BACKGROUND: Toxicology in the 21st Century (Tox21) assay data provide a valuable resource for the prediction of in vivo toxicity using machine learning models. However, the performances of these models previously developed using the pre-existing Tox21 assay data were less than ideal, likely due to insufficient coverage of the biological response space by the assay targets. OBJECTIVES: This study aimed to assess whether expanding the Tox21 portfolio with new assays that probe under-represented targets/pathways related to unanticipated adverse drug effects could improve the predictive capacity of in vitro assay data for in vivo toxicity such as drug-induced liver injury (DILI) and cardiotoxicity (DICT). METHODS: Models were constructed using data from the pre-existing panel of 36 assay targets and the expanded panel of 49 assay targets. A feature selection approach was used to determine the optimal number of assays needed for each model. The models were then applied to predict the potential hepatotoxicity and cardiotoxicity of compounds in the Tox21 10K compound library. RESULTS: For both DILI and DICT prediction, the best-performing models developed using the expanded assay panel required a smaller number of assays to achieve the same level of performance compared to those based on the pre-existing assays. Models constructed by combining both assay data (pre-existing + expanded) and chemical structure consistently outperformed those constructed based on assay data alone but showed similar performance to those constructed based on chemical structure. The compounds predicted to have the highest toxic potential were experimentally verified to demonstrate the effectiveness of our models in identifying new potentially toxic compounds. DISCUSSION: The expansion of the Tox21 assay panel has significantly enhanced the predictive capacity of assay data for predicting the DILI and DICT potential. This improvement underscores the importance of a diverse and comprehensive in vitro assay portfolio in advancing safety assessment.

Beta-1 adrenergic receptors (ADR beta 1) are critical regulators of cardiac function; however, the potential modulation of ADR beta 1 by environmental chemicals remains largely underexplored, raising concerns about unintended impacts on cardiovascular health. We applied a quantitative high-throughput screening (qHTS) approach to identify ADR beta 1 agonists within the Tox21 10K compound library, which includes environmental chemicals, pharmaceuticals, industrial agents, and consumer products, using an HTRF-based cAMP assay in ADR beta 1-overexpressing HEK293 cells. Primary screening of 8,947 unique compounds identified 118 potential ADR beta 1 agonists. Among these, 94 were confirmed and further evaluated for beta-adrenergic receptor subtype selectivity (ADR beta 2 and ADR beta 3) and hERG channel inhibition to assess potential cardiotoxicity liability. Known ADR beta 1 agonists, isoproterenol (EC50, 0.91 nM) and dobutamine (EC50, 10 nM), were identified, supporting the validity of the assay. In addition, several compounds with limited prior ADR beta 1-specific characterization, such as GR 103691 and N,N '-dibenzylethane-1,2-diamine, demonstrated subtype-selective or mixed agonist profiles, with some exhibiting minimal hERG inhibition. These findings expand the catalog of ADR beta 1 modulators and demonstrate the utility of qHTS for identifying chemicals that may affect cardiovascular signaling pathways.

INTRODUCTION:Selective modulation of cannabinoid receptors, particularly achieving CB2 selectivity over CB1, represents a promising strategy for developing safer therapeutics with reduced psychotropic effects. This review examines how machine learning (ML) approaches can address persistent challenges in cannabinoid receptors selectivity and accelerate drug discovery. AREAS COVERED:The authors summarize current ML-based methodologies applied to cannabinoid ligand discovery, focusing on strategies for predicting receptor affinity and selectivity. The literature covered was identified through a PubMed search followed by manual screening to retain studies directly relevant to cannabinoid-focused AI-driven ligand discovery. The review discusses feature engineering approaches, including molecular fingerprints, physicochemical descriptors, and SMILES-based representations, as well as classification and regression algorithms for selectivity prediction. The authors evaluate model performance metrics, dataset limitations, and interpretability challenges. Recent advances in deep learning and generative models for de novo molecular design are also highlighted, with emphasis on their potential to expand chemical space and improve selective ligand identification. EXPERT OPINION:ML has significantly advanced the prediction of cannabinoid receptor selectivity, yet progress remains constrained by data quality, endpoint inconsistency, and limited interpretability. Future efforts integrating curated datasets, mechanistically informed modeling, and generative AI frameworks are expected to substantially enhance the discovery of selective cannabinoid therapeutics.

