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New Approach Methodologies (NAMs) have ushered in a new era in the field of toxicology, aiming to replace animal testing. However, despite these advancements, they are not exempt from the inherent complexities associated with the study's endpoint. In this review, we have identified three major groups of complexities: mechanistic, chemical space, and methodological. The mechanistic complexity arises from interconnected biological processes within a network that are challenging to model in a single step. In the second group, chemical space complexity exhibits significant dissimilarity between compounds in the training and test series. The third group encompasses algorithmic and molecular descriptor limitations and typical class imbalance problems. To address these complexities, this work provides a guide to the usage of a combination of predictive Quantitative Structure-Activity Relationship (QSAR) models, known as metamodels. This combination of low-level models (LLMs) enables a more precise approach to the problem by focusing on different sub-mechanisms or sub-processes. For mechanistic complexity, multiple Molecular Initiating Events (MIEs) or levels of information are combined to form a mechanistic-based metamodel. Regarding the complexity arising from chemical space, two types of approaches were reviewed to construct a fragment-based chemical space metamodel: those with and without structure sharing. Metamodels with structure sharing utilize unsupervised strategies to identify data patterns and build low-level models for each cluster, which are then combined. For situations without structure sharing due to pharmaceutical industry intellectual property, the use of prediction sharing, and federated learning approaches have been reviewed. Lastly, to tackle methodological complexity, various algorithms are combined to overcome their limitations, diverse descriptors are employed to enhance problem definition and balanced dataset combinations are used to address class imbalance issues (methodological-based metamodels). Remarkably, metamodels consistently outperformed classical QSAR models across all cases, highlighting the importance of alternatives to classical QSAR models when faced with such complexities. • The different kinds of complexities in the realm of toxicology are discussed. • Combination of models is addressed to solve toxicological complexities. • Combination of models overcomes some of the limitations of classical QSAR. • QIVIVE models may improve the performance of metamodels. [ABSTRACT FROM AUTHOR]

Toxicology is an essential part of any drug development plan. Circumnavigating the risk of failure because of a toxicity issue can be a challenge, and failure in late development is extremely costly. To identify potential risks, it requires more than just understanding the biological target. The toxicologist needs to consider a compound's structure, it's physicochemical properties (including the impact of the overall formulation), as well as the biological target (e.g., receptor interactions). Understanding the impact of the physicochemical properties can be used to predict potential toxicities in advance by incorporating key endpoints in early screening strategies and/or used to compare toxicity profiles across lead candidates. This review discussed the risks of off-target and/or non-specific toxicities that may be associated with the physicochemical properties of compounds, especially those carrying dominant positive or negative charges, including amphiphilic small molecules, peptides, oligonucleotides and lipids/liposomes/lipid nanoparticles. The latter of which are being seen more and more in drug development, including the recent Covid pandemic, where mRNA and lipid nanoparticle technology is playing more of a role in vaccine development. The translation between non-clinical and clinical data is also considered, questioning how a physicochemical driven toxicity may be more universal across species, which means that such toxicity may be reassuringly translatable between species and as such, this information may also be considered as a support to the 3 R's, particularly in the early screening stages of a drug development plan. [ABSTRACT FROM AUTHOR]

The goal of PrecisionTox is to overcome conceptual barriers to replacing traditional mammalian chemical safety testing by accelerating the discovery of evolutionarily conserved toxicity pathways that are shared by descent among humans and more distantly related animals. An international consortium is systematically testing the toxicological effects of a diverse set of chemicals on a suite of five model species comprising fruit flies, nematodes, water fleas, and embryos of clawed frogs and zebrafish along with human cell lines. Multiple forms of omics and comparative toxicology data are integrated to map the evolutionary origins of biomolecular interactions that are predictive of adverse health effects, to major branches of the animal phylogeny. These conserved elements of adverse outcome pathways (AOPs) and their biomarkers are expected to provide mechanistic insight useful for regulating groups of chemicals based on their shared modes of action. PrecisionTox also aims to quantify risk variation within populations by recognizing susceptibility as a heritable trait that varies with genetic diversity. This initiative incorporates legal experts and collaborates with risk managers to address specific needs within European chemicals legislation, including the uptake of new approach methodologies (NAMs) for setting precise regulatory limits on toxic chemicals. [Display omitted] • PrecisionTox is funded by the EU's Horizon 2020 programme until the year 2026. • Conserved toxicity pathways will be identified across five model organisms and humans. • Artificial intelligence will reveal adverse outcome pathways and their biomarkers. • Quantitative genetics is used to examine susceptibility to toxicity as a variable trait. • Collaboration with policy makers facilitates use of biomarkers for safety assessment. [ABSTRACT FROM AUTHOR]

