Exploration of Drug Science

Table 1. The number and type of bonding between ligand molecules and CYP2C9 binding cavity residues, the bond distance, together with the binding interaction energy and IC₅₀ values from the in vitro inhibition assay

CYP-ligand (Binding energy, kcal/mol; IC₅₀, μM)^a	No of bonds	Bonding no	Residues involved^b	Bonding category	Type of bonding	Distance (Å)
CYP2C9-rhein (–35.22; 6.08)	14	1	A:ILE205:HN1 - Rhein:O17	Hydrogen Bond	Conventional Hydrogen Bond	2.562197
		2	Rhein:H27 - A:ASP293:O	Hydrogen Bond	Conventional Hydrogen Bond	3.090060
		3	A:ASP293:HA - Rhein:O21	Hydrogen Bond	Carbon Hydrogen Bond	2.804421
		4	A:ALA297:HA - Rhein:O18	Hydrogen Bond	Carbon Hydrogen Bond	2.870330
		5	Rhein:O20 - A:HEM500	Electrostatic	Pi-Anion	4.453505
		6	A:PHE114 - Rhein (ring C)	Hydrophobic	Pi-Pi T-shaped	5.281925
		7	A:GLY296:C,O; ALA297:N - Rhein (ring C)	Hydrophobic	Amide-Pi Stacked	4.010072
		8	Rhein (ring C) - A:VAL113	Hydrophobic	Pi-Alkyl	5.321472
		9	Rhein (ring C) - A:ILE205	Hydrophobic	Pi-Alkyl	5.113695
		10	Rhein (ring C) - A:LEU208	Hydrophobic	Pi-Alkyl	5.201962
		11	Rhein (ring B) - A:VAL113	Hydrophobic	Pi-Alkyl	5.146036
		12	Rhein (ring B) - A:ILE205	Hydrophobic	Pi-Alkyl	5.318006
		13	Rhein (ring B) - A:ALA297	Hydrophobic	Pi-Alkyl	5.331537
		14	Rhein (ring A) - A:LEU366	Hydrophobic	Pi-Alkyl	4.682293
CYP2C9-diacerein (–39.86; 32.23)	14	1	A:SER209:HN1 - Diacerein:O26	Hydrogen Bond	Conventional Hydrogen Bond	2.903369
		2	A:SER209:HA - Diacerein:O26	Hydrogen Bond	Conventional Hydrogen Bond	2.612144
		3	A:LEU201:HA - Diacerein:O22	Hydrogen Bond	Carbon Hydrogen Bond	2.743063
		4	Diacerein:O17 - A:HEM500	Electrostatic	Pi-Anion	4.831412
		5	A:PHE114 - Diacerein (ring B)	Hydrophobic	Pi-Pi T-shaped	5.611757
		6	A:PHE114 - Diacerein (ring A)	Hydrophobic	Pi-Pi T-shaped	4.960882
		7	A:GLY296:C,O; ALA297:N - Diacerein (ring C)	Hydrophobic	Amide-Pi Stacked	4.812890
		8	A:GLY296:C,O; ALA297:N - Diacerein (ring B)	Hydrophobic	Amide-Pi Stacked	3.823132
