Statistical methods for in silico tools used for risk assessment and toxicology
-
Nermin A. Osman
Abstract
In silico toxicology is one type of toxicity assessment that uses computational methods to visualize, analyze, simulate, and predict the toxicity of chemicals. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. Animal studies for the type of toxicological information needed are both expensive and time-consuming, and to that, ethical consideration is added. Many different types of in silico methods have been developed to characterize the toxicity of chemical materials and predict their catastrophic consequences to humans and the environment. In light of European legislation such as Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) and the Cosmetics Regulation, in silico methods for predicting chemical toxicity have become increasingly important and used extensively worldwide e.g., in the USA, Canada, Japan, and Australia. A popular problem, concerning these methods, is the deficiency of the necessary data for assessing the hazards. REACH has called for increased use of in silico tools for non-testing data as structure-activity relationships, quantitative structure-activity relationships, and read-across. The main objective of the review is to refine the use of in silico tools in a risk assessment context of industrial chemicals.
-
Author contributions: The author has accepted responsibility for the entire content of this submitted manuscript and approved submission.
-
Research funding: None declared.
-
Conflict of interest statement: The author declares no conflicts of interest regarding this article.
References
1. Koruga, D. Ultimate computing: biomolecular consciousness and nanotechnology. Biosystems 1988;22:83–4. https://doi.org/10.1016/0303-2647(88)90052-4.Search in Google Scholar
2. Breville, M. US environmental protection agency tribal environmental health research program. Epidemiology 2011;22:S115. https://doi.org/10.1097/01.ede.0000392021.64753.75.Search in Google Scholar
3. Hartung, T, Hoffmann, S. Food for thought on in silico methods in toxicology. ALTEX 2009;36:155–66. https://doi.org/10.14573/altex.2009.3.155.Search in Google Scholar PubMed
4. Ekins, S, Mestres, J, Testa, B. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 2007;152:9–20. https://doi.org/10.1038/sj.bjp.0707305.Search in Google Scholar PubMed PubMed Central
5. Ekins, S, Mestres, J, Testa, B. In silico pharmacology for drug discovery: applications to targets and beyond. Br J Pharmacol 2007;152:21–37. https://doi.org/10.1038/sj.bjp.0707305.Search in Google Scholar
6. Muster, W, Breidenbach, A, Fischer, H, Kirchner, S, Müller, L, Pähler, A. Computational toxicology in drug development. Drug Discov Today 2008;13:303–10. https://doi.org/10.1016/j.drudis.2007.12.007.Search in Google Scholar PubMed
7. Tennekes, H. Novel approaches to chemical risk assessment. Environ Risk Assess Remediat 2017;3:S1. https://doi.org/10.4066/2529-8046.1000e101.Search in Google Scholar
8. Kortagere, S, Krasowski, M, Ekins, S. The importance of discerning shape in molecular pharmacology. Trends Pharmacol Sci 2009;30:138–47. https://doi.org/10.1016/j.tips.2008.12.001.Search in Google Scholar PubMed PubMed Central
9. Valerio, LJr. In silico toxicology for the pharmaceutical sciences. Toxicol Appl Pharmacol 2009;241:356–70. https://doi.org/10.1016/j.taap.2009.08.022.Search in Google Scholar PubMed
10. Merlot, C. Computational toxicology—a tool for early safety evaluation. Drug Discov Today 2010;15:16–22. https://doi.org/10.1016/j.drudis.2009.09.010.Search in Google Scholar PubMed
11. Worth, A. The future of in silico chemical safety … and beyond. Comput Toxicol 2019;10:60–2. https://doi.org/10.1016/j.comtox.2018.12.005.Search in Google Scholar
