How well can carcinogenicity be predicted by high throughput "characteristics of carcinogens" mechanistic data?

A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations

Environmental chemicals, such as PFAS, exist as mixtures and are frequently encountered at varying concentrations, which can lead to serious health effects, such as cancer. Therefore, understanding the dose-dependent toxicity of chemical mixtures is essential for health risk assessment. However, comprehensive methods to assess toxicity and identify the mechanisms of these harmful mixtures are currently absent. In this study, the dose-dependent toxicity assessments of chemical mixtures are performed in three methodologically distinct phases. In the first phase, we evaluated our machine-learning method (AI-HNN) and pathophysiology method (CPTM) for predicting toxicity. In the second phase, we integrated AI-HNN and CPTM to establish a comprehensive new approach method (NAM) framework called AI-CPTM that is targeted at refining prediction accuracy and providing a comprehensive understanding of toxicity mechanisms. The third phase involved experimental validations of the AI-CPTM predictions. Initially, we developed binary, multiclass classification, and regression models to predict binary, categorical toxicity, and toxic potencies using nearly a thousand experimental mixtures. This empirical dataset was expanded with assumption-based virtual mixtures, compensating for the lack of experimental data and broadening the scope of the dataset. For comparison, we also developed machine-learning models based on RF, Bagging, AdaBoost, SVR, GB, KR, DT, KN, and Consensus methods. The AI-HNN achieved overall accuracies of over 80%, with the AUC exceeding 90%. In the final phase, we demonstrated the superior performance and predictive capability of AI-CPTM, including for PFAS mixtures and their interaction effects, through rigorous literature and statistical validations, along with experimental dose-response zebrafish-embryo toxicity assays. Overall, the AI-CPTM approach significantly improves upon the limitations of standalone AI models, showing extensive enhancements in identifying toxic chemicals and mixtures and their mechanisms. This study is the first to develop a hybrid NAM that integrates AI with a pathophysiology method to comprehensively predict chemical-mixture toxicity, carcinogenicity, and mechanisms.

Ten years of using key characteristics of human carcinogens to organize and evaluate mechanistic evidence in IARC Monographs on the identification of carcinogenic hazards to humans: Patterns and associations

Systematic review and evaluation of mechanistic evidence using the Key Characteristics approach was proposed by the International Agency for Research on Cancer (IARC) in 2012 and used by the IARC Monographs Working Groups since 2015. Key Characteristics are 10 features of agents known to cause cancer in humans. From 2015 to 2022, a total of 19 Monographs (73 agents combined) used Key Characteristics for cancer hazard classification. We hypothesized that a retrospective analysis of applications of the Key Characteristics approach to cancer hazard classification using heterogenous mechanistic data on diverse agents would be informative for systematic reviews in decision-making. We extracted information on the conclusions, data types, and the role mechanistic data played in the cancer hazard classification from each Monograph. Statistical analyses identified patterns in the use of Key Characteristics, as well as trends and correlations among Key Characteristics, data types, and ultimate decisions. Despite gaps in data for many agents and Key Characteristics, several significant results emerged. Mechanistic data from in vivo animal, in vitro animal, and in vitro human studies were most impactful in concluding that an agent could cause cancer via a Key Characteristic. To exclude the involvement of a Key Characteristic, data from large-scale systematic in vitro testing programs such as ToxCast, were most informative. Overall, increased availability of systemized data streams, such as human in vitro data, would provide the basis for more confident and informed conclusions about both positive and negative associations and inform expert judgments on cancer hazard.

Issue analysis: key characteristics approach for identifying endocrine disruptors

For more than a decade, weight of evidence (WoE) evaluations have been the standard method for determining whether a chemical meets the definition of an endocrine disrupting chemical (EDC). WoE methods consider all data pertinent to satisfying the EDC definition and evaluating those data with respect to relevance, reliability, strength, and coherence with established endocrine physiology and pharmacology. A new approach for identifying EDC hazards has been proposed that organizes and evaluates data according to ten so-called "Key Characteristics (KCs) of EDCs". The approach claims to address the lack of a widely accepted, systematic approach for identifying EDC hazards, but completely ignores the WoE literature for EDCs. In contrast to WoE methods, the KC approach fails to apply the consensus definition of EDC and is not amenable to empirical testing or validation, is fungible and ensures inconsistent and unreliable results, ignores principles of hormone action and characteristics of dose-response in endocrine pharmacology and toxicology, lacks a means of distinguishing endocrine-mediated from non-endocrine mediated mechanisms, lacks a means to reach a negative conclusion about a chemical's EDC properties or to distinguish EDCs from non-EDCs, and provides no means for developing a valid consensus among experts nor provides a means of resolving conflicting interpretations of data. Instead of shortcuts like the KC approach, which are prone to bias, error, and arbitrary conclusions, identifying EDCs should rely on WoE evaluations that supply the critical components and scientific rigor lacking in the proposed KCs for EDCs.

