSME Molecular Editor [Internet]. Alcalá de Henares: University of Alcalá; [cited 2024 Aug 16]. Available from: https://biomodel.uah.es/en/DIY/JSME/draw.en.htm
18- B. Bienfait and P. Ertl, JSME: a free molecule editor in JavaScript, Journal of Cheminformatics 5:24 (2013) DOI: https://doi.org/10.1186/1758-2946-5-24
19- Guedes, I.A., et al., New machine learning and physics-based scoring functions for drug discovery. Scientific reports, 2021. 11(1): p. 1-19. DOI: https://doi.org/10.1038/s41598-021-82410-1
20- Santos, K.B., et al., Highly flexible ligand docking: Benchmarking of the DockThor program on the LEADS-PEP protein–peptide data set. Journal of Chemical Information and Modeling, 2020. 60(2): p. 667-683. DOI: https://doi.org/10.1021/acs.jcim.9b00905
21- De Magalhães, C.S., et al., A dynamic niching genetic algorithm strategy for docking highly flexible ligands. Information Sciences, 2014. 289: p. 206-224. DOI: https://doi.org/10.1016/j.ins.2014.08.002
22- Pettersen E.F., et al., UCSF Chimera—a visualization system for exploratory research and analysis. Journal of computational chemistry, 2004. 25(13): p. 1605–1612. DOI: https://doi.org/10.1002/jcc.20084
23- Wallace, A.C., R.A. Laskowski, and J.M. Thornton, LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein engineering, design and selection, 1995. 8(2): p. 127-134. DOI: https://doi.org/10.1093/protein/8.2.127
24- Samer T. Jasim, Monther F. Mahdi. Molecular Modeling, Synthesis, and preliminary pharmacological evaluation of New Sulfonamide Derivatives as Selective Carbonic Anhydrase XII and IX inhibitors. Al Mustansiriyah Journal of Pharmaceutical Sciences, 2024, Vol.24, No.2 DOI: https://doi.org/10.32947/ajps.v24i2.1055
25- Ammash O.M., Shakir M. A., Al bakaa A., Ismail Alsharif S. n Silico Profiling of Histone Deacetylase 8 Inhibitory Activity: A Computational Analysis of Novel Dipeptide-Based Compounds Cross-Linked with Hydroxamic Acid. Al Mustansiriyah Journal of Pharmaceutical Sciences, 2024, Vol.24, No.2 DOI: https://doi.org/10.32947/ajps.v24i2.1059