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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 Within this knowledge base, we identified 278 pharmaceuticals containing 198 marketed or withdrawn oral drugs with data from the U.S. FDA new drug application and 98 active pharmaceutical ingredients from ToxCast.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

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.

California's Office of Environmental Health Hazard Assessment was tasked with conducting risk assessments for United States Food and Drug Administration-approved food dyes relative to neurobehavioral concerns. The purpose of this assessment was to evaluate the evidence for neurodevelopment effects based on three streams of evidence: 1) studies identified by OEHHA for consideration in a quantitative risk assessment; 2) studies relevant to understanding mechanisms of neurobehavioral effects; 3) an in silico assessment of the bioavailability of USFDA-approved food dyes. The results indicate a lack of adequate or consistent evidence of neurological effects, supported by a lack of bioavailability and brain penetration predicted by the in silico assessment. Further, the mechanistic evidence supports a lack of activity from in vitro neurotransmitter assays, and a lack of evidence to support molecular initiating events or key events in adverse outcome pathways associated with neurodevelopmental effects, supporting a lack of biological plausibility for neurobehavioral effects following food exposures to colors. These conclusions are consistent with other authoritative bodies, such as JECFA and EFSA, that have determined (i) other effects are more appropriate for estimating acceptable daily intakes and (ii) evidence from the neurobehavioral studies lack the strength to be relied upon for quantitative risk assessment.

A new database of antimicrobial-enriched chemicals for the Threshold of Toxicological Concern (TTC) approach has been compiled, comprising 1357 chemicals with 276, 54, and 1027 substances in Cramer Classes I, II, and III, respectively. To enrich the chemical space of the No-/Lowest-Observed-Adverse Effect Level (NOAEL/LOAEL) database, a reference Antimicrobial (AM) Inventory (681) was established for chemical inclusion. To this database, the three existing TTC datasets were combined via robust data fusion process. From the final AM TTC Dataset, the fifth percentiles were derived to be 2.7, 0.43, and 0.12 mg/kg-bw/day for Cramer Classes I, II, and III, respectively. Considering the high percentage of AMs being Cramer Class III, the thresholds are remarkably stable across various TTC datasets. Based on the AM-enriched database, a set of AM categories stratified across potency were developed to classify AMs beyond the capability of the conventional Cramer Tree approach. Grouping the query chemical within the AM category, further distribution analyses were conducted to identify subclasses and differentiate potency. This study proposes a new framework for potential assessment of chronic toxicity made possible with the power of modern reliable databases and chemoinformatic methods.

To facilitate the practical implementation of the guidance on the residue definition for dietary risk assessment, EFSA has organized an evaluation of applicability of existing in silico models for predicting the genotoxicity of pesticides and their metabolites, including literature survey, application of QSARs and development of Read Across methodologies. This paper summarizes the main results. For the Ames test, all (Q)SAR models generated statistically significant predictions, comparable with the experimental variability of the test. The reliability of the models for other assays/endpoints appears to be still far from optimality. Two new Read Across approaches were evaluated: Read Across was largely successful for predicting the Ames test results, but less for in vitro Chromosomal Aberrations. The worse results for non-Ames endpoints may be attributable to the several revisions of experimental protocols and evaluation criteria of results, that have made the databases qualitatively non-homogeneous and poorly suitable for modeling. Last, Parent/Metabolite structural differences (besides known Structural Alerts) that may, or may not cause changes in the Ames mutagenicity were identified and catalogued. The findings from this work are suitable for being integrated into Weight-of-Evidence and Tiered evaluation schemes. Areas needing further developments are pointed out.

