Antimicrobial analysis of three monoterpenes derived from citronellal : an in silico approach



Antimicrobial analysis of three monoterpenes derived from citronellal: an in silico approach


Análisis antimicrobiano de tres monoterpenos derivados decitronelal: un enfoque in silico



Heloísa Mara Batista Fernandes de Oliveira,I Abrahão Alves de Oliveira Filho,II José Pinto de Siqueira Júnior,I Edeltrudes de Oliveira LimaI

I Universidade Federal de Campina Grande, Patos, Paraíba, Brasil.
II Universidade Federal da Paraíba, João Pessoa, Paraíba, Brasil.




Introduction: Monoterpenes are known to exhibit a variety of effects in different biological systems, for example, antimicrobial effects.
Objective: In the study, the (R)-(+)-citronellal (RC), (S)-(-)-citronellal (SC) and 7-hydroxycitronellal (7-OH) were evaluated for their antimicrobial effects.
Methods: The online PASS program was used in the study for in silico activities. In silico models were applied for the evaluation of pharmacological and compound toxicity in metabolic environment of mammals.
Results: The analysis of percentage value of "Pa" for antimicrobial in silico activity in citronellal derivatives with antifungal activity were Pa-7-OH: 42,4 %, Pa-RC: 58 % and Pa-SC: 58 %.
Conclusions: The analysis of possible in silico antimicrobial activity for citronellal derivatives of monoterpenes revealed that all the studied compounds are more likely to have a significant antifungal effect.

Keywords: Monoterpenes; antimicrobial effect; in silico.


Introducción: los monoterpenos se sabe que presentan una variedad de efectos en diferentes sistemas biológicos, por ejemplo, efectos antimicrobianos.
Objetivo: en el estudio, la (R)-(+)-citronelal (RC), (S)-(-)-citronelal (SC) y 7-hidroxicitronelal (7-OH) se evaluaron por sus efectos antimicrobianos.
Métodos: el programa en línea PASS se utilizó en el estudio de las actividades in silico. In silico se están aplicando modelos para la evaluación de la toxicidad farmacológica y del compuesto en el ambiente metabólico de mamíferos.
Resultados: el análisis del valor porcentual de "Pa" para la actividad antimicrobiana in silico para derivados citronelal de actividad antifúngica fueron Pa-7-OH: 42,4 %, Pa-RC: 58 % y Pa-SC: 58 %.
Conclusión: el análisis de la posible actividad antimicrobiana in silico citronelal derivado de monoterpenos reveló que todos los compuestos estudiados son más propensos a tener un efecto antimicótico significativo.

Palabras clave: monoterpenos; efecto antimicrobiano; in silico.




Representative of a class of secondary metabolites, the monoterpenes are oils constituents essences present in species of aromatics herbs. Its biosynthetic origin derived from isoprênica units, which are composed of ten units of carbons.1 Despite having a simple structure, some biological activities assigned to i.t2-5

Among the various monoterpenes studied can highlight the citronellal, which occurs naturally in essential oils of various herbs of the caatinga, such as gender Eucalyptus (ex.: Eucalyptus citriodora). Other genera also produce citronellal, although in variables percentage, such as: Melissa, Mentha, Allium, Cinnamomum and Cymbopogon.6-9

Several plants that produce citronellal are used worldwide, mainly in South America, in treating various health conditions, including primarily the treatment of pain.10 Also this monoterpene already revealed antimicrobial,7,11 allelopathic,6,9 antioxidante12,13 and herbicide activity.6

Based on this information, aimed to analysein silico antimicrobial properties of three monoterpenes derived from citronellal, (R)-(+)-citronellal, (S)-(-)-citronellal and 7-hydroxycitronellal.




Prediction of Activity Spectra for Substances (PASS) online (http://​www.​way2drug.​com/​passonline) is designed to evaluate the general biological potential fan organic drug-like molecule. It provides simultaneous predictions of many types of biological activities based on the structure of organic compounds. The biological activity spectrum of a chemical compound is the set of different types of biological activity that reflect the results of the compound's interaction with various biological entities. PASS online gives various facets of the biological action of a compound. Pa (probability "to be active") and Pi (probability "to be inactive") estimates the categorization of potential compound dis belonging to the subclass active or in active compoundsrespectively.14

PASS gives hits based on the probability of new effects and mechanism of action with required activity spectra among the compounds from in house, old and commercial databases. PASS online predicts the biological activity spectrum for the modified imprints on the basis of its structural formula, along with different descriptors like antifungal, antiviral, antihelmintic, antiprotozoal, etc., so it is possible to estimate if new compounds have a particular effect.14


For methodology by antifungal activity, Candida albicansstrain (ATCC 76485) was selected. The microorganism strain was obtained from the Laboratory of Mycology collection (Federal University of Paraiba, Brazil).

