Tacrolimus and Azole Derivatives of Agricultural and Human Health Importance: Prediction of ADME Properties


Cite item

Full Text

Abstract

Introduction:Agricultural chemicals are impacting health nowadays. Recently, promising synergistic antifungal interaction between tacrolimus and some azole compounds was studied.

Objectives:To determine ADME parameters, potential side effects of test substances to reduce time and resources in the future

Methods:All descriptors and molecular parameters were obtained by the protocols of SwissADME and ProTox II.

Results:In the result, the following physicochemical and drug-likeness parameters were calculated.

Conclusion:Studied triazoles 1 and 2 showed good ADME characteristics and promising toxicity levels suitable to be checked for in vitro toxicology in case of future advanced results in the agricultural field.

About the authors

Lyudmyla Antypenko

Department of Bioscience and Biotechnology, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University

Author for correspondence.
Email: info@benthamscience.net

Konstyantyn Shabelnyk

Department of Pharmaceutical Chemistry, Zaporizhia State Medical University

Email: info@benthamscience.net

Sergiy Kovalenko

Research Institute of Chemistry and Geology,, Oles Honchar Dnipro National University

Email: info@benthamscience.net

References

  1. Berger, S.; El Chazli, Y.; Babu, A.F.; Coste, A.T. Azole resistance in aspergillus fumigatus: a consequence of antifungal use in agriculture? Front. Microbiol., 2017, 8, 1024. doi: 10.3389/fmicb.2017.01024 PMID: 28638374
  2. Antypenko, L.; Meyer, F.; Sadykova, Z.; Shabelnyk, K.; Kovalenko, S.; Steffens, K.G.; Garbe, L-A. Combined application of tacrolimus with cyproconazole, hymexazol and novel {2-(3-R-1H-1,2,4-triazol-5-yl)phenyl}amines as antifungals: in vitro growth inhibition and in silico molecular docking analysis to fungal chitin deacetylase. J. Fungi, 2023, 9(1), 79. doi: 10.3390/jof9010079 PMID: 36675900
  3. Liu, Y.; Ahmed, S.; Fang, Y.; Chen, M.; An, J.; Yang, G.; Hou, X.; Lu, J.; Ye, Q.; Zhu, R.; Liu, Q.; Liu, S. Discovery of chitin deacetylase inhibitors through structure-based virtual screening and biological assays. J. Microbiol. Biotechnol., 2022, 32(4), 504-513. doi: 10.4014/jmb.2201.01009 PMID: 35131956
  4. Cedergreen, N. Quantifying synergy: a systematic review of mixture toxicity studies within environmental toxicology. PLoS One, 2014, 9(5), e96580. doi: 10.1371/journal.pone.0096580 PMID: 24794244
  5. SwissADME. Swiss Institute of Bioinformatics., 2022. Available from: http://www.swissadme.ch/index.php#
  6. 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(1), 42717. doi: 10.1038/srep42717 PMID: 28256516
  7. Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubilityand permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 2001, 46(1-3), 3-26. doi: 10.1016/S0169-409X(00)00129-0 PMID: 11259830
  8. Ghose, A.K.; Viswanadhan, V.N.; Wendoloski, J.J. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J. Comb. Chem., 1999, 1(1), 55-68. doi: 10.1021/cc9800071 PMID: 10746014
  9. Veber, D.F.; Johnson, S.R.; Cheng, H.Y.; Smith, B.R.; Ward, K.W.; Kopple, K.D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem., 2002, 45(12), 2615-2623. doi: 10.1021/jm020017n PMID: 12036371
  10. Egan, W.J.; Merz, K.M., Jr; Baldwin, J.J. Prediction of drug absorption using multivariate statistics. J. Med. Chem., 2000, 43(21), 3867-3877. doi: 10.1021/jm000292e PMID: 11052792
  11. Muegge, I.; Heald, S.L.; Brittelli, D. Simple selection criteria for drug-like chemical matter. J. Med. Chem., 2001, 44(12), 1841-1846. doi: 10.1021/jm015507e PMID: 11384230
  12. Martin, Y.C. A bioavailability score. J. Med. Chem., 2005, 48(9), 3164-3170. doi: 10.1021/jm0492002 PMID: 15857122
  13. Lovering, F.; Bikker, J.; Humblet, C. Escape from flatland: increasing saturation as an approach to improving clinical success. J. Med. Chem., 2009, 52(21), 6752-6756. doi: 10.1021/jm901241e PMID: 19827778
  14. Lee, M.S.; Feig, M.; Salsbury, F.R., Jr; Brooks, C.L. III New analytic approximation to the standard molecular volume definition and its application to generalized Born calculations. J. Comput. Chem., 2003, 24(11), 1348-1356. doi: 10.1002/jcc.10272 PMID: 12827676