Development of a framework for risk assessment of dietary carcinogens

Although there are a number of guidance documents and frameworks for evaluation of carcinogenicity, none of the current methods fully reflects the state of the science. Common limitations include the absence of dose-response assessment and not considering the impact of differing exposure patterns (e.g., intermittent, high peaks vs. lower, continuous exposures). To address these issues, we have developed a framework for risk assessment of dietary carcinogens. This framework includes an enhanced approach for weight of evidence (WOE) evaluation for genetic toxicology data, with a focus on evaluating studies based on the most recent testing guidance to determine whether a chemical is a mutagen. Included alongside our framework is a discussion of resources for evaluating tissue dose and the temporal pattern of internal dose, taking into account the chemical's toxicokinetics. The framework then integrates the mode of action (MOA) and associated dose metric category with the exposure data to identify the appropriate approach(es) to low-dose extrapolation and level of concern associated with the exposure scenario. This framework provides risk managers with additional flexibility in risk management and risk communication options, beyond the binary choice of linear low-dose extrapolation vs. application of uncertainty factors.

Ten Years of Using Key Characteristics of Human Carcinogens to Organize and Evaluate Mechanistic Evidence in IARC Monographs on the Identification of Carcinogenic Hazards to Humans: Patterns and Associations

Systematic review and evaluation of the mechanistic evidence only recently been instituted in cancer hazard identification step of decision-making. One example of organizing and evaluating mechanistic evidence is the Key Characteristics approach of the International Agency for Research on Cancer (IARC) Monographs on the Identification of Carcinogenic Hazards to Humans. The Key Characteristics of Human Carcinogens were proposed almost 10 years ago and have been used in every IARC Monograph since 2015. We investigated the patterns and associations in the use of Key Characteristics by the independent expert Working Groups. We examined 19 Monographs (2015-2022) that evaluated 73 agents. We extracted information on the conclusions by each Working Group on the strength of evidence for agent-Key Characteristic combinations, data types that were available for decisions, and the role mechanistic data played in the final cancer hazard classification. We conducted both descriptive and association analyses within and across data types. We found that IARC Working Groups were cautious when evaluating mechanistic evidence: for only ∼13% of the agents was strong evidence assigned for any Key Characteristic. Genotoxicity and cell proliferation were most data-rich, while little evidence was available for DNA repair and immortalization Key Characteristics. Analysis of the associations among Key Characteristics revealed that only chemical's metabolic activation was significantly co-occurring with genotoxicity and cell proliferation/death. Evidence from exposed humans was limited, while mechanistic evidence from rodent studies in vivo was often available. Only genotoxicity and cell proliferation/death were strongly associated with decisions on whether mechanistic data was impactful on the final cancer hazard classification. The practice of using the Key Characteristics approach is now well-established at IARC Monographs and other government agencies and the analyses presented herein will inform the future use of mechanistic evidence in regulatory decision-making.

In Vitro Cell Transformation Assays: A Valuable Approach for Carcinogenic Potentiality Assessment of Nanomaterials

This review explores the application of in vitro cell transformation assays (CTAs) as a screening platform to assess the carcinogenic potential of nanomaterials (NMs) resulting from continuously growing industrial production and use. The widespread application of NMs in various fields has raised concerns about their potential adverse effects, necessitating safety evaluations, particularly in long-term continuous exposure scenarios. CTAs present a realistic screening platform for known and emerging NMs by examining their resemblance to the hallmark of malignancy, including high proliferation rates, loss of contact inhibition, the gain of anchorage-independent growth, cellular invasion, dysregulation of the cell cycle, apoptosis resistance, and ability to form tumors in experimental animals. Through the deliberate transformation of cells via chronic NM exposure, researchers can investigate the tumorigenic properties of NMs and the underlying mechanisms of cancer development. This article examines NM-induced cell transformation studies, focusing on identifying existing knowledge gaps. Specifically, it explores the physicochemical properties of NMs, experimental models, assays, dose and time requirements for cell transformation, and the underlying mechanisms of malignancy. Our review aims to advance understanding in this field and identify areas for further investigation.

Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations

Explainable machine learning for molecular toxicity prediction is a promising approach for efficient drug development and chemical safety. A predictive ML model of toxicity can reduce experimental cost and time while mitigating ethical concerns by significantly reducing animal and clinical testing. Herein, we use a deep learning framework for simultaneously modeling in vitro, in vivo, and clinical toxicity data. Two different molecular input representations are used; Morgan fingerprints and pre-trained SMILES embeddings. A multi-task deep learning model accurately predicts toxicity for all endpoints, including clinical, as indicated by the area under the Receiver Operator Characteristic curve and balanced accuracy. In particular, pre-trained molecular SMILES embeddings as input to the multi-task model improved clinical toxicity predictions compared to existing models in MoleculeNet benchmark. Additionally, our multitask approach is comprehensive in the sense that it is comparable to state-of-the-art approaches for specific endpoints in in vitro, in vivo and clinical platforms. Through both the multi-task model and transfer learning, we were able to indicate the minimal need of in vivo data for clinical toxicity predictions. To provide confidence and explain the model's predictions, we adapt a post-hoc contrastive explanation method that returns pertinent positive and negative features, which correspond well to known mutagenic and reactive toxicophores, such as unsubstituted bonded heteroatoms, aromatic amines, and Michael receptors. Furthermore, toxicophore recovery by pertinent feature analysis captures more of the in vitro (53%) and in vivo (56%), rather than of the clinical (8%), endpoints, and indeed uncovers a preference in known toxicophore data towards in vitro and in vivo experimental data. To our knowledge, this is the first contrastive explanation, using both present and absent substructures, for predictions of clinical and in vivo molecular toxicity.

Methyl-tert-butyl ether (MTBE): integration of rat and mouse carcinogenicity data with mode of action and human and rodent bioassay dosimetry and toxicokinetics indicates MTBE is not a plausible human carcinogen

Methyl-tert-butyl ether (MTBE) is a fuel oxygenate used in non-United States geographies. Multiple health reviews conclude that MTBE is not a human-relevant carcinogen, and this review provides updated mode of action (MOA), exposure, dosimetry and risk perspectives supporting those conclusions. MTBE is non-genotoxic and has large margins of exposure between blood concentrations at the overall rat 400 ppm inhalation NOAEL and blood concentrations in typical workplace or general population exposures. Non-cancer and threshold cancer hazard quotients range from a high of 0.046 for fuel-pump gasoline station attendants and are 100-1,000-fold lower for general population exposures. Cancer risks conservatively assuming genotoxicity for these same scenarios are all less than 1 × 10^-6. The onset of MTBE nonlinear toxicokinetics (TK) in rats at inhalation exposures less than 3,000 ppm, a dose that is also not practically achievable in fuel-use scenarios, indicates that high-dose specific male rat kidney and testes (3,000 and 8,000 ppm) and female mouse liver tumors (8000 ppm) are not quantitatively relevant to humans. Mode of action analyses also indicate MTBE male rat kidney tumors, and lesser so female mouse liver tumors, are not qualitatively relevant to humans. Thus, an integrated analysis of the toxicology, exposure/dosimetry, TK, and MOA data indicates that MTBE presents minimal human cancer and non-cancer risks.

Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches

Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.

A Collaborative Initiative to Establish Genomic Biomarkers for Assessing Tumorigenic Potential to Reduce Reliance on Conventional Rodent Carcinogenicity Studies

There is growing recognition across broad sectors of the scientific community that use of genomic biomarkers has the potential to reduce the need for conventional rodent carcinogenicity studies of industrial chemicals, agrochemicals, and pharmaceuticals through a weight-of-evidence approach. These biomarkers fall into 2 major categories: (1) sets of gene transcripts that can identify distinct tumorigenic mechanisms of action; and (2) cancer driver gene mutations indicative of rapidly expanding growth-advantaged clonal cell populations. This call-to-action article describes a collaborative approach launched to develop and qualify biomarker gene expression panels that measure widely accepted molecular pathways linked to tumorigenesis and their activation levels to predict tumorigenic doses of chemicals from short-term exposures. Growing evidence suggests that application of such biomarker panels in short-term exposure rodent studies can identify both tumorigenic hazard and tumorigenic activation levels for chemical-induced carcinogenicity. In the future, this approach will be expanded to include methodologies examining mutations in key cancer driver gene mutation hotspots as biomarkers of both genotoxic and nongenotoxic chemical tumor risk. Analytical, technical, and biological validation studies of these complementary genomic tools are being undertaken by multisector and multidisciplinary collaborative teams within the Health and Environmental Sciences Institute. Success from these efforts will facilitate the transition from current heavy reliance on conventional 2-year rodent carcinogenicity studies to more rapid animal- and resource-sparing approaches for mechanism-based carcinogenicity evaluation supporting internal and regulatory decision-making.

