Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development.

The opioid crisis in the United States is a significant public health issue, with a nearly threefold increase in opioid-related fatalities between 1999 and 2014. In response to this crisis, society has made numerous efforts to mitigate its impact. Recent advancements in understanding the structural intricacies of the κ opioid receptor (KOR) have improved our knowledge of how opioids interact with their receptors, triggering downstream signaling pathways that lead to pain relief. This review concentrates on the KOR, offering crucial structural insights into the binding mechanisms of both agonists and antagonists to the receptor. Through comparative analysis of the atomic details of the binding site, distinct interactions specific to agonists and antagonists have been identified. These insights not only enhance our understanding of ligand binding mechanisms but also shed light on potential pathways for developing new opioid analgesics with an improved risk-benefit profile.

Antiviral C-geranylated flavonoids from Artocarpus communis.

Ten C-geranylated flavonoids, along with three known analogues, were isolated from the leaves of Artocarpus communis. The chemical structures of these compounds were unambiguously determined via comprehensive spectroscopic analysis, single-crystal X-ray diffraction experiments, and quantum chemical electronic circular dichroism calculations. Structurally, artocarones A-I (1-9) represent a group of unusual, highly modified C-geranylated flavonoids, in which the geranyl chain is cyclised with the ortho-hydroxy group of flavonoids to form various heterocyclic scaffolds. Notably, artocarones E and G-I (5 and 7-9) feature a 6H-benzo[c]chromene core that is hitherto undescribed in C-geranylated flavonoids. Artocarone J (10) is the first example of C-9-C-16 connected C-geranylated aurone. Meanwhile, the plausible biosynthetic pathways for these rare C-geranylated flavonoids were also proposed. Notably, compounds 1, 2, 4, 8, 11, and 12 exhibited promising in vitro inhibitory activities against respiratory syncytial virus and herpes simplex virus type 1.

NAD activates olfactory receptor 1386 to regulate type I interferon responses in Plasmodium yoelii YM infection.

Olfactory receptors (Olfr) are G protein-coupled receptors that are normally expressed on olfactory sensory neurons to detect volatile chemicals or odorants. Interestingly, many Olfrs are also expressed in diverse tissues and function in cell-cell recognition, migration, and proliferation as well as immune responses and disease processes. Here, we showed that many Olfr genes were expressed in the mouse spleen, linked to Plasmodium yoelii genetic loci significantly, and/or had genome-wide patterns of LOD scores (GPLSs) similar to those of host Toll-like receptor genes. Expression of specific Olfr genes such as Olfr1386 in HEK293T cells significantly increased luciferase signals driven by IFN-ß and NF-κB promoters, with elevated levels of phosphorylated TBK1, IRF3, P38, and JNK. Mice without Olfr1386 were generated using the CRISPR/Cas9 method, and the Olfr1386-/- mice showed significantly lower IFN-α/ß levels and longer survival than wild-type (WT) littermates after infection with P. yoelii YM parasites. Inhibition of G protein signaling and P38 activity could affect cyclic AMP-responsive element promoter-driven luciferase signals and IFN-ß mRNA levels in HEK293T cells expressing the Olfr1386 gene, respectively. Screening of malaria parasite metabolites identified nicotinamide adenine dinucleotide (NAD) as a potential ligand for Olfr1386, and NAD could stimulate IFN-ß responses and phosphorylation of TBK1 and STAT1/2 in RAW264.7 cells. Additionally, parasite RNA (pRNA) could significantly increase Olfr1386 mRNA levels. This study links multiple Olfrs to host immune response pathways, identifies a candidate ligand for Olfr1386, and demonstrates the important roles of Olfr1386 in regulating type I interferon (IFN-I) responses during malaria parasite infections.

Use of Tox21 screening data to profile PFAS bioactivities on nuclear receptors, cellular stress pathways, and cytochrome p450 enzymes.

