Bioinfo Chem
System biology and Infochemistry | Online ISSN 3071-4826
1
Citations
13.7k
Views
32
Articles
Volume 2 Number 1 2020
REVIEWS (Open Access)
AI-Driven Predictive Toxicology: Integrating Systems Biology and Machine Learning for the Future of Drug Safety
Meenakshi Singh 1*, Sonia Yadav 2*
Bioinfo Chem 2 (1) 1-13 https://doi.org/10.25163/bioinformatics.2110738
Submitted: 17 June 2020 Revised: 10 August 2020 Accepted: 16 August 2020 Published: 18 August 2020
Abstract
Toxicology, as a discipline, seems to be standing at a subtle yet decisive crossroads. For decades, safety assessment relied on animal-based models—robust in some respects, yet increasingly questioned for their cost, ethical implications, and, perhaps most critically, their imperfect translation to human biology. This review explores the emerging paradigm of Integrative Predictive Toxicology (IPT), where systems biology, machine learning, and biokinetic modeling converge to offer a more mechanistically grounded and human-relevant framework.Rather than focusing solely on late-stage apical outcomes, IPT shifts attention toward early molecular perturbations, often structured through Adverse Outcome Pathways (AOPs). Advances in high-throughput screening and multi-omics technologies now generate data at a scale that, not long ago, would have seemed unmanageable. Yet, when paired with machine learning algorithms and supported by curated databases, these datasets begin to reveal predictive patterns of toxicity. Importantly, the integration of physiologically based pharmacokinetic (PBPK) modeling and QIVIVE provides a necessary bridge between in vitro signals and real-world human exposure. Still, this transition is not without uncertainty. Questions of model interpretability, data harmonization, and regulatory acceptance remain. And yet, taken together, these approaches suggest something quite compelling: a shift from observing toxicity after it occurs to anticipating it before it manifests—arguably redefining the future of drug safety itself.
Keywords: Predictive toxicology; Machine learning; Systems biology; PBPK modeling; Adverse outcome pathways
References
Ankley, G. T., Bennett, R. S., Erickson, R. J., Hoff, D. J., Hornung, M. W., Johnson, R. D., Mount, D. R., Nichols, J. W., Russom, C. L., Schmieder, P. K., Serrrano, J. A., Tietge, J. E., & Villeneuve, D. L. (2010). Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment. Environmental Toxicology and Chemistry, 29(3), 730–741. https://doi.org/10.1002/etc.34
Berggren, E., White, A., Ouedraogo, G., Paini, A., Richarz, A. N., Bois, F. Y., ... & Worth, A. (2017). Ab initio chemical safety assessment: A workflow based on exposure considerations and non-animal methods. Computational Toxicology, 4, 31–44.
Berggren, E., White, A., Ouedraogo, G., Paini, A., Richarz, A. N., Bois, F. Y., Exner, T., Leite, S., van Grunsven, L. A., Worth, A., & Mahony, C. (2017). Ab initio chemical safety assessment: A workflow based on exposure considerations and non-animal methods. Computational Toxicology, 4, 31–44.
Bessems, J. G., Loizou, G., Krishnan, K., Clewell, H. J., Bernasconi, C., Bois, F., Coecke, S., Collnot, E. M., Diembeck, W., Farcal, L., Geraets, L., Gundert-Remy, U., Kramer, N., Küsters, G., Leite, S. B., Pelkonen, O., Schröder, K., Testai, E., Wilk-Zasadna, I., & Zaldívar-Comenges, J. M. (2014). PBTK modelling platforms and parameter estimation tools to enable animal-free risk assessment. Regulatory Toxicology and Pharmacology, 68(1), 119–139. https://doi.org/10.1016/j.yrtph.2013.11.008
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Council, N. R. (2007). Toxicity testing in the 21st century: A vision and a strategy. National Academies Press. https://doi.org/10.17226/11970
Cramer, G. M., Ford, R. A., & Hall, R. L. (1978). Estimation of toxic hazard—a decision tree approach. Food and Cosmetics Toxicology, 16(3), 255–276.
Dix, D. J., Houck, K. A., Martin, M. T., Richard, A. M., Setzer, R. W., & Kavlock, R. J. (2007). The ToxCast program for prioritizing toxicity testing of environmental chemicals. Toxicological Sciences, 95(1), 5–12.
