The probable future of toxicology - probabilistic risk assessment.

Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.

Probabilistic risk assessment, initially from engineering, is applied in toxicology to understand chemical-related hazards and their consequences. In toxicology, uncertainties abound ­ unclear molecular events, varied proposed outcomes, and population-level assessments for issues like neurodevelopmental disorders. Establishing links between chemical exposures and diseases, especially rare events like birth defects, often demands extensive studies. Existing methods struggle with subtle effects or those affecting specific groups. Future risk assessments must address developmental disease origins, presenting challenges beyond current capabilities. The intricate nature of many toxicological processes, lack of consensus on mechanisms and outcomes, and the need for nuanced population-level assessments highlight the complexities in understanding and quantifying risks associated with chemical exposures in the field of toxicology.

Using in silico tools to predict flame retardant metabolites for more informative exposomics-based approaches.

Introduction: The positive identification of xenobiotics and their metabolites in human biosamples is an integral aspect of exposomics research, yet challenges in compound annotation and identification continue to limit the feasibility of comprehensive identification of total chemical exposure. Nonetheless, the adoption of in silico tools such as metabolite prediction software, QSAR-ready structural conversion workflows, and molecular standards databases can aid in identifying novel compounds in untargeted mass spectral investigations, permitting the assessment of a more expansive pool of compounds for human health hazard. This strategy is particularly applicable when it comes to flame retardant chemicals. The population is ubiquitously exposed to flame retardants, and evidence implicates some of these compounds as developmental neurotoxicants, endocrine disruptors, reproductive toxicants, immunotoxicants, and carcinogens. However, many flame retardants are poorly characterized, have not been linked to a definitive mode of toxic action, and are known to share metabolic breakdown products which may themselves harbor toxicity. As U.S. regulatory bodies begin to pursue a subclass- based risk assessment of organohalogen flame retardants, little consideration has been paid to the role of potentially toxic metabolites, or to expanding the identification of parent flame retardants and their metabolic breakdown products in human biosamples to better inform the human health hazards imposed by these compounds. Methods: The purpose of this study is to utilize publicly available in silico tools to 1) characterize the structural and metabolic fates of proposed flame retardant classes, 2) predict first pass metabolites, 3) ascertain whether metabolic products segregate among parent flame retardant classification patterns, and 4) assess the existing coverage in of these compounds in mass spectral database. Results: We found that flame retardant classes as currently defined by the National Academies of Science, Engineering and Medicine (NASEM) are structurally diverse, with highly variable predicted pharmacokinetic properties and metabolic fates among member compounds. The vast majority of flame retardants (96%) and their predicted metabolites (99%) are not present in spectral databases, posing a challenge for identifying these compounds in human biosamples. However, we also demonstrate the utility of publicly available in silico methods in generating a fit for purpose synthetic spectral library for flame retardants and their metabolites that have yet to be identified in human biosamples. Discussion: In conclusion, exposomics studies making use of fit-for-purpose synthetic spectral databases will better resolve internal exposure and windows of vulnerability associated with complex exposures to flame retardant chemicals and perturbed neurodevelopmental, reproductive, and other associated apical human health impacts.

Application of physiologically based kinetic (PBK) modelling in the next generation risk assessment of dermally applied consumer products.

Next Generation Risk Assessment (NGRA) is a procedure that integrates new approach methodologies (NAMs) to assure safety of a product without generating data from animal testing. One of the major challenges in the application of NGRA to consumer products is how to extrapolate from the in vitro points of departure (PoDs) to the human exposure level associated with product use. To bridge the gap, physiologically based kinetic (PBK) modelling is routinely used to predict systemic exposure (Cmax or AUC) from external exposures. A novel framework was developed for assessing the exposure of new ingredients in dermally applied products based on the construction of PBK models describing consumer habits and practices, formulation type, and ADME (absorption, distribution, metabolism and excretion) properties exclusively obtained from NAMs. This framework aims to quantify and reduce the uncertainty in predictions and is closely related to the risk assessment process (i.e., is the margin of safety sufficient to cover the uncertainties in the extrapolation between the in vitro and in vivo toxicodynamics and toxicokinetics?). Coumarin, caffeine, and sulforaphane in four product types (kitchen cleaner liquid, face cream, shampoo, and body lotion) were selected to exemplify how this framework could be used in practise. Our work shows initial levels of the framework provide a conservative estimate of Cmax in most cases which can be refined using sensitivity analysis to inform the choice of follow-up in vitro experiments. These case studies show the framework can increase confidence in use of PBK predictions for safety assessment.