Per- and poly-fluoroalkyl substances (PFAS) are synthetic chemicals widely used in commercial products. PFAS are a global concern due to their persistence in the environment and extensive associations with adverse health outcomes. While legacy PFAS have been extensively studied, many non-legacy PFAS lack sufficient toxicity information. In this study, we first analyzed the bioactivity of PFAS using Tox21 screening data surveying more than 75 assay endpoints (e.g., nuclear receptors, stress response, and metabolism) to understand the toxicity of non-legacy PFAS and investigate potential new targets of PFAS. From the Tox21 screening data analysis, we confirmed several known PFAS targets/pathways and identified several potential novel targets/pathways of PFAS. To confirm the effect of PFAS on these novel targets/pathways, we conducted several cell- and enzyme-based assays in the follow-up studies. We found PFAS inhibited cytochromes P450s (CYPs), especially CYP2C9 with IC50 values of < 1 µM. Considering PFAS affected other targets/pathways at > 10 µM, PFAS have a higher affinity to CYP2C9. This PFAS-CYP2C9 interaction was further investigated using molecular docking analysis. The result suggested that PFAS directly bind to the active sites of CYP2C9. These findings have important implications to understand the mechanism of PFAS action and toxicity.

Paleo-geomorphic features of pre-Jurassic and its oil-controlling effect in Wuqi-Dingbian area.

The Fuxian-Yan10 layers are the main oil-producing reservoirs of Jurassic in Wuqi-Dingbian area of Ordos Basin. However, due to the lack of understanding of the pattern and distribution characteristics of oil reservoirs, the benefits of exploration and development are restricted. In order to provide theoretical guidance for the study of similar geological features, based on the analysis of paleogeomorphic features and evolution, the analysis focuses on the influence of palaeo-geomorphology on oil reservoir distribution, and summarizes the main types of reservoir models in the study area. The results show that there are four types of palaeo-geomorphic units in the Wuding area: palaeo-river, slope, highland and interriver hill. In the study area, the Jurassic paleogeomorphology controls the sedimentary development and distribution from Fuxian Formation to Yan 9 Formation. The compacted structure and lithologic barrier provide good trapping conditions for the paleogeomorphic oil. Moreover, the swampy coal measures and mudstone at the top of Yan 9 play a sealing role for oil accumulation, and the bottom water was obviously driven. In addition, the pre-Jurassic deep valley was the main channels for oil migration. On this basis, it is concluded that there are four reservoir-forming models in Wuding area: slope type, river hill type, ancient river type and highland type.

Anti-inflammatory naphthoquinone-monoterpene adducts and neolignans from Eugenia caryophyllata.

A phytochemical investigation on the buds of edible medicinal plant, Eugenia carvophyllata, led to the discovery of seven new compounds, caryophones A-G (1-7), along with two biogenetically-related known ones, 2-methoxy-7-methyl-1,4-naphthalenedione (8) and eugenol (9). Compounds 1-3 represent the first examples of C-5-C-1' connected naphthoquinone-monoterpene adducts with a new carbon skeleton. Compounds 4-7 are a class of novel neolignans with unusual linkage patterns, in which the C-9 position of one phenylpropene unit coupled with the aromatic core of another phenylpropene unit. The chemical structures of the new compounds were determined based on extensive spectroscopic analysis, X-ray diffraction crystallography, and quantum-chemical calculation. Among the isolates, compounds (-)-2, 3, 6, and 9 showed significant in vitro inhibitory activities against respiratory syncytial virus (RSV)-induced nitric oxide (NO) production in RAW264.7 cells.

Use of in vitro methods combined with in silico analysis to identify potential skin sensitizers in the Tox21 10K compound library.

Introduction: Skin sensitization, which leads to allergic contact dermatitis, is a key toxicological endpoint with high occupational and consumer prevalence. This study optimized several in vitro assays listed in OECD skin sensitization test guidelines for use on a quantitative high-throughput screening (qHTS) platform and performed in silico model predictions to assess the skin sensitization potential of prioritized compounds from the Tox21 10K compound library. Methods: First, we screened the entire Tox21 10K compound library using a qHTS KeratinoSensTM (KS) assay and built a quantitative structure-activity relationship (QSAR) model based on the KS results. From the qHTS KS screening results, we prioritized 288 compounds to cover a wide range of structural chemotypes and tested them in the solid phase extraction-tandem mass spectrometry (SPE-MS/MS) direct peptide reactivity assay (DPRA), IL-8 homogeneous time-resolved fluorescence (HTRF) assay, CD86 and CD54 surface expression in THP1 cells, and predicted in silico sensitization potential using the OECD QSAR Toolbox (v4.5). Results: Interpreting tiered qHTS datasets using a defined approach showed the effectiveness and efficiency of in vitro methods. We selected structural chemotypes to present this diverse chemical collection and to explore previously unidentified structural contributions to sensitization potential. Discussion: Here, we provide a skin sensitization dataset of unprecedented size, along with associated tools, and analysis designed to support chemical assessments.