Enoch, S. J., Cronin, M. T., & Ellison, C. M. (2011). The use of a chemistry-based profiler for covalent DNA binding in the development of chemical categories for read-across for genotoxicity. ATLA - Alternatives to Laboratory Animals, 39(2), 131–145.
Gajewska, M., Paini, A., Sala Benito, J. V., Burton, J., Worth, A., Urani, C., Briesen, H., & Schramm, K. W. (2015). In vitro to in vivo correlation of the skin penetration, liver clearance and hepatotoxicity of caffeine. Food and Chemical Toxicology, 75, 39–49. https://doi.org/10.1016/j.fct.2014.10.018
Ganter, B., Snyder, R. D., Halbert, D. N., & Lee, M. D. (2006). Toxicogenomics in drug discovery and development: Mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. Pharmacogenomics, 7(7), 1025–1044.
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., & Overington, J. P. (2012). ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Research, 40(D1), D1100–D1107.
Hardy, B., Apic, G., Carthew, P., Clark, D., Cook, D., Dix, I., Escher, S., Hastings, J., Heard, D. J., Jeliazkova, N., Judson, P., Matis-Mitchell, S., Mitic, D., Myatt, G., Shah, I., Spjuth, O., Tcheremenskaia, O., Toldo, L., Watson, D., ... & Yang, C. (2012). A toxicology ontology roadmap. ALTEX, 29(2), 129–137. https://doi.org/10.14573/altex.2012.2.129
Hartung, T. (2010). Lessons learned from alternative methods and their validation for a new toxicology in the 21st century. Journal of Toxicology and Environmental Health, Part B, 13(2-4), 277–290. https://doi.org/10.1080/10937401003734435
Igarashi, Y., Nakatsu, N., Yamashita, T., Yamada, H., & Urushidani, T. (2015). Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Research, 43(D1), D921–D927.
Ingle, B. L., Veber, B. C., Nichols, J. W., & Tornero-Velez, R. (2016). Informing the human plasma protein binding of environmental chemicals by machine learning in the pharmaceutical space: Applicability domain and limits of predictability. Journal of Chemical Information and Modeling, 56(11), 2243–2252.
Judson, R. S., Martin, M. T., Egeghy, P., Gangwal, S., Reif, D. M., Kothiya, P., Wolf, M., Cathey, T., Transue, T., Smith, D., ... & Richard, A. M. (2012). Aggregating data for computational toxicology applications: The U.S. Environmental Protection Agency (EPA) Aggregated Computational Toxicology Resource (ACToR) system. International Journal of Molecular Sciences, 13(2), 1805–1831.
Kavlock, R., Chandler, K., Houck, K., Hunter, S., Judson, R., Kleinstreuer, N., Knudsen, T., Martin, M., Padilla, S., Reif, D., Richard, A. M., Rotroff, D., Sipes, N. S., & Dix, D. (2012). Update on EPA’s ToxCast program: Providing high throughput decision support tools for chemical risk management. Chemical Research in Toxicology, 25(7), 1287–1302. https://doi.org/10.1021/tx3000939
Kuhn, M., Letunic, I., Jensen, L. J., & Bork, P. (2015). The SIDER database of drugs and side effects. Nucleic Acids Research, 44(D1), D1075–D1079.
Leist, M., Hasiwa, N., Rovida, C., Daneshian, M., Basketter, D., Kimber, I., Clewell, H., Gocht, T., Goldberg, A., Busquet, F., Rossi, A. M., Schwarz, M., Stephens, M., Taalman, R., Knudsen, T. B., McKim, J., Harris, G., Pamies, D., & Hartung, T. (2014). Consensus report on the future of animal-free systemic toxicity testing. ALTEX, 31(3), 341–356. https://doi.org/10.14573/altex.1406091
Lipscomb, J. C., Haddad, S., Poet, T., & Krishnan, K. (2012). Physiologically-based pharmacokinetic (PBPK) models in toxicity testing and risk assessment. Advances in Experimental Medicine and Biology, 745, 76–95.
National Research Council (NRC). (1983). Risk assessment in the federal government: Managing the process. National Academies Press.