The antifungal activity assays were carried out according to the protocols fromClinical and Laboratory Standards Institute (CSLI) (2008).15

The Minimal Inhibitory Concentration (MIC) of the monoterpenes were determinate against Candida strain by broth microdilution technique. Initially was distributed 100 µL of Sabouraud dextrose broth doubly concentrated in the holes of microdilution plates. Then, 100 µL of the emulsion products also doubly concentrated, were dispensed in the wells of the first row of the plate. Follow, serial dilution at a ratio of two concentrations were obtained from 2 µg/mL to 1024 µg/mL, so that the first line of the plate was meet the highest concentration and last, the lowest concentration. Finally, it was added 10 µL of the inoculum of the species in the cavities, where each plate column refereed to a fungal strain, in particular.

In parallel, it was carried out feasibility control of the tested strains. Also, sensitivity control these forward strains to antifungal action considered standards in clinical use (Nistatin 100 UI/mL, Sigma Aldrich, Brazil). To verify the absence of interference in the results for the solvent used in the preparation of the substance in the event the dimethyl sulfoxide (DMSO), in which a control was placed in the cavities 100 µL of the double-concentrated broth, 100 µL of DMSO and 10 µL of the suspension was made.

The plates were sealed aseptically and incubated at 35 °C for 24-48 hours to the reading performed. MIC was defined for the products tested as the lowest concentration able to produce inhibition of visible fungal growth recorded in the holes, compared with the control growth. Testing was performed in duplicate and the result expressed by the arithmetic mean of the MIC's obtained in the two tests.



The analysis of percentual value of "Pa" for antimicrobial activity in silico for citronellal derivatives, after asingle analysis, revealed that all the studied monoterpenes are more likely to have a significant antifungal effect (Pa-7-OH: 42,4 %, Pa-RC: 58 % and Pa-SC: 58 %) (Figs. 1, 2, 3).

Observing these results for MIC for C. Albicans strain can be seen that the monoterpenes presented MIC of the values of 256 µg/mL for all test with the fungi strain, even afterrepeat testing. The positive control (Nystatin) inhibited fungal growthat the concentration tested.



In silico models are being applied for the evaluation of pharmacology and toxicity of compoundin metabolic environment of mammals. Hence, several efficient statistical machine learning methods have been used to develop in silicotools for the prediction of pharmacological and toxicological hazards ofmolecularstructure.16

Computer-assisted prediction models, so-called predictive tools, play an essential role in the proposed repertoire of alternative methods besides in vitro models. Hence, these tools are used to study both existing and hypothetical compounds, which are fast, reproducible and are typically based onhumanbio-regulators.14,17 In this study, the citronellal derivatives showed different effects, but the major effect was antifungal activity among the other effects, which were confirmed with in vitro tests against C. albicans strain.

Furthermore, the in silico studies, the Pa value of monoterpene 7-hydroxycitronellal showed a lower value than the other two compounds, however, this profile was not observed in the test with the fungal strain. This can be justified, because more tests with other strains are needed to close the antifungal activity of the compounds.

Knowing that is a close relationship enters the pharmacological activity and the enantiomeric of active substances due to stereo selectivity of biological receptors and enzymes.18 We sought to compare the results between the isomers (R)-(+)-citronellal and (S)-(-)-citronellal, analyzing the data can be seen that these compounds have the same percentage of probability in relation to its possible pharmacological effects, suggesting that in such cases the isomerism does not interfere with potential antimicrobial effects presented by these monoterpenes. In silico study of three monoterpenes derived from citronellal, (R)-(+)-citronellal, (S)-(-)-citronellal and 7-hydroxycitronellal, demonstrated that these compounds have several possible biological effects on the pathogenic microorganisms, in special fungi species.

Conflictos de intereses

Los autores declaran no presentar conflicto de intereses.



