High-Throughput Screening to Advance In Vitro Toxicology: Accomplishments, Challenges, and Future Directions.

Traditionally, chemical toxicity is determined by in vivo animal studies, which are low throughput, expensive, and sometimes fail to predict compound toxicity in humans. Due to the increasing number of chemicals in use and the high rate of drug candidate failure due to toxicity, it is imperative to develop in vitro, high-throughput screening methods to determine toxicity. The Tox21 program, a unique research consortium of federal public health agencies, was established to address and identify toxicity concerns in a high-throughput, concentration-responsive manner using a battery of in vitro assays. In this article, we review the advancements in high-throughput robotic screening methodology and informatics processes to enable the generation of toxicological data, and their impact on the field; further, we discuss the future of assessing environmental toxicity utilizing efficient and scalable methods that better represent the corresponding biological and toxicodynamic processes in humans.

Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond.

The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.

Alternative lung cell model systems for toxicology testing strategies: Current knowledge and future outlook.

Due to the current relevance of pulmonary toxicology (with focus upon air pollution and the inhalation of hazardous materials), it is important to further develop and implement physiologically relevant models of the entire respiratory tract. Lung model development has the aim to create human relevant systems that may replace animal use whilst balancing cost, laborious nature and regulatory ambition. There is an imperative need to move away from rodent models and implement models that mimic the holistic characteristics important in lung function. The purpose of this review is therefore, to describe and identify the various alternative models that are being applied towards assessing the pulmonary toxicology of inhaled substances, as well as the current and potential developments of various advanced models and how they may be applied towards toxicology testing strategies. These models aim to mimic various regions of the lung, as well as implementing different exposure methods with the addition of various physiologically relevent conditions (such as fluid-flow and dynamic movement). There is further progress in the type of models used with focus on the development of lung-on-a-chip technologies and bioprinting, as well as and the optimization of such models to fill current knowledge gaps within toxicology.

Ninety-eight semesters of cytochrome P450 enzymes and related topics-What have I taught and learned?

This Reflection article begins with my family background and traces my career through elementary and high school, followed by time at the University of Illinois, Vanderbilt University, the University of Michigan, and then for 98 semesters as a Vanderbilt University faculty member. My research career has dealt with aspects of cytochrome P450 enzymes, and the basic biochemistry has had applications in fields as diverse as drug metabolism, toxicology, medicinal chemistry, pharmacogenetics, biological engineering, and bioremediation. I am grateful for the opportunity to work with the Journal of Biological Chemistry not only as an author but also for 34 years as an Editorial Board Member, Associate Editor, Deputy Editor, and interim Editor-in-Chief. Thanks are extended to my family and my mentors, particularly Profs. Harry Broquist and Minor J. Coon, and the more than 170 people who have trained with me. I have never lost the enthusiasm for research that I learned in the summer of 1968 with Harry Broquist, and I have tried to instill this in the many trainees I have worked with. A sentence I use on closing slides is "It's not just a laboratory-it's a fraternity."

E-Cigarette Toxicology.

Since the spread of tobacco from the Americas hundreds of years ago, tobacco cigarettes and, more recently, alternative tobacco products have become global products of nicotine addiction. Within the evolving alternative tobacco product space, electronic cigarette (e-cigarette) vaping has surpassed conventional cigarette smoking among adolescents and young adults in the United States and beyond. This review describes the experimental and clinical evidence of e-cigarette toxicity and deleterious health effects. Adverse health effects related to e-cigarette aerosols are influenced by several factors, including e-liquid components, physical device factors, chemical changes related to heating, and health of the e-cigarette user (e.g., asthmatic). Federal, state, and local regulations have attempted to govern e-cigarette flavors, manufacturing, distribution, and availability, particularly to underaged youths. However, the evolving e-cigarette landscape continues to impede timely toxicological studies and hinder progress made toward our understanding of the long-term health consequence of e-cigarettes.

Concept drift detection in toxicology datasets using discriminative subgraph-based drift detector.

Due to the increasing importance of graphs and graph streams in data representation in today's era, concept drift detection in graph streaming scenarios is more important than ever. Contributions to concept drift detection in graph streams are minimal and practically non-existent in the field of toxicology. This paper applied the discriminative subgraph-based drift detector (DSDD) to graph streams generated from real-world toxicology datasets. We used four toxicology datasets, each of which yielded two graph streams - one with abrupt drift points and one with gradual drift points. We used DSDD both with the standard minimum description length (MDL) heuristic and after replacing MDL with a much simpler heuristic SIZE (number of vertices + number of edges), and applied it to all generated graph streams containing abrupt drift points and gradual drift points for varying window sizes. Following that, we compared and analyzed the results. Finally, we applied a long short-term memory based graph stream classification model to all the generated streams and compared the difference in the performances obtained with and without detecting drift using DSDD. We believe that the results and analysis presented in this paper will provide insight into the task of concept drift detection in the toxicology domain and aid in the application of DSDD in a variety of scenarios.

