| Title: | QSAR modelling of inhalation toxicity of diverse volatile organic molecules using no observed adverse effect concentration (NOAEC) as the endpoint |
| Address: | "Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India. Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India. Electronic address: kunal.roy@jadavpuruniversity.in" |
| DOI: | 10.1016/j.chemosphere.2021.131954 |
| ISSN/ISBN: | 1879-1298 (Electronic) 0045-6535 (Linking) |
| Abstract: | "Nowadays, air pollution due to urbanization and reduction of forestry is emerging as a serious threat to humans and the environment. According to the World Health Organization, respiratory diseases are the third most mortality factor in the world. Chemical research organizations and industries are producing a large number of new chemical compounds continuously. Although toxicity testing of those chemicals on animals is costly, resource and time consuming, these data cannot be properly extrapolated to humans and other animals, and also these raise ethical issues. In this background, we have developed Quantitative Structure-Activity Relationship (QSAR) models using the No Observed Adverse Effect Concentration (NOAEC) as the endpoint to assess inhalation toxicity of diverse organic chemicals, commonly used and exposed by us in our daily life. No Observed Adverse Effect Concentration (NOAEC) can be used for long term toxicity studies towards the human inhalation risk assessment, as recommended by Organization for Economic Co-operation and Development (OECD) in guidance document 39. A particular QSAR model may not be equally effective for prediction of all query compounds from a given set of compounds; therefore, we have developed multiple models, which are robust, sound and well predictive from the statistical point of view to forecast the NOAEC values for the new untested compounds. Subsequently the validated individual models were employed to generate consensus models, in order to improve the quality of predictions and to reduce prediction errors. We have investigated some crucial structural features from these models which may regulate inhalation toxicity for newly produced molecules. Thus, our developed models may help in toxicity assessment towards reducing the health hazards for new chemicals" |
| Keywords: | Animals *Drug-Related Side Effects and Adverse Reactions Humans Organic Chemicals *Quantitative Structure-Activity Relationship Risk Assessment Toxicity Tests Diverse volatile organic molecules Inhalation toxicity Noaec OECD guidance Document 39 Qsar; |
| Notes: | "MedlineNath, Aniket De, Priyanka Roy, Kunal eng England 2021/09/04 Chemosphere. 2022 Jan; 287(Pt 1):131954. doi: 10.1016/j.chemosphere.2021.131954. Epub 2021 Aug 27" |