12. Stenner, R, Kees Van Leeuwen, Theo Vermeire (Eds.): Risk assessment of chemicals—an introduction. Environ Sci Pollut Res 2008;15:450–1. https://doi.org/10.1007/s11356-008-0017-0.Search in Google Scholar
13. Myatt, G, Bower, D, Cross, K, Hasselgren, C, Miller, S, Quigley, D. In silico toxicology protocols and software platforms. Toxicol Lett 2017;280:S286. https://doi.org/10.1016/j.toxlet.2017.07.802.Search in Google Scholar
14. Ma, J, Tong, C, Liaw, A, Sheridan, R, Szumiloski, J, Svetnik, V. Generating hypotheses about molecular structure-activity relationships (SARs) by solving an optimization problem. Stat Anal Data Min: ASA Data Sci J 2009;2:161–74. https://doi.org/10.1002/sam.10040.Search in Google Scholar
15. Gao, G. Statistical modeling of SAR images: a survey. Sensors 2010;10:775–95. https://doi.org/10.3390/s100100775.Search in Google Scholar PubMed PubMed Central
16. Gupta-Ostermann, D, Shanmugasundaram, V, Bajorath, J. Neighborhood-based prediction of novel active compounds from SAR matrices. J Chem Inf Model 2014;54:801–9. https://doi.org/10.1021/ci5000483.Search in Google Scholar PubMed
17. Roy, K, Kar, S, Das, R. Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment, 2nd ed. Amsterdam: Academic Press, an imprint of Elsevier; 2015.Search in Google Scholar
18. Gramatica, P. Principles of QSAR modeling. Int J Quant Struct-Property Relat 2020;5:61–97. https://doi.org/10.4018/ijqspr.20200701.oa1.Search in Google Scholar
19. Gramatica, P. Principles of QSAR models validation: internal and external. QSAR Comb Sci 2007;26:694–701. https://doi.org/10.1002/qsar.200610151.Search in Google Scholar
20. Migut, M, Worring, M. Visual exploration of classification models for various data types in risk assessment. Inf Visual 2012;11:237–51. https://doi.org/10.1177/1473871611433715.Search in Google Scholar
21. Brereton, R, Lloyd, G. Partial least squares discriminant analysis: taking the magic away. J Chemom 2014;28:213–25. https://doi.org/10.1002/cem.2609.Search in Google Scholar
22. Salvador-Meneses, J, Ruiz-Chavez, Z, Garcia-Rodriguez, J. Compressed kNN: K-nearest neighbors with data compression. Entropy 2019;21:234. https://doi.org/10.3390/e21030234.Search in Google Scholar PubMed PubMed Central
23. Kumar, R. Signature verification using support vector machine (SVM). Int J Sci Res Manag 2017;5:5327–30. https://doi.org/10.18535/ijsrm/v5i5.07.Search in Google Scholar
24. Kovari, A, Andersson, N, Bell, D, Cartlidge, G, Fedtke, N, Kojo, A, et al.. Read-across in REACH and the read-across assessment framework (RAAF). Toxicol Lett 2018;295:S9. https://doi.org/10.1016/j.toxlet.2018.06.035.Search in Google Scholar
25. Benfenati, E, Chaudhry, Q, Gini, G, Dorne, J. Integrating in silico models and read-across methods for predicting toxicity of chemicals: a step-wise strategy. Environ Int 2019;131:105060. https://doi.org/10.1016/j.envint.2019.105060.Search in Google Scholar PubMed
26. Cherkasov, A, Muratov, E, Fourches, D, Varnek, A, Baskin, I, Cronin, M, et al.. QSAR modeling: where have you been? Where are you going to? J Med Chem 2014;57:4977–5010. https://doi.org/10.1021/jm4004285.Search in Google Scholar PubMed PubMed Central
27. Muratov, E, Bajorath, J, Sheridan, R, Tetko, I, Filimonov, D, Poroikov, V, et al.. QSAR without borders. Chem Soc Rev 2020;49:3525–64. https://doi.org/10.1039/D0CS00098A.Search in Google Scholar PubMed PubMed Central
28. Matthews, E, Contrera, J. In silico approaches to explore toxicity end points: issues and concerns for estimating human health effects. Expet Opin Drug Metabol Toxicol 2007;3:125–34. https://doi.org/10.1517/17425255.3.1.125.Search in Google Scholar PubMed
