CYTOTOXICITY PROFILING OF METAL OXIDE NANOPARTICLES TOWARDS ESCHERICHIA COLI USING QSAR MODELING
Main Article Content
Keywords
Neural network, computational, bioactivity, metal oxide nanoparticles, descriptors
Abstract
Nanotechnology is quickly developing, manufacturing nanomaterials of different genera, of which, metal oxide nanoparticles, is the most demanding class of nanomaterials. Currently, the expedient heightening of metal oxide nanoparticle's usage poses an impact on the environment. As, nanotoxicity profile of the vast majority of these metal oxide nanoparticles is as yet unclear and the information with respect to their physicochemical properties that contributes towards their bioactivity is also scant. In addition, trial assessment of each and every current and recently integrated metal oxide nanoparticle is very costly, arduous and tedious. Likewise, low sufficiency of invivo/invitro experimental designs encumbers the toxicity evaluation of nanoparticles. Consequently, computational insilico QSAR (quantitative structure- activity relationship) approach have been investigated as successful technique for assessing the harmful cytotoxic effects of metal oxide nanoparticles in Escherichia coli. In addition, both the models were assessed for their prediction accuracy based on the merit of F-measure. In the current work, nano-QSAR models have been developed employing computed size autonomous nano-explicit descriptors using machine learning algorithm including linear multiple linear regression (MLR) and non-linear neural network (NN). Deciphered from the developed models, nanodescriptors including ∑χ=nO, χox and ∆H Me+ effectively encodes the metal oxide nanoparticle's cytotoxicity mechanism in Escherichia coli. And furthermore, the value of F-measure for linear MLR based model was higher i.e., 85% than non-linear NN model i.e.,74% indicating improved prediction competence of linear MLR model over non-linear NN model in Escherichia coli. Thus, MLR based QSAR model displayed high statistical robustness in the proposed specie for toxicity profiling. Subsequently, this study underscores on the importance of nano-QSAR modeling in nanotoxicology and is expected to enhance the advancement of more secure nanomaterials in the future.
References
1. Shekhar, S., Gautam, S., Sharma, B., Sharma, S., Das, P. P., & Chaudhary, V. (2021). Deciphering the pathways for evaluation of nanotoxicity: Stumbling block in nanotechnology. Cleaner Engineering and Technology, 100311.https://doi.org/10.1016/j.clet.2021.100311
2. Ettlinger, R., Lächelt, U., Gref, R., Horcajada, P., Lammers, T., Serre, C., ... & Wuttke, S. (2022). Toxicity of metal–organic framework nanoparticles: from essential analyses to potential applications. Chemical Society Reviews. https://doi.org/10.1039/D1CS00918D
3. Jose, J. P. A., Younus, L. A., Reddy, K. B., Sana, S. S., Gangadhar, L., Hou, T., ... & Bhardwaj, P. (2022). Environmental impact on toxicity of nanomaterials. In Biogenic Sustainable Nanotechnology (pp. 315-355). Elsevier. https://doi.org/10.1016/B978-0-323-88535-5.00011-1
4. Warheit, D. B., & Donner, E. M. (2010). Rationale of genotoxicity testing of nanomaterials: regulatory requirements and appropriateness of available OECD test guidelines. Nanotoxicology, 4(4), 409-413. https://doi.org/10.3109/17435390.2010.485704
5. Card, J. W., Jonaitis, T. S., Tafazoli, S., & Magnuson, B. A. (2011). An appraisal of the published literature on the safety and toxicity of food-related nanomaterials. Critical reviews in toxicology, 41(1), 20-49. https://doi.org/10.3109/10408444.2010.524636
