In-silico ADME Toxicity Analysis of Impurities in Commercially Marketed Δ8-THC Products for Potential Bio-medical Applications

Published: September 30, 2025

Authors

Munagala Alivelu and Natte Kavitha

Keywords
Δ8-THC, ADMET, Cannabinoid impurities, In-silico

Abstract

Background: Δ8-Tetrahydrocannabinol (Δ8-THC) is a psychoactive cannabinoid compound naturally occurring in the Cannabis sativa plant. The commercial Δ8-THC products are typically synthesized from cannabidiol (CBD), which may lead to the formation of various impurities. These impurities may contribute to unintended pharmacological or toxicological effects, highlighting the need for comprehensive safety assessment.

Purpose: This study aims to assess the pharmacokinetic and toxicity profiles of Δ8-THC and its structurally related impurities using in silico methods, thereby providing preliminary safety insights before in vitro or in vivo experimentation.

Method: In silico ADMET predictions were performed using the pkCSM web server.

Results: All analyzed compounds possess good membrane permeability and showed favorable values for intestinal absorption. The skin permeability values were within acceptable limits, with the exception of compound 10 (log Kp value -2.443). This suggests that compound 10 may have significantly reduced dermal permeability. All compounds were also predicted to exhibit high Caco-2 cell permeability. Compounds 3, 6, 7, 8, 9 (0.704, 0.542, 0.531, 0.531, 0.648), and 11 (0.227) showed relatively low VDss values. This could influence their duration of action and tissue-specific effects. All the compounds are unlikely to penetrate the blood-brain barrier (BBB), based on predicted log BB and CNS permeability indices. Our predictions indicate that impurities 6, 7, 8, 10, and 12 have the potential to inhibit the hERG channel, flagging them as possible cardiotoxic agents.

Conclusion: Δ8-THC and its structurally related impurities exhibited favorable absorption and distribution characteristics; variations in volume of distribution and dermal permeability, particularly for compound 10, may influence their pharmacological behavior. The predicted hERG inhibition by impurities 6, 7, 8, 10, and 12 raises potential cardiotoxicity concerns. Future work should include in vitro and in vivo validation of these predictions, as well as expansion to include additional impurities formed under various synthetic and storage conditions.

