Development of a New Threshold of Toxicological Concern Database of Non-cancer Toxicity Endpoints for Industrial Chemicals

Quantitative prediction of toxicological points of departure using two-stage machine learning models: A new approach methodology (NAM) for chemical risk assessment

Point of departure (POD) is a concept used in risk assessment to calculate the reference dose of exposure that is likely to have no appreciable risk on health. POD can be directly utilized from no observed adverse effect levels (NOAEL) which is the dose or exposure level at which there is little or no risk of adverse effects. However, NOAEL values are unavailable for most of the chemicals due to inconsistent animal toxicity data. Hence, the current study utilizes a two-stage machine learning (ML) model for predicting NOAEL values, based on data curated from diverse toxicity exposures. In the first stage, a random forest regressor is used for supervised outlier detection and removal addressing any variability in data and poor correlations. The refined data is then used for toxicity prediction using several ML models; random forest and XGBoost show relatively higher performance with an R2 value of 0.4 and 0.43, respectively, for predicting NOAEL in chronic toxicity. Similarly, feature combinations with absorption distribution metabolism and excretion (ADME) indicate better NOAEL prediction for acute toxicity. External validation is performed by predicting NOAEL values for cosmetic pigments and calculating reference doses (RfD). Notably, pigments like orange and red show higher RfD values, indicating broader safety margins. This study provides a practical framework for addressing variability and data limitations in toxicity prediction while offering insights into its applicability in risk evaluation.

Incorporating new approach methods (NAMs) data in dose-response assessments: The future is now!

Regulatory dose-response assessments traditionally rely on in vivo data and default assumptions. New Approach Methods (NAMs) present considerable opportunities to both augment traditional dose-response assessments and accelerate the evaluation of new/data-poor chemicals. This review aimed to determine the potential utilization of NAMs through a unified conceptual framework that compartmentalizes derivation of toxicity values into five sequential Key Dose-response Modules (KDMs): (1) point-of-departure (POD) determination, (2) test system-to-human (e.g. inter-species) toxicokinetics and (3) toxicodynamics, (4) human population (intra-species) variability in toxicodynamics, and (5) toxicokinetics. After using several "traditional" dose-response assessments to illustrate this framework, a review is presented where existing NAMs, including in silico, in vitro, and in vivo approaches, might be applied across KDMs. Further, the false dichotomy between "traditional" and NAMs-derived data sources is broken down by organizing dose-response assessments into a matrix where each KDM has Tiers of increasing precision and confidence: Tier 0: Default/generic values, Tier 1: Computational predictions, Tier 2: Surrogate measurements, and Tier 3: Direct measurements. These findings demonstrated that although many publications promote the use of NAMs in KDMs (1) for POD determination and (5) for human population toxicokinetics, the proposed matrix of KDMs and Tiers reveals additional immediate opportunities for NAMs to be integrated across other KDMs. Further, critical needs were identified for developing NAMs to improve in vitro dosimetry and quantify test system and human population toxicodynamics. Overall, broadening the integration of NAMs across the steps of dose-response assessment promises to yield higher throughput, less animal-dependent, and more science-based toxicity values for protecting human health.

Application of the threshold of toxicological concern (TTC) in the evaluation of drinking water contact chemicals

The Threshold of Toxicological Concern (TTC) is an approach for assessing the safety of chemicals with low levels of exposure for which limited toxicology data are available. The original TTC criteria were derived for oral exposures from a distributional analysis of a dataset of 613 chemicals that identified 5th percentile no observed effect level (NOEL) values grouped within three tiers of compounds having specific structural functional groups and/or toxic potencies known as Cramer I, II and III classifications. Subsequent assessments of the TTC approach have established current thresholds to be scientifically robust. While the TTC has gained acknowledgment and acceptance by many regulatory agencies and organizations, use of the TTC approach in evaluating drinking water chemicals has been limited. To apply the TTC concept to drinking water chemicals, an exposure-based approach that incorporates the current weight of evidence for the target chemical is presented. Such an approach provides a comparative point of departure to the 5th percentile TTC NOEL using existing data, while conserving the allocation of toxicological resources for quantitative risk assessment to chemicals with greater exposure or toxicity. This approach will be considered for incorporation into NSF/ANSI/CAN 600, a health effects standard used in the safety evaluation of chemicals present in drinking water from drinking water contact additives and materials certified to NSF/ANSI/CAN 60 and 61, respectively.