Nanoparticle Toxicology.

Nanoparticles from natural and anthropogenic sources are abundant in the environment, thus human exposure to nanoparticles is inevitable. Due to this constant exposure, it is critically important to understand the potential acute and chronic adverse effects that nanoparticles may cause to humans. In this review, we explore and highlight the current state of nanotoxicology research with a focus on mechanistic understanding of nanoparticle toxicity at organ, tissue, cell, and biomolecular levels. We discuss nanotoxicity mechanisms, including generation of reactive oxygen species, nanoparticle disintegration, modulation of cell signaling pathways, protein corona formation, and poly(ethylene glycol)-mediated immunogenicity. We conclude with a perspective on potential approaches to advance current understanding of nanoparticle toxicity. Such improved understanding may lead to mitigation strategies that could enable safe application of nanoparticles in humans. Advances in nanotoxicity research will ultimately inform efforts to establish standardized regulatory frameworks with the goal of fully exploiting the potential of nanotechnology while minimizing harm to humans.

Iguratimod, an allosteric inhibitor of macrophage migration inhibitory factor (MIF), prevents mortality and oxidative stress in a murine model of acetaminophen overdose.

BACKGROUND: Macrophage migration inhibitory factor (MIF) is a pleiotropic cytokine that has been implicated in multiple inflammatory and non-inflammatory diseases, including liver injury induced by acetaminophen (APAP) overdose. Multiple small molecule inhibitors of MIF have been described, including the clinically available anti-rheumatic drug T-614 (iguratimod); however, this drug's mode of inhibition has not been fully investigated. METHODS: We conducted in vitro testing including kinetic analysis and protein crystallography to elucidate the interactions between MIF and T-614. We also performed in vivo experiments testing the efficacy of T-614 in a murine model of acetaminophen toxicity. We analyzed survival in lethal APAP overdose with and without T-614 and using two different dosing schedules of T-614. We also examined MIF and MIF inhibition effects on hepatic hydrogen peroxide (H2O2) as a surrogate of oxidative stress in non-lethal APAP overdose. RESULTS: Kinetic analysis was consistent with a non-competitive type of inhibition and an inhibition constant (Ki) value of 16 µM. Crystallographic analysis revealed that T-614 binds outside of the tautomerase active site of the MIF trimer, with only the mesyl group of the molecule entering the active site pocket. T-614 improved survival in lethal APAP overdose when given prophylactically, but this protection was not observed when the drug was administered late (6 h after APAP). T-614 also decreased hepatic hydrogen peroxide concentrations during non-lethal APAP overdose in a MIF-dependent fashion. CONCLUSIONS: T-614 is an allosteric inhibitor of MIF that prevented death and decreased hepatic hydrogen peroxide concentrations when given prophylactically in a murine model of acetaminophen overdose. Further studies are needed to elucidate the mechanistic role of MIF in APAP toxicity.

The role of the cytochrome P450 superfamily in the skin.

In mammals, the skin acts as a barrier to prevent harmful environmental stimuli from entering the circulation. CYP450s are involved in drug biotransformation, exogenous and endogenous substrate metabolism, and maintaining the normal physiological function of the skin, as well as facilitating homeostasis of the internal environment. The expression pattern of CYP450s in the skin is tissue-specific and thus differs from the liver and other organs. The development of skin topical medications, and knowledge of the toxicity and side effects of these medications require a detailed understanding of the expression and function of skin-specific CYP450s. Thus, we summarized the expression of CYP450s in the skin, their function in endogenous metabolic physiology, aberrant CYP450 expression in skin diseases and the influence of environmental variables and medications. This information will serve as a crucial foundation for future studies on the skin, as well as for the design and development of new drugs for skin diseases including topical medications.

Sex differences in the neural and behavioral effects of acute high-dose edible cannabis consumption in rats.