Advancing the Adverse Outcome Pathway for PPARγ Inactivation Leading to Pulmonary Fibrosis Using Bradford-Hill Consideration and the Comparative Toxicogenomics Database

Pulmonary fibrosis is regulated by transforming growth factor-β (TGF-β) and peroxisome proliferator-activated receptor-gamma (PPARγ). An adverse outcome pathway (AOP) for PPARγ inactivation leading to pulmonary fibrosis has been previously developed. To advance the development of this AOP, the confidence of the overall AOP was assessed using the Bradford-Hill considerations as per the recommendations from the Organisation for Economic Co-operation and Development (OECD) Users' Handbook. Overall, the essentiality of key events (KEs) and the biological plausibility of key event relationships (KERs) were rated high. In contrast, the empirical support of KERs was found to be moderate. To experimentally evaluate the KERs from the molecular initiating event (MIE) and KE1, PPARγ (MIE) and TGF-β (KE1) inhibitors were used to examine the effects of downstream events following inhibition of their upstream events. PPARγ inhibition (MIE) led to TGF-β activation (KE1), upregulation in vimentin expression (KE3), and an increase in the fibronectin level (KE4). Similarly, activated TGF-β (KE1) led to an increase in vimentin (KE3) and fibronectin expression (KE4). In the database analysis using the Comparative Toxicogenomics Database, 31 genes related to each KE were found to be highly correlated with pulmonary fibrosis, and the top 21 potential stressors were suggested. The AOP for pulmonary fibrosis evaluated in this study will be the basis for the screening of inhaled toxic substances in the environment.

Acrylonitrile induction of rodent neoplasia: Potential mechanism of action and relevance to humans

Acrylonitrile, an industrial chemical, is a multisite carcinogen in rats and mice, producing tumors in four tissues with barrier function, that is, brain, forestomach, Zymbal’s gland, and Harderian gland. To assess mechanism(s) of action (MoA) for induction of neoplasia and to evaluate whether the findings in rodents are indicative of human hazard, data on the potential key effects produced by acrylonitrile in the four rodent target tissues of carcinogenicity were evaluated. A notable finding was depletion of glutathione in various organs, including two target tissues, the brain, and forestomach, suggesting that this effect could be a critical initiating event. An additional combination of oxidative DNA damage and cytotoxic effects of acrylonitrile and its metabolites, cyanide, and 2-cyanoethylene oxide, could initiate pro-inflammatory signaling and sustained cell and tissue injury, leading to compensatory cell proliferation and neoplastic development. The in vivo DNA-binding and genotoxicity of acrylonitrile has been studied in several target tissues with no compelling positive results. Thus, while some mutagenic effects were reported in acrylonitrile-exposed rodents, data to determine whether this mutagenicity stems from direct DNA reactivity of acrylonitrile are insufficient. Accordingly, the induction of tumors in rodents is consistent primarily with a non-genotoxic MoA, although a contribution from weak mutagenicity cannot be ruled out. Mechanistic data to support conclusions regarding human hazard from acrylonitrile exposure is weak. Comparison of metabolism of acrylonitrile between rodents and humans provide little support for human hazard. Three of the tissues affected in bioassays (forestomach, Zymbal’s gland, and Harderian gland) are present only in rodents, while the brain is anatomically different between rodents and humans, diminishing relevance of tumor induction in these tissues to human hazard. Extensive epidemiological data has not revealed causation of human cancer by acrylonitrile.

Predictive modeling of biological responses in the rat liver using in vitro Tox21 bioactivity: Benefits from high-throughput toxicokinetics

Computational methods are needed to more efficiently leverage data from in vitro cell-based models to predict what occurs within whole body systems after chemical insults. This study set out to test the hypothesis that in vitro high-throughput screening (HTS) data can more effectively predict in vivo biological responses when chemical disposition and toxicokinetic (TK) modeling are employed. In vitro HTS data from the Tox21 consortium were analyzed in concert with chemical disposition modeling to derive nominal, aqueous, and intracellular estimates of concentrations eliciting 50% maximal activity. In vivo biological responses were captured using rat liver transcriptomic data from the DrugMatrix and TG-Gates databases and evaluated for pathway enrichment. In vivo dosing data were translated to equivalent body concentrations using HTTK modeling. Random forest models were then trained and tested to predict in vivo pathway-level activity across 221 chemicals using in vitro bioactivity data and physicochemical properties as predictor variables, incorporating methods to address imbalanced training data resulting from high instances of inactivity. Model performance was quantified using the area under the receiver operator characteristic curve (AUC-ROC) and compared across pathways for different combinations of predictor variables. All models that included toxicokinetics were found to outperform those that excluded toxicokinetics. Biological interpretation of the model features revealed that rather than a direct mapping of in vitro assays to in vivo pathways, unexpected combinations of multiple in vitro assays predicted in vivo pathway-level activities. To demonstrate the utility of these findings, the highest-performing model was leveraged to make new predictions of in vivo biological responses across all biological pathways for remaining chemicals tested in Tox21 with adequate data coverage (n = 6617). These results demonstrate that, when chemical disposition and toxicokinetics are carefully considered, in vitro HT screening data can be used to effectively predict in vivo biological responses to chemicals.