Ice flavours and non-menthol synthetic cooling agents in e-cigarette products: a review

E-cigarettes with cooling flavours have diversified in ways that complicate tobacco control with the emergence of: (1) Ice-hybrid flavours (eg, 'Raspberry Ice') that combine cooling and fruity/sweet properties; and (2) Products containing non-menthol synthetic cooling agents (eg, Wilkinson Sword (WS), WS-3, WS-23 (termed 'koolada')). This paper reviews the background, chemistry, toxicology, marketing, user perceptions, use prevalence and policy implications of e-cigarette products with ice-hybrid flavours or non-menthol coolants. Scientific literature search supplemented with industry-generated and user-generated information found: (a) The tobacco industry has developed products containing synthetic coolants since 1974, (b) WS-3 and WS-23 are detected in mass-manufactured e-cigarettes (eg, PuffBar); (c) While safe for limited oral ingestion, inhalational toxicology and health effects from daily synthetic coolant exposure are unknown and merit scientific inquiry and attention from regulatory agencies; (d) Ice-hybrid flavours are marketed with themes incorporating fruitiness and/or coolness (eg, snow-covered raspberries); (e) WS-23/WS-3 concentrates also are sold as do-it-yourself additives, (f) Pharmacology research and user-generated and industry-generated information provide a premise to hypothesise that e-cigarette products with ice flavours or non-menthol cooling agents generate pleasant cooling sensations that mask nicotine's harshness while lacking certain aversive features of menthol-only products, (g) Adolescent and young adult use of e-cigarettes with ice-hybrid or other cooling flavours may be common and cross-sectionally associated with more frequent vaping and nicotine dependence in convenience samples. Evidence gaps in the epidemiology, toxicology, health effects and smoking cessation-promoting potential of using these products exist. E-cigarettes with ice flavours or synthetic coolants merit scientific and regulatory attention.

High Throughput Read-Across for Screening a Large Inventory of Related Structures by Balancing Artificial Intelligence/Machine Learning and Human Knowledge

Read-across is an in silico method applied in chemical risk assessment for data-poor chemicals. The read-across outcomes for repeated-dose toxicity end points include the no-observed-adverse-effect level (NOAEL) and estimated uncertainty for a particular category of effects. We have previously developed a new paradigm for estimating NOAELs based on chemoinformatics analysis and experimental study qualities from selected analogues, not relying on quantitative structure-activity relationships (QSARs) or rule-based SAR systems, which are not well-suited to end points for which the underpinning data are weakly grounded in specific chemical-biological interactions. The central hypothesis of this approach is that similar compounds have similar toxicity profiles and, hence, similar NOAEL values. Analogue quality (AQ) quantifies the suitability of an analogue candidate for reading across to the target by considering similarity from structure, physicochemical, ADME (absorption, distribution, metabolism, excretion), and biological perspectives. Biological similarity is based on experimental data; assay vectors derived from aggregations of ToxCast/Tox21 data are used to derive machine learning (ML) hybrid rules that serve as biological fingerprints to capture target-analogue similarity relevant to specific effects of interest, for example, hormone receptors (ER/AR/THR). Once one or more analogues have been qualified for read-across, a decision theory approach is used to estimate confidence bounds for the NOAEL of the target. The confidence interval is dramatically narrowed when analogues are constrained to biologically related profiles. Although this read-across process works well for a single target with several analogues, it can become unmanageable when, for example, screening multiple targets (e.g., virtual screening library) or handling a parent compound having numerous metabolites. To this end, we have established a digitalized framework to enable the assessment of a large number of substances, while still allowing for human decisions for filtering and prioritization. This workflow was developed and validated through a use case of a large set of bisphenols and their metabolites.