		9	A:GLY296:C,O; ALA297:N - Diacerein (ring A)	Hydrophobic	Amide-Pi Stacked	4.320433
		10	Diacerein (ring C) - A:ALA297	Hydrophobic	Pi-Alkyl	4.973498
		11	Diacerein (ring B) - A:VAL113	Hydrophobic	Pi-Alkyl	4.854490
		12	Diacerein (ring B) - A:ILE205	Hydrophobic	Pi-Alkyl	5.201749
		13	Diacerein (ring B) - A:ALA297	Hydrophobic	Pi-Alkyl	5.027617
		14	Diacerein (ring A) - A:VAL113	Hydrophobic	Pi-Alkyl	5.227213
CYP2C9-sulfaphenazole (–36.50; 0.95)	9	1	A:HEM500 - Sulfaphenazole	Hydrophobic	Pi-Pi T-shaped	5.566038
		2	A:GLY296:C,O; ALA297:N - Sulfaphenazole (ring A)	Hydrophobic	Amide-Pi Stacked	4.557513
		3	Sulfaphenazole (ring A) - A:LEU208	Hydrophobic	Pi-Alkyl	5.469036
		4	Sulfaphenazole (ring B) - A:LEU208	Hydrophobic	Pi-Alkyl	4.861211
		5	Sulfaphenazole (ring B) - A:LEU366	Hydrophobic	Pi-Alkyl	5.116724
		6	Sulfaphenazole (ring C) - A:VAL113	Hydrophobic	Pi-Alkyl	5.492285
		7	Sulfaphenazole (ring C) - A:ALA297	Hydrophobic	Pi-Alkyl	4.923605
		8	Sulfaphenazole (ring C) - A:LEU362	Hydrophobic	Pi-Alkyl	5.275490
		9	Sulfaphenazole (ring C) - A:LEU366	Hydrophobic	Pi-Alkyl	4.735068
CYP2C9-glucosamine 3 sulfate (–25.54; > 1,000)	4	1	Glucosamine 3-sulphate:H24 - A:LEU208:O	Hydrogen Bond	Conventional Hydrogen Bond	2.158468
		2	A:ALA297:HA - Glucosamine 3-sulphate:O21	Hydrogen Bond	Carbon Hydrogen Bond	2.379240
		3	Glucosamine 3-sulphate:H18 - A:SER209:OG	Hydrogen Bond	Carbon Hydrogen Bond	2.714800
		4	Glucosamine 3-sulphate:N12 - A:PHE114	Electrostatic	Pi-Cation	3.699830
CYP2C9-glucosamine 6 sulfate (–24.41; > 1,000)	4	1	A:ARG108:HH22 - Glucosamine 6-sulphate:O21	Hydrogen Bond	Conventional Hydrogen Bond	3.028392
		2	A:GLY296:HA2 - Glucosamine 6-sulphate:O22	Hydrogen Bond	Carbon Hydrogen Bond	2.743366
		3	Glucosamine 6-sulphate:H18 - A:GLY296:O	Hydrogen Bond	Carbon Hydrogen Bond	2.573529
		4	Glucosamine 6-sulphate:S20 - A:PHE114	Other	Pi-Sulfur	5.314108
CYP2C9-chondroitin disaccharide Δdi-4S (–7.59; > 1,000)	0		-	-	-	-

