Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods

A tiered next-generation risk assessment framework integrating toxicokinetics and NAM-based toxicodynamics: "proof of concept" case study using pyrethroids

New Approach Methodologies (NAMs) in Next-Generation Risk Assessment (NGRA), integrating toxicokinetics (TK) with toxicodynamics (TD), provides an accurate evaluation of combined chemical exposures. This study assesses pyrethroids, which pose regulatory challenges due to their widespread use and cumulative exposure risks. A tiered NGRA framework was compared with conventional risk assessment (RA) to evaluate regulatory applicability. In Tier 1, ToxCast data established gene and tissue bioactivity indicators, facilitating hypothesis-driven hazard identification. Tier 2 examined combined risk assessments, rejecting the hypothesis of the same mode of action and highlighting inconsistencies in in vitro data and NOAEL/ADI correlations. Tier 3 applied Margin of Exposure (MoE) analysis and TK modeling to realistic exposure estimations for risk assessment screening based on internal doses, identifying tissue-specific pathways as critical risk drivers. Tier 4 refined bioactivity indicators using TK approaches to improve the NAM-based effect assessment, including an in vitro vs. in vivo comparison, with coherent results based on interstitial concentrations, though intracellular estimations remained uncertain. Tier 5 confirmed that dietary exposure in healthy adults is close to but below levels of concern, with bioactivity MoE values remaining below concern thresholds, and in vivo MoEs within the standard safety factors. Nevertheless, the MoEs are insufficient for addressing the additional non-dietary exposure expected from other pyrethroid uses such as biocides or pharmaceuticals. Results demonstrate that NGRA with TK-NAM-based TD offers a nuanced, regulatory-relevant framework for risk assessment. The proposed approach integrates the information on individual pyrethroids using bioactivity indicators; and the re-assessment of regulatory toxicity studies to select organ-relevant NOAELs allowed an improved in vitro-in vivo comparison, demonstrating the capacity of bioactivity-based MoEs for combined exposure assessments. This tiered approach provides key insights for regulatory decision-making, establishing a robust model for evaluating pyrethroids and similar chemical classes.

Integration of new approach methods for the assessment of data-poor chemicals

The use of new approach methods (NAMs), including high-throughput, in vitro bioactivity data, in setting a point-of-departure (POD) will accelerate the pace of human health hazard assessments. Combining hazard and exposure predictions into a bioactivity:exposure ratio (BER) for use in risk-based prioritization and utilizing NAM-based bioactivity flags to indicate potential hazards of interest for further prediction or mechanism-based screening together comprise a prospective approach for management of substances with limited traditional toxicity testing data. In this work, we demonstrate a NAM-based assessment case study conducted via the Accelerating the Pace of Chemical Risk Assessment initiative, a consortium of international research and regulatory scientists. The primary objective was to develop a reusable and adaptable approach for addressing chemicals with limited traditional toxicity data using a NAM-based POD, BER, and bioactivity-based flags for indication of putative endocrine, developmental, neurological, and immunosuppressive effects via data generation and interpretation for 200 substances. Multiple data streams, including in silico and in vitro NAMs, were used. High-throughput transcriptomics and phenotypic profiling data, as well as targeted biochemical and cell-based assays, were combined with generic high-throughput toxicokinetic models parameterized with chemical-specific data to estimate dose for comparison to exposure predictions. This case study further enables regulatory scientists from different international purviews to utilize efficient approaches for prospective chemical management, addressing hazard and risk-based data needs, while reducing the need for animal studies. This work demonstrates the feasibility of using a battery of toxicodynamic and toxicokinetic NAMs to provide a NAM-based POD for screening-level assessment.

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.

Systematic Approaches for the Encoding of Chemical Groups: A Case Study

Regulatory authorities aim to organize substances into groups to facilitate prioritization within hazard and risk assessment processes. Often, such chemical groupings are not explicitly defined by structural rules or physicochemical property information. This is largely due to how these groupings are developed, namely, a manual expert curation process, which in turn makes updating and refining groupings, as new substances are evaluated, a practical challenge. Herein, machine learning methods were leveraged to build models that could preliminarily assign substances to predefined groups. A set of 86 groupings containing 2,184 substances as published on the European Chemicals Agency (ECHA) website were mapped to the U.S. Environmental Protection Agency (EPA) Distributed Toxicity Structure Database (DSSTox) content to extract chemical and structural information. Substances were represented using Morgan fingerprints, and two machine learning approaches were used to classify test substances into 56 groups containing at least 10 substances with a structural representation in the data set: k-nearest neighbor (kNN) and random forest (RF), that led to mean 5-fold cross-validation test accuracies (average F1 scores) of 0.781 and 0.853, respectively. With a 9% improvement, the RF classifier was significantly more accurate than KNN (p-value = 0.001). The approach offers promise as a means of the initial profiling of new substances into predefined groups to facilitate prioritization efforts and streamline the assessment of new substances when earlier groupings are available. The algorithm to fit and use these models has been made available in the accompanying repository, thereby enabling both use of the produced models and refitting of these models, as new groupings become available by regulatory authorities or industry.

Evaluating scientific confidence in the concordance of in vitro and in vivo protective points of departure

To fulfil the promise of reducing reliance on mammalian in vivo laboratory animal studies, new approach methods (NAMs) need to provide a confident basis for regulatory decision-making. However, previous attempts to develop in vitro NAMs-based points of departure (PODs) have yielded mixed results, with PODs from U.S. EPA's ToxCast, for instance, appearing more conservative (protective) but poorly correlated with traditional in vivo studies. Here, we aimed to address this discordance by reducing the heterogeneity of in vivo PODs, accounting for species differences, and enhancing the biological relevance of in vitro PODs. However, we only found improved in vitro-to-in vivo concordance when combining the use of Bayesian model averaging-based benchmark dose modeling for in vivo PODs, allometric scaling for interspecies adjustments, and human-relevant in vitro assays with multiple induced pluripotent stem cell-derived models. Moreover, the available sample size was only 15 chemicals, and the resulting level of concordance was only fair, with correlation coefficients <0.5 and prediction intervals spanning several orders of magnitude. Overall, while this study suggests several ways to enhance concordance and thereby increase scientific confidence in vitro NAMs-based PODs, it also highlights challenges in their predictive accuracy and precision for use in regulatory decision making.