Screening Saffron-Derived Apocarotenoids Against NUDT1 in Hepatocellular Carcinoma: An Integrated Computational Study

Authors

  • Nethraa Kannan University College Dublin, Ireland Author
  • Pernilla Siljehag Karolinska Institutet, Sweden Author
  • Hager Saleh Karolinska Institutet, Sweden Author

Keywords:

Crocus sativus, crocin, crocetin, NUDT1, hepatocellular carcinoma, docking

Abstract

Background: Saffron contains apocarotenoids with reported antioxidant, anti-inflammatory and anticancer activity, yet their interactions with oxidative-nucleotide defence proteins remain incompletely explored.

Objective: To evaluate saffron-derived metabolites as potential NUDT1 ligands in hepatocellular carcinoma.

Methods: Twenty-seven reported saffron constituents and metabolites were curated, geometry-optimised and screened against NUDT1. Drug-likeness, predicted metabolism and off-target liabilities were assessed before 100-ns simulations of the three strongest complexes.

Results: Crocetin, picrocrocin metabolite PM-4 and safranal analogue SA-2 achieved docking scores below -8.0 kcal/mol. Crocetin formed a stable hydrogen-bond network near the catalytic pocket and maintained an average ligand RMSD of 1.84±0.27 Å. Its mean MM/GBSA energy was -31.6±4.9 kcal/mol. PM-4 showed slightly weaker binding but superior predicted solubility. Crocin itself was penalised by size and polarity despite favourable contact density. Network mapping of predicted secondary targets connected the leading compounds to oxidative-stress response, apoptosis and inflammatory signalling. No candidate generated high-confidence mutagenicity alerts, although crocetin and SA-2 showed potential CYP interactions that would require experimental verification.

Conclusion: Saffron-derived metabolites demonstrated plausible computational affinity for NUDT1, with crocetin emerging as the strongest candidate for enzyme-based validation. The findings connect phytochemical pharmacology with oxidative-nucleotide biology and provide a testable framework for investigating whether saffron metabolites can influence cancer-cell redox adaptation.

Publication format: research abstract. No full-text manuscript accompanies this record.

References

Alam SR, Nasrin N, Chowdhury NS, Akter R, Rahman MA, Chowdhury RN, Tahsin MR, Arnob TA, Morshed N, Mia MSH, Opo FADM, Aktar F, Miah MM, Büyüker SM, Reza R, Ayubee MS, Reza MS, Chowdhury JA, Chowdhury AA, Kabir S, Amran MS. Exploring the abundance of de-oiled non-resin analgesic and anti-inflammatory compounds in Crocus sativus through polarity-directed quaternary solvent extraction in both diabetic and non-diabetic conditions: Integrating in silico and in vivo analysis. Acta Pharm Sci. 2026;64(2):181-232. doi:10.23893/1307-2080.APS64012.

Tasnim J, Hasan MM, Hossain A, Miah MM, Hossain KR, Molla MTH, Ahmed Z, Reza AFGM, Alam MT. In silico identification of a potential NUDT1 inhibitor for cancer treatment. Sci Rep. 2026. doi:10.1038/s41598-026-66781-x.

Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2^-Delta Delta CT method. Methods. 2001;25(4):402-408.

Hayden MS, Ghosh S. Shared principles in NF-kappaB signaling. Cell. 2008;132(3):344-362.

Ma Q. Role of Nrf2 in oxidative stress and toxicity. Annu Rev Pharmacol Toxicol. 2013;53:401-426.

Sies H. Oxidative stress: a concept in redox biology and medicine. Redox Biol. 2015;4:180-183.

Forman HJ, Maiorino M, Ursini F. Signaling functions of reactive oxygen species. Biochemistry. 2010;49(5):835-842.

Nathan C, Ding A. Nonresolving inflammation. Cell. 2010;140(6):871-882.

Medzhitov R. Origin and physiological roles of inflammation. Nature. 2008;454(7203):428-435.

Hotamisligil GS. Inflammation and metabolic disorders. Nature. 2006;444(7121):860-867.

Donath MY, Shoelson SE. Type 2 diabetes as an inflammatory disease. Nat Rev Immunol. 2011;11(2):98-107.

Srinivasan K, Ramarao P. Animal models in type 2 diabetes research: an overview. Indian J Med Res. 2007;125(3):451-472.

OECD. Test No. 425: Acute Oral Toxicity: Up-and-Down Procedure. OECD Guidelines for the Testing of Chemicals, Section 4. Paris: OECD Publishing; 2008.

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;7:42717.

Banerjee P, Kemmler E, Dunkel M, Preissner R. ProTox 3.0: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2024;52(W1):W513-W520.

Hollingsworth SA, Dror RO. Molecular dynamics simulation for all. Neuron. 2018;99(6):1129-1143.

Genheden S, Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov. 2015;10(5):449-461.

Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455-461.

Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;4(11):682-690.

Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput. 2015;11(8):3696-3713.

Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA. Development and testing of a general AMBER force field. J Comput Chem. 2004;25(9):1157-1174.

Rácz A, Mihalovits LM, Bajusz D, Héberger K, Miranda-Quintana RA. Molecular dynamics simulations and diversity selection by extended continuous similarity indices. J Chem Inf Model. 2022;62(14):3415-3425.

Moukengue B, Brown HK, Charrier C, Battaglia S, Baud'Huin M, Quillard T, et al. TH1579, MTH1 inhibitor, delays tumour growth and inhibits metastases development in osteosarcoma model. EBioMedicine. 2020;53:102704.

Zhou W, Ma L, Yang J, Qiao H, Li L, Guo Q, et al. Potent and specific MTH1 inhibitors targeting gastric cancer. Cell Death Dis. 2019;10(6):434.

Zhan D, Zhang X, Li J, Ding X, Cui Y, Jia J. MTH1 inhibitor TH287 suppresses gastric cancer development through the regulation of PI3K/AKT signaling. Cancer Biother Radiopharm. 2020;35(3):223-232.

Kang N, Ma J, Hu Y, Di R, Wang L, Zhang X, et al. Targeting MutT Homolog 1 (MTH1) for breast cancer suppression by using a novel MTH1 inhibitor MA-24 with tumor-selective toxicity. Pharmaceuticals. 2024;17(3):291.

Azuaje F. Computational models for predicting drug responses in cancer research. Brief Bioinform. 2017;18(5):820-829.

Beckers M, Sturm N, Sirockin F, Fechner N, Stiefl N. Prediction of small-molecule developability using large-scale in silico ADMET models. J Med Chem. 2023;66(20):14047-14060.

Padhye A, Konen JM, Rodriguez BL, Fradette JJ, Ochieng JK, Diao L, Gibbons DL. Targeting CDK4 overcomes EMT-mediated tumor heterogeneity and therapeutic resistance in KRAS-mutant lung cancer. JCI Insight. 2021;6(17):e148392.

Bouclier C, Simon M, Laconde G, Pellerano M, Diot S, Lantuejoul S, et al. Stapled peptide targeting the CDK4/Cyclin D interface combined with abemaciclib inhibits KRAS mutant lung cancer growth. Theranostics. 2020;10(5):2008-2022.

Published

2026-10-01