The consumption of D9-tetrahydrocannabinol (THC)- or cannabis-containing edibles has increased in recent years; however, the behavioral and neural circuit effects of such consumption remain unknown, especially in the context of ingestion of higher doses resulting in cannabis intoxication. We examined the neural and behavioral effects of acute high-dose edible cannabis consumption (AHDECC). Sprague-Dawley rats (6 males, 7 females) were implanted with electrodes in the prefrontal cortex (PFC), dorsal hippocampus (dHipp), cingulate cortex (Cg), and nucleus accumbens (NAc). Rats were provided access to a mixture of Nutella (6 g/kg) and THC-containing cannabis oil (20 mg/kg) for 10 minutes, during which they voluntarily consumed all of the provided Nutella and THC mixture. Cannabis tetrad and neural oscillations were examined 2, 4, 8, and 24-h after exposure. In another cohort (16 males, 15 females), we examined the effects of AHDECC on learning and prepulse inhibition, and serum and brain THC and 11-hydroxy-THC concentrations. AHDECC resulted in higher brain and serum THC and 11-hydroxy-THC levels in female rats over 24 h. AHDECC also produced: 1) Cg, dHipp, and NAc gamma power suppression, with the suppression being greater in female rats, in a time-dependent manner; 2) hypolocomotion, hypothermia, and anti-nociception in a time-dependent manner; and 3) learning and prepulse inhibition impairments. Additionally, most neural activity and behavior changes appear 2 h post-ingestion, suggesting that interventions around this time might be effective in reversing/reducing the effects of AHDECC. Significance Statement The effects of high-dose edible cannabis on behaviour and neural circuitry are poorly understood. We found that the effects of acute high-dose edible cannabis consumption, which include decreased gamma power, hypothermia, hypolocomotion, analgesia, and learning and information processing impairments, are time- and sex-dependent. Moreover, these effects begin 2 h after AHDECC and last for at least 24 h, suggesting that treatments should target this time window in order to be effective.

Neonicotinoids differentially modulate nicotinic acetylcholine receptors in immature and antral follicles in the mouse ovary†.

Neonicotinoids are the most widely used insecticides in the world. They are synthetic nicotine derivatives that act as nicotinic acetylcholine receptor agonists. Although parent neonicotinoids have low affinity for the mammalian nicotinic acetylcholine receptor, they can be activated in the environment and the body to positively charged metabolites with high affinity for the mammalian nicotinic acetylcholine receptor. Imidacloprid, the most popular neonicotinoid, and its bioactive metabolite desnitro-imidacloprid differentially interfere with ovarian antral follicle physiology in vitro, but their effects on ovarian nicotinic acetylcholine receptor subunit expression are unknown. Furthermore, ovarian nicotinic acetylcholine receptor subtypes have yet to be characterized in the ovary. Thus, this work tested the hypothesis that ovarian follicles express nicotinic acetylcholine receptors and their expression is differentially modulated by imidacloprid and desnitro-imidacloprid in vitro. We used polymerase chain reaction, RNA in situ hybridization, and immunohistochemistry to identify and localize nicotinic acetylcholine receptor subunits (α2, 4, 5, 6, 7 and ß1, 2, 4) expressed in neonatal ovaries (NO) and antral follicles. Chrnb1 was expressed equally in NO and antral follicles. Chrna2 and Chrnb2 expression was higher in antral follicles compared to NO and Chrna4, Chrna5, Chrna6, Chrna7, and Chrnb4 expression was higher in NO compared to antral follicles. The α subunits were detected throughout the ovary, especially in oocytes and granulosa cells. Imidacloprid and desnitro-imidacloprid dysregulated the expression of multiple nicotinic acetylcholine receptor subunits in NO, but only dysregulated one subunit in antral follicles. These data indicate that mammalian ovaries contain nicotinic acetylcholine receptors, and their susceptibility to imidacloprid and desnitro-imidacloprid exposure varies with the stage of follicle maturity.

AI for predicting chemical-effect associations at the chemical universe level-deepFPlearn.

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods-even if high throughput-are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful-experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.

Safety Evaluation of Bifidobacterium animalis subsp. lactis BLa80 under In Vitro, and In Vivo Conditions.