References

  • Al Azzam, K. (2022). SwissADME and pkCSM webservers predictors: An integrated online platform for accurate and comprehensive predictions for in silico ADME/T properties of artemisinin and its derivatives. Complex Use of Mineral Resources, 325(2), 14–21. https://doi.org/10.31643/2023/6445.13
  • deBruyn, T., Choo, E., & Rowland, M. (2023). Mechanistic prediction of renal drug clearance and its application in drug development. Clinical Pharmacokinetics, 62(2), 147–165. https://doi.org/10.1007/s40262-022-01183-6
  • Domański, J., Polak, S., & Gawron, A. (2023). Assessment of ADMET and drug-likeness properties of diquinothiazine derivatives using pkCSM predictions. Pharmaceuticals, 16(6), 725. https://doi.org/10.3390/ph16060725
  • Ekins, S., Mestres, J., & Testa, B. (2007). In silico pharmacology for drug discovery: Applications to targets and beyond. British Journal of Pharmacology, 152(1), 21–37. https://doi.org/10.1038/sj.bjp.0707305
  • ElSohly, M. A., Gul, W., Wanas, A. S., & Radwan, M. M. (2017). Phytochemistry of Cannabis sativa L. In S. Chandra, H. Lata, & M. A. ElSohly (Eds.), Cannabis sativa L. – Botany and biotechnology (pp. 67–89). Springer.
  • Franco, P., Narayan, M., & ElSohly, M. A. (2023). Toxicological and pharmacological concerns surrounding Δ8-THC and its impurities: A regulatory science perspective. Frontiers in Pharmacology, 14, 1121984. https://doi.org/10.3389/fphar.2023.1121984
  • Gallardo, A. A., Gutierrez, M. R., Gomez, L. A. J., Delos Reyes, P. A. O., Dones, S. A. A., Dumbrique, M. M. U., … Labrador, A. M. (2024). A comparative analysis on the potential anticancer properties of tetrahydrocannabinol, cannabidiol, and tetrahydrocannabivarin compounds through in silico approach. Asian Pacific Journal of Cancer Prevention, 25(3), 839–856. https://doi.org/10.31557/APJCP.2024.25.3.839
  • Gleeson, M. P., Hersey, A., Montanari, D., & Overington, J. (2011). Probing the links between in vitro potency, ADMET and physicochemical parameters. Nature Reviews Drug Discovery, 10(3), 197–208. https://doi.org/10.1038/nrd3367
  • Jamei, M., Bajot, F., Neuhoff, S., Barter, Z. E., Yang, J., Rostami-Hodjegan, A., & Rostami, A. (2019). Impacts of impurities on drug safety: Computational toxicology approaches. Toxicology and Applied Pharmacology, 370, 1–10. https://doi.org/10.1016/j.taap.2019.04.014
  • Li, A. P. (2001). In vitro assessment of drug properties. Annual Review of Pharmacology and Toxicology, 41, 447–470. https://doi.org/10.1146/annurev.pharmtox.41.1.447
  • Lipinski, C. A. (2004). Lead- and drug-like compounds: The Rule-of-Five revolution. Drug Discovery Today: Technologies, 1(4), 337–341. https://doi.org/10.1016/j.ddtec.2004.11.007
  • Meehan-Atrash, J., Mahadevan, N., & Rahman, I. (2021). Δ8-THC: Legal loophole or safety concern? Chemical Research in Toxicology, 34(9), 1954–1956. https://doi.org/10.1021/acs.chemrestox.1c00118
  • Mohammad, A., Albadr, M., Ahmed, A., & Al-Qahtani, W. (2023). Identification and characterization of Δ8-THC-related impurities in commercial products using advanced chromatographic and spectroscopic techniques. Journal of Cannabis Research, 5(1), 15. https://doi.org/10.1186/s42238-023-00123-y
  • Onyango, T. C., Akingbade, L. O., & Camara, J. K. (2024). In silico ADMET profiling of novel flavonoids reveals distribution and CNS penetration trends via pkCSM predictions. Phytochemistry Letters, 55, 104–113. https://doi.org/10.1016/j.phytol.2024.01.012
  • Pires, D. E. V., Blundell, T. L., & Ascher, D. B. (2023). pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. Journal of Medicinal Chemistry, 66(3), 1122–1134. https://doi.org/10.1021/acs.jmedchem.2c01842
  • Pires, D. E., Blundell, T. L., & Ascher, D. B. (2015). pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. Journal of Medicinal Chemistry, 58(9), 4066–4072. https://doi.org/10.1021/acs.jmedchem.5b00104
  • pkCSM: Pharmacokinetics and toxicity predictions. (n.d.). Biosignature Lab, University of Melbourne. http://biosig.unimelb.edu.au/pkcsm/prediction
  • Poklis, J. L., Charles, J., Wolf, C. E., Poklis, A., Peace, M. R., & Pearson, J. M. (2022). Evaluation of Δ8-THC products for cannabinoid content and purity. Journal of Analytical Toxicology. https://doi.org/10.1093/jat/bkac035
  • Radwan, M. M., Wanas, A. S., Gul, W., Ibrahim, E. A., & ElSohly, M. A. (2023). Isolation and characterization of impurities in commercially marketed Δ8-THC products. Journal of Natural Products, 86(4), 822–829. https://doi.org/10.1021/acs.jnatprod.2c01008
  • Snyder, R. D., Green, J. W., & Cross, B. E. (2001). Structural alerts for toxicity in drug discovery. Current Opinion in Drug Discovery & Development, 4(1), 31–40.
  • Thomas, B. F., & Pollard, G. T. (2018). Synthetic cannabinoids: A summary of research on adverse effects and mechanisms of action. In Handbook of Experimental Pharmacology (Vol. 252, pp. 443–462). https://doi.org/10.1007/164-2018-155
  • Van de Waterbeemd, H., & Gifford, E. (2003). ADMET in silico modelling: Towards prediction paradise? Nature Reviews Drug Discovery, 2(3), 192–204. https://doi.org/10.1038/nrd1032
  • Zanger, U. M., & Schwab, M. (2023). Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacology & Therapeutics, 246, 108403. https://doi.org/10.1016/j.pharmthera.2023.108403
  • Zhang, R., Wen, H., Lin, Z., Li, B., & Zhou, X. (2025). Artificial intelligence driven drug toxicity prediction: Advances, challenges, and future directions. Toxics, 13(7), 525. https://doi.org/10.3390/toxics13070525

How to Cite

Munagala Alivelu and Natte Kavitha. In-silico ADME Toxicity Analysis of Impurities in Commercially Marketed Δ8-THC Products for Potential Bio-medical Applications. J. Multidiscip. Res. Healthcare. 2025, 11, 1-9
In-silico ADME Toxicity Analysis of Impurities in Commercially Marketed Δ8-THC Products for Potential Bio-medical Applications

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