Return to article view

^a The CDOCKER interaction energy and IC₅₀ values were derived from previously published data [13]. ^b Ring labeling was the same as the labels used in Figure 4

Declarations

Author contributions

BHT: Data curation, Formal analysis, Investigation, Writing—original draft. NA: Formal analysis, Methodology, Software, Validation, Visualization, Writing—review & editing. YP: Data curation, Formal analysis, Investigation, Writing—review & editing. UDP: Methodology, Data curation, Formal analysis, Investigation, Supervision. CEO: Conceptualization, Data curation, Formal analysis, Funding acquisition, Supervision, Writing—review & editing. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.

Funding

This work was supported by the Monash University Seed Grant [grant no BCHH-SS-4-02-2010] and the Science Fund [project code 02-02-10-SF0077] of the Malaysian Ministry of Science, Technology and Innovation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright

Publisher’s note

Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.

References

Sangkuhl K, Claudio-Campos K, Cavallari LH, Agundez JAG, Whirl-Carrillo M, Duconge J, et al. PharmVar GeneFocus: CYP2C9. Clin Pharmacol Ther. 2021;110:662–76. [DOI] [PubMed] [PMC]

Ma CH, Tworek KB, Kung JY, Kilcommons S, Wheeler K, Parker A, et al. Systemic Nonsteroidal Anti-Inflammatories for Analgesia in Postoperative Critical Care Patients: A Systematic Review and Meta-Analysis of Randomized Control Trials. Crit Care Explor. 2023;5:e0938. [DOI] [PubMed] [PMC]

Zou Y, Liu C, Wang Z, Li G, Xiao J. Neural and immune roles in osteoarthritis pain: Mechanisms and intervention strategies. J Orthop Translat. 2024;48:123–32. [DOI] [PubMed] [PMC]

Rabade A, Viswanatha GL, Nandakumar K, Kishore A. Evaluation of efficacy and safety of glucosamine sulfate, chondroitin sulfate, and their combination regimen in the management of knee osteoarthritis: a systematic review and meta-analysis. Inflammopharmacology. 2024;32:1759–75. [DOI] [PubMed]

Debord P, Louchahi K, Tod M, Cournot A, Perret G, Petitjean O. Influence of renal function on the pharmacokinetics of diacerein after a single oral dose. Eur J Drug Metab Pharmacokinet. 1994;19:13–9. [DOI] [PubMed]

Żęgota Z, Goździk J, Głogowska-Szeląg J. Efficacy of herbal and naturally-derived dietary supplements for the management of knee osteoarthritis: a mini-review. Wiad Lek. 2021;74:1975–83. [PubMed]

Panova E, Jones G. Benefit-risk assessment of diacerein in the treatment of osteoarthritis. Drug Saf. 2015;38:245–52. [DOI] [PubMed]

Persiani S, Canciani L, Larger P, Rotini R, Trisolino G, Antonioli D, et al. In vitro study of the inhibition and induction of human cytochromes P450 by crystalline glucosamine sulfate. Drug Metabol Drug Interact. 2009;24:195–209. [DOI] [PubMed]

Iovu M, Héroux L, Vergés J, Montell E, Paiement J, Souich Pd. Effect of chondroitin sulfate on turpentine-induced down-regulation of CYP1A2 and CYP3A6. Carbohydr Res. 2012;355:63–8. [DOI] [PubMed]

10.

Yokotani K, Nakanishi T, Chiba T, Sato Y, Umegaki K. Glucosamine and chondroitin sulfate do not enhance anticoagulation activity of warfarin in mice in vivo. Shokuhin Eiseigaku Zasshi. 2014;55:183–7. [DOI] [PubMed]

11.

Tang J, Yang H, Song X, Song X, Yan S, Shao J, et al. Inhibition of cytochrome P450 enzymes by rhein in rat liver microsomes. Phytother Res. 2009;23:159–64. [DOI] [PubMed]

12.

Djuv A, Nilsen OG. Aloe vera juice: IC₅₀ and dual mechanistic inhibition of CYP3A4 and CYP2D6. Phytother Res. 2012;26:445–51. [DOI] [PubMed]

13.

Tan BH, Ahemad N, Pan Y, Palanisamy UD, Othman I, Yiap BC, et al. Cytochrome P450 2C9-natural antiarthritic interactions: Evaluation of inhibition magnitude and prediction from in vitro data. Biopharm Drug Dispos. 2018;39:205–17. [DOI] [PubMed]

14.

Wu G, Robertson DH, 3rd CLB, Vieth M. Detailed analysis of grid-based molecular docking: A case study of CDOCKER-A CHARMm-based MD docking algorithm. J Comput Chem. 2003;24:1549–62. [DOI] [PubMed]

15.

Hevener KE, Zhao W, Ball DM, Babaoglu K, Qi J, White SW, et al. Validation of molecular docking programs for virtual screening against dihydropteroate synthase. J Chem Inf Model. 2009;49:444–60. [DOI] [PubMed] [PMC]

16.

Wester MR, Yano JK, Schoch GA, Yang C, Griffin KJ, Stout CD, et al. The structure of human cytochrome P450 2C9 complexed with flurbiprofen at 2.0-A resolution. J Biol Chem. 2004;279:35630–7. [DOI] [PubMed]

17.