  15. Daina, A.; Michielin, O.; Zoete, V. iLOGP: a simple, robust, and efficient description of n-octanol/water partition coefficient for drug design using the GB/SA approach. J. Chem. Inf. Model., 2014, 54(12), 3284-3301. doi: 10.1021/ci500467k PMID: 25382374
  16. Moriguchi, I.; Hirono, S.; Liu, Q.; Nakagome, I.; Matsushita, Y. Simple method of calculating octanol/water partition coefficient. Chem. Pharm. Bull., 1992, 40(1), 127-130. doi: 10.1248/cpb.40.127
  17. Cheng, T.; Zhao, Y.; Li, X.; Lin, F.; Xu, Y.; Zhang, X.; Li, Y.; Wang, R.; Lai, L. Computation of octanol-water partition coefficients by guiding an additive model with knowledge. J. Chem. Inf. Model., 2007, 47(6), 2140-2148. doi: 10.1021/ci700257y PMID: 17985865
  18. Delaney, J.S. ESOL: estimating aqueous solubility directly from molecular structure. J. Chem. Inf. Comput. Sci., 2004, 44(3), 1000-1005. doi: 10.1021/ci034243x PMID: 15154768
  19. Ali, J.; Camilleri, P.; Brown, M.B.; Hutt, A.J.; Kirton, S.B. Revisiting the general solubility equation: in silico prediction of aqueous solubility incorporating the effect of topographical polar surface area. J. Chem. Inf. Model., 2012, 52(2), 420-428. doi: 10.1021/ci200387c PMID: 22196228
  20. Zhang, A.Y.; Camp, W.L.; Elewski, B.E. Advances in topical and systemic antifungals. Dermatol. Clin., 2007, 25(2), 165-183. vi. doi: 10.1016/j.det.2007.01.002 PMID: 17430754
  21. Kaur, I.P.; Kakkar, S. Topical delivery of antifungal agents. Expert Opin. Drug Deliv., 2010, 7(11), 1303-1327. doi: 10.1517/17425247.2010.525230 PMID: 20961206
  22. Gűngőr, S.; Erdal, M.S.; Aksu, B. New formulation strategies in topical antifungal therapy. J. Cosm. Dermatol. Sci. Appl, 2013, 3, 56-65.
  23. Ritchie, T.J.; Ertl, P.; Lewis, R. The graphical representation of ADME-related molecule properties for medicinal chemists. Drug Discov. Today, 2011, 16(1-2), 65-72. doi: 10.1016/j.drudis.2010.11.002 PMID: 21074634
  24. Potts, R.O.; Guy, R.H. Predicting skin permeability. Pharm. Res., 1992, 9(5), 663-669. doi: 10.1023/A:1015810312465 PMID: 1608900
  25. Montanari, F.; Ecker, G.F. Prediction of drug–ABC-transporter interaction-recent advances and future challenges. Adv. Drug Deliv. Rev., 2015, 86, 17-26. doi: 10.1016/j.addr.2015.03.001 PMID: 25769815
  26. Szakács, G.; Váradi, A.; Özvegy-Laczka, C.; Sarkadi, B. The role of ABC transporters in drug absorption, distribution, metabolism, excretion and toxicity (ADME–Tox). Drug Discov. Today, 2008, 13(9-10), 379-393. doi: 10.1016/j.drudis.2007.12.010 PMID: 18468555
  27. Saad, A.H.; DePestel, D.D.; Carver, P.L. Factors influencing the magnitude and clinical significance of drug interactions between azole antifungals and select immunosuppressants. Pharmacotherapy, 2006, 26(12), 1730-1744. doi: 10.1592/phco.26.12.1730 PMID: 17125435
  28. Tavira, B.; Gómez, J.; Díaz-Corte, C.; Coronel, D.; Lopez-Larrea, C.; Suarez, B.; Coto, E. The donor ABCB1 (MDR-1) C3435T polymorphism is a determinant of the graft glomerular filtration rate among tacrolimus treated kidney transplanted patients. J. Hum. Genet., 2015, 60(5), 273-276. doi: 10.1038/jhg.2015.12 PMID: 25673014
  29. Piletta-Zanin, A.; De Mul, A.; Rock, N.; Lescuyer, P.; Samer, C.F.; Rodieux, F. Case Report: Low hematocrit leading to tacrolimus toxicity. Front. Pharmacol., 2021, 12, 717148. doi: 10.3389/fphar.2021.717148 PMID: 34483924
  30. Di, L. The role of drug metabolizing enzymes in clearance. Expert Opin. Drug Metab. Toxicol., 2014, 10(3), 379-393. doi: 10.1517/17425255.2014.876006 PMID: 24392841
  31. Oral toxicity prediction results for input compound. 2022. Available from: https://tox-new.charite.de/protox_II/index.php?site=compound_input
  32. Baell, J.B.; Holloway, G.A. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J. Med. Chem., 2010, 53(7), 2719-2740. doi: 10.1021/jm901137j PMID: 20131845
  33. Brenk, R.; Schipani, A.; James, D.; Krasowski, A.; Gilbert, I.H.; Frearson, J.; Wyatt, P.G. Lessons learnt from assembling screening libraries for drug discovery for neglected diseases. ChemMedChem, 2008, 3(3), 435-444. doi: 10.1002/cmdc.200700139 PMID: 18064617
  34. Daina, A.; Zoete, V. A BOILED-Egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem, 2016, 11(11), 1117-1121. doi: 10.1002/cmdc.201600182 PMID: 27218427

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2024 Bentham Science Publishers