Metals and metalloids are widely used in industrial applications, and increasing experimental and epidemiological evidence has linkded metal exposure to adverse health outcomes. However, the underlying mechanisms for these effects have not been fully understood. As part of the Toxicology in 21st century (Tox21) program, we have screened more than 150 metal-containing compounds and their salt forms across over 90 biological endpoints. In this study, we analyzed the comprehensive toxicity of metal compounds using Tox21 screening data to enhance the understanding of their mechanism and molecular pathways involved in molecular initiating events. Integrated data analysis and in vitro confirmation experiments identified three potential novel targets of metal compounds (i.e., sonic hedgehog pathway, thyroid-stimulating hormone receptor, and thyrotropin-releasing hormone receptor). We also found that mercury- and tin-containing substances were highly bioactive. Furthermore, cell painting analysis uncovered metal-induced bioactivity could be classified into two patterns depending on their respective associations to mitochondrial-related morphology changes. Our results provide a comprehensive analysis of metals-association bioactivity data within Tox21 assays, which can be applied to estimate the potency ranges for metal-induced bioactivity that support risk assessment efforts for metal and metalloid exposures. These findings identify previously undercharacterized molecular targets of metal compounds, offering new insights into mechanisms of metal toxicity and informing improved risk assessment methodologies.

G-protein-coupled receptors (GPCRs) are a diverse family of seven-transmembrane domain receptors that play pivotal roles in various physiological and neurological processes by mediating extracellular signals through G proteins. Notable GPCRs such as ADRB2, CHRM1, DRD2, and HTR2A are important therapeutic targets linked to conditions ranging from asthma to schizophrenia. The human ether-à-go-go-related gene (hERG), encoding the Kv11.1 potassium channel, is critical for cardiac repolarization, the inhibition of which can lead to prolonged QT intervals and an increased risk of arrhythmias. Consequently, assessing hERG-GPCR interactions is essential during drug development to enhance safety and ensure regulatory compliance. In this study, we utilized quantitative high-throughput screening (qHTS) to identify GPCR agonists and inhibitors in the Tox21 10K compound library. We applied machine-learning (ML)-based quantitative structure-activity relationship (QSAR) models to predict selective GPCR-targeting compounds with reduced hERG liability, employing different data processing sequences. Our models trained on the Tox21 10K library screening data were subsequently validated by using the Library of Pharmacologically Active Compounds (LOPAC). Furthermore, the models were applied to virtually screen approximately 360 K diverse compounds, with the top predictions experimentally validated, revealing new GPCR modulators with minimal hERG liability. The findings provide efficient strategies for the development of lead compounds targeting GPCRs while minimizing the cardiac risks associated with hERG inhibition.

Endoplasmic reticulum-associated degradation (ERAD) is a critical protein quality control mechanism that also regulates lipid metabolism and calcium homeostasis. Dysregulation of ERAD and unfolded protein response underlies diseases including cancer, neurodegenerative disorders, and metabolic syndromes. Small molecule modulators of ERAD could enable mechanistic discovery and therapeutic intervention, but few have been identified. Using a high-content screening, we discovered several ERAD-modulating compounds, including NCATS-SM0225, an ERAD inhibitor that unexpectedly binds all three isoforms of VDAC, outer mitochondrial membrane proteins enriched at mitochondria-associated membranes. This led us to discover an essential role for VDACs in ERAD and ER-phagy. NCATS-SM0225 elevates cytosolic, ER, and mitochondrial calcium through calcium influx and IP3R–MCU activity. This calcium imbalance strengthens VDAC1–IP3R coupling and activates PERK, which phosphorylates STIM1 and drives degradation of key ERAD regulators. Loss of these components amplifies PERK signaling and selectively kills cancer cells while sparing normal cells. These findings uncover a cancer-specific role of VDACs in ERAD regulation and calcium signaling, highlighting a therapeutically actionable vulnerability. Here the authors present NCATS-SM0225, a small molecule that inhibits ERAD and selectively kills cancer cells by binding VDACs, disrupting calcium homeostasis, and triggering the PERK-STIM1 pathway to degrade ERAD regulators.