Tautomeric cinnamoylphloroglucinol-monoterpene adducts from Cleistocalyx operculatus and their antiviral activities.

Guided by 1H NMR spectroscopic experiments using the characteristic enol proton signals as probes, three pairs of new tautomeric cinnamoylphloroglucinol-monoterpene adducts (1-3) were isolated from the buds of Cleistocalyx operculatus. Their structures with absolute configurations were established by spectroscopic analysis, modified Mosher's method, and quantum chemical electronic circular dichroism calculation. Compounds 1-3 represent a novel class of cinnamoylphloroglucinol-monoterpene adducts featuring an unusual C-4-C-1' linkage between 2,2,4-trimethyl-cinnamyl-ß-triketone and modified linear monoterpenoid motifs. Notably, compounds 1-3 exhibited significant in vitro antiviral activity against respiratory syncytial virus (RSV).

3D-Suspension culture platform for high throughput screening of neurotoxic chemicals using LUHMES dopaminergic neurons.

Three-dimensional (3D) cell culture in vitro promises to improve representation of neuron physiology in vivo. This inspired development of a 3D culture platform for LUHMES (Lund Human Mesencephalic) dopaminergic neurons for high-throughput screening (HTS) of chemicals for neurotoxicity. Three culture platforms, adhesion (2D-monolayer), 3D-suspension, and 3D-shaken, were compared to monitor mRNA expression of seven neuronal marker genes, DCX, DRD2, ENO2, NEUROD4, SYN1, TH, and TUBB3. These seven marker genes reached similar maxima in all three formats, with the two 3D platforms showing similar kinetics, whereas several markers peaked earlier in 2D adhesion compared to both 3D culture platforms. The differentiated LUHMES (dLUHMES) neurons treated with ziram, methylmercury or thiram dynamically increased expression of metallothionein biomarker genes MT1G, MT1E and MT2A at 6 h. These gene expression increases were generally more dynamic in 2D adhesion cultures than in 3D cultures, but were generally comparable between 3D-suspension and 3D-u plate (low binding) platforms. Finally, we adapted 3D-suspension culture of dLUHMES and neural stem cells to 1536 well plates with a HTS cytotoxicity assay. This HTS assay revealed that cytotoxicity IC50 values were not significantly different between adhesion and 3D-suspension platforms for 31 of 34 (91%) neurotoxicants tested, whereas IC50 values were significantly different for at least two toxicants. In summary, the 3D-suspension culture platform for LUHMES dopaminergic neurons supported full differentiation and reproducible assay results, enabling quantitative HTS (qHTS) for cytotoxicity in 1536 well format with a Robust Z' score of 0.68.

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in ß2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

High-throughput screening of novel TFEB agonists in protecting against acetaminophen-induced liver injury in mice.

Macroautophagy (referred to as autophagy hereafter) is a major intracellular lysosomal degradation pathway that is responsible for the degradation of misfolded/damaged proteins and organelles. Previous studies showed that autophagy protects against acetaminophen (APAP)-induced injury (AILI) via selective removal of damaged mitochondria and APAP protein adducts. The lysosome is a critical organelle sitting at the end stage of autophagy for autophagic degradation via fusion with autophagosomes. In the present study, we showed that transcription factor EB (TFEB), a master transcription factor for lysosomal biogenesis, was impaired by APAP resulting in decreased lysosomal biogenesis in mouse livers. Genetic loss-of and gain-of function of hepatic TFEB exacerbated or protected against AILI, respectively. Mechanistically, overexpression of TFEB increased clearance of APAP protein adducts and mitochondria biogenesis as well as SQSTM1/p62-dependent non-canonical nuclear factor erythroid 2-related factor 2 (NRF2) activation to protect against AILI. We also performed an unbiased cell-based imaging high-throughput chemical screening on TFEB and identified a group of TFEB agonists. Among these agonists, salinomycin, an anticoccidial and antibacterial agent, activated TFEB and protected against AILI in mice. In conclusion, genetic and pharmacological activating TFEB may be a promising approach for protecting against AILI.

Exploring NCATS in-house biomedical data for evidence-based drug repurposing.

