Research Article

Predictive Ecotoxicology of Naturally Derived Terpenoids: A Computational Approach

Number: 10 June 14, 2026
EN

Predictive Ecotoxicology of Naturally Derived Terpenoids: A Computational Approach

Abstract

Estimations based on data accelerate the assessment of chemical toxicity related to bioactive compounds, serving as an effective preliminary stage for toxicological screening and ecological evaluations. In silico approaches provide major advantages for environmental toxicity testing by reducing time, cost, and resource use, while maintaining high sensitivity for humans and other ecological species. This study aimed to predict the acute and chronic ecotoxicity of six terpenoids-carvacrol, menthol, 1,8-cineole, thymol, limonene, and linalool-used as essential oil components or directly as herbicides, insecticides, and pesticides, across four aquatic organisms. ECOSAR (Ecological Structure-Activity Relationship) software was employed to estimate potential toxicity for fish (96 h), Daphnia magna (48 h), green algae (96 h), and mysid shrimp (48 h). Molecular docking was then conducted on Danio rerio RNase5 and Polo-like Kinase 1 to explore molecular mechanisms, followed by molecular dynamics simulations of the docked complexes. Results indicated that limonene exhibited the highest ecotoxicity. In conclusion, in silico analyses revealed potential toxic effects of terpenoids, highlighting the importance of ecotoxicological evaluation to ensure safe and sustainable applications

Keywords

References

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Details

Primary Language

English

Subjects

Field Crops and Pasture Production (Other)

Journal Section

Research Article

Publication Date

June 14, 2026

Submission Date

August 25, 2025

Acceptance Date

March 17, 2026

Published in Issue

Year 2026 Number: 10

APA
Kaymak, S., Tosyagülü Çelik, H., & Vural, N. (2026). Predictive Ecotoxicology of Naturally Derived Terpenoids: A Computational Approach. International Journal of Chemistry and Technology, 10, 1-11. https://doi.org/10.32571/ijct.1772194
AMA
1.Kaymak S, Tosyagülü Çelik H, Vural N. Predictive Ecotoxicology of Naturally Derived Terpenoids: A Computational Approach. Int. J. Chem. Technol. 2026;(10):1-11. doi:10.32571/ijct.1772194
Chicago
Kaymak, Sibel, Hatice Tosyagülü Çelik, and Nilufer Vural. 2026. “Predictive Ecotoxicology of Naturally Derived Terpenoids: A Computational Approach”. International Journal of Chemistry and Technology, nos. 10: 1-11. https://doi.org/10.32571/ijct.1772194.
EndNote
Kaymak S, Tosyagülü Çelik H, Vural N (June 1, 2026) Predictive Ecotoxicology of Naturally Derived Terpenoids: A Computational Approach. International Journal of Chemistry and Technology 10 1–11.
IEEE
[1]S. Kaymak, H. Tosyagülü Çelik, and N. Vural, “Predictive Ecotoxicology of Naturally Derived Terpenoids: A Computational Approach”, Int. J. Chem. Technol., no. 10, pp. 1–11, June 2026, doi: 10.32571/ijct.1772194.
ISNAD
Kaymak, Sibel - Tosyagülü Çelik, Hatice - Vural, Nilufer. “Predictive Ecotoxicology of Naturally Derived Terpenoids: A Computational Approach”. International Journal of Chemistry and Technology. 10 (June 1, 2026): 1-11. https://doi.org/10.32571/ijct.1772194.
JAMA
1.Kaymak S, Tosyagülü Çelik H, Vural N. Predictive Ecotoxicology of Naturally Derived Terpenoids: A Computational Approach. Int. J. Chem. Technol. 2026;:1–11.
MLA
Kaymak, Sibel, et al. “Predictive Ecotoxicology of Naturally Derived Terpenoids: A Computational Approach”. International Journal of Chemistry and Technology, no. 10, June 2026, pp. 1-11, doi:10.32571/ijct.1772194.
Vancouver
1.Sibel Kaymak, Hatice Tosyagülü Çelik, Nilufer Vural. Predictive Ecotoxicology of Naturally Derived Terpenoids: A Computational Approach. Int. J. Chem. Technol. 2026 Jun. 1;(10):1-11. doi:10.32571/ijct.1772194