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In Silico Analysis of Metabolite Compounds from the Essential Oil of Cinnamomum burmannii Bark with COX-1 and COX-2 as Target Molecules

Budiastuti Budiastuti¹, Vitra Nuraini Helmi², Mustofa Helmi Effendi³,

Hani Plumeriastuti^4,*, Aswin Rafif Khairullah⁵, Emmanuel Nnabuike Ugbo⁶,

Wiwiek Tyasningsih⁷, Mo Awwanah⁸, Bima Putra Pratama⁹, Agung Prasetyo¹⁰,

Ikechukwu Benjamin Moses⁶ and Riza Zainuddin Ahmad⁵

¹Study Program of Pharmacy Science, Faculty of Health Science, Universitas Muhammadiyah Surabaya,

Surabaya, East Java, Indonesia

²Postgraduate Program, Faculty of Dentistry, Universitas Airlangga, Surabaya, East Java, Indonesia

³Division of Veterinary Public Health, Faculty of Veterinary Medicine, Universitas Airlangga, Surabaya,

East Java, Indonesia

⁴Division of Veterinary Pathology, Faculty of Veterinary Medicine, Universitas Airlangga, Surabaya,

East Java, Indonesia

⁵Research Center for Veterinary Science, National Research and Innovation Agency (BRIN), Bogor,

West Java, Indonesia

⁶Department of Applied Microbiology, Faculty of Science, Ebonyi State University, Abakaliki, Nigeria

⁷Division of Veterinary Microbiology, Faculty of Veterinary Medicine, Universitas Airlangga, Surabaya,

East Java, Indonesia

⁸Research Center for Applied Botany, National Research and Innovation Agency (BRIN), Bogor, West Java, Indonesia

⁹Research Center for Agroindustry, National Research and Innovation Agency (BRIN), South Tangerang,

Banten, Indonesia

¹⁰Research Center for Estate Crops, National Research and Innovation Agency (BRIN), Bogor, West Java, Indonesia

(^*Corresponding author’s e-mail: [email protected])

Received: 1 April 2025, Revised: 27 April 2025, Accepted: 10 May 2025, Published: 5 July 2025

Abstract

In silico approaches, including molecular docking, have emerged as powerful tools in predicting the interaction between natural compounds and molecular targets such as COX-1 and COX-2. These computational methods provide valuable insights into the binding affinity and selectivity of these compounds, making them indispensable in modern drug discovery. The interactions between metabolites from C. burmanii essential oil and COX-1 and COX-2 were investigated through in silico analysis. This analysis involves several stages, including In Silico Activity Analysis, Drug-likeness Test, Anti-inflammatory Agent Probability, and Molecular Docking. The study suggests that specific compounds could serve as anti-inflammatory adjuvants alongside conventional anti-inflammatory drugs (NSAIDs), potentially reducing dosages or minimizing the side effects associated with NSAID use. The in silico analysis results obtained by simulating the binding of metabolites with COX-1 and COX-2 target proteins using PyRx 0.8 software indicated that 12 out of 14 volatile oil metabolites might contribute to anti-inflammatory activity. However, the anti-inflammatory effects of cinnamon oil require further validation through in vivo testing.

Keywords: In silico, Cinnamomum burmannii, COX-1, COX-2, Anti-inflammatory

Introduction

Table 6 Results of specific molecular docking of 3 metabolite compounds with COX-1 target protein (1EQG).

