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Herbal supplements are a significant source of drug-drug interactions (DDIs), herb-drug interactions, and hepatotoxicity. Cytochrome P450 (CYP450) enzymes metabolize a large number of FDA-approved pharmaceuticals and herbal supplements. This metabolism of pharmaceuticals and supplements can be augmented by concomitant use of either pharmaceuticals or supplements. The xenobiotic receptors constitutive androstane receptor (CAR) and the pregnane X receptor (PXR) can respond to xenobiotics by increasing the expression of a large number of genes that are involved in the metabolism of xenobiotics, including CYP450s. Conversely, but not exclusively, many xenobiotics can inhibit the activity of CYP450s. Induction of the expression or inhibition of the activity of CYP450s can result in DDIs and toxicity. Currently, the United States (US) Food and Drug Administration does not require the investigation of the interactions of herbal supplements and CYP450s. This review provides a summary of herbal supplements that inhibit CYP450s, induce the expression of CYP450s, and/or whose toxicity is mediated by CYP450s.

Complementary structural biology approaches reveal how an agonist and a covalent inhibitor simultaneously bind to a nuclear receptor.Complementary structural biology approaches reveal how an agonist and a covalent inhibitor simultaneously bind to a nuclear receptor.

Many drugs bind to and activate human pregnane X receptor (hPXR) to upregulate drug-metabolizing enzymes, resulting in decreased drug efficacy and increased resistance. This suggests that hPXR antagonists have therapeutic value. Here we report that SPA70 is a potent and selective hPXR antagonist. SPA70 inhibits hPXR in human hepatocytes and humanized mouse models and enhances the chemosensitivity of cancer cells, consistent with the role of hPXR in drug resistance. Unexpectedly, SJB7, a close analog of SPA70, is an hPXR agonist. X-ray crystallography reveals that SJB7 resides in the ligand-binding domain (LBD) of hPXR, interacting with the AF-2 helix to stabilize the LBD for coactivator binding. Differential hydrogen/deuterium exchange analysis demonstrates that SPA70 and SJB7 interact with the hPXR LBD. Docking studies suggest that the lack of the para-methoxy group in SPA70 compromises its interaction with the AF-2, thus explaining its antagonism. SPA70 is an hPXR antagonist and promising therapeutic tool. The xenobiotic-activated human pregnane X receptor (hPXR) regulates drug metabolism. Here the authors develop hPXR modulators, which are of potential therapeutic interest and functionally and structurally characterize the antagonist SPA70 and the structurally related agonist SJB7.

Nuclear receptors are ligand-activated transcription factors that can often be useful drug targets. Unfortunately, ligand promiscuity leads to two-thirds of receptors remaining clinically untargeted. PXR is a nuclear receptor that can be activated by diverse compounds to elevate metabolism, negatively impacting drug efficacy and safety. This presents a barrier to drug development because compounds designed to target other proteins must avoid PXR activation while retaining potency for the desired target. This problem could be avoided by using PXR antagonists, but these compounds are rare, and their molecular mechanisms remain unknown. Here, we report structurally related PXR-selective agonists and antagonists and their corresponding co-crystal structures to describe mechanisms of antagonism and selectivity. Structural and computational approaches show that antagonists induce PXR conformational changes incompatible with transcriptional coactivator recruitment. These results guide the design of compounds with predictable agonist/antagonist activities and bolster efforts to generate antagonists to prevent PXR activation interfering with other drugs. PXR is a receptor activated by diverse compounds that triggers detoxification pathways in the cell, and blocking this receptor may increase the effectiveness of certain drugs. Here, the authors present the structural basis of PXR inhibition.

A lack of relevant genetic models and cell lines hampers our understanding of hepatoblastoma pathogenesis and the development of new therapies for this neoplasm. Here, we report an improved MYC-driven hepatoblastoma-like murine model that recapitulates the pathological features of embryonal type of hepatoblastoma, with transcriptomics resembling the high-risk gene signatures of the human disease. Single-cell RNA-sequencing and spatial transcriptomics identify distinct subpopulations of hepatoblastoma cells. After deriving cell lines from the mouse model, we map cancer dependency genes using CRISPR-Cas9 screening and identify druggable targets shared with human hepatoblastoma (e.g., CDK7, CDK9, PRMT1, PRMT5). Our screen also reveals oncogenes and tumor suppressor genes in hepatoblastoma that engage multiple, druggable cancer signaling pathways. Chemotherapy is critical for human hepatoblastoma treatment. A genetic mapping of doxorubicin response by CRISPR-Cas9 screening identifies modifiers whose loss-of-function synergizes with (e.g., PRKDC) or antagonizes (e.g., apoptosis genes) the effect of chemotherapy. The combination of PRKDC inhibition and doxorubicin-based chemotherapy greatly enhances therapeutic efficacy. These studies provide a set of resources including disease models suitable for identifying and validating potential therapeutic targets in human high-risk hepatoblastoma. The availability of relevant animal models that can recapitulate high-risk hepatoblastoma will help to better understand its pathogenesis. Here the authors report and characterize a hepatocyte-specific, MYC-driven hepatoblastoma mouse model and show it recapitulates the human hepatoblastoma pathophysiology.