Bifidobacterium animalis subsp. lactis BLa80 is a new probiotic strain with extensive applications in food products both domestically and internationally. Given the rising consumption of this probiotic, its safety assessment is increasingly crucial in the food industry. This study evaluates the safety of strain BLa80 using a combination of in vitro and in vivo assays along with genomic analysis. Methods included exposing the strain to artificial gastric and intestinal fluids, as well as a medium containing bile salts, to stimulate human digestive conditions. The strain showed high tolerance to gastric fluid at pH of 2.5 and to 0.3% bile salts. It maintained a 99.92% survival rate in intestinal fluid. Additional tests assessed hemolytic activity, antibiotic susceptibility (revealing sensitivity to 7 antibiotics), and biogenic amine production using HPLC-ELSD, confirming the absence of histamine, and other harmful amines. Bile salt hydrolase activity was demonstrated qualitatively, and metabolic byproducts were quantitatively analyzed using a D-/L-lactic acid assay kit, showing that BLa80 produces 1.48 mg/mL of L-lactic acid and no harmful D-lactic acid. Genomic analysis confirmed the absence of virulence or pathogenicity genes, and a 90-day oral toxicity study in rats confirmed no toxic effects at various doses. Overall, these findings support the safety classification of the strain BLa80.

Elucidating the relationship between metabolites and breast cancer: A Mendelian randomization study.

The evidence about the causal roles of metabolites in breast cancer is lacking. This study conducted a systematic evaluation of the potential causal relationship between 1091 human blood metabolites, 309 metabolite ratios, and the likelihood of developing breast cancer and its subtype by employing a two-sample bidirectional Mendelian randomization (MR) approach Four metabolites, including tryptophan betaine (Odds Ratio [OR] = 1.07, 95%CI = 1.04-1.10, Bonferroni-corrected P = 0.007), X-21312 (OR = 0.90, 95%CI = 0.86-0.94, Bonferroni-corrected P = 0.02), 3-bromo-5-chloro-2,6-dihydroxybenzoic acid (OR = 0.94, 95%CI = 0.91-0.96, Bonferroni-corrected P = 0.03) and X-18921 (OR = 0.96, 95%CI = 0.94-0.98, Bonferroni-corrected P = 0.04) were significantly associated with overall breast cancer using inverse-variance weighted (IVW) method. Tryptophan betaine was also significantly associated with estrogen receptor (ER)-positive breast cancer (OR = 1.08, 95%CI = 1.04-1.11, Bonferroni-corrected P = 0.03). X-23680 (OR = 1.10, 95%CI = 1.05-1.15, Bonferroni-corrected P = 0.04) and glycine to phosphate ratio (OR = 1.07, 95%CI = 1.04-1.10, Bonferroni-corrected P = 0.04) were associated with ER-negative breast cancer. Reverse MR analysis showed no significant associations between breast cancer and metabolites. This MR study indicated compelling evidence of a causal association between metabolites and the risk of breast cancer and its subtypes, underscoring the potential impact of metabolic interference on breast cancer risk and indicating the drug targets for breast cancer.

Network toxicology, molecular docking technology, and experimental verification revealed the mechanism of cantharidin-induced testicular injury in mice.

As a protein kinase inhibitor, cantharidin (CTD) exhibits antitumor activities. However, CTD is highly toxic, thereby limiting clinical applications. Moreover, relatively few studies have investigated CTD-induced reproductive toxicity, thus the underlying mechanism remains unclear. In this study, the toxic effects of CTD on mouse testis were confirmed in vivo and the potential mechanism was predicted by network toxicology (NT) and molecular docking technology. Proteins involved in the signaling pathways and core targets were verified. The results showed that different concentrations of CTD induced weight loss increased the testicular coefficient, and caused obvious pathological damage to testicular cells. The NT results showed that the main targets of CTD-induced testicular injury (TI) included AKT1, Caspase 3, Bcl-2, and Bax. The results of pathway enrichment analysis showed that CTD-induced TI was closely related to apoptosis and the PI3K/AKT and HIF-1 signaling pathways. Molecular docking methods confirmed high affinity between CTD and key targets. Western blot analysis showed that CTD inhibited expression of PI3K, AKT, and the anti-apoptotic protein Bcl-2, while promoting expression of the pro-apoptotic proteins Bax and Caspase 3. These results suggest that CTD-induced TI involves multiple targets and pathways, and the underlying mechanism was associated with inhibition of the apoptosis-related PI3K/AKT signaling pathway.

Integrated network toxicology, transcriptomics and gut microbiomics reveals hepatotoxicity mechanism induced by benzo[a]pyrene exposure in mice.