1. Las Heras B, Rodríguez B, Bosca L, Villar AM. Terpenoids: sources, structure elucidation and therapeutic potential in inflammation. Curr. Top. Med. Chem. 2003;3(2):171-85.

2. Bhalla Y, Gupta VK, Jaitak V. Anticancer activity of essential oils: a review. J. Sci. Food Agric. 2013;93(15):3643-53.

3. Guimarães AG, Quintans JSS, Quintans-Júnior LJ. Monoterpenes with Analgesic Activity-A Systematic Review. Phytother. Res. 2013;27:1-15.

4. Riella KR, Marinho RR, Santos JS, Pereira-Filho RN, Cardoso JC, Albuquerque-Junior RLC, Thomazzi SM. Anti-inflammatory and cicatrizing activities of thymol, a monoterpene of the essential oil from Lippiagracilis, in rodents. J. Ethnopharmacol. 2012;143:656-63.

5. Santana MT, De Oliveira MG, Santana MF, De Sousa DP, Santana DG, Camargo EA, et al. Citronellal, a monoterpene present in Java citronella oil, attenuates mechanical nociception response in mice. Pharm. Biol. 2013;51(9):1144-9.

6. Brito DV, Ootani MA, Ramos ACC, Sertão WC, Aguiar RWS. Effect of citronella oil, eucalipto and citronellal compound of myco flora and devolopment of maize plants. J. Biotechonol. Biodiv. 2012;3:184-92.

7. Cavalcanti YM, Almeida LFD, Padilha WWN. Screening of essential oils antifungal activity on Candida strains. Odontol. Clínica-Científica. 2011:243-6.

8. Quintans-Júnior L. Antinociceptive action and redox properties of citronelal, an essential oil present in lemongrass. J. Med. Food. 2011;14(6):630-9.

9. Tomaz MA, Costa A, Rodrigues WN, Pinheiro PF, Parreira PA, Rinaldo D. Chemical Composition and Allelopathic Activity of the Eucalyptus Essential Oil. Biosc. J. 2014;30:475-83.

10. Lu YH, Zhang CW, Bucheli P, Wei DZ. Citrus flavonoids in fruit and traditional Chinese medicinal food ingredients in China. Plant Foods Hum. Nutrition. 2006;61:57-65.

11. Seixas TL, Castro HC, Santos GR, Cardoso DP. Controle fitopatológico do Fusarium subglutinans pelo óleo essencial do capim-citronela (Cymbopogonnardus L.) e do compostocitronelal. Rev Bras Plant Med. 2011;13:523-6.

12. Scherer R, Wagner R, Duarte MCT, Godoy HT. Composição e atividades antioxidante e antimicrobiana dos óleos essenciais de cravo-da-india, citronela e palmarosa. Rev Bras Plant Med. 2009;11(4):442-9.

13. Andrade MA, Cardoso MG, Batista LB, Mallet ACT, Machado SMF. Óleos essenciais de Cymbopogonnardus, Cinnamomumzeylanicume Zingiberofficinale: composição, atividades antioxidante e antibacteriana. Rev Cienc. Agron. 2012;43(2):399-408.

14. Srinivas N, Sandeep KS, Anusha Y, Devendra BN. In Vitro Cytotoxic Evaluation and Detoxification of Monocrotaline (Mct) Alkaloid: An In Silico Approach. Int. Inv. J. Biochem. Bioinform. 2014;2(3):20-9.

15. CLSI. Reference method for broth dilution antifungal susceptibility testing of yeasts. Pennsylvania: CLSI. Document M27-A3. 2008;28(14):10.

16. Marchant CA. Computational toxicology: a tool for all industries. WIREs Comp. Mol. Sci. 2012;2:424-4.

17. Angelo V, Max D, Markus AL. The Challenge of Predicting Drug Toxicity in silico. Bas. Clin. Phar. Tox. 2006;99:195-208.

18. Bielory L, Leonov A. Stereo configuration of antiallergic and immunologic drugs. Ann. Allergy AsthmIm. 2008;100:1-9.



Recibido: 24 de septiembre de 2015.
Aprobado: 21 de octubre de 2015.



Heloísa Mara Batista Fernandes de Oliveira. Universidade Federal de Campina Grande, Patos, Paraíba, Brasil.
Dirección electrónica:

Enlaces refback

  • No hay ningún enlace refback.