Drug-drug interactions associate with concurrent uses of multiple medications. Cytochrome P450 (CYP) 3A4 metabolizes a large portion of marketed drugs. To maintain the efficacy of drugs metabolized by CYP3A4, pan-CYP3A inhibitors such as ritonavir are often co-administered. Although selective CYP3A4 inhibitors have greater therapeutic benefits as they avoid inhibiting unintended CYPs and undesirable clinical consequences, the high homology between CYP3A4 and CYP3A5 has hampered the development of such selective inhibitors. Here, we report a series of selective CYP3A4 inhibitors with scaffolds identified by high-throughput screening. Structural, functional, and computational analyses reveal that the differential C-terminal loop conformations and two distinct ligand binding surfaces disfavor the binding of selective CYP3A4 inhibitors to CYP3A5. Structure-guided design of compounds validates the model and yields analogs that are selective for CYP3A4 versus other major CYPs. These findings demonstrate the feasibility to selectively inhibit CYP3A4 and provide guidance for designing better CYP3A4 selective inhibitors. To maintain the efficacy of drugs metabolized by CYP3A4, pan-CYP3A inhibitor is often co-administered, but the high homology between CYP3A4 and CYP3A5 has hampered the development of selective CYP3A4 inhibitors. Here, the authors report a series of selective CYP3A4 inhibitors and show that differential C-terminal loop conformations and two distinct ligand binding surfaces disfavour the binding of selective CYP3A4 inhibitors to CYP3A5.

Pregnane X receptor (PXR) is activated by chemicals to transcriptionally regulate drug disposition and possibly decrease drug efficacy and increase resistance, suggesting therapeutic value for PXR antagonists. We previously reported the antagonist SPA70 and its analog SJB7, which unexpectedly is an agonist. Here, we describe another unexpected observation: mutating a single residue (W299A) within the PXR ligand-binding domain converts SPA70 to an agonist. After characterizing wild-type and W299A PXR activity profiles, we used molecular dynamics simulations to reveal that in wild-type PXR, agonists stabilize the activation function 2 (AF-2) helix in an “inward” position, but SPA70 displaces the AF-2. In W299A, however, SPA70 stabilizes the AF-2 “inward”, like agonists. We validated our model by predicting the antagonist SJC2 to be a W299A agonist, which was confirmed experimentally. Our work correlates previously unobserved ligand-induced conformational changes to PXR cellular activity and, for the first time, reveals how PXR antagonists work.

Drug combinations are essential to modern medicine, but their discovery remains slow and inefficient as experimental complexity expands rapidly with each additional drug tested. Although modern liquid handling systems enable complex and highly customizable experimental designs, a lack of strategies integrating these technologies with combination-specific analytical methods has limited throughput. Here we introduce Combocat, an open-source and streamlined framework that combines acoustic liquid handling protocols with machine learning-based inference to achieve ultrahigh-throughput drug combination screening. Using Combocat, we generate a reference dataset of over 800 unique combinations in a dense 10 × 10 matrix format across multiple cell types, and use this to train a predictive model that accurately infers drug combination effects from sparse data, drastically reducing the number of experimental measurements required. As proof of concept, we screened 9,045 combinations in a neuroblastoma cell line—the largest number of combinations tested in a single cell line to date—achieved using minimal resources. By integrating advanced drug dispensing technologies with predictive computational modeling, Combocat provides a scalable solution to accelerate the discovery of novel drug combinations. Drug combination discovery remains slow and challenging. Here, the authors introduce Combocat, an open-source framework that combines acoustic liquid handling protocols with machine learning to achieve ultrahigh-throughput drug combination screening; as proof of concept, they use Combocat to screen 9,045 drug combinations in a neuroblastoma cell line.

Doxorubicin is a commonly used anticancer agent that can cause debilitating and irreversible cardiac injury. The initiating mechanisms contributing to this side effect remain unknown, and current preventative strategies offer only modest protection. Using stemcell–derived cardiomyocytes from patients receiving doxorubicin, we probed the transcriptomic landscape of solute carriers and identified organic cation transporter 3 (OCT3) (SLC22A3) as a critical transporter regulating the cardiac accumulation of doxorubicin. Functional validation studies in heterologous overexpression models confirmed that doxorubicin is transported into cardiomyocytes by OCT3 and that deficiency of OCT3 protected mice from acute and chronic doxorubicin-related changes in cardiovascular function and genetic pathways associated with cardiac damage. To provide proof-of-principle and demonstrate translational relevance of this transport mechanism, we identified several pharmacological inhibitors of OCT3, including nilotinib, and found that pharmacological targeting of OCT3 can also preserve cardiovascular function following treatment with doxorubicin without affecting its plasma levels or antitumor effects in multiple models of leukemia and breast cancer. Finally, we identified a previously unrecognized, OCT3-dependent pathway of doxorubicin-induced cardiotoxicity that results in a downstream signaling cascade involving the calcium-binding proteins S100A8 and S100A9. These collective findings not only shed light on the etiology of doxorubicin-induced cardiotoxicity, but also are of potential translational relevance and provide a rationale for the implementation of a targeted intervention strategy to prevent this debilitating side effect.

Deep convolutional neural networks (DCNNs) have been widely applied in various modern artificial intelligence (AI) applications. DCNN's inference is a process with high calculation costs, which usually requires billions of multiply‐accumulate operations. On mobile platforms such as embedded systems or robotics, an efficient implementation of DCNNs is significant. However, most previous field‐programmable gate array‐based works on accelerators for DCNNs just support one DCNN or just support convolution layers. In order to address this limitation, this work proposes a reconfigurable accelerator. The accelerator is flexible and can support multiple DCNNs and different layer types, such as convolution, pooling, activation function, and full connection layers. It is equipped with a five‐level pipeline convolution engine whose main component is two processing element arrays. Furthermore, a design space exploration method is proposed to make full advantage of the proposed accelerator. This accelerator is implemented with the ZYNQ‐7 ZC706 evaluation board and achieves a high performance of 53.29 Giga operations per second (GOPS) on AlexNet and 45.09 GOPS on YOLOv2‐tiny at 100 MHz. Further performance of the accelerator is compared with the previous works, and it achieves multiple advantages: High performance, high configurability, and efficient resource utilisation.