6. Baun, A., & Grieger, K. (2022). Environmental Risk Assessment of Emerging Contaminants—The Case of Nanomaterials. In Advances in Toxicology and Risk Assessment of Nanomaterials and Emerging Contaminants (pp. 349-371). Springer, Singapore. https://doi.org/10.1007/978-981-16-9116-4_15
7. Yan, X., Yue, T., Zhu, H., & Yan, B. (2022). Bridging the Gap Between Nanotoxicological Data and the Critical Structure–Activity Relationships. In Advances in Toxicology and Risk Assessment of Nanomaterials and Emerging Contaminants (pp. 161-183). Springer, Singapore. https://doi.org/10.1007/978-981-16-9116-4_7
8. Winkler, D. A. (2016). Recent advances, and unresolved issues, in the application of computational modeling to the prediction of the biological effects of nanomaterials. Toxicology and applied pharmacology, 299, 96-100. https://doi.org/10.1016/j.taap.2015.12.016
9. Tang, K., Liu, X., Harper, S. L., Steevens, J. A., & Xu, R. (2013). NEIMiner: nanomaterial environmental impact data miner. International journal of nanomedicine, 8(Suppl 1), 15. https://doi.org/10.2147/IJN.S40974
10. Fu, P. P., Xia, Q., Hwang, H. M., Ray, P. C., & Yu, H. (2014). Mechanisms of nanotoxicity: generation of reactive oxygen species. Journal of food and drug analysis, 22(1), 64-75. https://doi.org/10.1016/j.jfda.2014.01.005
11. Adams, L. K., Lyon, D. Y., & Alvarez, P. J. (2006). Comparative eco-toxicity of nanoscale TiO2, SiO2, and ZnO water suspensions. Water research, 40(19), 3527-3532. https://doi.org/10.1016/j.watres.2006.08.004
12. Buglak, A. A., Zherdev, A. V., & Dzantiev, B. B. (2019). Nano-(Q) SAR for cytotoxicity prediction of engineered nanomaterials. Molecules, 24(24), 4537. https://doi.org/10.3390/molecules24244537
13. Yap, C. W. (2011). PaDEL‐descriptor: An open-source software to calculate molecular descriptors and fingerprints. Journal of computational chemistry, 32(7), 1466-1474. https://doi.org/10.1002/jcc.21707
14. Darlington, R. B. (1990). Regression and linear models. McGraw-Hill College.
15. Choi, J. S., Ha, M. K., Trinh, T. X., Yoon, T. H., & Byun, H. G. (2018). Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Scientific reports, 8(1), 1-10. https://doi.org/10.1038%2Fs41598-018-24483-z
16. Pratim Roy, P., Paul, S., Mitra, I., & Roy, K. (2009). On two novel parameters for validation of predictive QSAR models. Molecules, 14(5), 1660-1701. https://doi.org/10.3390/molecules14051660
17. Powers, K. W., Carpinone, P. L., & Siebein, K. N. (2012). Characterization of nanomaterials for toxicological studies. In Nanotoxicity (pp. 13-32). Humana Press, Totowa, NJ. DOI 10.1007/978-1-62703-002-1_2
18. Gramatica, P. (2007). Principles of QSAR models validation: internal and external. QSAR & combinatorial science, 26(5), 694-701. https://doi.org/10.1002/qsar.200610151
19. Kar, S., Das, R. N., Roy, K., & Leszczynski, J. (2016). Can toxicity for different species be correlated? the concept and emerging applications of interspecies quantitative structure-toxicity relationship (i-QSTR) modeling. International Journal of Quantitative Structure-Property Relationships (IJQSPR), 1(2), 23-51. 10.4018/IJQSPR.2016070102
20. Fjodorova, N., Novic, M., Gajewicz, A., & Rasulev, B. (2017). The way to cover prediction for cytotoxicity for all existing nano-sized metal oxides by using neural network method. Nanotoxicology, 11(4), 475-483. https://doi.org/10.1080/17435390.2017.1310949
