FXR antagonism of NSAIDs contributes to drug-induced liver injury identified by systems pharmacology approach

Farnesoid X Receptor Regulated Sepsis-Induced Abnormal Bile Acid Metabolism via the Fibroblast Growth Factor 15/Fibroblast Growth Factor Receptor 4 Pathway

OBJECTIVE

The study aims to investigate the mechanism of Farnesoid X receptor (FXR) activation in sepsis-induced abnormal bile acid metabolism and the metabolism status of each bile acid type.

METHODS

The sepsis mouse model was developed via lipopolysaccharide intraperitoneal injection and confirmed via hematoxylin and eosin (H&E) staining. FXR agonist activated the FXR/fibroblast growth factor (FGF)15/FGFR pathway via quantitative real-time polymerase chain reaction and Western blot. Consequently, metabolomics and bioinformatics analysis were conducted to identify the alterations in each kind of bile acid content following FXR agonist/inhibitor intervention.

RESULTS

The H&E staining indicated that FXR activation alleviates the liver injury of the sepsis mouse model. The increased FGF15 and FXFR expression levels and decreased CYP7A1 demonstrated FXR/FGF15/FGFR pathway activation following FXR agonist treatment. Furthermore, total bile acid, interleukin (IL)-6, and tumor necrosis factor-α concentrations were downregulated after FXR activation, whereas IL-10 concentration was upregulated, indicating the alleviated effect of FXR agonist in sepsis. Consequently, metabolomics and bioinformatics analysis determined that T-a-MCA were downregulated in both FXR agonist and inhibitor groups, whereas six bile acid types were altered in the control group.

CONCLUSION

FXR activation was crucial in alleviating sepsis-induced hepatic injury and cholestasis through the FGF15/FGFR signaling pathway, and FXR may act as a potential preventive and intervention target of sepsis.

Naproxen-induced liver injury in a case of adult-onset Still's disease

We present the case of a young female with fever, rash, and joint pain without any other history of connective tissue disorder, who was found on evaluation to have neutrophilic leucocytosis, elevated inflammatory markers and lymphadenopathy with hepatosplenomegaly. After extensive workup, the possibility of adult onset Still's disease was suggested by Yamaguchi criteria. She was started on naproxen, but later on developed drug-induced liver injury. She already had central serous retinopathy due to steroid use, and it was difficult to treat the adult-onset Stills disease due to the liver injury in view of contraindication to use of anti-inflammatory drugs but her illness was monocyclic and did not require further immunosuppression.

Farnesoid X receptor: From Structure to Function and Its Pharmacology in Liver Fibrosis

The farnesoid X receptor (FXR), a ligand-activated transcription factor, plays a crucial role in regulating bile acid metabolism within the enterohepatic circulation. Beyond its involvement in metabolic disorders and immune imbalances affecting various tissues, FXR is implicated in microbiota modulation, gut-to-brain communication, and liver disease. The liver, as a pivotal metabolic and detoxification organ, is susceptible to damage from factors such as alcohol, viruses, drugs, and high-fat diets. Chronic or recurrent liver injury can culminate in liver fibrosis, which, if left untreated, may progress to cirrhosis and even liver cancer, posing significant health risks. However, therapeutic options for liver fibrosis remain limited in terms of FDA-approved drugs. Recent insights into the structure of FXR, coupled with animal and clinical investigations, have shed light on its potential pharmacological role in hepatic fibrosis. Progress has been achieved in both fundamental research and clinical applications. This review critically examines recent advancements in FXR research, highlighting challenges and potential mechanisms underlying its role in liver fibrosis treatment.

Development and Challenges of Diclofenac-Based Novel Therapeutics: Targeting Cancer and Complex Diseases

Simple Summary

Diclofenac is a widely used drug for its anti-inflammatory and pain alleviating properties. This review summarizes the current understanding about the drug diclofenac. The potential applications of diclofenac beyond its well-known anti-inflammatory properties for other diseases such as cancer are discussed, along with existing limitations.

