A new paradigm in threshold of toxicological concern based on chemoinformatics analysis of a highly curated database enriched with antimicrobials

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.

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.

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.

Evaluating the utility of the Threshold of Toxicological Concern (TTC) and its exclusions in the biocompatibility assessment of extractable chemical substances from medical devices

The Threshold of Toxicological Concern (TTC) is a pragmatic approach used to establish safe thresholds below which there can be no appreciable risk to human health. Here, a large inventory of ~45,000 substances (referred to as the LRI dataset) was profiled through the Kroes TTC decision module within Toxtree v3.1 to assign substances into their respective TTC categories. Four thousand and two substances were found to be not applicable for the TTC approach. However, closer examination of these substances uncovered several implementation issues: substances represented in their salt forms were automatically assigned as not appropriate for TTC when many of these contained essential metals as counter ions which would render them TTC applicable. High Potency Carcinogens and dioxin-like substances were not fully captured based on the rules currently implemented in the software. Phosphorus containing substances were considered exclusions when many of them would be appropriate for TTC. Refinements were proposed to address the limitations in the current software implementation. A second component of the study explored a set of substances representative of those released from medical devices and compared them to the LRI dataset as well as other toxicity datasets to investigate their structural similarity. A third component of the study sought to extend the exclusion rules to address application to substances released from medical devices that lack toxicity data. The refined rules were then applied to this dataset and the TTC assignments were compared. This case study demonstrated the importance of evaluating the software implementation of an established TTC workflow, identified certain limitations and explored potential refinements when applying these concepts to medical devices.

Read-across approaches: current applications and regulatory acceptance in Korea, Japan, and China

The aim of this paper was to investigate the current status of read-across approaches in the Republic of Korea, Japan, and China in terms of applications and regulatory acceptance. In the Republic of Korea, over the last 6 years, approximately 8% of safety data records used for chemical registrations were based upon read-across, and a guideline published on the use of read-across results in 2017. In Japan, read-across is generally accepted for screening hazard classification of toxicological endpoints according to the Chemical Substances Control Law (CSCL). In China, read-across data, along with data from other animal alternatives are accepted as a data source for chemical registrations, but could be only considered when testing is not technically feasible. At present, read-across is not widely used for chemical registrations and regulatory acceptance of read-across may differ among countries in Asia. With consideration of the advantages and limitations of read-across, it is expected that read-across may soon gradually be employed in Asian countries. Thus, regulatory agencies need to prepare for this progression.

In Silico Models for Repeated-Dose Toxicity (RDT): Prediction of the No Observed Adverse Effect Level (NOAEL) and Lowest Observed Adverse Effect Level (LOAEL) for Drugs

Many regulatory contexts require the evaluation of repeated-dose toxicity (RDT) studies conducted in laboratory animals. The main outcome of RDT studies is the identification of the no observed adverse effect level (NOAEL) and the lowest observed adverse effect level (LOAEL) that are normally used as point of departure for the establishment of health-based guidance values. Since in vivo RDT studies are expensive and time-consuming, in silico approaches could offer a valuable alternative. However, NOAEL and LOAEL modeling suffer some limitations since they do not refer to a single end point but to several different effects, and the doses used in experimental studies strongly influence the results. Few attempts to model NOAEL and LOAEL have been reported. The available database and models for the prediction of NOAEL and LOAEL are reviewed here.

Threshold of Toxicological Concern: Extending the chemical space by inclusion of a highly curated dataset for organosilicon compounds

The Threshold of Toxicological Concern (TTC) for non-genotoxic substances, a risk assessment tool to establish safe exposure levels for chemicals with insufficient toxicological data, is based on the 5th percentile of cumulated distributions of Point of Departures in a high amount of repeat-dose, developmental and reproductive toxicity studies, grouped by Cramer Classes. The lack of organosilicon compounds in this dataset has resulted in regulatory concerns over the applicability of the TTC concept for this chemistry. We collected publicly available, scientifically robust oral repeat-dose and DART studies for 71 organosilicon substances for inclusion in the existing TTC dataset, using criteria for evaluation of studies and derivation of points of departure analogous to the Munro and COSMOS TTC publications. The resulting 5th percentile of this dataset was 13-fold higher than the 5th percentile for Cramer Class III compounds reported by Munro (which is the default for silicon-containing substances). Both the existing TTC for Cramer Class III compounds from Munro (1.5 μg/kg bw/day) and the COSMOS TTC (2.3 μg/kg bw/day), recommended by the SCCS for cosmetics-related substances, provide a conservative and sufficiently protective approach for this class of chemistry.