Assessment of Mechanistic Data for Hexavalent Chromium-Induced Rodent Intestinal Cancer Using the Key Characteristics of Carcinogens

Oral exposure to hexavalent chromium (Cr[VI]) induces intestinal tumors in mice. Mutagenic and nonmutagenic modes of action (MOAs) have been accepted by different regulatory bodies globally, the latter involving cytotoxicity-induced regenerative cell proliferation. However, concerns persist that all possible MOAs have not been fully considered. To address the potential for alternative MOAs, mechanistic data not represented in the existing two MOAs were evaluated. Relevant data were identified and organized by key characteristics of carcinogens (KCCs); literature related to epigenetics, immunosuppression, receptor-mediated effects, and immortalization were reviewed to identify potential key events associated with an alternative MOA. Over 200 references were screened for these four KCCs and further prioritized based on relevance to the research objective (ie, in vivo, oral exposure, gastrointestinal tissue). Minimal data were available specific to the intestine for these KCCs, and there was no evidence of any underlying mechanisms or key events that are not already represented in the two proposed MOAs. For example, while epigenetic dysregulation of DNA repair genes has been demonstrated, epigenetic effects were not measured in intestinal tissue, and it has been shown that Cr(VI) does not cause DNA damage in intestinal tissue. High-throughput screening data related to the KCCs were also evaluated, with activity generally limited to the two recognized MOAs. Collectively, no plausible alternative MOAs (or key events) were identified in addition to those previously proposed for Cr(VI) small intestine tumors.

Lack of potential carcinogenicity for steviol glycosides - Systematic evaluation and integration of mechanistic data into the totality of evidence

Steviol glycosides are present in the leaves of the Stevia rebaudiana plant, have a sweet taste, and have been used as a sweetener for centuries. To build on previous authoritative safety assessments of steviol glycosides, a systematic assessment of mechanistic data related to key characteristics of carcinogens (KCCs) was conducted. Over 900 KCC-relevant endpoints from peer-reviewed literature and high-throughput screening data (ToxCast/Tox21) were identified across individual steviol glycosides and derivatives, metabolites, and whole leaf extracts. Most data (both in vivo and in vitro, including human cells), showed inactivity. Studies were weighted according to quality and relevance. Although data were available for eight of the ten KCC, genotoxicity, oxidative stress, inflammation, and cell proliferation/cell death represent the KCCs with the most data. The data for these KCC primarily show beneficial activity (anti-inflammatory, antioxidant, and anti-proliferative). Following integration across all data, and accounting for study quality and relevance, the totality of the evidence demonstrated an overall lack of genotoxic and carcinogenic activity for steviol glycosides. This is in agreement with previous regulatory decisions, and is consistent with the lack of tumor response in two-year rodent cancer bioassays. The findings support prior conclusions that steviol glycosides are unlikely to be carcinogenic in humans.

Modeling Bioavailable Concentrations in Zebrafish Cell Lines and Embryos Increases the Correlation of Toxicity Potencies across Test Systems

Linking cellular toxicity to low-tier animal toxicity and beyond is crucial within the adverse outcome pathway concept and the 3R framework. This study aimed to determine and compare the bioavailable effect concentrations in zebrafish cell lines and embryos. Acute, short-term toxicity (48 h) of eight veterinary pharmaceuticals was measured in two zebrafish cell lines (hepatocytes, fibroblasts) and zebrafish embryos. Seven endpoints of cytotoxicity were recorded. The fish embryo acute toxicity test was modified by adding sublethal endpoints. Chemical distribution modeling (mass balance) was applied to compute the bioavailable compound concentrations in cells (Cfree) and embryos (Cint;aq) based on nominal effect concentrations (Cnom). Effect concentration ratios were calculated (cell effects/embryo effects). A low correlation was observed between cytotoxicity and embryo toxicity when nominal concentrations were used. Modeled bioavailable effect concentrations strongly increased correlations and placed regression lines close to the line of unity and axis origin. Cytotoxicity endpoints showed differences in sensitivity and predictability. The hepatocyte cell line depicted closer proximity to the embryo data. Conclusively, the high positive correlation between the cell- and embryo-based test systems emphasizes the appropriate modulation of toxicity when linked to bioavailable concentrations. Furthermore, it highlights the potential of fish cell lines to be utilized in integrated testing strategies.