Abstract

Diclofenac is a highly prescribed non-steroidal anti-inflammatory drug (NSAID) that relieves inflammation, pain, fever, and aches, used at different doses depending on clinical conditions. This drug inhibits cyclooxygenase-1 and cyclooxygenase-2 enzymes, which are responsible for the generation of prostaglandin synthesis. To improve current diclofenac-based therapies, we require new molecular systematic therapeutic approaches to reduce complex multifactorial effects. However, the critical challenge that appears with diclofenac and other drugs of the same class is their side effects, such as signs of stomach injuries, kidney problems, cardiovascular issues, hepatic issues, and diarrhea. In this article, we discuss why defining diclofenac-based mechanisms, pharmacological features, and its medicinal properties are needed to direct future drug development against neurodegeneration and imperfect ageing and to improve cancer therapy. In addition, we describe various advance molecular mechanisms and fundamental aspects linked with diclofenac which can strengthen and enable the better designing of new derivatives of diclofenac to overcome critical challenges and improve their applications.

Detailed Molecular Mechanisms Involved in Drug-Induced Non-Alcoholic Fatty Liver Disease and Non-Alcoholic Steatohepatitis: An Update

Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) are some of the biggest public health challenges due to their spread and increasing incidence around the world. NAFLD is characterized by intrahepatic lipid deposition, accompanied by dyslipidemia, hypertension, and insulin resistance, leading to more serious complications. Among the various causes, drug administration for the treatment of numerous kinds of diseases, such as antiarrhythmic and antihypertensive drugs, promotes the onset and progression of steatosis, causing drug-induced hepatic steatosis (DIHS). Here, we reviewed in detail the major classes of drugs that cause DIHS and the specific molecular mechanisms involved in these processes. Eight classes of drugs, among the most used for the treatment of common pathologies, were considered. The most diffused mechanism whereby drugs can induce NAFLD/NASH is interfering with mitochondrial activity, inhibiting fatty acid oxidation, but other pathways involved in lipid homeostasis are also affected. PubMed research was performed to obtain significant papers published up to November 2021. The key words included the class of drugs, or the specific compound, combined with steatosis, nonalcoholic steatohepatitis, fibrosis, fatty liver and hepatic lipid deposition. Additional information was found in the citations listed in other papers, when they were not displayed in the original search.

Teaching computational systems biology with an eye on quantitative systems pharmacology at the undergraduate level: Why do it, who would take it, and what should we teach?

Computational systems biology (CSB) is a field that emerged primarily as the product of research activities. As such, it grew in several directions in a distributed and uncoordinated manner making the area appealing and fascinating. The idea of not having to follow a specific path but instead creating one fueled innovation. As the field matured, several interdisciplinary graduate programs emerged attempting to educate future generations of computational systems biologists. These educational initiatives coordinated the dissemination of information across student populations that had already decided to specialize in this field. However, we are now entering an era where CSB, having established itself as a valuable research discipline, is attempting the next major step: Entering undergraduate curricula. As interesting as this endeavor may sound, it has several difficulties, mainly because the field is not uniformly defined. In this manuscript, we argue that this diversity is a significant advantage and that several incarnations of an undergraduate-level CSB biology course could, and should, be developed tailored to programmatic needs. In this manuscript, we share our experiences creating a course as part of a Biomedical Engineering program.

Preclinical models of idiosyncratic drug-induced liver injury (iDILI): Moving towards prediction

Idiosyncratic drug-induced liver injury (iDILI) encompasses the unexpected harms that prescription and non-prescription drugs, herbal and dietary supplements can cause to the liver. iDILI remains a major public health problem and a major cause of drug attrition. Given the lack of biomarkers for iDILI prediction, diagnosis and prognosis, searching new models to predict and study mechanisms of iDILI is necessary. One of the major limitations of iDILI preclinical assessment has been the lack of correlation between the markers of hepatotoxicity in animal toxicological studies and clinically significant iDILI. Thus, major advances in the understanding of iDILI susceptibility and pathogenesis have come from the study of well-phenotyped iDILI patients. However, there are many gaps for explaining all the complexity of iDILI susceptibility and mechanisms. Therefore, there is a need to optimize preclinical human in vitro models to reduce the risk of iDILI during drug development. Here, the current experimental models and the future directions in iDILI modelling are thoroughly discussed, focusing on the human cellular models available to study the pathophysiological mechanisms of the disease and the most used in vivo animal iDILI models. We also comment about in silico approaches and the increasing relevance of patient-derived cellular models.