• The transcriptome of iPSC-derived cortical neurons and brain biopsies were assessed. • sAD iPSC-derived neurons & brain samples showed many similar AD-related processes. • iPSCs exposed to toxicants increases our knowledge of their AD-inducing properties. Alzheimer's disease (AD) is the most common cause of dementia, characterized by the progressive impairment of cognition and memory loss. Sporadic AD (sAD) represents approximately 95 % of the AD cases and is induced by a complex interplay between genetic and environmental factors called "Alzheimerogens". Heavy metals (e.g. copper) and pesticides (e.g. fipronil) can affect many AD-related processes, including neuroinflammation (considered as AD-inducing factor). Research would benefit from in vitro models to investigate effects of Alzheimerogens. We compared transcriptomics changes in sAD induced pluripotent stem cell (iPSC) derived cortical neurons to differentially expressed genes (DEGs) identified in post-mortem AD brain tissue. These analyses showed that many AD-related processes could be identified in the sAD iPSC-derived neurons, and furthermore, could even identify more DEGs functioning in these processes than post-mortem AD-brain tissue. Thereafter, we exposed the iPSCs to AD-inducing factors (copper(II)chloride, fipronil sulfone and an inflammatory cytokine cocktail). Cytokine exposure induced expression of immune related genes while copper-exposure affected genes involved in lipid and cholesterol metabolism, which are known AD-related processes. Fipronil-exposure did not result in significant transcriptomic changes, although prolonged exposures or higher doses may be necessary. Overall, we show that iPSC-derived cortical neurons can be beneficial in vitro models to identify Alzheimerogens and AD-related molecular mechanisms. [ABSTRACT FROM AUTHOR]

• A 99.9 % probability of carcinogenicity was estimated for 8 benzidine-related-dyes. • A 98.9 % probability of carcinogenicity was forecast for other 251 BZ-related-colorants. • Bayesian forecasting exhibited the potential to enhance predictive toxicology. • Bayesian forecasting showed the potential to increase the impact of nonclinical toxicology. The preclinical identification of health hazards relies on the performance (the historic concordance to the respective gold standard) of regulatorily recommended bioassays. However, any testing with less than 100% sensitivity (or 100% specificity) can deliver false results (outcomes discordant to the respective gold standard). Conversely, the predictive values approach (a.k.a. Bayesian forecasting) weighs (1) the performance of the predictive bioassay (battery, or framework) with (2) the prevalence of -positivity to the respective gold standard- in the most representative category to which the test substance can be allocated. Thus, the predictive values approach (PVA) provides the numeric probability for the toxicity to humans of chemicals that, circumstantially, are evaluable only through nonclinical data. Consequently, the PVA improves the predictivity of nonclinical toxicology, and increases the impact of hazard identifications entirely based on preclinical data. This article aimed to introduce the PVA through a worked example. Due to their toxicological homogeneity and public health relevance, the superfamily of colorants synthesized from benzidine (BZ) or some mutagenic congeners was selected (colorings hereafter mentioned as BZ-related-colorants). Through the PVA, the numeric probability of innate carcinogenicity to humans of 259 BZ-related-colorants was either estimated from rodent carcinogenesis bioassays (RCBs) or predicted from methods alternative to the RCB. A discussion was provided on (1) some limitations and implications of the PVA, and (2) the probable significance of the predictive values figured here for 259 BZ-related-colorings. [ABSTRACT FROM AUTHOR]

The role of the aryl hydrocarbon receptor (AhR) in hemostasis has recently gained increased attention. Here, we demonstrate, by qRT-PCR and western blot, that human platelets express both AhR mRNA and AhR protein. AhR protein levels increase in a dose dependent manner when incubated with either 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) or omeprazole. Treatment of platelets with puromycin blocks increased AhR protein synthesis in the presence of AhR activators. Additionally, treatment of platelets with either activator results in phosphorylation of p38MAPK and cPLA2, two key signaling molecules in platelet activation pathways. Using the AhR competitive inhibitors alpha naphthoflavone and CH-223191, we show that phosphorylation of p38MAPK is AhR dependent. Further, inhibition of p38MAPK blocks downstream cPLA2 phosphorylation induced by TCDD or omeprazole. Treatment with AhR activators results in platelet priming, as demonstrated by increased platelet aggregation, which is inhibited by AhR antagonists. Our data support a model of the platelet AhR non-genomic pathway in which treatment with AhR activators results in increased expression of the AhR, phosphorylation of p38MAPK and cPLA2, leading to platelet priming in response to agonist.