Assessment of the biochemical pathways for acetaminophen toxicity: Implications for its carcinogenic hazard potential

In 2019 California's Office of Environmental Health Hazard Assessment (OEHHA) initiated a review of the carcinogenic hazard potential of acetaminophen. In parallel with this review, herein we evaluated the mechanistic data related to the steps and timing of cellular events following therapeutic recommended (≤4 g/day) and higher doses of acetaminophen that may cause hepatotoxicity to evaluate whether these changes indicate that acetaminophen is a carcinogenic hazard. At therapeutic recommended doses, acetaminophen forms limited amounts of N-acetyl-p-benzoquinone-imine (NAPQI) without adverse cellular effects. Following overdoses of acetaminophen, there is potential for more extensive formation of NAPQI and depletion of glutathione, which may result in mitochondrial dysfunction and DNA damage, but only at doses that result in cell death - thus making it implausible for acetaminophen to induce the kind of stable, genetic damage in the nucleus indicative of a genotoxic or carcinogenic hazard in humans. The collective data demonstrate a lack of a plausible mechanism related to carcinogenicity and are consistent with rodent cancer bioassays, epidemiological results reviewed in companion manuscripts in this issue, as well as conclusions of multiple international health authorities.

Lack of potential carcinogenicity for acesulfame potassium - Systematic evaluation and integration of mechanistic data into the totality of the evidence

The safety of low- and no-calorie sweeteners remains a topic of general interest. Substantial evidence exists demonstrating a lack of carcinogenicity of the no-calorie sweetener acesulfame potassium (Ace K). The objective of this evaluation was to conduct a systematic assessment of available mechanistic data using a framework that quantitatively integrates proposed key characteristics of carcinogens (KCCs) into the totality of the evidence. Over 800 KCC-relevant endpoints from a variety of in vitro and in vivo assays were assessed for quality, relevance, and activity, and integrated to determine the overall strength of the evidence for plausibility that Ace K acts through the KCC. Overall, there was a lack of activity across the KCCs (overall integrated score <0 and no "strong" categorization for evidence of activity) in which data were identified. Together with the absence of treatment-related tumor effects in rodent bioassays, these results support the conclusion that Ace K is unlikely to induce a carcinogenic response. This assessment employed a weight of the evidence analysis that includes the consideration of factors such as reliability, strength of the model system, activity, and dose in a complex and heterogeneous dataset, and the ultimate integration of multiple data streams in the cancer hazard evaluation.

Potential of ToxCast Data in the Safety Assessment of Food Chemicals

Tox21 and ToxCast are high-throughput in vitro screening programs coordinated by the U.S. National Toxicology Program and the U.S. Environmental Protection Agency, respectively, with the goal of forecasting biological effects in vivo based on bioactivity profiling. The present study investigated whether mechanistic insights in the biological targets of food-relevant chemicals can be obtained from ToxCast results when the chemicals are grouped according to structural similarity. Starting from the 556 direct additives that have been identified in the ToxCast database by Karmaus et al. [Karmaus, A. L., Trautman, T. D., Krishan, M., Filer, D. L., and Fix, L. A. (2017). Curation of food-relevant chemicals in ToxCast. Food Chem. Toxicol. 103, 174-182.], the results showed that, despite the limited number of assays in which the chemical groups have been tested, sufficient results are available within so-called "DNA binding" and "nuclear receptor" target families to profile the biological activities of the defined chemical groups for these targets. The most obvious activity identified was the estrogen receptor-mediated actions of the chemical group containing parabens and structurally related gallates, as well the chemical group containing genistein and daidzein (the latter 2 being particularly active toward estrogen receptor β as a potential health benefit). These group effects, as well as the biological activities of other chemical groups, were evaluated in a series of case studies. Overall, the results of the present study suggest that high-throughput screening data could add to the evidence considered for regulatory risk assessment of food chemicals and to the evaluation of desirable effects of nutrients and phytonutrients. The data will be particularly useful for providing mechanistic information and to fill data gaps with read-across.

Recommendations for further revisions to improve the International Agency for Research on Cancer (IARC) Monograph program

In 2019, the International Agency for Research on Cancer (IARC) "Preamble to the IARC Monographs" expanded guidance regarding the scientific approaches that should be employed in its monographs. These amendments to the monograph development process are an improvement but still fall short in several areas. While the revised Preamble lays out broad methods and approaches to evaluate scientific evidence, there is a lack of specificity with regard to how IARC Working Groups will conduct consistent evaluations in a standardized, objective, and transparent manner; document systematic review and evidence integration actions, and substantiate how these actions and decisions inform the ultimate classifications. Furthermore, no guidance is provided to ensure Working Groups consistently incorporate mechanistic evidence in a robust manner using a defined approach in the context of 21st century knowledge of modes of action. Nor are the conclusions of the working groups subjected to outside, independent scientific peer review. Continued improvements and modernization of the procedures for evaluating, presenting, and communicating study quality, and in the methods used to conduct and peer-review evidence-based decision making will benefit the Working Group members, the IARC Monographs Programme overall, and the international regulatory community and public who rely upon the monographs.

Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs)

Adverse outcome pathway Bayesian networks (AOPBNs) are a promising avenue for developing predictive toxicology and risk assessment tools based on adverse outcome pathways (AOPs). Here, we describe a process for developing AOPBNs. AOPBNs use causal networks and Bayesian statistics to integrate evidence across key events. In this article, we use our AOPBN to predict the occurrence of steatosis under different chemical exposures. Since it is an expert-driven model, we use external data (i.e., data not used for modeling) from the literature to validate predictions of the AOPBN model. The AOPBN accurately predicts steatosis for the chemicals from our external data. In addition, we demonstrate how end users can utilize the model to simulate the confidence (based on posterior probability) associated with predicting steatosis. We demonstrate how the network topology impacts predictions across the AOPBN, and how the AOPBN helps us identify the most informative key events that should be monitored for predicting steatosis. We close with a discussion of how the model can be used to predict potential effects of mixtures and how to model susceptible populations (e.g., where a mutation or stressor may change the conditional probability tables in the AOPBN). Using this approach for developing expert AOPBNs will facilitate the prediction of chemical toxicity, facilitate the identification of assay batteries, and greatly improve chemical hazard screening strategies.

Considerations for the Use of Mutation as a Regulatory Endpoint in Risk Assessment

Assessment of a chemical's potential to cause permanent changes in the genetic code has been a common practice in the industry and regulatory settings for decades. Furthermore, the genetic toxicity battery of tests has typically been employed during the earliest stages of the research and development programs of new product development. A positive outcome from such battery has a major impact on the chemical's utility, industrial hygiene, product stewardship practices, and product life cycle analysis, among many other decisions that need to be taken by the industry, even before the registration of a chemical is undertaken. Under the prevailing regulatory paradigm, the dichotomous (yes/no) evaluation of the chemical's genotoxic potential leads to a conservative, linear no-threshold (LNT) risk assessment, unless compelling and undeniable data to the contrary can be provided to satisfy regulators, typically in a number of different global jurisdictions. With the current advent of predictive methods, new testing paradigms, mode-of-action/adverse outcome pathways, and quantitative risk assessment approaches, various stakeholders are starting to employ these state-of-the-science methodologies to further the conversation on decision making and advance the regulatory paradigm beyond the dominant LNT status quo. This commentary describes these novel methodologies, relevant biological responses, and how these can affect internal and regulatory risk assessment approaches. Environ. Mol. Mutagen. 61:84-93, 2020. © 2019 Wiley Periodicals, Inc.

Lack of potential carcinogenicity for sucralose - Systematic evaluation and integration of mechanistic data into the totality of the evidence

Sucralose is widely used as a sugar substitute. Many studies and authoritative reviews have concluded that sucralose is non-carcinogenic, based primarily on animal cancer bioassays and genotoxicity data. To add to the body of knowledge on the potential carcinogenicity of sucralose, a systematic assessment of mechanistic data was conducted. This entailed using a framework developed for the quantitative integration of data related to the proposed key characteristics of carcinogens (KCCs). Data from peer-reviewed literature and the ToxCast/Tox21 database were evaluated using an algorithm that weights data for quality and relevance. The resulting integration demonstrated an overall lack of activity for sucralose across the KCCs, with no "strong" activity observed for any KCC. Almost all data collected demonstrated inactivity, including those conducted in human models. The overall lack of activity in mechanistic data is consistent with findings from animal cancer bioassays. The few instances of activity across the KCC were generally accompanied by limitations in study design in the context of either quality and/or dose and model relevance, highlighted upon integration of the totality of the evidence. The findings from this comprehensive and integrative evaluation of mechanistic data support prior conclusions that sucralose is unlikely to be carcinogenic in humans.

Progress in data interoperability to support computational toxicology and chemical safety evaluation

New approach methodologies (NAMs) in chemical safety evaluation are being explored to address the current public health implications of human environmental exposures to chemicals with limited or no data for assessment. For over a decade since a push toward "Toxicity Testing in the 21st Century," the field has focused on massive data generation efforts to inform computational approaches for preliminary hazard identification, adverse outcome pathways that link molecular initiating events and key events to apical outcomes, and high-throughput approaches to risk-based ratios of bioactivity and exposure to inform relative priority and safety assessment. Projects like the interagency Tox21 program and the US EPA ToxCast program have generated dose-response information on thousands of chemicals, identified and aggregated information from legacy systems, and created tools for access and analysis. The resulting information has been used to develop computational models as viable options for regulatory applications. This progress has introduced challenges in data management that are new, but not unique, to toxicology. Some of the key questions require critical thinking and solutions to promote semantic interoperability, including: (1) identification of bioactivity information from NAMs that might be related to a biological process; (2) identification of legacy hazard information that might be related to a key event or apical outcomes of interest; and, (3) integration of these NAM and traditional data for computational modeling and prediction of complex apical outcomes such as carcinogenesis. This work reviews a number of toxicology-related efforts specifically related to bioactivity and toxicological data interoperability based on the goals established by Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles. These efforts are essential to enable better integration of NAM and traditional toxicology information to support data-driven toxicology applications.