Autophagy is a cellular degradation process that plays a critical role in maintaining homeostasis and preventing stress-induced damage, making it a promising therapeutic target for cancer and neurodegenerative diseases. In this study, we utilized mouse embryonic fibroblasts expressing GFP-labeled microtubule-associated protein 1 light chain 3, a widely used biomarker of autophagy activation, to screen 3733 clinically approved or investigational drugs using a high-throughput and high-content screening platform. From the primary screening, 117 compounds were identified as potential autophagy inducers. Subsequent confirmation studies narrowed this group to 5 previously uncharacterized autophagy-inducing candidates for further investigation. Follow-up studies assessed the mechanisms underlying autophagy modulation by these compounds, with a focus on key pathways such as mechanistic target of rapamycin inhibition, endoplasmic reticulum stress activation, and p53 activation. To further explore their therapeutic potential in cancer, we conducted an angiogenesis inhibition assay. This study successfully identified several autophagy inducers that may be repurposed for the treatment of cancer, highlighting their potential for future therapeutic development. SIGNIFICANT STATEMENT: This study identifies novel autophagy inducers from high-throughput screening of approved and investigational drugs. The findings highlight key pathways such as mechanistic target of rapamycin inhibition and endoplasmic reticulum stress activation, and demonstrate the ability of these compounds to inhibit angiogenesis, suggesting their potential for repurposing in cancer therapy.

Beta-1 adrenergic receptors (ADRβ1) are critical regulators of cardiac function; however, the potential modulation of ADRβ1 by environmental chemicals remains largely underexplored, raising concerns about unintended impacts on cardiovascular health. We applied a quantitative high-throughput screening (qHTS) approach to identify ADRβ1 agonists within the Tox21 10K compound library, which includes environmental chemicals, pharmaceuticals, industrial agents, and consumer products, using an HTRF-based cAMP assay in ADRβ1-overexpressing HEK293 cells. Primary screening of 8,947 unique compounds identified 118 potential ADRβ1 agonists. Among these, 94 were confirmed and further evaluated for β-adrenergic receptor subtype selectivity (ADRβ2 and ADRβ3) and hERG channel inhibition to assess potential cardiotoxicity liability. Known ADRβ1 agonists, isoproterenol (EC50, 0.91 nM) and dobutamine (EC50, 10 nM), were identified, supporting the validity of the assay. In addition, several compounds with limited prior ADRβ1-specific characterization, such as GR 103691 and N,N'-dibenzylethane-1,2-diamine, demonstrated subtype-selective or mixed agonist profiles, with some exhibiting minimal hERG inhibition. These findings expand the catalog of ADRβ1 modulators and demonstrate the utility of qHTS for identifying chemicals that may affect cardiovascular signaling pathways.