Compound name	2D Structure	Rerank score	RMSD	Hydrogen bond	Electrostatic interactions	Steric interactions
Native Ligand Ibuprofen	2-[4-(2-methylpropyl)phenyl] propanoic acid	1) –82.0501 2) –80.2243 3) –81.8767 Average –81.38370 ± 1.00781	1.18584 1.18940 1.41182 Average 1.26235 ± 0.12945	3 bond with Try 355, 2 dengan Arg 120	2 interaction with Arg 120	-
Ibuprofen	2-[4-(2-methylpropyl)phenyl] propanoic acid	1) –80.9704 2) –82.3334 3) –80.4241 Average –81.24263 ± 0.98333	8.14495 8.20340 8.73232 Average 8.36022 ± 0.32357	3 bond with Try 355, 2 dengan Arg 120	2 interaction with Arg 120	15 bond with Tyr 355, Leu 359, Gln 358, Leu 357 Leu 93, Val 116
α-Terpineol	2-[(1S)-4-methylcyclohex-3-en-1-yl]propan-2-ol	1) –60.0217 2) –60.0624 3) –59.9281 Average –60.00407 ± 0.06886	7.18158 7.58395 7.16914 Average 7.31156 ± 0.23598	1 bond with Arg 120	-	11 bond with Ile 523, Ala 527, Tyr 355, Ser 353 Leu 359
Trans-Cinnamal-dehyde	(E)-3-phenylprop-2-enal	1) –59.7091 2) –59.6834 3) –59.6730 Average –59.6885 ± 0.01858	7.73425 7.73730 7.73631 Average 7.73595 ± 0.00156	1 Bond with Arg 120	-	14 bond with Val 349, Leu 359 Ser 353, Gin 350 Tyr 355
ɣ-Murrulene	(1S,4aS,8aR)-7-methyl-4-methylidene-1-propan-2-yl-2,3,4a,5,6,8a-hexahydro-1H-naphthalene	1) –72.0711 2) –72.0627 3) –72.0680 Average 72.06727 ± 0.00425	4.87238 4.87311 4.87265 Average 4.87271 ± 0.00037	-	-	14 bond with Ile 523, Ser 153 Gln 350, Val 349 Tyr 355, Arg 120

Table 7 Results of specific molecular docking of 3 metabolite compounds with COX-2 target protein (4PH9).

Compound name	2D Structure	Rerank score	RMSD	Hydrogen bond	Electrostatic interactions	Steric interactions
Native Ligand Ibuprofen	2-[4-(2-methylpropyl)phenyl] propanoic acid	1) –78.5474 2) –78.6864 3) –76.6223 Average –77.95203 ± 1.15368	0.568205 0.682736 0.531957 Average 0.59430 ± 0.07870	1 bond with Tyr 356 2 bond with Arg 121	2 bond with Arg 121	-
Ibuprofen	2-[4-(2-methylpropyl)phenyl] propanoic acid	1) –80.3256 2) –79.6909 3) –82.5077 Average –80.8414 ± 1.47754	5.90367 1.38703 5.96873 Average 4.41981 ± 2.62666	2 bond with Arg 121	2 bond with Arg 121	8 interaction with Val 350, Ala 528 Ser 531, Gly 527 Ser 354, Tyr 356
α-Terpineol	2-[(1S)-4-methylcyclohex-3-en-1-yl]propan-2-ol	1) –58.8908 2) –58.8858 3) –58.8803 Average –58.88563 ± 0.00525	5.24059 5.24315 5.24590 Average 5.24321 ± 0.00265	2 bond with Arg 121 Tyr 356	-	7 bond with Arg 121, Tyr356, Ala 528, Val 117, Val 350
Trans-Cinnamal-dehyde	(E)-3-phenylprop-2-enal	1) –59.6735 2) –59.9385 3) –59.5923 Average –59.73477 ± 0.18105	6.87610 7.47409 6.87256 Average 7.07425 ± 0.34628	2 bond with Arg 121 Tyr 356	-	12 bond with 6873Leu 360, Gln 351 Ile 346, Val 350
ɣ-Murrulene	(1S,4aS,8aR)-7-methyl-4-methylidene-1-propan-2-yl-2,3,4a,5,6,8a-hexahydro-1H-naphthalene	1) –74.7544 2) –74.7005 3) –74.7220 Average –74.72563 ± 0.02713	7.10789 7.10582 7.10550 Average 7.10640 0.00130	-		19 bond with Leu 360, Leu 532 Tyr 356, Arg 121 Ser 354, Gln 351, Val 350, Val 117

After being identified as drug-like molecules, the 14 essential oil compounds from C. burmannii were analyzed for their probability as anti-inflammatory agents using the PASS Online server (http://www.pharmaexpert.ru/ passonline/). With an average accuracy of more than 95%, PASS Online forecasts almost 4,000 biological activities, including as pharmacological activities, metabolic enzyme and transporter interactions, mechanisms of action, toxicity, side effects, and influences on gene expression [14]. Using their structural formulas or SMILES strings, this server assesses the biological activity of query compounds in silico [15].