The role of farnesoid X receptor in metabolic diseases, and gastrointestinal and liver cancer

Farnesoid X receptor (FXR) is a ligand-activated transcription factor involved in the control of bile acid (BA) synthesis and enterohepatic circulation. FXR can influence glucose and lipid homeostasis. Hepatic FXR activation by obeticholic acid is currently used to treat primary biliary cholangitis. Late-stage clinical trials investigating the use of obeticholic acid in the treatment of nonalcoholic steatohepatitis are underway. Mouse models of metabolic disease have demonstrated that inhibition of intestinal FXR signalling reduces obesity, insulin resistance and fatty liver disease by modulation of hepatic and gut bacteria-mediated BA metabolism, and intestinal ceramide synthesis. FXR also has a role in the pathogenesis of gastrointestinal and liver cancers. Studies using tissue-specific and global Fxr-null mice have revealed that FXR acts as a suppressor of hepatocellular carcinoma, mainly through regulating BA homeostasis. Loss of whole-body FXR potentiates progression of spontaneous colorectal cancer, and obesity-induced BA imbalance promotes intestinal stem cell proliferation by suppressing intestinal FXR in Apc^min/+ mice. Owing to altered gut microbiota and FXR signalling, changes in overall BA levels and specific BA metabolites probably contribute to enterohepatic tumorigenesis. Modulating intestinal FXR signalling and altering BA metabolites are potential strategies for gastrointestinal and liver cancer prevention and treatment. In this Review, studies on the role of FXR in metabolic diseases and gastrointestinal and liver cancer are discussed, and the potential for development of targeted drugs are summarized.

Insights into the Molecular Mechanisms of Liuwei Dihuang Decoction via Network Pharmacology

The traditional Chinese medicines (TCMs) have been used to treat diseases over a long history, but it is still a great challenge to uncover the underlying mechanisms for their therapeutic effects due to the complexity of their ingredients. Based on a novel network pharmacology-based approach, we explored in this study the potential therapeutic targets of Liuwei Dihuang (LWDH) decoction in its neuroendocrine immunomodulation (NIM) function. We not only collected the known targets of the compounds in LWDH but also predicted the targets for these compounds using the balanced substructure-drug-target network-based inference (bSDTNBI), which is a target prediction method based on network inferring developed by our laboratory. A "target-(pathway)-target" (TPT) network, in which targets of LWDH were connected by relevant pathways, was constructed and divided into several separate modules with strong internal connections. Then the target module that contributes the most to NIM function was determined through a contribution scoring algorithm. Finally, the targets with the highest contribution score to NIM-related diseases in this target module were recommended as potential therapeutic targets of LWDH.

In vitro assessment of farnesoid X receptor antagonism to predict drug-induced liver injury risk