Since its creation in 2002, the European Food Safety Authority (EFSA) has produced risk assessments for over 5000 substances in >2000 Scientific Opinions, Statements and Conclusions through the work of its Scientific Panels, Units and Scientific Committee. OpenFoodTox is an open source toxicological database, available both for download and data visualisation which provides data for all substances evaluated by EFSA including substance characterisation, links to EFSA’s outputs, applicable legislations regulations, and a summary of hazard identification and hazard characterisation data for human health, animal health and ecological assessments. The database has been structured using OECD harmonised templates for reporting chemical test summaries (OHTs) to facilitate data sharing with stakeholders with an interest in chemical risk assessment, such as sister agencies, international scientific advisory bodies, and others. This manuscript provides a description of OpenFoodTox including data model, content and tools to download and search the database. Examples of applications of OpenFoodTox in chemical risk assessment are discussed including new quantitative structure–activity relationship (QSAR) models, integration into tools (OECD QSAR Toolbox and AMBIT-2.0), assessment of environmental footprints and testing of threshold of toxicological concern (TTC) values for food related compounds. Finally, future developments for OpenFoodTox 2.0 include the integration of new properties, such as physico-chemical properties, exposure data, toxicokinetic information; and the future integration within in silico modelling platforms such as QSAR models and physiologically-based kinetic models. Such structured in vivo, in vitro and in silico hazard data provide different lines of evidence which can be assembled, weighed and integrated using harmonised Weight of Evidence approaches to support the use of New Approach Methodologies (NAMs) in chemical risk assessment and the reduction of animal testing.

The existing Carcinogenicity Potency Database (CPDB) has been curated and extended with new data to facilitate a re-evaluation ofthe Threshold of Toxicological Concern (TTC) for carcinogenicity. New data have been added from the National Toxicology Program (NTP) and other open sources. All data were subject to a thorough review to ensure accuracy of the chemical structure as well as toxicological information. The new CPDB now comprises data for more than 650 chemicals with acceptable studies, according to defined criteria, of which >570 were carcinogens with >450 associated with reliable genotoxicity data. The CPDB was subjected to various analyses to determine points of departure, notably TD25 and TD50 as well as Benchmark Dose Levels. These analyses were compared and demonstrated the need for appropriate dose response data. A strategy was also implemented to identify mode of action with regard to genotoxicity. In vivo, in vitro and in silico data were applied through a defined strategy to identify genotoxic and non-genotoxic carcinogens. Specifically, where available, experimental genotoxicity (mutagenicity or clastogenicity) data or information from computational models (QSARs and structural alerts) for DNA reactivity, in vivo micronucleus effects and chromosomal aberration were utilised. Analysis of the new CPDB confirmed the conservative nature of the current TTC values for carcinogens. The new CPDB is publicly available as an Access file and searchable via COSMOS DB (cosmosdb.eu) and is intended to support further TTC evaluations for carcinogenicity. The funding of the CEFIC LRI-B18 Project is gratefully acknowledged.

Pharmaceuticals are considered as a new, important group of pollutants. These compounds can enter the environment via several routes and can disturb the natural balance of ecosystems. Factors affecting the environmental fate of medical substances can be determined with computational modelling. The routine application of the modelling methodology in the environmental risk assessment for newly designed pharmaceuticals would enable prediction of their important physical/chemical properties and forecasting their long-range transport and fate. In this contribution, we present the existing state-of-the-art and review the currently available modelling tools of two groups: Quantitative Structure-Property Relationship techniques and Multimedia Mass-balance models. We discuss the current research needs in the context of extending the applicability of the existing tools onto pharmaceuticals, being a more structurally diversified group of contaminants than persistence organic pollutants, for which the majority of the existing models have been originally developed. Multimedia fate models applied for pharmaceuticals are reviewed.QSPR approach to predict physical/chemical properties of pharmaceuticals is reviewed.QSPR-MM combined model is suggested to predict Pov and LRTP of chemical pollutants.

This paper reviews in silico models currently available for the prediction of skin permeability. A comprehensive discussion on the developed methods is presented, focusing on quantitative structure-permeability relationships. In addition, the mechanistic models and comparative studies that analyse different models are discussed. Limitations and strengths of the different approaches are highlighted together with the emergent issues and perspectives.