EC Pharmacology and Toxicology

The implementation of the Russian Technical Regulation of the Eurasian Economic Union 041/2017 “On the Safety of Chemical Products” highlights the need to develop and validate alternative methods for assessing the hazard of new chemicals as part of their notification procedure. The objective of this study was to evaluate the possibility of using the QSAR Toolbox software of the Organization for Economic Co-operation and Development to predict toxicity and hazard indicators in accordance with the requirements of the Globally Harmonized System of Classification and Labelling of Chemicals in the framework of the notification procedure. The article analyzes the prognostic ability of the program in relation to assessment of acute toxicity, skin and eyes irritation, mutagenic effects, sensitizing, reproductive toxicity effects and acute toxicity to aquatic biota. Samples of chemicals with known experimental data were used to verify the forecasts. It was found that the accuracy of the forecast varies significantly depending on the estimated effect: the highest rates were achieved for estimation of skin sensitization when using profilers based on the Аdverse Outcome Pathway concept (up to 75%), for acute toxicity to aquatic biota with respect to Daphnia magna (84%); acceptable accuracy - for acute toxicity with intragastric intake (70%) and for acute toxicity to fish (71 - 75%). It is shown that the Quantitative Structure-Activity Relationship Toolbox can be used as an effective tool at the initial stages of assessment. However, the forecast data obtained require experimental confirmation for hazard classification as part of the notification procedure for new chemicals, especially compounds beyond the scope of the model.

 Keywords: QSAR Toolbox; OECD Methods; Prediction of Toxic Effects; GHS; In Silico Methods; Notification; Technical Regulation

1. Technical Regulation of the Eurasian Economic Union "On the Safety of Chemical products" (EAEU TR 041/2017).
2. GOST 12.1.007-76 "Occupational Safety Standards System (OSSS) “Hazardous substances. Classification and general safety requirements".
3. Methodological recommendations MR 1.2.0313-22 "Assessment and classification of the hazard of endocrine disruptors".
4. Methodological recommendations MR 1.2.0378-25 "Assessment and classification of the hazard of mutagens".
5. Methodological recommendations MR 1.2.0321-23 "Assessment and classification of reproductive toxicants".
6. Methodological recommendations MR 1.2.0406-25 "Assessment and determination of the hazard class of chemicals by sensitizing effect".
7. Guideline R 1.2.3156-13 "Assessment of the toxicity and hazard of chemicals and their mixtures to human health".
8. Sachchidanand Pathak., et al. “Chapter 1 - In silico pharmacology”. Editor(s): Rupesh Kumar Gautam, Mohammad Amjad Kamal, Pooja Mittal, Computational Approaches in Drug Discovery, Development and Systems Pharmacology, Academic Press (2023): 1-52.
9. Globally Harmonized System of Classification and Labelling of Chemicals. Eleventh revised version.
10. OECD, (Q)SAR Assessment Framework: Guidance for the regulatory assessment of (Quantitative) Structure Activity Relationship models and predictions, Second Edition, OECD Series on Testing and Assessment.405, OECD Publishing, Paris (2024).
11. OECD Guidelines for the Testing of Chemicals, Section 4.
12. Kolle SN., et al. “A review of substances found positive in 1 of 3 in vitro tests for skin sensitization”. Regulatory Toxicology and Pharmacology 106 (2019): 352-368.
13. Cronin MTD and Worth AP. “(Q)SARs for predicting effects relating to reproductive toxicity”. QSAR and Combinatorial Science1 (2008): 91-100.
14. Novič M and Vračko M. “QSAR models for reproductive toxicity and endocrine disruption activity”. Molecules 3 (2010): 1987-1999.
15. Hewitt M., et al. “Integrating (Q)SAR models, expert systems and read-across approaches for the prediction of developmental toxicity”. Reproductive Toxicology1 (2010): 147-160.
16. Sussman NB., et al. “Decision tree SAR models for developmental toxicity based on an FDA/TERIS database”. SAR and QSAR in Environmental Research2 (2003): 83-96.
17. Piparo EL and Worth AP. “Review of QSAR models and software tools for predicting developmental and reproductive toxicity”. JRC Scientific and Technical Reports (2010): 1-24.
18. ENV/JM/MONO. Guidance on grouping of chemicals, second edition. OECD Environment, Health and Safety Publications. Series on Testing and Assessment 194 (2014): 1-141.
19. Khamidulina Kh., et al. “Application of the OECD QSAR Toolbox software for calculating the parameters of acute aquatic toxicity of chemicals”. Toxicological Review1 (2022): 45-54.
20. Khamidulina Kh., et al. “Application of OECD QSAR Toolbox software for predicting the mutagenic effects of chemicals”. Toxicological Review6 (2022): 403-413.
21. Khamidulina Kh., et al. “Prediction of the biodegradation of chemicals using OECD QSAR Toolbox software”. Toxicological Review1 (2024): 20-30.
22. Khamidulina Kh., et al. “Prognostic systems in preventive toxicology (literature review)”. Toxicological Review2 (2025): 134-143.
23. The Adverse Outcome Pathway for Skin Sensitisation Initiated by Covalent Binding to Proteins, OECD Series on Testing and Assessment. 168, OECD Publishing, Paris (2014).
24. QSAR Toolbox.
25. Automated Distributed Information Retrieval System (ARIPS) "Dangerous Substances".
26. Database of the European Chemical Agency (ECHA).
27. Rita HR Branco., et al. “Influence of redox condition and inoculum on micropollutant biodegradation by soil and activated sludge communities”. Science of The Total Environment 897 (2023): 165233.
28. Başak Kılıç and Ferhan Çeçen. “Review of experimental biodegradation data on pharmaceuticals and comparison with predictive BIOWIN models”. Journal of Environmental Management 344 (2023): 118310.
29. Wu S., et al. “Framework for identifying chemicals with structural features associated with the potential to act as developmental or reproductive toxicants”. Chemical Research in Toxicology12 (2013): 1840-1861.
30. Thresher A., et al. “Generation of TD₅₀ values for carcinogenicity study data”. Toxicology Research (2019).
31. Gold LS., et al. “Summary of carcinogenic potency and positivity for 492 rodent carcinogens in the carcinogenic potency database”. Environmental Health Perspectives 79 (1989): 259-272.
32. Dybing E., et al. “T25: A simplified carcinogenic potency index: description of the system and study of correlations between carcinogenic potency and species/site specificity and mutagenicity”. Pharmacology and Toxicology6 (1997): 272-279.