Drug-induced liver injury (DILI) continues to be a major cause of drug attrition and restrictive labeling. Given the importance of farnesoid X receptor (FXR) in bile acid homeostasis, drug-related FXR antagonism may be an important mechanism of DILI. However, a comprehensive assessment of this phenomenon broadly in the context of DILI is lacking. As such, we used an orthogonal approach comprising a FXR target gene assay in primary human hepatocytes and a commercially available FXR reporter assay to investigate the potential FXR antagonistic effects of an extensive test set of 159 compounds with and without association with clinical DILI. Data were omitted from analysis based on the presence of cytotoxicity to minimize false positive assay signals and other complications in data interpretation. Based on the experimental approaches employed and corresponding data, the prevalence of FXR antagonism was relatively low across this broad DILI test set, with 16-24% prevalence based on individual assay results or combined signals in both assays. Moreover, FXR antagonism was not highly predictive for identifying clinically relevant hepatotoxicants retrospectively, where FXR antagonist classification alone had minimal to moderate predictive value as represented by positive and negative likelihood ratios of 2.24-3.84 and 0.72-0.85, respectively. The predictivity did not increase significantly when considering only compounds with high clinical exposure (maximal or efficacious plasma exposures > 1.0 μM). In contrast, modest gains in predictive value of FXR antagonism were observed considering compounds that also inhibit bile salt export pump. In addition, we have identified novel FXR antagonistic effects of well-studied hepatotoxic drugs, including bosentan, tolcapone and ritonavir. In conclusion, this work represents a comprehensive evaluation of FXR antagonism in the context of DILI, including its overall predictivity and challenges associated with detecting this phenomenon in vitro.

Obeticholic Acid Ameliorates Valproic Acid-Induced Hepatic Steatosis and Oxidative Stress

Farnesoid X receptor (FXR), or NR1H4, protects the liver from insults of various etiologies. A role of FXR in drug-induced liver injury has also been hypothesized yet only marginally investigated. The aim of this study was to assess the effect of FXR activation on gene expression and phenotype of the liver of mice treated with valproic acid (VPA), or 2-propylpentanoic acid, a prototypical hepatotoxic drug. Obeticholic acid (OCA) was used to activate FXR both in mice and in human hepatocellular carcinoma (Huh-7) cells. Next-generation sequencing of mouse liver tissues was performed from control, VPA, and VPA + OCA-treated mice. Pathway analysis validation was performed using real-time reverse-transcription polymerase chain reaction, Western blotting, immunohistochemistry, and fluorometric assays. FXR activation induced antioxidative pathways, which was confirmed by a marked reduction in VPA-induced lipid peroxidation and endoplasmic reticulum stress. In vitro, VPA-induced oxidative stress was independent of lipid accumulation, stemmed from the cytoplasm, and was mitigated by OCA. In the liver of the mice treated with OCA, the levels of cytochrome P450 potentially involved in VPA metabolism were increased. The hepatic lipid-lowering effect observed in animals cotreated with VPA and OCA in comparison with that of animals treated with VPA was associated with regulation of the genes involved in the steatogenic nuclear receptor peroxisome proliferator-activated γ (PPARγ) pathway. In conclusion, pronounced antioxidant activity, repression of the PPARγ pathway, and higher expression of P450 enzymes involved in VPA metabolism may underlie the hepatoprotective of FXR activation during VPA treatment. SIGNIFICANCE STATEMENT: Valproic acid-induced oxidative stress occurs in absence of lipid accumulation and is not of mitochondrial origin. Valproic acid exposure induces the expression of the steatogenic nuclear receptor peroxisome proliferator-activated γ (PPARγ) and its downstream target genes. Constitutive activation of the farnesoid X receptor (FXR) reduces PPARγ hepatic expression and induces hepatic antioxidant activity. The variability in FXR expression level/activity, for instance in individuals carrying loss-of-function genetic variants of the FXR gene, could contribute to valproic acid pharmacokinetic and toxicokinetic profile.

New paradigm for expediting drug development in Asia

Some Asian regulators currently require Phase I data in Asians before joining global Phase II/III trials. Here, we discuss inherent limitations of Phase I ethnic sensitivity studies (ESS) to identify potential interethnic differences. We review recent new drug applications (NDAs) for Japan and China to critically assess the value of separate ESSs in Asian populations. Given that the observed value of ESS was limited, we propose a new global drug development paradigm: if relevant safety, pharmacokinetic (PK), and pharmacogenetic (PG) data are available from the original Phase I study population, it might be possible to extrapolate those data to Asian populations for their inclusion in Phase II/III trials, without an ESS. This could help to streamline drug development in Asia while still addressing regulatory requirements.