Abstract Background Cosmetics are defined by the U.S. Food and Drug Administration (FDA) as “articles intended to be rubbed, poured, sprinkled, or sprayed on, introduced into, or otherwise applied to the human body…for cleansing, beautifying, promoting attractiveness, or altering the appearance”. However, the safety of cosmetic ingredients is the responsibility of the manufacturer and exposure to some of these chemicals can result in unintentional harmful effects in humans. Thus, some states are banning specific chemicals, at the state legislation level, from being used in cosmetics, which includes known endocrine disruptors such as dibutyl phthalate, diethylhexyl phthalate, and per- and polyfluoroalkyl substances (PFAS). In this study, we aim to determine the toxicity pathways significantly affected by the cosmetic ingredients (both banned and non-banned), compared to the non-cosmetics compounds in the Tox21 10K library. Methods The Tox21 10K compound library (which includes 113 banned, 927 non-banned cosmetic ingredients and 8,185 non-cosmetic compounds) was previously screened against a panel of ~ 90 cell-based and biochemical assays. The hit rates (i.e., the percentage of compounds active in an assay) of the cosmetic and non-cosmetic compounds, as well as banned and non-banned compounds, in each Tox21 assay were compared using a Fisher’s exact test, and a p-value < 0.05 was considered statistically significant. Results We found that the evaluated cosmetic compounds were significantly more active in 11 antagonist mode assays and 23 agonist mode assays compared to the non-cosmetic compounds. The banned compounds were significantly more active in 7 antagonist mode and 6 agonist mode assays compared to the non-banned compounds. The targets of the antagonist assays included enzymes such as CYP2C19 and aromatase (CYP19A1), and the agonist assays included the Keap1/Nrf2 antioxidant response element pathway. Conclusions In the present study, we compared the bioactivity profiles of cosmetic ingredients and non-cosmetic compounds (not used in cosmetics), as well as the banned and non-banned cosmetic ingredients, across the Tox21 assays. The analyses revealed distinct molecular pathways for these chemicals which furthers our mechanistic understanding of cosmetics ingredient toxicity and may help direct the selection of cosmetic ingredients in the future with potentially less bioactivity. Clinical trial number Not applicable.

The Tox21 10K chemical library, an in vitro toxicology toolbox consisting of more than 8900 unique chemical entities including environmental chemicals and drugs, has undergone analytical quality control (QC) testing after storage at room temperature for 0 and 4 months (T0 and T4). Each chemical was previously assigned a QC grade based on purity, identity, and concentration. In parallel, the Tox21 10K library has been tested across approximately 90 in vitro assays in a quantitative high-throughput screening (qHTS) format, generating >120 M data points to date. These data were used to analyze the correlation between chemical quality and bioassay activity, as well as chemical structure. The chemical characteristics of poor-quality and unstable compounds were explored to identify structural features that should be avoided. In addition, one of the high-throughput assays measuring the induction of p53 activity by small molecules was used to test the Tox21 10K compound library at T0 and T4 due to its robust performance and reproducibility. Approximately 2% of compounds in the library showed a significant change in activity in the p53 assay between T0 and T4 (active to inactive or vice versa), which also correlated with chemical stability. Here, machine learning models were constructed using bioassay data or chemical structures to predict poor-quality (low QC grades at T0) and unstable (grade drop from T0 to T4) chemicals. Chemical structure was found to be highly predictive (0.75) of chemical quality and stability, whereas bioassay data was less predictive (0.66) but still showed better than random performance. Taken together, these findings provide valuable guidance for interpreting the Tox21 assay results and informing best practices for future chemical selection and handling.

The Wnt/β-catenin signaling pathway plays an important role in development and tissue homeostasis, and its dysregulation is implicated in various pathologies, including cancer, fibrosis, and neurodegeneration. However, the discovery of small-molecule modulators of this pathway remains challenging due to the pathway’s inherent complexity, characterized by ligand redundancy, overlapping receptor usage, and compensatory downstream signaling. In this study, we optimized a cell-based LEF/TCF-β-lactamase reporter assay for quantitative high-throughput screening in a 1536-well format. Screening 1280 compounds from the Library of Pharmacologically Active Compounds alongside 88 compounds from the Tox21 collection identified twelve potential antagonists of Wnt/β-catenin signaling. Follow-up studies confirmed the activity of 10 compounds, demonstrating consistent activity across two independent reporter systems (β-lactamase and luciferase). Western blot analysis showed that all compounds except for cytosine-1-beta-D-arabinofuranoside and PMEG reduced accumulation of both non-phosphorylated β-catenin (active) and total β-catenin, providing orthogonal validation of pathway inhibition. The identification of known Wnt inhibitors such as emetine, tyrphostin A9, niclosamide, ouabain, and podophyllotoxin further validated the assay’s robustness. Collectively, this study establishes a robust 1536-well screening platform for identifying Wnt pathway modulators and identifies topotecan, amsacrine, brefeldin A, and tyrphostin AG 879 as candidate small-molecule antagonist, thereby expanding the chemical tools for investigating Wnt/β-catenin signaling.