National Research Council (NRC). (2007). Toxicity testing in the 21st century: A vision and a strategy. National Academies Press.
Paini, A., Sala Benito, J. V., Bessems, J., & Worth, A. P. (2017). From in vitro to in vivo: Integration of the virtual cell based assay with physiologically based kinetic modelling. Toxicology in Vitro, 45, 241–248.
Pihan, E., Colliandre, L., Guichard, G., & Bonnet, P. (2012). e-Drug3D: 3D structures and physicochemical properties of FDA-approved drugs. Journal of Computer-Aided Molecular Design, 26(11), 1273–1280.
Raies, A. B., & Bajic, V. B. (2016). In silico toxicology: Computational methods for the prediction of chemical toxicity. WIREs Computational Molecular Science, 6(2), 147–172.
Rodgers, T., & Rowland, M. (2006). Physiologically based pharmacokinetic modelling 2: Predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. Journal of Pharmaceutical Sciences, 95(6), 1238–1257.
Rotroff, D. M., Wetmore, B. A., Dix, D. J., Ferguson, S. S., Clewell, H. J., Houck, K. A., Lecluyse, E. L., Andersen, M. E., Judson, R. S., Smith, C. M., Sochaski, M. A., Kavlock, R. J., Boellmann, F., Martin, M. T., Reif, D. M., Wambaugh, J. F., & Thomas, R. S. (2010). Incorporating human dosimetry and exposure into high-throughput in vitro toxicity screening. Toxicological Sciences, 117(2), 348–358. https://doi.org/10.1093/toxsci/kfq220
Russell, W. M. S., & Burch, R. L. (1960). The principles of humane experimental technique. Medical Journal of Australia, 1(13), 500.
Russell, W. M. S., & Burch, R. L. (1960). The principles of humane experimental technique. Methuen.
Schmitt, W. (2008). General approach for the calculation of tissue to plasma partition coefficients. Toxicology in Vitro, 22(2), 457–467.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.
Tice, R. R., Austin, C. P., Kavlock, R. J., & Bucher, J. R. (2013). Improving the human hazard characterization of chemicals: A Tox21 update. Environmental Health Perspectives, 121(7), 756–765. https://doi.org/10.1289/ehp.1205784
Wang, Y., Xiao, J., Suzek, T. O., Zhang, J., Wang, J., & Bryant, S. H. (2009). PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Research, 37(suppl_2), W623–W633.
Wetmore, B. A. (2015). Quantitative in vitro-to-in vivo extrapolation in a high-throughput environment. Toxicology, 332, 94–101.
Whelan, M., & Schwarz, M. (2011). SEURAT: Vision, research strategy and execution. Towards the Replacement of in vivo Repeated Dose Systemic Toxicity, 1, 47–57.
Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., ... & Wilson, M. (2018). DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074–D1082.
Zaldívar Comenges, J. M., & Baraibar, J. (2011). A virtual cell based assay model for the prediction of in vitro toxicity. Toxicology in Vitro, 25(8), 1670–1679.
Zaldívar-Comenges, J. M., Joossens, E., Sala Benito, J. V., Worth, A., & Paini, A. (2016). Theoretical and mathematical foundation of the virtual cell based assay – A review. Toxicology in Vitro, 45, 209–221. https://doi.org/10.1016/j.tiv.2016.07.013
Zhang, J. H., Fraczkiewicz, R., Bolger, M. B., Waldman, M., Woltosz, W. S., & Enslein, K. (2008). Predicting kinetic parameters Km and Vmax for substrates of human cytochrome P450 1A2, 2C9, 2C19, 2D6, and 3A4. Drug Metabolism Reviews, 40(S1), 119.
Recommended articles
Illuminating Biological Dark Matter: Integrating Metagenomics, Synthetic Biology, and AI to Unlock Microbial and Genomic Potential for Therapeutics and Biotechnology
An Integrated AI-Driven Framework for Maternal Resource Intelligence Shortages Across U.S. Hospital
Interpretable Machine Learning Data Modeling for Liver Disease Risk Profiling: Insights from the Indian Liver Patient Dataset
0
Save
Save
0
Citation
Citation
11
View
View
0
Share
Share