Gotoh O. Substrate recognition sites in cytochrome P450 family 2 (CYP2) proteins inferred from comparative analyses of amino acid and coding nucleotide sequences. J Biol Chem. 1992;267:83–90. [PubMed]

18.

Mo S, Zhou Z, Yang L, Wei MQ, Zhou S. New insights into the structural features and functional relevance of human cytochrome P450 2C9. Part I. Curr Drug Metab. 2009;10:1075–126. [DOI] [PubMed]

19.

Kronbach T, Kemper B, Johnson EF. A hypervariable region of P450IIC5 confers progesterone 21-hydroxylase activity to P450IIC1. Biochemistry. 1991;30:6097–102. [DOI] [PubMed]

20.

Straub P, Johnson EF, Kemper B. Hydrophobic side chain requirements for lauric acid and progesterone hydroxylation at amino acid 113 in cytochrome P450 2C2, a potential determinant of substrate specificity. Arch Biochem Biophys. 1993;306:521–7. [DOI] [PubMed]

21.

Avsar O. Identification of the effects of pathogenic genetic variations of human CYP2C9 and CYP2D6: an in silico approach. Nucleosides Nucleotides Nucleic Acids. 2024;43:356–76. [DOI] [PubMed]

22.

Lewis DFV. Essential requirements for substrate binding affinity and selectivity toward human CYP2 family enzymes. Arch Biochem Biophys. 2003;409:32–44. [DOI] [PubMed]

23.

Haining RL, Jones JP, Henne KR, Fisher MB, Koop DR, Trager WF, et al. Enzymatic determinants of the substrate specificity of CYP2C9: role of B'-C loop residues in providing the pi-stacking anchor site for warfarin binding. Biochemistry. 1999;38:3285–92. [DOI] [PubMed]

24.

Cheng S, Flora DR, Rettie AE, Brundage RC, Tracy TS. A Physiological-Based Pharmacokinetic Model Embedded with a Target-Mediated Drug Disposition Mechanism Can Characterize Single-Dose Warfarin Pharmacokinetic Profiles in Subjects with Various CYP2C9 Genotypes under Different Cotreatments. Drug Metab Dispos. 2023;51:257–67. [DOI] [PubMed] [PMC]

25.

Takanashi K, Tainaka H, Kobayashi K, Yasumori T, Hosakawa M, Chiba K. CYP2C9 Ile359 and Leu359 variants: enzyme kinetic study with seven substrates. Pharmacogenetics. 2000;10:95–104. [DOI] [PubMed]

26.

Ridderström M, Masimirembwa C, Trump-Kallmeyer S, Ahlefelt M, Otter C, Andersson TB. Arginines 97 and 108 in CYP2C9 are important determinants of the catalytic function. Biochem Biophys Res Commun. 2000;270:983–7. [DOI] [PubMed]

27.

Dickmann LJ, Locuson CW, Jones JP, Rettie AE. Differential roles of Arg97, Asp293, and Arg108 in enzyme stability and substrate specificity of CYP2C9. Mol Pharmacol. 2004;65:842–50. [DOI] [PubMed]

28.

Wang J, Bhattarai A, Do HN, Miao Y. Challenges and frontiers of computational modelling of biomolecular recognition. QRB Discov. 2022;3:e13. [DOI] [PubMed] [PMC]

29.

Han SB, Teuffel J, Mukherjee G, Wade RC. Multiresolution molecular dynamics simulations reveal the interplay between conformational variability and functional interactions in membrane-bound cytochrome P450 2B4. Protein Sci. 2024;33:e5165. [DOI] [PubMed] [PMC]

30.

Pompon D, Garcia-Alles LF, Urban P. Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity control. Sci Rep. 2025;15:7512. [DOI] [PubMed] [PMC]