Benzo[a]pyrene (BaP) is a ubiquitous environmental pollutant posing various toxicity effects on organisms. Previous studies demonstrated that BaP could induce hepatotoxicity, while the underlying mechanism remains incompletely elucidated. In this study, a comprehensive strategy including network toxicology, transcriptomics and gut microbiomics was applied to investigate the hepatotoxicity and the associated mechanism of BaP exposure in mice. The results showed that BaP induced liver damage, liver oxidative stress and hepatic lipid metabolism disorder. Mechanistically, BaP may disrupt hepatic lipid metabolism through increasing the uptake of free fatty acid (FFA), promoting the synthesis of FA and triglyceride (TG) in the liver and suppressing lipid synthesis in white adipose tissue. Moreover, integrated network toxicology and hepatic transcriptomics revealed that BaP induced hepatotoxicity by acting on several core targets, such as signal transducer and activator of transcription 1 (STAT1), C-X-C motif chemokine ligand 10 (CXCL10) and toll-like receptor 2 (TLR2). Further analysis suggested that BaP inhibited JAK2-STAT3 signaling pathway, as supported by molecular docking and western blot. The 16S rRNA sequencing showed that BaP changed the composition of gut microbiota which may link to the hepatotoxicity based on the correlation analysis. Taken together, this study demonstrated that BaP caused liver injury, hepatic lipid metabolism disorder and gut microbiota dysbiosis, providing novel insights into the hepatotoxic mechanism induced by BaP exposure.

Redox-activity and in vitro effects of regional atmospheric aerosol pollution: Seasonal differences and correlation between oxidative potential and in vitro toxicity of PM1.

Particulate Matter (PM) is a complex and heterogeneous mixture of atmospheric particles recognized as a threat to human health. Oxidative Potential (OP) measurement is a promising and integrative method for estimating PM-induced health impacts since it is recognized as more closely associated with adverse health effects than ordinarily used PM mass concentrations. OP measurements could be introduced in the air quality monitoring, along with the parameters currently evaluated. PM deposition in the lungs induces oxidative stress, inflammation, and DNA damage. The study aimed to compare the OP measurements with toxicological effects on BEAS-2B and THP-1 cells of winter and summer PM1 collected in the Po Valley (Italy) during 2021. PM1 was extracted in deionized water by mechanical agitation and tested for OP and, in parallel, used to treat cells. Cytotoxicity, genotoxicity, oxidative stress, and inflammatory responses were assessed by MTT test, DCFH-DA assay, micronucleus, γ-H2AX, comet assay modified with endonucleases, ELISA, and Real-Time PCR. The evaluation of OP was performed by applying three different assays: dithiothreitol (OPDTT), ascorbic acid (OPAA), and 2',7'-dichlorofluorescein (OPDCFH), in addition, the reducing potential was also analysed (RPDPPH). Seasonal differences were detected in all the parameters investigated. The amount of DNA damage detected with the Comet assay and ROS formation highlights the presence of oxidative damage both in winter and in summer samples, while DNA damage (micronucleus) and genes regulation were mainly detected in winter samples. A positive correlation with OPDCFH (Spearman's analysis, p < 0.05) was detected for IL-8 secretion and γ-H2AX. These results provide a biological support to the implementation in air quality monitoring of OP measurements as a useful proxy to estimate PM-induced cellular toxicological responses. In addition, these results provide new insights for the assessment of the ability of secondary aerosol in the background atmosphere to induce oxidative stress and health effects.

High-Throughput Transcriptomics screen of ToxCast chemicals in U-2 OS cells.

New approach methodologies (NAMs) aim to accelerate the pace of chemical risk assessment while simultaneously reducing cost and dependency on animal studies. High Throughput Transcriptomics (HTTr) is an emerging NAM in the field of chemical hazard evaluation for establishing in vitro points-of-departure and providing mechanistic insight. In the current study, 1201 test chemicals were screened for bioactivity at eight concentrations using a 24-h exposure duration in the human- derived U-2 OS osteosarcoma cell line with HTTr. Assay reproducibility was assessed using three reference chemicals that were screened on every assay plate. The resulting transcriptomics data were analyzed by aggregating signal from genes into signature scores using gene set enrichment analysis, followed by concentration-response modeling of signatures scores. Signature scores were used to predict putative mechanisms of action, and to identify biological pathway altering concentrations (BPACs). BPACs were consistent across replicates for each reference chemical, with replicate BPAC standard deviations as low as 5.6 × 10-3 µM, demonstrating the internal reproducibility of HTTr-derived potency estimates. BPACs of test chemicals showed modest agreement (R2 = 0.55) with existing phenotype altering concentrations from high throughput phenotypic profiling using Cell Painting of the same chemicals in the same cell line. Altogether, this HTTr based chemical screen contributes to an accumulating pool of publicly available transcriptomic data relevant for chemical hazard evaluation and reinforces the utility of cell based molecular profiling methods in estimating chemical potency and predicting mechanism of action across a diverse set of chemicals.