Drug repurposing is a strategy for identifying new uses of approved or investigational drugs that are outside the scope of the original medical indication. Even though many repurposed drugs have been found serendipitously in the past, the increasing availability of large volumes of biomedical data has enabled more systemic, data-driven approaches for drug candidate identification. At National Center of Advancing Translational Sciences (NCATS), we invent new methods to generate new data and information publicly available to spur innovation and scientific discovery. In this study, we aimed to explore and demonstrate biomedical data generated and collected via two NCATS research programs, the Toxicology in the 21st Century program (Tox21) and the Biomedical Data Translator (Translator) for the application of drug repurposing. These two programs provide complementary types of biomedical data from uncovering underlying biological mechanisms with bioassay screening data from Tox21 for chemical clustering, to enrich clustered chemicals with scientific evidence mined from the Translator towards drug repurposing. 129 chemical clusters have been generated and three of them have been further investigated for drug repurposing candidate identification, which is detailed as case studies.

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.

Auranofin targets UBA1 and enhances UBA1 activity by facilitating ubiquitin trans-thioesterification to E2 ubiquitin-conjugating enzymes.

UBA1 is the primary E1 ubiquitin-activating enzyme responsible for generation of activated ubiquitin required for ubiquitination, a process that regulates stability and function of numerous proteins. Decreased or insufficient ubiquitination can cause or drive aging and many diseases. Therefore, a small-molecule enhancing UBA1 activity could have broad therapeutic potential. Here we report that auranofin, a drug approved for the treatment of rheumatoid arthritis, is a potent UBA1 activity enhancer. Auranofin binds to the UBA1's ubiquitin fold domain and conjugates to Cys1039 residue. The binding enhances UBA1 interactions with at least 20 different E2 ubiquitin-conjugating enzymes, facilitating ubiquitin charging to E2 and increasing the activities of seven representative E3s in vitro. Auranofin promotes ubiquitination and degradation of misfolded ER proteins during ER-associated degradation in cells at low nanomolar concentrations. It also facilitates outer mitochondrial membrane-associated degradation. These findings suggest that auranofin can serve as a much-needed tool for UBA1 research and therapeutic exploration.

Exploring NCATS In-House Biomedical Data for Evidence-based Drug Repurposing.

Drug repurposing is a strategy for identifying new uses of approved or investigational drugs that are outside the scope of the original medical indication. Even though many repurposed drugs have been found serendipitously in the past, the increasing availability of large volumes of biomedical data has enabled more systemic, data-driven approaches for drug candidate identification. At National Center of Advancing Translational Sciences (NCATS), we invent new methods to generate new data and information publicly available to spur innovation and scientific discovery. In this study, we aimed to explore and demonstrate biomedical data generated and collected via two NCATS research programs, the Toxicology in the 21st Century program (Tox21) and the Biomedical Data Translator (Translator) for the application of drug repurposing. These two programs provide complementary types of biomedical data from uncovering underlying biological mechanisms with bioassay screening data from Tox21 for chemical clustering, to enrich clustered chemicals with scientific evidence mined from the Translator towards drug repurposing. 129 chemical clusters have been generated and three of them have been further investigated for drug repurposing candidate identification, which is detailed as case studies.

In silico modeling-based new alternative methods to predict drug and herb-induced liver injury: A review.

New approach methods (NAMs) have been developed to predict a wide range of toxicities through innovative technologies. Liver injury is one of the most extensively studied endpoints due to its severity and frequency, occurring among populations that consume drugs or dietary supplements. In this review, we focus on recent developments of in silico modeling for liver injury prediction using deep learning and in vitro data based on adverse outcome pathways (AOPs). Despite these models being mainly developed using datasets generated from drug-like molecules, they were also applied to the prediction of hepatotoxicity caused by herbal products. As deep learning has achieved great success in many different fields, advanced machine learning algorithms have been actively applied to improve the accuracy of in silico models. Additionally, the development of liver AOPs, combined with big data in toxicology, has been valuable in developing in silico models with enhanced predictive performance and interpretability. Specifically, one approach involves developing structure-based models for predicting molecular initiating events of liver AOPs, while others use in vitro data with structure information as model inputs for making predictions. Even though liver injury remains a difficult endpoint to predict, advancements in machine learning algorithms and the expansion of in vitro databases with relevant biological knowledge have made a huge impact on improving in silico modeling for drug-induced liver injury prediction.

Evaluating the utility of a high throughput thiol-containing fluorescent probe to screen for reactivity: A case study with the Tox21 library.