Toxicological safety assessment of essential oils used as food supplements to establish safe oral recommended doses

Essential oils (EOs) are increasingly consumed as food supplements. The few published recommended doses available generally lack details both on the methodology used and concentration limits for substances of concern, including genotoxic carcinogens. We propose a tiered approach based on the toxicological evaluation of maximized concentrations of each constituent present in the EO investigated. The genotoxic potential of each constituent is assessed using literature data or QSAR analyses. Genotoxic constituents are evaluated according to the methodology provided in the ICHM7 guideline. A Toxicological Reference Value (TRV) is associated to each non-genotoxic constituent, using one of the following methodologies (decision-tree successive steps): extraction from recognized databases or clinical studies, application of adequate safety factors to NOAELs established in animal studies, read-across analyses and when none was possible, TTC of Cramer classes. An EO recommended dose is considered safe when the safety margin (ratio between TRV and systemic exposure) for all constituents is all at least equal to 1. In conclusion, this methodology has proven to be robust to establish safe recommended doses for EOs used as food supplements, consistent with those publicly available, and avoiding unnecessary dedicated new animal testing.

An overview of current practices for regulatory risk assessment with lessons learnt from cosmetics in the European Union

Risk assessments of various types of chemical compounds are carried out in the European Union (EU) foremost to comply with legislation and to support regulatory decision-making with respect to their safety. Historically, risk assessment has relied heavily on animal experiments. However, the EU is committed to reduce animal experimentation and has implemented several legislative changes, which have triggered a paradigm shift towards human-relevant animal-free testing in the field of toxicology, in particular for risk assessment. For some specific endpoints, such as skin corrosion and irritation, validated alternatives are available whilst for other endpoints, including repeated dose systemic toxicity, the use of animal data is still central to meet the information requirements stipulated in the different legislations. The present review aims to provide an overview of established and more recently introduced methods for hazard assessment and risk characterisation for human health, in particular in the context of the EU Cosmetics Regulation (EC No 1223/2009) as well as the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) Regulation (EC 1907/2006).

COSMOS next generation - A public knowledge base leveraging chemical and biological data to support the regulatory assessment of chemicals

The COSMOS Database (DB) was originally established to provide reliable data for cosmetics-related chemicals within the COSMOS Project funded as part of the SEURAT-1 Research Initiative. The database has subsequently been maintained and developed further into COSMOS Next Generation (NG), a combination of database and in silico tools, essential components of a knowledge base. COSMOS DB provided a cosmetics inventory as well as other regulatory inventories, accompanied by assessment results and in vitro and in vivo toxicity data. In addition to data content curation, much effort was dedicated to data governance - data authorisation, characterisation of quality, documentation of meta information, and control of data use. Through this effort, COSMOS DB was able to merge and fuse data of various types from different sources. Building on the previous effort, the COSMOS Minimum Inclusion (MINIS) criteria for a toxicity database were further expanded to quantify the reliability of studies. COSMOS NG features multiple fingerprints for analysing structure similarity, and new tools to calculate molecular properties and screen chemicals with endpoint-related public profilers, such as DNA and protein binders, liver alerts and genotoxic alerts. The publicly available COSMOS NG enables users to compile information and execute analyses such as category formation and read-across. This paper provides a step-by-step guided workflow for a simple read-across case, starting from a target structure and culminating in an estimation of a NOAEL confidence interval. Given its strong technical foundation, inclusion of quality-reviewed data, and provision of tools designed to facilitate communication between users, COSMOS NG is a first step towards building a toxicological knowledge hub leveraging many public data systems for chemical safety evaluation. We continue to monitor the feedback from the user community at support@mn-am.com.

Systemic absorption of benzalkonium chloride after maximal use of a consumer antiseptic wash product

An in vivo pharmacokinetic study was conducted using consumer antiseptic wash containing 0.13% benzalkonium chloride (BAC) to assess the effect of dermal absorption on long-term systemic exposure to BAC. The objective of the study was to determine blood levels of BAC under maximal use conditions. Subjects were enlisted to wash their hands 60 s with soap containing 0.13% BAC 30 times per day over an 8-9 h time period for 5 consecutive days. The test product with the highest absorption potential was selected based on market share and results from in vitro permeation testing. Blood plasma was collected from subjects on 32 occasions over the 6-day study period. Plasma samples were analyzed for the C₁₂ and C₁₄ homologs of BAC using LC-MS/MS with a lower limit of quantitation (LLOQ) of 106.9 and 32.6 ng/L, respectively. For the 32 subjects, C₁₂ homolog was detected above the LLOQ in only four of 1,024 plasma samples at 117.8-191.7 ng/L, and C₁₄ homolog was detected in only one sample at 59.5 ng/L. Consequently, systemic exposure to BAC in antimicrobial soap is very low and below the level of concern identified by the U.S. Food and Drug Administration (500 ng/L) even under maximal use conditions.