Combination therapy is a central strategy to overcome drug resistance in hepatocellular carcinoma (HCC), yet systematic identification of synergistic combinations is constrained by the combinatorial search space. We developed a data-driven workflow to integrate two orthogonal single-agent resources for 244 drugs, including cell viability profiles across 11 liver cancer cell lines and bioactivity signatures across 1,925 biochemical and cell-based assays. We calculated the correlation of the bioactivity profiles between each drug pair yielding 821 high-confidence combinations involving 190 unique drugs, from which eight anchors and a 125-drug library were selected by frequency-guided prioritization. Fixed-concentration screening of 992 anchor-library combinations in the Hep 3B2.1-7 cell line yielded 89 combinations with potency enhancement. Panobinostat showed the largest potency gains when combined with briciclib or thiocolchicine (69.5-fold and 81.1-fold lower IC₅₀, respectively). Matrix-based screening of these two representative combinations across 11 cell lines revealed pronounced concentration- and context-dependence. At optimal dose pairs, strong synergy was observed in Hep 3B2.1-7 (Zero Interaction Potency (ZIP) = 40.80 ± 4.22 with 58.53 ± 6.54

ICRF193 is a catalytic inhibitor of Topoisomerase 2 (TOP2), one of the major targets in cancer therapy. Although ICRF193 has not been approved for clinical use, it has potential implications in chemotherapy. In this study, we aimed to investigate the use of ICRF193 in chemotherapy in co-treatment with other drugs. To identify compounds that have synergistic effects with ICRF193, we optimized a cytotoxicity assay with combinations of ICRF193 in a 1536-well plate format and screened 2678 compounds, including clinically approved and investigational drugs, for their cytotoxicity in the presence and absence of ICRF193. From the screening and confirmation assays, etoposide, a known TOP2-targeting drug, was found to have a synergistic effect with 200 nM ICRF193 across multiple cancer cell lines, including HCT116, MCF7, and T47D. On the other hand, ICRF193 suppressed the toxicity of etoposide at higher concentrations (> 10 µM). In the follow-up studies, we found that ICRF193 and etoposide synergistically induced DNA double-strand breaks and subsequent G2 phase accumulation. Interestingly, this synergistic effect was observed only with etoposide and not with other TOP2 inhibitors in the tested compound library. Taken together, our results indicate that ICRF193 has a specific functional interaction with etoposide that enhances its genotoxic potential.

In this article, we provide a proof of concept evaluating the utility of the U.S. Tox21 high-throughput screening approach to assess the hazard of chemical mixtures using 2 estrogen receptor (ER) assays. A subset of chemicals identified in Phase I of the Tox21 program as active in the ER agonist assay were used to design mixtures for testing in Phase II. Individual chemicals and mixtures were evaluated in 2 cell-based ER alpha (ERα) activation assays: One incorporating a transfected ligand-binding domain in an ERα β-lactamase reporter cell line (ER-bla) and the full-length endogenous receptor in the MCF7 cell line with a luciferase reporter gene (ER-luc). Concentration-response data from individual chemicals were used to predict the joint effect based on mixtures modeling methods and were compared with observed mixtures data to assess model fit. The models tended to overpredict mixture responses in the ER-bla assay, whereas predictions were closer to observed responses in the ER-luc assay, indicating that a full-length endogenous ER is a preferred model for high-throughput mixture analysis. Lessons learned from this research include the importance of analyzing the individual chemicals used for predictions and the mixtures in the same experimental paradigm to minimize variation, developing methods for imputing missing values from incomplete concentration-response curves, and establishing criteria to determine when inactive chemicals should be omitted from mixture predictions.

