Comparison of Points of Departure for Health Risk Assessment Based on High-Throughput Screening Data

Sand, Salomon; Parham, Frederick; Portier, Christopher J.; Tice, Raymond R.; Krewski, Daniel

doi:10.1289/ehp408

Cited by 19 publications

(10 citation statements)

References 34 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In risk assessment and toxicology, dose-dependent modeling is an established tool to identify points of departure (POD) that can be used as a guidance value denoting the adverse effect levels, such as benchmark dose (BMD), no observed adverse effect level (NOAEL), and lowest observed adverse effect level (LOAEL) [15]. Similar modeling strategies have emerged for the analysis of transcriptomic experiments to identify the genes directly and progressively affected with respect to the reference dose or concentration [16][17][18][19] and to characterize their POD.…”

Section: Introductionmentioning

confidence: 99%

Characterization of ENM Dynamic Dose-Dependent MOA in Lung with Respect to Immune Cells Infiltration

et al. 2022

View full text Add to dashboard Cite

The molecular effects of exposures to engineered nanomaterials (ENMs) are still largely unknown. In classical inhalation toxicology, cell composition of bronchoalveolar lavage (BAL) is a toxicity indicator at the lung tissue level that can aid in interpreting pulmonary histological changes. Toxicogenomic approaches help characterize the mechanism of action (MOA) of ENMs by investigating the differentially expressed genes (DEG). However, dissecting which molecular mechanisms and events are directly induced by the exposure is not straightforward. It is now generally accepted that direct effects follow a monotonic dose-dependent pattern. Here, we applied an integrated modeling approach to study the MOA of four ENMs by retrieving the DEGs that also show a dynamic dose-dependent profile (dddtMOA). We further combined the information of the dddtMOA with the dose dependency of four immune cell populations derived from BAL counts. The dddtMOA analysis highlighted the specific adaptation pattern to each ENM. Furthermore, it revealed the distinct effect of the ENM physicochemical properties on the induced immune response. Finally, we report three genes dose-dependent in all the exposures and correlated with immune deregulation in the lung. The characterization of dddtMOA for ENM exposures, both for apical endpoints and molecular responses, can further promote toxicogenomic approaches in a regulatory context.

show abstract

Section: Introductionmentioning

confidence: 99%

Characterization of ENM Dynamic Dose-Dependent MOA in Lung with Respect to Immune Cells Infiltration

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Also, the standardized nature of the approach may enable comparative risk characterization across different chemical exposure scenarios, which for example could facilitate risk-based prioritization. Suggestions regarding dose-response modeling approaches and estimation of (standardized) RPs across different health effects and data types ( Murrell et al , 1998 ; Sand et al , 2011 , 2017 ; Shockley 2015 ; Slob, 2017 ; Wignall et al , 2014 ) may help to refine the method at the technical level.…”

Section: Discussionmentioning

confidence: 99%

Dose-Related Severity Sequence, and Risk-Based Integration, of Chemically Induced Health Effects

Sand

Lindqvist

Rosen

et al. 2018

Toxicological Sciences

Self Cite

View full text Add to dashboard Cite

Risk assessment of chemical hazards is typically based on single critical health effects. This work aims to expand the current approach by characterizing the dose-related sequence of the development of multiple (lower- to higher-order) toxicological health effects caused by a chemical. To this end a “reference point profile” is defined as the relation between benchmark doses for considered health effects, and a standardized severity score determined for these effects. For a given dose of a chemical or mixture the probability for exceeding the reference point profile, thereby provoking lower- to higher-order effects, can be assessed. The overall impact at the same dose can also be derived by integrating contributions across all health effects following severity-weighting. In its generalized form the new impact metric relates to the probability of response for the most severe health effects. Reference points (points of departure) corresponding to defined levels of response can also be estimated. The proposed concept, which is evaluated for dioxin-like chemicals, provides an alternative for characterizing the low-dose region below the reference point for a severe effect like cancer. The shape and variability of the reference point profile add new dimensions to risk assessment, which for example extends the characterization of chemical potency, and the concept of acceptable effect sizes for individual health effects. Based on the present data the method shows high stability at low doses/responses, and is also robust to differences in severity categorization of effects. In conclusion, the novel method proposed enables risk-based integration of multiple dose-related health effects. It provides a first step towards a more comprehensive characterization of chemical toxicity, and suggests a potential for improved low-dose risk assessment.

show abstract

“…While using these biomarkers are valuable in ranking the potency of chemicals and prioritizing them for further testing, their usage as in vitro PoD to infer adverse health outcome is questionable due to lack of quantitative correlations. In certain cases, the benchmark-dose (BMD) approach or its variants are used to define in vitro PoD ( 53 ). This approach considers the shape of the entire in vitro concentration-response curve and variability.…”

Section: Computational Approaches For Dose-response and Extrapolationmentioning

confidence: 99%

Bridging the Data Gap From in vitro Toxicity Testing to Chemical Safety Assessment Through Computational Modeling

Zhang

Jin

Middleton

et al. 2018

Front. Public Health

View full text Add to dashboard Cite

Chemical toxicity testing is moving steadily toward a human cell and organoid-based in vitro approach for reasons including scientific relevancy, efficiency, cost, and ethical rightfulness. Inferring human health risk from chemical exposure based on in vitro testing data is a challenging task, facing various data gaps along the way. This review identifies these gaps and makes a case for the in silico approach of computational dose-response and extrapolation modeling to address many of the challenges. Mathematical models that can mechanistically describe chemical toxicokinetics (TK) and toxicodynamics (TD), for both in vitro and in vivo conditions, are the founding pieces in this regard. Identifying toxicity pathways and in vitro point of departure (PoD) associated with adverse health outcomes requires an understanding of the molecular key events in the interacting transcriptome, proteome, and metabolome. Such an understanding will in turn help determine the sets of sensitive biomarkers to be measured in vitro and the scope of toxicity pathways to be modeled in silico. In vitro data reporting both pathway perturbation and chemical biokinetics in the culture medium serve to calibrate the toxicity pathway and virtual tissue models, which can then help predict PoDs in response to chemical dosimetry experienced by cells in vivo. Two types of in vitro to in vivo extrapolation (IVIVE) are needed. (1) For toxic effects involving systemic regulations, such as endocrine disruption, organism-level adverse outcome pathway (AOP) models are needed to extrapolate in vitro toxicity pathway perturbation to in vivo PoD. (2) Physiologically-based toxicokinetic (PBTK) modeling is needed to extrapolate in vitro PoD dose metrics into external doses for expected exposure scenarios. Linked PBTK and TD models can explore the parameter space to recapitulate human population variability in response to chemical insults. While challenges remain for applying these modeling tools to support in vitro toxicity testing, they open the door toward population-stratified and personalized risk assessment.

show abstract

Comparison of Points of Departure for Health Risk Assessment Based on High-Throughput Screening Data

Cited by 19 publications

References 34 publications

Characterization of ENM Dynamic Dose-Dependent MOA in Lung with Respect to Immune Cells Infiltration

Characterization of ENM Dynamic Dose-Dependent MOA in Lung with Respect to Immune Cells Infiltration

Dose-Related Severity Sequence, and Risk-Based Integration, of Chemically Induced Health Effects

Bridging the Data Gap From in vitro Toxicity Testing to Chemical Safety Assessment Through Computational Modeling

Contact Info

Product

Resources

About