21. Cronin, M. T., & Schultz, T. W. (2003). Pitfalls in QSAR. Journal of Molecular Structure: THEOCHEM, 622(1-2), 39-51. https://doi.org/10.1016/S0166-1280(02)00616-4
22. Ahmadi, S. (2020). Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria. Chemosphere, 242, 125192 https://doi.org/10.1016/j.chemosphere.2019.125192
23. Dearden, J. C., Cronin, M. T., & Kaiser, K. L. (2009). How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR). SAR and QSAR in Environmental Research, 20(3-4), 241-266. https://doi.org/10.1080/10629360902949567
2. Ettlinger, R., Lächelt, U., Gref, R., Horcajada, P., Lammers, T., Serre, C., ... & Wuttke, S. (2022). Toxicity of metal–organic framework nanoparticles: from essential analyses to potential applications. Chemical Society Reviews. https://doi.org/10.1039/D1CS00918D
3. Jose, J. P. A., Younus, L. A., Reddy, K. B., Sana, S. S., Gangadhar, L., Hou, T., ... & Bhardwaj, P. (2022). Environmental impact on toxicity of nanomaterials. In Biogenic Sustainable Nanotechnology (pp. 315-355). Elsevier. https://doi.org/10.1016/B978-0-323-88535-5.00011-1
4. Warheit, D. B., & Donner, E. M. (2010). Rationale of genotoxicity testing of nanomaterials: regulatory requirements and appropriateness of available OECD test guidelines. Nanotoxicology, 4(4), 409-413. https://doi.org/10.3109/17435390.2010.485704
5. Card, J. W., Jonaitis, T. S., Tafazoli, S., & Magnuson, B. A. (2011). An appraisal of the published literature on the safety and toxicity of food-related nanomaterials. Critical reviews in toxicology, 41(1), 20-49. https://doi.org/10.3109/10408444.2010.524636
6. Baun, A., & Grieger, K. (2022). Environmental Risk Assessment of Emerging Contaminants—The Case of Nanomaterials. In Advances in Toxicology and Risk Assessment of Nanomaterials and Emerging Contaminants (pp. 349-371). Springer, Singapore. https://doi.org/10.1007/978-981-16-9116-4_15
7. Yan, X., Yue, T., Zhu, H., & Yan, B. (2022). Bridging the Gap Between Nanotoxicological Data and the Critical Structure–Activity Relationships. In Advances in Toxicology and Risk Assessment of Nanomaterials and Emerging Contaminants (pp. 161-183). Springer, Singapore. https://doi.org/10.1007/978-981-16-9116-4_7
8. Winkler, D. A. (2016). Recent advances, and unresolved issues, in the application of computational modeling to the prediction of the biological effects of nanomaterials. Toxicology and applied pharmacology, 299, 96-100. https://doi.org/10.1016/j.taap.2015.12.016
9. Tang, K., Liu, X., Harper, S. L., Steevens, J. A., & Xu, R. (2013). NEIMiner: nanomaterial environmental impact data miner. International journal of nanomedicine, 8(Suppl 1), 15. https://doi.org/10.2147/IJN.S40974
10. Fu, P. P., Xia, Q., Hwang, H. M., Ray, P. C., & Yu, H. (2014). Mechanisms of nanotoxicity: generation of reactive oxygen species. Journal of food and drug analysis, 22(1), 64-75. https://doi.org/10.1016/j.jfda.2014.01.005
11. Adams, L. K., Lyon, D. Y., & Alvarez, P. J. (2006). Comparative eco-toxicity of nanoscale TiO2, SiO2, and ZnO water suspensions. Water research, 40(19), 3527-3532. https://doi.org/10.1016/j.watres.2006.08.004
12. Buglak, A. A., Zherdev, A. V., & Dzantiev, B. B. (2019). Nano-(Q) SAR for cytotoxicity prediction of engineered nanomaterials. Molecules, 24(24), 4537. https://doi.org/10.3390/molecules24244537
13. Yap, C. W. (2011). PaDEL‐descriptor: An open-source software to calculate molecular descriptors and fingerprints. Journal of computational chemistry, 32(7), 1466-1474. https://doi.org/10.1002/jcc.21707