In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine

BACKGROUNDS AND AIMS

Recently, a growing number of hepatotoxicity cases aroused by Traditional Chinese Medicine (TCM) have been reported, causing increasing concern. To date, the reported predictive models for drug induced liver injury show low prediction accuracy and there are still no related reports for hepatotoxicity evaluation of TCM systematically. Additionally, the mechanism of herb induced liver injury (HILI) still remains unknown. The aim of the study was to identify potential hepatotoxic ingredients in TCM and explore the molecular mechanism of TCM against HILI.

MATERIALS AND METHODS

In this study, we developed consensus models for HILI prediction by integrating the best single classifiers. The consensus model with best performance was applied to identify the potential hepatotoxic ingredients from the Traditional Chinese Medicine Systems Pharmacology database (TCMSP). Systems pharmacology analyses, including multiple network construction and KEGG pathway enrichment, were performed to further explore the hepatotoxicity mechanism of TCM.

RESULTS

16 single classifiers were built by combining four machine learning methods with four different sets of fingerprints. After systematic evaluation, the best four single classifiers were selected, which achieved a Matthews correlation coefficient (MCC) value of 0.702, 0.691, 0.659, and 0.717, respectively. To improve the predictive capacity of single models, consensus prediction method was used to integrate the best four single classifiers. Results showed that the consensus model C-3 (MCC = 0.78) outperformed the four single classifiers and other consensus models. Subsequently, 5,666 potential hepatotoxic compounds were identified by C-3 model. We integrated the top 10 hepatotoxic herbs and discussed the hepatotoxicity mechanism of TCM via systems pharmacology approach. Finally, Chaihu was selected as the case study for exploring the molecular mechanism of hepatotoxicity.

CONCLUSION

Overall, this study provides a high accurate approach to predict HILI and an in silico perspective into understanding the hepatotoxicity mechanism of TCM, which might facilitate the discovery and development of new drugs.

In Silico Oncology Drug Repositioning and Polypharmacology

Network-aided in silico approaches have been widely used for prediction of drug-target interactions and evaluation of drug safety to increase the clinical efficiency and productivity during drug discovery and development. Here we review the advances and new progress in this field and summarize the translational applications of several new network-aided in silico approaches we developed recently. In addition, we describe the detailed protocols for a network-aided drug repositioning infrastructure for identification of new targets for old drugs, failed drugs in clinical trials, and new chemical entities. These state-of-the-art network-aided in silico approaches have been used for the discovery and development of broad-acting and targeted clinical therapies for various complex diseases, in particular for oncology drug repositioning. In this chapter, the described network-aided in silico protocols are appropriate for target-centric drug repositioning to various complex diseases, but expertise is still necessary to perform the specific oncology projects based on the cancer targets of interest.

In silico polypharmacology of natural products

Natural products with polypharmacological profiles have demonstrated promise as novel therapeutics for various complex diseases, including cancer. Currently, many gaps exist in our knowledge of which compounds interact with which targets, and experimentally testing all possible interactions is infeasible. Recent advances and developments of systems pharmacology and computational (in silico) approaches provide powerful tools for exploring the polypharmacological profiles of natural products. In this review, we introduce recent progresses and advances of computational tools and systems pharmacology approaches for identifying drug targets of natural products by focusing on the development of targeted cancer therapy. We survey the polypharmacological and systems immunology profiles of five representative natural products that are being considered as cancer therapies. We summarize various chemoinformatics, bioinformatics and systems biology resources for reconstructing drug-target networks of natural products. We then review currently available computational approaches and tools for prediction of drug-target interactions by focusing on five domains: target-based, ligand-based, chemogenomics-based, network-based and omics-based systems biology approaches. In addition, we describe a practical example of the application of systems pharmacology approaches by integrating the polypharmacology of natural products and large-scale cancer genomics data for the development of precision oncology under the systems biology framework. Finally, we highlight the promise of cancer immunotherapies and combination therapies that target tumor ecosystems (e.g. clones or 'selfish' sub-clones) via exploiting the immunological and inflammatory 'side' effects of natural products in the cancer post-genomics era.

Update on FXR Biology: Promising Therapeutic Target?