Docosahexaenoic acid (22:6n3, DHA) is an n-3 polyunsaturated fatty acid (PUFA) known to affect numerous biological functions. While DHA possesses many properties that impact cell survival such as suppressing cell growth and inducing apoptosis, the exact molecular and cellular mechanism(s) remain unknown. Peroxisome proliferator-activated receptors (PPARs) are a family of nuclear receptors that regulate many cell pathways including cell death. As DHA acts as a ligand to PPARs the aim of this study was to examine the involvement of PPARδ in DHA-mediated cytotoxicity toward H9c2 cells. Treatment with DHA (100μM) resulted in a significant decline in cell viability, cellular metabolic activity and total antioxidant capacity coinciding with increased total proteasome activities and activity of released lactate dehydrogenase (LDH). No changes in reactive oxygen species (ROS) production or accumulation of lipid peroxidation products were observed but DHA promoted apoptotic cell death as detected by flow cytometry, increased caspase-3 activity and decreased phosphorylation of Akt. Importantly, DHA enhanced PPARδ DNA binding activity in H9c2 cells strongly signifying that the cytotoxic effect of DHA might be mediated via PPARδ signaling. Co-treatment with the selective PPARδ antagonist GSK 3787 (1μM) abolished the cytotoxic effects of DHA in H9c2 cells. Cytotoxic effects of DHA were attenuated by co-treatment with myriocin, a selective inhibitor of serine palmitoyl transferase (SPT), preventing de novo ceramide biosynthesis. LC/MS analysis revealed that treatment with DHA resulted in the accumulation of ceramide, which was blocked by GSK 3787. Interestingly, inhibition of cytochrome P450 (CYP) oxidase with MS-PPOH (50μM) abolished DHA-mediated cytotoxicity suggesting downstream metabolites as the active mediators. We further demonstrate that CYP oxidase metabolites of DHA, methyl epoxy docosapentaenoate (EDP methyl esters, 1μM) (mix 1:1:1:1:1:1; 4,5-, 7,8-, 10,11-, 13,14-, 16,17- and 19,20-EDP methyl esters) and 19,20-EDP cause cytotoxicity via activation of PPARδ signaling leading to increased levels of intracellular ceramide. These results illustrate novel pathways for DHA-induced cytotoxicity that suggest an important role for CYP-derived metabolites, EDPs.

Polycyclic aromatic hydrocarbons (PAHs), including benzo[a]pyrene (BaP), are ubiquitous environmental pollutants found in tobacco smoke, air pollution, and grilled foods. Reactive metabolites and reactive oxygen species generated during PAH metabolism are detoxified by reactions involving glutathione (GSH). Early life exposures to tobacco smoke and air pollution have been linked to increased risk of obesity and metabolic syndrome. We investigated the independent and interactive effects of prenatal exposure to BaP and GSH deficiency due to deletion of the modifier subunit of glutamate cysteine ligase (Gclm), the rate-limiting enzyme in GSH synthesis, on adiposity and hepatic steatosis in adult female F1 offspring. We mated Gclm(+/-) dams with Gclm(+/-) males and treated the pregnant dams with 0, 2, or 10 mg/kg/day BaP in sesame oil by oral gavage daily from gestational day 7 through 16. We analyzed metabolic endpoints in female Gclm(-/-) and Gclm(+/+) littermate F1 offspring. Prenatal BaP exposure significantly increased visceral adipose tissue weight, weight gain between 3 weeks and 7.5 months of age, hepatic lipid content measured by oil red O staining, and hepatic fatty acid beta-oxidation gene expression in Gclm(+/+), but not in Gclm(-/-), female offspring. Hepatic expression of lipid biosynthesis and antioxidant genes were decreased and increased, respectively, in Gclm(-/-) mice. Our results suggest that reported effects of pre- and peri-natal air pollution and tobacco smoke exposure on obesity may be mediated in part by PAHs. GSH deficiency is protective against the metabolic effects of prenatal BaP exposure.