Multi-dimensional in vitro bioactivity profiling for grouping of glycol ethers

High-content screening data derived from physiologically-relevant in vitro models promise to improve confidence in data-integrative groupings for read-across in human health safety assessments. The biological data-based read-across concept is especially applicable to bioactive chemicals with defined mechanisms of toxicity; however, the challenge of data-derived groupings for chemicals that are associated with little or no bioactivity has not been explored. In this study, we apply a suite of organotypic and population-based in vitro models for comprehensive bioactivity profiling of twenty E-Series and P-Series glycol ethers, solvents with a broad variation in toxicity ranging from relatively non-toxic to reproductive and hematopoetic system toxicants. Both E-Series and P-Series glycol ethers elicited cytotoxicity only at high concentrations (mM range) in induced pluripotent stem cell-derived hepatocytes and cardiomyocytes. Population-variability assessment comprised a study of cytotoxicity in 94 human lymphoblast cell lines from 9 populations and revealed differences in inter-individual variability across glycol ethers, but did not indicate population-specific effects. Data derived from various phenotypic and transcriptomic assays revealed consistent bioactivity trends between both cardiomyocytes and hepatocytes, indicating a more universal, rather than cell-type specific mode-of-action for the tested glycol ethers in vitro. In vitro bioactivity-based similarity assessment using Toxicological Priority Index (ToxPi) showed that glycol ethers group according to their alcohol chain length, longer chains were associated with increased bioactivity. While overall in vitro bioactivity profiles did not correlate with in vivo toxicity data on glycol ethers, in vitro bioactivity of E-series glycol ethers were indicative of and correlated with in vivo irritation scores.

Identifying Attributes That Influence In Vitro-to-In Vivo Concordance by Comparing In Vitro Tox21 Bioactivity Versus In Vivo DrugMatrix Transcriptomic Responses Across 130 Chemicals

Recent efforts aimed at integrating in vitro high-throughput screening (HTS) data into chemical toxicity assessments are necessitating increased understanding of concordance between chemical-induced responses observed in vitro versus in vivo. This investigation set out to (1) measure concordance between in vitro HTS data and transcriptomic responses observed in vivo, focusing on the liver, and (2) identify attributes that can influence concordance. Signal response profiles from 130 substances were compared between in vitro data produced through Tox21 and liver transcriptomic data through DrugMatrix, collected from rats exposed to a chemical for ≤5 days. A global in vitro-to-in vivo comparative analysis based on pathway-level responses resulted in an overall average percent agreement of 79%, ranging on a per-chemical basis between 41% and 100%. Whereas concordance amongst inactive chemicals was high (89%), concordance amongst chemicals showing in vitro activity was only 13%, suggesting that follow-up in vivo and/or orthogonal in vitro assays would improve interpretations of in vitro activity. Attributes identified to influence concordance included experimental design attributes (eg, cell type), target pathways, and physicochemical properties (eg, logP). The attribute that most consistently increased concordance was dose applicability, evaluated by filtering for experimental doses administered to rats that were within 10-fold of those related to likely bioactivity, derived using Tox21 data and high-throughput toxicokinetic modeling. Together, findings suggest that in vitro screening approaches to predict in vivo toxicity are viable particularly when certain attributes are considered, including whether activity versus inactivity is observed, experimental design, chemical properties, and dose applicability.

IARC use of oxidative stress as key mode of action characteristic for facilitating cancer classification: Glyphosate case example illustrating a lack of robustness in interpretative implementation

The International Agency for Research on Cancer (IARC) has formulated 10 key characteristics of human carcinogens to incorporate mechanistic data into cancer hazard classifications. The analysis used glyphosate as a case example to examine the robustness of IARC's determination of oxidative stress as "strong" evidence supporting a plausible cancer mechanism in humans. The IARC analysis primarily relied on 14 human/mammalian studies; 19 non-mammalian studies were uninformative of human cancer given the broad spectrum of test species and extensive use of formulations and aquatic testing. The mammalian studies had substantial experimental limitations for informing cancer mechanism including use of: single doses and time points; cytotoxic/toxic test doses; tissues not identified as potential cancer targets; glyphosate formulations or mixtures; technically limited oxidative stress biomarkers. The doses were many orders of magnitude higher than human exposures determined in human biomonitoring studies. The glyphosate case example reveals that the IARC evaluation fell substantially short of "strong" supporting evidence of oxidative stress as a plausible human cancer mechanism, and suggests that other IARC monographs relying on the 10 key characteristics approach should be similarly examined for a lack of robust data integration fundamental to reasonable mode of action evaluations.