Adverse Outcome Pathway (AOP) Informed Modeling of Aquatic Toxicology: QSARs, Read-Across, and Interspecies Verification of Modes of Action.

Alternative approaches have been promoted to reduce the number of vertebrate and invertebrate animals required for the assessment of the potential of compounds to cause harm to the aquatic environment. A key philosophy in the development of alternatives is a greater understanding of the relevant adverse outcome pathway (AOP). One alternative method is the fish embryo toxicity (FET) assay. Although the trends in potency have been shown to be equivalent in embryo and adult assays, a detailed mechanistic analysis of the toxicity data has yet to be performed; such analysis is vital for a full understanding of the AOP. The research presented herein used an updated implementation of the Verhaar scheme to categorize compounds into AOP-informed categories. These were then used in mechanistic (quantitative) structure-activity relationship ((Q)SAR) analysis to show that the descriptors governing the distinct mechanisms of acute fish toxicity are capable of modeling data from the FET assay. The results show that compounds do appear to exhibit the same mechanisms of toxicity across life stages. Thus, this mechanistic analysis supports the argument that the FET assay is a suitable alternative testing strategy for the specified mechanisms and that understanding the AOPs is useful for toxicity prediction across test systems.

Development of a list of reference chemicals for evaluating alternative methods to in vivo fish bioaccumulation tests.

The aim to reduce the number of animals in experiments has highlighted the need to develop and validate nonanimal methods as alternatives to bioaccumulation studies using fish. The present study details a novel 3-tier approach to develop a list of reference compounds to aid this process. The approach was based on 1) the inclusion of relevant chemical classes supported by high-quality in vivo data for the bioconcentration factor (BCF), whole-body biotransformation rates (K(met)), and metabolism characterization for rainbow trout (Oncorhynchus mykiss) and common carp (Cyprinus carpio) (tiers I and II); and 2) the refinement to ensure a broad coverage of hydrophobicity, bioconcentration potential, molecular weight, maximum molecular diameter, whole-body biotransformation half-lives, and metabolic pathways (tier III). In silico techniques were employed to predict maximal log BCF and molecular and metabolic properties. Of the 157 compounds considered as reference compounds, 144 were supported by high-quality BCF data, 8 were supported by K(met) data, and 5 were supported by in vivo metabolism data. Additional criteria for refinement of the list of reference compounds were suggested to aid practical implementation in experimental efforts. The present list of reference compounds is anticipated to facilitate the development of alternative approaches, enhance understanding of in vivo and in vitro bioaccumulation relationships, and refine in silico BCF and metabolism predictions.

Pragmatic approaches to using computational methods to predict xenobiotic metabolism.

In this study the performance of a selection of computational models for the prediction of metabolites and/or sites of metabolism was investigated. These included models incorporated in the MetaPrint2D-React, Meteor, and SMARTCyp software. The algorithms were assessed using two data sets: one a homogeneous data set of 28 Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and paracetamol (DS1) and the second a diverse data set of 30 top-selling drugs (DS2). The prediction of metabolites for the diverse data set (DS2) was better than for the more homogeneous DS1 for each model, indicating that some areas of chemical space may be better represented than others in the data used to develop and train the models. The study also identified compounds for which none of the packages could predict metabolites, again indicating areas of chemical space where more information is needed. Pragmatic approaches to using metabolism prediction software have also been proposed based on the results described here. These approaches include using cutoff values instead of restrictive reasoning settings in Meteor to reduce the output with little loss of sensitivity and for directing metabolite prediction by preselection based on likely sites of metabolism.