High-throughput screening (HTS) assays for bioactivity in the Tox21 program aim to evaluate an array of different biological targets and pathways, but a significant barrier to interpretation of these data is the lack of high-throughput screening (HTS) assays intended to identify non-specific reactive chemicals. This is an important aspect for prioritising chemicals to test in specific assays, identifying promiscuous chemicals based on their reactivity, as well as addressing hazards such as skin sensitisation which are not necessarily initiated by a receptor-mediated effect but act through a non-specific mechanism. Herein, a fluorescence-based HTS assay that allows the identification of thiol-reactive compounds was used to screen 7,872 unique chemicals in the Tox21 10K chemical library. Active chemicals were compared with profiling outcomes using structural alerts encoding electrophilic information. Random Forest classification models based on chemical fingerprints were developed to predict assay outcomes and evaluated through 10-fold stratified cross validation (CV). The mean CV Balanced Accuracy of the validation set was 0.648. The model developed shows promise as a tool to screen untested chemicals for their potential electrophilic reactivity based solely on chemical structural features.

In vitro profiling of pesticides within the Tox21 10K compound library for bioactivity and potential toxicity.

Pesticides include a diverse class of toxic chemicals, often having numerous modes of actions when used in agriculture against targeted organisms to control insect infestation, halt unwanted vegetation, and prevent the spread of disease. In this study, the in vitro assay activity of pesticides within the Tox21 10K compound library were examined. The assays in which pesticides showed significantly more activities than non-pesticide chemicals revealed potential targets and mechanisms of action for pesticides. Furthermore, pesticides that showed promiscuous activity against many targets and cytotoxicity were identified, which warrant further toxicological evaluation. Several pesticides were shown to require metabolic activation, demonstrating the importance of introducing metabolic capacity to in vitro assays. Overall, the activity profiles of pesticides highlighted in this study can contribute to the knowledge gaps surrounding pesticide mechanisms and to the better understanding of the on- and off-target organismal effects of pesticides.

Quantitative Bioactivity Signatures of Dietary Supplements and Natural Products.

Dietary supplements and natural products are often marketed as safe and effective alternatives to conventional drugs, but their safety and efficacy are not well regulated. To address the lack of scientific data in these areas, we assembled a collection of Dietary Supplements and Natural Products (DSNP), as well as Traditional Chinese Medicinal (TCM) plant extracts. These collections were then profiled in a series of in vitro high-throughput screening assays, including a liver cytochrome p450 enzyme panel, CAR/PXR signaling pathways, and P-glycoprotein (P-gp) transporter assay activities. This pipeline facilitated the interrogation of natural product-drug interaction (NaPDI) through prominent metabolizing pathways. In addition, we compared the activity profiles of the DSNP/TCM substances with those of an approved drug collection (the NCATS Pharmaceutical Collection or NPC). Many of the approved drugs have well-annotated mechanisms of action (MOAs), while the MOAs for most of the DSNP and TCM samples remain unknown. Based on the premise that compounds with similar activity profiles tend to share similar targets or MOA, we clustered the library activity profiles to identify overlap with the NPC to predict the MOAs of the DSNP/TCM substances. Our results suggest that many of these substances may have significant bioactivity and potential toxicity, and they provide a starting point for further research on their clinical relevance.

Identification of Potent and Selective Acetylcholinesterase/Butyrylcholinesterase Inhibitors by Virtual Screening.

Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) play important roles in human neurodegenerative disorders such as Alzheimer's disease. In this study, machine learning methods were applied to develop quantitative structure-activity relationship models for the prediction of novel AChE and BChE inhibitors based on data from quantitative high-throughput screening assays. The models were used to virtually screen an in-house collection of ∼360K compounds. The optimal models achieved good performance with area under the receiver operating characteristic curve values ranging from 0.83 ± 0.03 to 0.87 ± 0.01 for the prediction of AChE/BChE inhibition activity and selectivity. Experimental validation showed that the best-performing models increased the assay hit rate by several folds. We identified 88 novel AChE and 126 novel BChE inhibitors, 25% (AChE) and 53% (BChE) of which showed potent inhibitory effects (IC50 < 5 µM). In addition, structure-activity relationship analysis of the BChE inhibitors revealed scaffolds for chemistry design and optimization. In conclusion, machine learning models were shown to efficiently identify potent and selective inhibitors against AChE and BChE and novel structural series for further design and development of potential therapeutics against neurodegenerative disorders.