Metabolically active compounds can cause toxicity which would otherwise be undetected using traditional in vitro assays with limited proficiency for xenobiotic metabolism. Introduction of liver microsomes to assay systems enables enhanced identification of compounds that require biotransformation to induce toxicity. Previously, metabolically active compounds from the Tox21 10 K compound library were identified using assays probing two targets, p53 and acetylcholinesterase (AChE), in the presence and absence of human or rat liver microsomes, due to the established roles of cytochrome P450 (CYP) enzymes in human drug metabolism. To further explore the role of metabolic activation, the activities of the identified metabolically active compounds were evaluated against five CYP enzymes: CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4. CYP bioactivities were found to be highly predictive (>80 % accuracy) of compounds that required metabolic activation in these assays. Chemical features significantly enriched in metabolically active compounds, as well as chemical features that were specific for each of the five CYPs, were identified. Product use exposures of the metabolically active compounds were examined in this study, with "pesticides" appearing to be the largest category that may produce harmful metabolites. Additionally, the compound interactions with different CYPs were assessed and frequencies for both classes of compounds, drugs and environmental chemicals, were found to be proportionally similar across the five CYP isoforms.

Viral genome sequencing provides valuable information for antiviral development, yet its integration with machine learning for virtual screening remains underexplored. To bridge this gap, viral genome sequences were combined with structural data of approved and investigational antivirals to identify virus-selective agents. In parallel, quantitative structure-activity relationship (QSAR) models were built to predict pan-antivirals. Robust models were generated with the area under the receiver operating characteristic curve (AUC-ROC) >0.72 for virus-selective and >0.79 for pan-antiviral predictions. These models were applied to virtually screen ~360 K compounds for anti-SARS-CoV-2 activity. The 346 compounds identified by the models were tested using two in vitro assays, yielding hit rates of 9.4% (24/256) in the pseudotyped particle (PP) entry assay and 37% (47/128) in the RNA-dependent RNA polymerase (RdRp) assay. The top compounds showed potencies around 1 µM. This study provides a framework for virtual screening of virus-selective and pan- antivirals against emerging pathogens.

β-adrenergic receptors play important roles in heart failure and drug-induced cardiotoxicity (DICT). The Tox21 10 K library of drugs and environmental chemicals have been tested for their activity against β-adrenergic receptor subtypes 1 and 2 (ADRB1 and ADRB2), as well as inhibition of the human ether-à-go-go-related gene (hERG) in a quantitative high-throughput screening (qHTS) format. In this study, the Tox21 compound activity profiles in the ADRB1/2 and hERG assays were compared in relation to their DICT potential. The results showed that compounds that acted as ADRB1 agonists, ADRB2 antagonists, or hERG inhibitors were more likely to exhibit DICT. The ADRB1 and ADRB2 assays shared similar compound activity profiles, while the hERG inhibition assay identified a distinct set of active compounds. In addition, we identified structural features that may differentiate the cardiotoxic and non-toxic ADRB1 agonists. Finally, machine learning models were developed for ADRB1 activity prediction based on chemical structure. The models were used to virtually screen a collection of approximately 360 K diverse compounds, with the highest-ranked compounds selected for experimental validation. This work represents the first systematic study of drugs and environmental chemicals against ADRB1/2, providing important insights into β-adrenergic receptor-related cardiotoxicity mechanisms. By clarifying how specific pharmacological interactions contribute to cardiac risk, it provides a framework for early cardiotoxicity prediction and the design of safer therapeutics through integrated profiling and modeling.