Farnesoid X receptor (FXR), a metabolic nuclear receptor, plays critical roles in the maintenance of systemic energy homeostasis and the integrity of many organs, including liver and intestine. It regulates bile acid, lipid, and glucose metabolism, and contributes to inter-organ communication, in particular the enterohepatic signaling pathway, through bile acids and fibroblast growth factor-15/19 (FGF-15/19). The metabolic effects of FXR are also involved in gut microbiota. In addition, FXR has various functions in the kidney, adipose tissue, pancreas, cardiovascular system, and tumorigenesis. Consequently, the deregulation of FXR may lead to abnormalities of specific organs and metabolic dysfunction, allowing the protein as an attractive therapeutic target for the management of liver and/or metabolic diseases. Indeed, many FXR agonists have been being developed and are under pre-clinical and clinical investigations. Although obeticholic acid (OCA) is one of the promising candidates, significant safety issues have remained. The effects of FXR modulation might be multifaceted according to tissue specificity, disease type, and/or energy status, suggesting the careful use of FXR agonists. This review summarizes the current knowledge of systemic FXR biology in various organs and the gut–liver axis, particularly regarding the recent advancement in these fields, and also provides pharmacological aspects of FXR modulation for rational therapeutic strategies and novel drug development.

A Computational Systems Pharmacology Approach to Investigate Molecular Mechanisms of Herbal Formula Tian-Ma-Gou-Teng-Yin for Treatment of Alzheimer's Disease

Traditional Chinese medicine (TCM) is typically prescribed as formula to treat certain symptoms. A TCM formula contains hundreds of chemical components, which makes it complicated to elucidate the molecular mechanisms of TCM. Here, we proposed a computational systems pharmacology approach consisting of network link prediction, statistical analysis, and bioinformatics tools to investigate the molecular mechanisms of TCM formulae. Taking formula Tian-Ma-Gou-Teng-Yin as an example, which shows pharmacological effects on Alzheimer’s disease (AD) and its mechanism is unclear, we first identified 494 formula components together with corresponding 178 known targets, and then predicted 364 potential targets for these components with our balanced substructure-drug–target network-based inference method. With Fisher’s exact test and statistical analysis we identified 12 compounds to be most significantly related to AD. The target genes of these compounds were further enriched onto pathways involved in AD, such as neuroactive ligand–receptor interaction, serotonergic synapse, inflammatory mediator regulation of transient receptor potential channel and calcium signaling pathway. By regulating key target genes, such as ACHE, HTR2A, NOS2, and TRPA1, the formula could have neuroprotective and anti-neuroinflammatory effects against the progression of AD. Our approach provided a holistic perspective to study the relevance between TCM formulae and diseases, and implied possible pharmacological effects of TCM components.

SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning

Computational prediction of drug-target interactions (DTIs) and drug repositioning provides a low-cost and high-efficiency approach for drug discovery and development. The traditional social network-derived methods based on the naïve DTI topology information cannot predict potential targets for new chemical entities or failed drugs in clinical trials. There are currently millions of commercially available molecules with biologically relevant representations in chemical databases. It is urgent to develop novel computational approaches to predict targets for new chemical entities and failed drugs on a large scale. In this study, we developed a useful tool, namely substructure-drug-target network-based inference (SDTNBI), to prioritize potential targets for old drugs, failed drugs and new chemical entities. SDTNBI incorporates network and chemoinformatics to bridge the gap between new chemical entities and known DTI network. High performance was yielded in 10-fold and leave-one-out cross validations using four benchmark data sets, covering G protein-coupled receptors, kinases, ion channels and nuclear receptors. Furthermore, the highest areas under the receiver operating characteristic curve were 0.797 and 0.863 for two external validation sets, respectively. Finally, we identified thousands of new potential DTIs via implementing SDTNBI on a global network. As a proof-of-principle, we showcased the use of SDTNBI to identify novel anticancer indications for nonsteroidal anti-inflammatory drugs by inhibiting AKR1C3, CA9 or CA12. In summary, SDTNBI is a powerful network-based approach that predicts potential targets for new chemical entities on a large scale and will provide a new tool for DTI prediction and drug repositioning. The program and predicted DTIs are available on request.