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

In cases where chemical-specific toxicity data are absent or limited, the threshold of toxicological concern (TTC) offers an alternative to assess human exposure below which “there would be no appreciable risk to human health.” The application of TTC to non-cancer systemic endpoints has been pursued for decades using a chemical classification and Point of Departure (POD). This study presents a new POD dataset of oral subacute/subchronic toxicity studies in rats for 656 industrial chemicals retrieved from the Hazard Evaluation Support System (HESS) Integrated Platform, which contains hundreds of reliable repeated-dose toxicity test data of industrial chemicals under the Chemical Substances of Control Law in Japan. The HESS TTC dataset was found to have less duplication with substances in other reported TTC datasets. Each chemical was classified into a Cramer Class, with 68, 3, and 29% of these 656 chemicals distributed in Classes III, II, and I, respectively. For each Cramer Class, a provisional Tolerable Daily Intake (TDI) was derived from the 5th percentile of the lognormal distribution of PODs. The TDIs were 1.9 and 30 μg/kg bw/day for Classes III and I, respectively. The TDI for Cramer Class II could not be determined due to insufficient sample size. This work complements previous studies of the TTC approach and increases the confidence of the thresholds for non-cancer endpoints by including unique chemical structures. This new TTC dataset is publicly available and can be merged with existing databases to improve the TTC approach.

Development of a Battery of In Silico Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment

Drug-induced liver injury (DILI) remains a challenge when translating knowledge from the preclinical stage to human use cases. Attempts to model human DILI directly based on the information from drug labels have had some success; however, the approach falls short of providing insights or addressing uncertainty due to the difficulty of decoupling the idiosyncratic nature of human DILI outcomes. Our approach in this comparative analysis is to leverage existing preclinical and clinical data as well as information on metabolism to better translate mammalian to human DILI. The human DILI knowledge base from the United States Food and Drug Administration (U.S. FDA) National Center for Toxicology Research contains 1036 pharmaceuticals from diverse therapeutic categories. A human DILI training set of 305 oral marketed drugs was prepared and a binary classification scheme applied. The second knowledge base consists of mammalian repeated dose toxicity with liver toxicity data from various regulatory sources. Within this knowledge base, we identified 278 pharmaceuticals containing 198 marketed or withdrawn oral drugs with data from the U.S. FDA new drug application and 98 active pharmaceutical ingredients from ToxCast. From this collection, a set of 225 oral drugs was prepared as the mammalian hepatotoxicity training set with particular end points of pathology findings in the liver and bile duct. Both human and mammalian data sets were processed using various learning algorithms, including artificial intelligence approaches. The external validations for both models were comparable to the training statistics. These data sets were also used to extract species-differentiating chemotypes that differentiate DILI effects on humans from mammals. A systematic workflow was devised to predict human DILI and provide mechanistic insights. For a given query molecule, both human and mammalian models are run. If the predictions are discordant, both metabolites and parents are investigated for quantitative structure-activity relationship and species-differentiating chemotypes. Their results are combined using the Dempster-Shafer decision theory to yield a final outcome prediction for human DILI with estimated uncertainty. Finally, these tools are implementable within an in silico platform for systematic evaluation.

Do Similar Structures Have Similar No Observed Adverse Effect Level (NOAEL) Values? Exploring Chemoinformatics Approaches for Estimating NOAEL Bounds and Uncertainties

Determination of the no observed adverse effect level (NOAEL) of a substance is an important step in safety and regulatory assessments. Application of conventional in silico strategies, for example, quantitative structure-activity relationship (QSAR) models, to predict NOAEL values is inherently problematic. Whereas QSAR models for well-defined toxicity endpoints such as Ames mutagenicity or skin sensitization can be developed from mechanistic knowledge of molecular initiating events and adverse outcome pathways, QSAR is not appropriate for predicting a NOAEL value, a concentration at which "no effect" is observed. This paper presents a chemoinformatics approach and explores how it can be further refined through the incorporation of toxicity endpoint-specific information to estimate confidence bounds for the NOAEL of a target substance, given experimentally determined NOAEL values for one or more suitable analogues. With a sufficiently large NOAEL database, we analyze how a difference in NOAEL values for pairs of structures depends on their pairwise similarity, where similarity takes both structural features and physicochemical properties into account. The width of the estimate NOAEL confidence interval is proportional to the uncertainty. Using the new threshold of toxicological concern (TTC) database enriched with antimicrobials, examples are presented to illustrate how uncertainty decreases with increasing analogue quality and also how NOAEL bounds estimation can be significantly improved by filtering the full database to include only substances that are in structure categories relevant to the target and analogue.