29. Raies, AB, Bajic, VB. In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip Rev Comput Mol Sci 2016;6:147–72. https://doi.org/10.1002/wcms.1240.Search in Google Scholar PubMed PubMed Central
30. Raunio, H. In silico toxicology – non-testing methods. Front Pharmacol 2011;2:33. https://doi.org/10.3389/fphar.2011.00033.Search in Google Scholar PubMed PubMed Central
31. Basilevsky, A. The ratio estimator and maximum-likelihood weighted least squares regression. Qual Quantity 1980;14:377–95. https://doi.org/10.1007/bf00144097.Search in Google Scholar
32. Fletcher, J. Multiple linear regression. BMJ 2009;338:b167. https://doi.org/10.1136/bmj.b167.Search in Google Scholar
33. Nagy, G. Sector based linear regression, a new robust method for the multiple linear regression. Acta Cybern 2018;23:1017–38. https://doi.org/10.14232/actacyb.23.4.2018.3.Search in Google Scholar
34. Beran, R. Prediction in random coefficient regression. J Stat Plann Inference 1995;43:205–13. https://doi.org/10.1016/0378-3758(94)00020-v.Search in Google Scholar
35. Stoltzfus, J. Logistic regression: a brief primer. Acad Emerg Med 2011;18:1099–104. https://doi.org/10.1111/j.1553-2712.2011.01185.x.Search in Google Scholar PubMed
36. Zhang, Z. Residuals and regression diagnostics: focusing on logistic regression. Ann Transl Med 2016;4:195–6. https://doi.org/10.21037/atm.2016.03.36.Search in Google Scholar PubMed PubMed Central
37. Rynkiewicz, J. General bound of overfitting for MLP regression models. Neurocomputing 2012;90:106–10. https://doi.org/10.1016/j.neucom.2011.11.028.Search in Google Scholar
38. Kumar, R. Errors in use of multivariable regression analysis. Indian J Pharmacol 2015;47:571–2. https://doi.org/10.4103/0253-7613.165187.Search in Google Scholar PubMed PubMed Central
39. Senaviratna, NA, Cooray, T. Diagnosing ulticollinearity of logistic regression model. Asian J Probab Stat 2019;2:1–9. https://doi.org/10.9734/ajpas/2019/v5i230132.Search in Google Scholar
40. Hansen, M, Cai, L, Monroe, S, Li, Z. Limited-information goodness-of-fit testing of diagnostic classification item response models. Br J Math Stat Psychol 2016;69:225–52. https://doi.org/10.1111/bmsp.12074.Search in Google Scholar PubMed
41. Kartoun, U. A glimpse of the difference between predictive modeling and classification modeling. J Clin Epidemiol 2019;109:142. https://doi.org/10.1016/j.jclinepi.2019.01.001.Search in Google Scholar PubMed
42. Krupinski, E. Receiver operating characteristic (ROC) analysis. Frontline Learn Res 2017;5:31–42. https://doi.org/10.14786/flr.v5i2.250.Search in Google Scholar
43. Topliss, J, Edwards, R. Chance factors in studies of quantitative structure-activity relationships. J Med Chem 1979;22:1238–44. https://doi.org/10.1021/jm00196a017.Search in Google Scholar PubMed
44. Heinze, G, Wallisch, C, Dunkler, D. Variable selection – a review and recommendations for the practicing statistician. Biom J 2018;60:431–49. https://doi.org/10.1002/bimj.201700067.Search in Google Scholar PubMed PubMed Central
45. Cheng, F, Ikenaga, Y, Zhou, Y, Yu, Y, Li, W, Shen, J, et al.. In silico assessment of chemical biodegradability. J Chem Inf Model 2012;52:655–69. https://doi.org/10.1021/ci200622d.Search in Google Scholar PubMed
© 2021 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Reviews
- Magnetic characterization of magnetoactive elastomers containing magnetic hard particles using first-order reversal curve analysis
- Microscopic understanding of particle-matrix interaction in magnetic hybrid materials by element-specific spectroscopy
- Biodeinking: an eco-friendly alternative for chemicals based recycled fiber processing
- Bio-based polyurethane aqueous dispersions
- Cellulose-based polymers
- Biodegradable shape-memory polymers and composites
- Natural substances in cancer—do they work?
