Systems Toxicology is the integration of classical toxicology with quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization. Society demands increasingly close scrutiny of the potential health risks associated with exposure to chemicals present in our everyday life, leading to an increasing need for more predictive and accurate risk-assessment approaches. Developing such approaches requires a detailed mechanistic understanding of the ways in which xenobiotic substances perturb biological systems and lead to adverse outcomes. Thus, Systems Toxicology approaches offer modern strategies for gaining such mechanistic knowledge by combining advanced analytical and computational tools. Furthermore, Systems Toxicology is a means for the identification and application of biomarkers for improved safety assessments. In Systems Toxicology, quantitative systems-wide molecular changes in the context of an exposure are measured, and a causal chain of molecular events linking exposures with adverse outcomes (i.e., functional and apical end points) is deciphered. Mathematical models are then built to describe these processes in a quantitative manner. The integrated data analysis leads to the identification of how biological networks are perturbed by the exposure and enables the development of predictive mathematical models of toxicological processes. This perspective integrates current knowledge regarding bioanalytical approaches, computational analysis, and the potential for improved risk assessment.
Multiple non-animal-based test methods have never been formally validated. In order to use such new approach methods (NAMs) in a regulatory context, criteria to define their readiness are necessary. The field of developmental neurotoxicity (DNT) testing is used to exemplify the application of readiness criteria. The costs and number of untested chemicals are overwhelming for in vivo DNT testing. Thus, there is a need for inexpensive, high-throughput NAMs to obtain initial information on potential hazards, and to allow prioritization for further testing. A background on the regulatory and scientific status of DNT testing is provided showing different types of test readiness levels, depending on the intended use of data from NAMs. Readiness criteria, compiled during a stakeholder workshop that united scientists from academia, industry and regulatory authorities, are presented. An important step beyond the listing of criteria was the suggestion of a preliminary scoring scheme. On this basis a (semi)-quantitative analysis process was assembled on test readiness of 17 NAMs with respect to various uses (e.g., prioritization/screening, risk assessment). The scoring results suggest that several assays are currently at high readiness levels. Therefore, suggestions are made on how DNT NAMs may be assembled into an integrated approach to testing and assessment (IATA). In parallel, the testing state in these assays was compiled for more than 1000 compounds. Finally, a vision is presented on how further NAM development may be guided by knowledge of signaling pathways necessary for brain development, DNT pathophysiology, and relevant adverse outcome pathways (AOP).
Background: Paraquat is a herbicide with a good occupational safety record, but a high mortality after intentional ingestion that has proved refractory to treatment. For nearly three decades paraquat concentration–time data have been used to predict the outcome following ingestion. However, none of the published methods has been independently or prospectively validated. We aimed to use prospectively collected data to test the published predictive methods and to determine if any is superior.Methods: Plasma paraquat concentrations were measured on admission for 451 patients in 10 hospitals in Sri Lanka as part of large prospective cohort study. All deaths in hospital were recorded; patients surviving to hospital discharge were followed up after 3 months to detect delayed deaths. Five prediction methods that are based on paraquat concentration–time data were then evaluated in all eligible patients.Results: All methods showed comparable performance within their range of application. For example, between 4- and 24-h prediction of prognosis was most variable between Sawada and Proudfoot methods but these differences were relatively small [specificity 0.96 (95% CI: 0.90–0.99) vs. 0.89 (0.82–0.95); sensitivity 0.57 vs. 0.79, positive and negative likelihood ratios 14.8 vs. 7.40 and 0.44 vs. 0.23 and positive predictive values 0.96 vs. 0.92, respectively].Conclusions: All five published methods were better at predicting death than survival. These predictions may also serve as tools to identify patients who need treatment and for some assessment to be made of new treatments that are trialled without a control group.
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