Generalized Read-Across (GenRA): A workflow implemented into the EPA CompTox Chemicals Dashboard

Helman, George; Shah, Imran; Williams, Antony; Edwards, Jeff; Dunne, Jeremy; Patlewicz, Grace

doi:10.14573/altex.1811292

Cited by 33 publications

(26 citation statements)

References 8 publications

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“…CBRA integrates chemical similarity and comparison of biological responses from multiple NAM assays into the assessment (Low et al 2013). This approach was further developed into a more general approach, which predicts the toxicity of a target chemical using a similarity weighted activity of nearest neighbours and is now implemented within the EPA's CompTox Chemicals Dashboard (Shah et al 2016;Helman et al 2019).…”

Section: Read-across Examples and Challengesmentioning

confidence: 99%

Towards grouping concepts based on new approach methodologies in chemical hazard assessment: the read-across approach of the EU-ToxRisk project

et al. 2019

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Read-across is one of the most frequently used alternative tools for hazard assessment, in particular for complex endpoints such as repeated dose or developmental and reproductive toxicity. Read-across extrapolates the outcome of a specific toxicological in vivo endpoint from tested (source) compounds to "similar" (target) compound(s). If appropriately applied, a read-across approach can be used instead of de novo animal testing. The read-across approach starts with structural/ physicochemical similarity between target and source compounds, assuming that similar structural characteristics lead to similar human hazards. In addition, similarity also has to be shown for the toxicokinetic and toxicodynamic properties of the grouped compounds. To date, many read-across cases fail to demonstrate toxicokinetic and toxicodynamic similarities. New concepts, in vitro and in silico tools are needed to better characterise these properties, collectively called new approach methodologies (NAMs). This white paper outlines a general read-across assessment concept using NAMs to support hazard characterization of the grouped compounds by generating data on their dynamic and kinetic properties. Based on the overarching read-across hypothesis, the read-across workflow suggests targeted or untargeted NAM testing also outlining how mechanistic knowledge such as adverse outcome pathways (AOPs) can be utilized. Toxicokinetic models (biokinetic and PBPK), enriched by in vitro parameters such as plasma protein binding and hepatocellular clearance, are proposed to show (dis)similarity of target and source compound toxicokinetics. Furthermore, in vitro to in vivo extrapolation is proposed to predict a human equivalent dose, as potential point of departure for risk assessment. Finally, the generated NAM data are anchored to the existing in vivo data of source compounds to predict the hazard of the target compound in a qualitative and/ or quantitative manner. To build this EU-ToxRisk read-across concept, case studies have been conducted and discussed with the regulatory community. These case studies are briefly outlined.

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Section: Read-across Examples and Challengesmentioning

confidence: 99%

Towards grouping concepts based on new approach methodologies in chemical hazard assessment: the read-across approach of the EU-ToxRisk project

et al. 2019

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“…Helman and coworkers could show that GenRA achieves a better predictivity when applied to structurally‐related clusters of chemicals . Recently, GenRA was also implemented in the EPAs ToxDashboard …”

Section: Expert Methodsmentioning

confidence: 99%

“…54 Recently, GenRA was also implemented in the EPAs ToxDashboard. 55 The most important step of a read-across, besides the assessment of the compound similarity, is the assessment of the remaining uncertainty. For uncertainty assessment, many analyses were done defining different sources such as the similarity of the analogs, confidence in the underlying data or the weight of the available evidence.…”

Section: Read-acrossmentioning

confidence: 99%

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

Hemmerich

Ecker

2020

WIREs Comput Mol Sci

121

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In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Chemoinformatics

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“…[10][11][12] The Health Level 7 Fast Healthcare Interoperability Resources (FHIR) system is the new standard for exchanging electronic health records. 10,13 The FHIR system is fast and easy to implement and is more suitable for mobile phone and tablet devices. 14,15 These features hold more importance when applied to time-critical conditions in complex clinical settings, such as the emergency department (ED).…”

Section: An Automated Fast Healthcare Interoperability Resources-basementioning

confidence: 99%

An Automated Fast Healthcare Interoperability Resources-Based 12-Lead Electrocardiogram Mobile Alert System for Suspected Acute Coronary Syndrome

et al. 2020

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Purpose: For patients with time-critical acute coronary syndrome, reporting electrocardiogram (ECG) findings is the most important component of the treatment process. We aimed to develop and validate an automated Fast Healthcare Interoperability Resources (FHIR)-based 12-lead ECG mobile alert system for use in an emergency department (ED). Materials and Methods: An automated FHIR-based 12-lead ECG alert system was developed in the ED of an academic tertiary care hospital. The system was aimed at generating an alert for patients with suspected acute coronary syndrome based on interpretation by the legacy device. The alert is transmitted to physicians both via a mobile application and the patient's electronic medical record (EMR). The automated FHIR-based 12-lead ECG alert system processing interval was defined as the time from ED arrival and 12-lead ECG capture to the time when the FHIR-based notification was transmitted. Results: During the study period, 3812 emergency visits and 1581 12-lead ECGs were recorded. The FHIR system generated 155 alerts for 116 patients. The alerted patients were significantly older [mean (standard deviation): 68.1 (12.4) years vs. 59.6 (16.8) years, p<0.001], and the cardiac-related symptom rate was higher (34.5% vs. 19%, p<0.001). Among the 155 alerts, 146 (94%) were transmitted successfully within 5 minutes. The median interval from 12-lead ECG capture to FHIR notification was 2.7 min [interquartile range (IQR) 2.2-3.1 min] for the group with cardiac-related symptoms and 3.0 min (IQR 2.5-3.4 min) for the group with non-cardiac-related symptoms. Conclusion: An automated FHIR-based 12-lead ECG mobile alert system was successfully implemented in an ED.

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Generalized Read-Across (GenRA): A workflow implemented into the EPA CompTox Chemicals Dashboard

Cited by 33 publications

References 8 publications

Towards grouping concepts based on new approach methodologies in chemical hazard assessment: the read-across approach of the EU-ToxRisk project

Towards grouping concepts based on new approach methodologies in chemical hazard assessment: the read-across approach of the EU-ToxRisk project

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

An Automated Fast Healthcare Interoperability Resources-Based 12-Lead Electrocardiogram Mobile Alert System for Suspected Acute Coronary Syndrome

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