Androgen receptor (AR) is a nuclear receptor with a well-established role in sexual function and development. Modifications in AR can lead to endocrine disruption, cancer, and other diseases, making it imperative to identify compounds that influence these changes. AR modulators have been identified using immortalized cell lines in a high-throughput screening assay. However, most of these methods do not incorporate metabolism, leading to misclassification of compounds that normally require it to become AR modulators. Metabolism transforms exogenous parent compounds into metabolites that are easier to excrete, and normally less active than the parent. However, some metabolites modulate AR more effectively than the parent compound. Incorporating metabolism into a large compound screen can identify active metabolites as potential AR modulators. In this study, we optimized a high-throughput screening assay that included rat liver microsomes (RLM) as an exogenous metabolic system to detect AR antagonists. A robotic screen of the LOPAC library + 88 Tox21 compounds (a total of 1365 unique compounds) was then performed to validate the assay and identify any bioactivated AR modulators within the test library. Fifty-five compounds were identified as potential AR antagonists; 9 compounds out of these 55 compounds were found to have significant potency shifts between RLM free and RLM assays, suggesting the necessity of metabolism for their AR activity. A concurrent assay using heat-inactivated RLM was conducted to discern the true activity of each compound. Metabolic stability assays were also performed on the top compounds to clarify their ability to transition from parent to metabolite using RLM. Four compounds were identified as novel parent compounds requiring metabolism to become more potent AR antagonists. However, only 4,5-dianilinophthalimide (DAPH) displayed a clear concentration-response curve with a more potent IC50 when RLM was included compared to its parallel screens, identifying it as a true AR antagonist requiring metabolism.

Although multiple pesticides and solvents are risk factors for Parkinson’s disease [1] and other neurodegenerative diseases, most risk factors remain undiscovered. We previously identified the metallothionein gene MT1G as a biomarker for neurotoxicity induced by all seven neurotoxicants tested in LUHMES dopaminergic neurons. Here we used CRISP/R technology to insert a HiBiT tag into the MT1G gene of the LUHMES cell line. The engineered LUHMES MT1G::HiBiT cell lines were used to develop a quantitative high throughput screening [2] assay in a 3D-suspension culture platform with 1536 well microplates. We validated this qHTS assay by screening the LOPAC (Library of Pharmacologically Active Compounds) collection composed of 1280 compounds plus 88 selected Tox21 chemicals, demonstrating high signal-to-noise and reproducibility. In screening this library, 49 compounds were confirmed to significantly increase MT1G-HiBiT activity, including 35 compounds that exhibited cytotoxicity below 50 μM, and 14 noncytotoxic compounds. Most of these MT1G-HiBiT inducers killed cells at concentrations moderately higher than their MT1G-HiBiT activation potencies (AC50), however 14 showed MT1G-HiBiT AC50 values more than 3-fold lower than cytotoxicity IC50 values, and two showed higher values. Among the 49 MT1G-HiBiT inducers, 45 compounds resembled chelators. To test this apparent association, 27 known chelators were gathered and tested. Of these, 23 were active in the MT1G-HiBiT activity assay, confirming the propensity of chelators to activate MT1G transcription. Screening chemical libraries with this validated assay and characterizing the effects of active chemicals on cultured neurons may enable the identification of neurotoxicants or neurotoxic chemotypes that may cause neurodegenerative diseases.

The pathogenesis of cancer is complicated, and different types of cancer often exhibit different gene mutations resulting in different omics profiles. The purpose of this study was to systematically identify cancer-specific biological pathways and potential cancer-targeting drugs. We collectively analyzed the transcriptomics and proteomics data from 16 common types of human cancer to study the mechanism of carcinogenesis and seek potential treatment. Statistical approaches were applied to identify significant molecular targets and pathways related to each cancer type. Potential anti-cancer drugs were subsequently retrieved that can target these pathways. The number of significant pathways linked to each cancer type ranged from four (stomach cancer) to 112 (acute myeloid leukemia), and the number of therapeutic drugs that can target these cancer related pathways, ranged from one (ovarian cancer) to 97 (acute myeloid leukemia and non-small-cell lung carcinoma). As a validation of our method, some of these drugs are FDA approved therapies for their corresponding cancer type. Our findings provide a rich source of testable hypotheses that can be applied to deconvolute the complex underlying mechanisms of human cancer and used to prioritize and repurpose drugs as anti-cancer therapies.