This study used medium evidence predictions, where compounds with a Pa value > 0.3 were categorized as good candidates for anti-inflammatory agents [10]. Among the 14 compounds, 2—eucalyptol and benzenepropanal—had Pa values equal to 0.3 and were therefore not considered strong candidates for anti-inflammatory activity compared to other compounds.

Specific molecular docking with the anti-inflammatory control compound ibuprofen was performed on 2 compounds with the lowest binding energy affinity from the screening results, namely α-Terpineol and γ-Muurolene, as well as the compound with the highest concentration, trans-cinnamaldehyde, to predict the orientation and binding affinity using Molegro Virtual Docker. This study used ibuprofen as the control compound [12,13].

The Rerank Score represents the compound’s binding energy to the receptor. The activity is indicated by a lower binding energy value, as it reflects a more stable bond, thereby predicting greater activity [16]. The docking results for α-Terpineol, γ-Muurolene, and trans-Cinnamaldehyde suggest that they have the potential to inhibit the inflammatory response through COX-1 inhibition, consistent with previous studies [17]. The Rerank Score values show that α-Terpineol, trans-Cinnamaldehyde, and γ-Muurolene have higher binding energies compared to the ibuprofen control compound, indicating that their potential anti-inflammatory activity through interaction with the COX-1 target protein is lower than that of ibuprofen.

In COX-1, the compound Ibuprofen, serving as the control, exhibits an average rerank score of –81.38 kcal/mol, with an RMSD of approximately 1.26 Å. The detected interactions include hydrogen bonds with Try 355 and Arg 120, along with electrostatic interactions involving Arg 120. Conversely, the compounds α-Terpineol and trans-Cinnamaldehyde present lower rerank scores of –60.00 and –59.69 kcal/mol, respectively. These 2 compounds demonstrate fewer interactions with the COX-1 target, yet they do exhibit hydrogen bonding and electrostatic interactions with Arg 120. These results suggest that while these substances might not be as effective in reducing inflammation compared to ibuprofen, they could potentially serve as adjuvants to reduce NSAID dosages [18].

For the COX-2 target, the compound ibuprofen shows an average rerank score of –77.95 kcal/mol and a low RMSD (0.59 Å). Detected interactions include 1 hydrogen bond with Tyr 356 and 2 hydrogen bonds with Arg 121, supporting the strong anti-inflammatory effects of ibuprofen. Other compounds, such as γ-Muurolene and trans-cinnamaldehyde, exhibit lower rerank scores (around –74 to –59 kcal/mol), with hydrogen and electrostatic interactions detected at Arg 121 and Tyr 356, but fewer interactions compared to ibuprofen. These fewer interactions indicate that while these compounds may have anti-inflammatory properties, their effectiveness might be lower than ibuprofen [5]. Thus, it is necessary to conduct preclinical and clinical trials.

Conclusions

The results of in silico analysis by simulating the binding of metabolites with COX-1 and COX-2 target proteins on PyRx 0.8 software (https://www.rcsb.org/) on 14 volatile oil metabolites showed that there were 12 compounds thought to contribute to the activity of anti-inflammatory. These metabolites are α-Terpineol, α-Pinene, α-Muurulene, ɣ-Muurulene, trans-α-Bergamotene, transCinnamaldehyde, Caryophyllene oxide, Caryophylene, D-Limonene, Bornyl acetate, Copaene, and Linalool. The lowest binding affinity value with COX-1 target protein is α -Terpineol, which is –6.9 kcal/mol. The lowest binding affinity value with COX-2 target protein is ɣ-Muurulene which is –7.4. Specific molecular docking with Molegro Virtual Docker software against α-Terpineol and ɣ-Muurulene and trans-Cinnamaldehyde with ibuprofen as control compounds to strengthen the prediction that these 3 compounds have potential as anti-inflammatory.

Acknowledgements

This study was partly funded by the Lembaga Penelitian dan Pengabdian Masyarakat, by the Airlangga Doctoral Dissertation Research Scheme (PDDA) in fiscal year 2024, grant number 444/UN3.LPPM/PT.01.03/2024.

Declaration of Generative AI in Scientific Writing

The authors declare that no generative AI tools were used in the writing or preparation of this manuscript.

CRediT Author Statement

BB, MHE, and VNH conducted data collection. ARK, BPP, and MA drafted the manuscript. AP, ENU, and IBM performed data analysis. HP, RZA, and WT conducted the concept research. All authors have read and approved the final manuscript.

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