14. Darlington, R. B. (1990). Regression and linear models. McGraw-Hill College.
15. Choi, J. S., Ha, M. K., Trinh, T. X., Yoon, T. H., & Byun, H. G. (2018). Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Scientific reports, 8(1), 1-10. https://doi.org/10.1038%2Fs41598-018-24483-z
16. Pratim Roy, P., Paul, S., Mitra, I., & Roy, K. (2009). On two novel parameters for validation of predictive QSAR models. Molecules, 14(5), 1660-1701. https://doi.org/10.3390/molecules14051660
17. Powers, K. W., Carpinone, P. L., & Siebein, K. N. (2012). Characterization of nanomaterials for toxicological studies. In Nanotoxicity (pp. 13-32). Humana Press, Totowa, NJ. DOI 10.1007/978-1-62703-002-1_2
18. Gramatica, P. (2007). Principles of QSAR models validation: internal and external. QSAR & combinatorial science, 26(5), 694-701. https://doi.org/10.1002/qsar.200610151
19. Kar, S., Das, R. N., Roy, K., & Leszczynski, J. (2016). Can toxicity for different species be correlated? the concept and emerging applications of interspecies quantitative structure-toxicity relationship (i-QSTR) modeling. International Journal of Quantitative Structure-Property Relationships (IJQSPR), 1(2), 23-51. 10.4018/IJQSPR.2016070102
20. Fjodorova, N., Novic, M., Gajewicz, A., & Rasulev, B. (2017). The way to cover prediction for cytotoxicity for all existing nano-sized metal oxides by using neural network method. Nanotoxicology, 11(4), 475-483. https://doi.org/10.1080/17435390.2017.1310949
21. Cronin, M. T., & Schultz, T. W. (2003). Pitfalls in QSAR. Journal of Molecular Structure: THEOCHEM, 622(1-2), 39-51. https://doi.org/10.1016/S0166-1280(02)00616-4
22. Ahmadi, S. (2020). Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria. Chemosphere, 242, 125192 https://doi.org/10.1016/j.chemosphere.2019.125192
23. Dearden, J. C., Cronin, M. T., & Kaiser, K. L. (2009). How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR). SAR and QSAR in Environmental Research, 20(3-4), 241-266. https://doi.org/10.1080/10629360902949567
Article Sidebar
Published
Jul 17, 2024
Article Details
How to Cite
Nida Tabassum Khan, Samiullah Khan, Arooj Khan, Aemad Tabasum, & Usman Azhar. (2024). CYTOTOXICITY PROFILING OF METAL OXIDE NANOPARTICLES TOWARDS ESCHERICHIA COLI USING QSAR MODELING. Journal of Population Therapeutics and Clinical Pharmacology, 31(7), 1997-2004. https://doi.org/10.53555/z5kp9t12
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
Author Biographies
Nida Tabassum Khan
Department of Biotechnology, Life Sciences & Informatics, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, Pakistan
Samiullah Khan
Department of Biotechnology, Life Sciences & Informatics, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, Pakistan
Arooj Khan
Department of Biotechnology, Life Sciences & Informatics, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, Pakistan
Aemad Tabasum
Department of Biotechnology, Alhamd Islamic University, Quetta, Pakistan
Usman Azhar
Department of Economics, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, Pakistan
How to Cite
Nida Tabassum Khan, Samiullah Khan, Arooj Khan, Aemad Tabasum, & Usman Azhar. (2024). CYTOTOXICITY PROFILING OF METAL OXIDE NANOPARTICLES TOWARDS ESCHERICHIA COLI USING QSAR MODELING. Journal of Population Therapeutics and Clinical Pharmacology, 31(7), 1997-2004. https://doi.org/10.53555/z5kp9t12