- Personalized and targeted therapies
- Identification of potential histone deacetylase inhibitory biflavonoids from Garcinia kola (Guttiferae) using in silico protein-ligand interaction
- Chemical computational approaches for optimization of effective surfactants in enhanced oil recovery
- Social media and learning in an era of coronavirus among chemistry students in tertiary institutions in Rivers State
- Techniques for the detection and quantification of emerging contaminants
- Occurrence, fate, and toxicity of emerging contaminants in a diverse ecosystem
- Updates on the versatile quinoline heterocycles as anticancer agents
- Trends in microbial degradation and bioremediation of emerging contaminants
- Power to the city: Assessing the rooftop solar photovoltaic potential in multiple cities of Ecuador
- Phytoremediation as an effective tool to handle emerging contaminants
- Recent advances and prospects for industrial waste management and product recovery for environmental appliances: a review
- Integrating multi-objective superstructure optimization and multi-criteria assessment: a novel methodology for sustainable process design
- A conversation on the quartic equation of the secular determinant of methylenecyclopropene
- Recent developments in the synthesis and anti-cancer activity of acridine and xanthine-based molecules
- An overview of in silico methods used in the design of VEGFR-2 inhibitors as anticancer agents
- Fragment based drug design
- Advances in heterocycles as DNA intercalating cancer drugs
- Systems biology–the transformative approach to integrate sciences across disciplines
- Pharmaceutical interest of in-silico approaches
- Membrane technologies for sports supplementation
- Fused pyrrolo-pyridines and pyrrolo-(iso)quinoline as anticancer agents
- Membrane applications in the food industry
- Membrane techniques in the production of beverages
- Statistical methods for in silico tools used for risk assessment and toxicology
- Dicarbonyl compounds in the synthesis of heterocycles under green conditions
- Green synthesis of triazolo-nucleoside conjugates via azide–alkyne C–N bond formation
- Anaerobic digestion fundamentals, challenges, and technological advances
- Survival is the driver for adaptation: safety engineering changed the future, security engineering prevented disasters and transition engineering navigates the pathway to the climate-safe future
Articles in the same Issue
- Frontmatter
- Reviews
- Magnetic characterization of magnetoactive elastomers containing magnetic hard particles using first-order reversal curve analysis
- Microscopic understanding of particle-matrix interaction in magnetic hybrid materials by element-specific spectroscopy
- Biodeinking: an eco-friendly alternative for chemicals based recycled fiber processing
- Bio-based polyurethane aqueous dispersions
- Cellulose-based polymers
- Biodegradable shape-memory polymers and composites
- Natural substances in cancer—do they work?
- Personalized and targeted therapies
- Identification of potential histone deacetylase inhibitory biflavonoids from Garcinia kola (Guttiferae) using in silico protein-ligand interaction
- Chemical computational approaches for optimization of effective surfactants in enhanced oil recovery
- Social media and learning in an era of coronavirus among chemistry students in tertiary institutions in Rivers State
- Techniques for the detection and quantification of emerging contaminants
- Occurrence, fate, and toxicity of emerging contaminants in a diverse ecosystem
- Updates on the versatile quinoline heterocycles as anticancer agents
- Trends in microbial degradation and bioremediation of emerging contaminants
- Power to the city: Assessing the rooftop solar photovoltaic potential in multiple cities of Ecuador
- Phytoremediation as an effective tool to handle emerging contaminants
- Recent advances and prospects for industrial waste management and product recovery for environmental appliances: a review
- Integrating multi-objective superstructure optimization and multi-criteria assessment: a novel methodology for sustainable process design
- A conversation on the quartic equation of the secular determinant of methylenecyclopropene
- Recent developments in the synthesis and anti-cancer activity of acridine and xanthine-based molecules
- An overview of in silico methods used in the design of VEGFR-2 inhibitors as anticancer agents
- Fragment based drug design
- Advances in heterocycles as DNA intercalating cancer drugs
- Systems biology–the transformative approach to integrate sciences across disciplines
- Pharmaceutical interest of in-silico approaches
- Membrane technologies for sports supplementation
- Fused pyrrolo-pyridines and pyrrolo-(iso)quinoline as anticancer agents
- Membrane applications in the food industry
- Membrane techniques in the production of beverages
- Statistical methods for in silico tools used for risk assessment and toxicology
- Dicarbonyl compounds in the synthesis of heterocycles under green conditions
- Green synthesis of triazolo-nucleoside conjugates via azide–alkyne C–N bond formation
- Anaerobic digestion fundamentals, challenges, and technological advances
- Survival is the driver for adaptation: safety engineering changed the future, security engineering prevented disasters and transition engineering navigates the pathway to the climate-safe future