Quantitative and Systems Pharmacology 3. Network-Based Identification of New Targets for Natural Products Enables Potential Uses in Aging-Associated Disorders

Aging that refers the accumulation of genetic and physiology changes in cells and tissues over a lifetime has been shown a high risk of developing various complex diseases, such as neurodegenerative disease, cardiovascular disease and cancer. Over the past several decades, natural products have been demonstrated as anti-aging interveners via extending lifespan and preventing aging-associated disorders. In this study, we developed an integrated systems pharmacology infrastructure to uncover new indications for aging-associated disorders by natural products. Specifically, we incorporated 411 high-quality aging-associated human genes or human-orthologous genes from mus musculus (MM), saccharomyces cerevisiae (SC), caenorhabditis elegans (CE), and drosophila melanogaster (DM). We constructed a global drug-target network of natural products by integrating both experimental and computationally predicted drug-target interactions (DTI). We further built the statistical network models for identification of new anti-aging indications of natural products through integration of the curated aging-associated genes and drug-target network of natural products. High accuracy was achieved on the network models. We showcased several network-predicted anti-aging indications of four typical natural products (caffeic acid, metformin, myricetin, and resveratrol) with new mechanism-of-actions. In summary, this study offers a powerful systems pharmacology infrastructure to identify natural products for treatment of aging-associated disorders.

Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy

Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.

Editor's Highlight: Farnesoid X Receptor Protects Against Low-Dose Carbon Tetrachloride-Induced Liver Injury Through the Taurocholate-JNK Pathway

Hepatotoxicity is of major concern for humans exposed to industrial chemicals and drugs. Disruption of farnesoid X receptor (FXR), a master regulator of bile acid (BA) metabolism, enhanced the sensitivity to liver injury in mice after toxicant exposure, but the precise mechanism remains unclear. In this study, the interconnection between BA metabolism, FXR, and chemically induced hepatotoxicity was investigated using metabolomics, Fxr-null mice (Fxr-/-) and hepatocytes, and recombinant adenoviruses. A single low-dose intraperitoneal injection of carbon tetrachloride (CCl4), an inducer of acute hepatitis in mice, resulted in more severe hepatocyte damage and higher induction of pro-inflammatory mediators, such as chemokine (C-C motif) ligand 2 (Ccl2), in Fxr-/-. Serum metabolomics analysis revealed marked increases in circulating taurocholate (TCA) and tauro-β-muricholate (T-β-MCA) in these mice, and forced expression of bile salt export protein (BSEP) by recombinant adenovirus in Fxr-/- ameliorated CCl4-induced liver damage. Treatment of Fxr-null hepatocytes with TCA, but not T-β-MCA, significantly increased c-Jun-N-terminal kinase (JNK) activation and Ccl2 mRNA levels, and up-regulation of Ccl2 mRNA was attenuated by co-treatment with a JNK inhibitor SP600125, indicating that TCA directly amplifies hepatocyte inflammatory signaling mainly mediated by JNK under FXR-deficiency. Additionally, pretreatment with SP600125 or restoration of FXR expression in liver by use of recombinant adenovirus, attenuated CCl4-induced liver injury. Collectively, these results suggest that the TCA-JNK axis is likely associated with increased susceptibility to CCl4-induced acute liver injury in Fxr-/-, and provide clues to the mechanism by which FXR and its downstream gene targets, such as BSEP, protects against chemically induced hepatotoxicity.

Saccharomyces boulardii Administration Changes Gut Microbiota and Attenuates D-Galactosamine-Induced Liver Injury

Growing evidence has shown that gut microbiome is a key factor involved in liver health. Therefore, gut microbiota modulation with probiotic bacteria, such as Saccharomyces boulardii, constitutes a promising therapy for hepatosis. In this study, we aimed to investigate the protective effects of S. boulardii on D-Galactosamine-induced liver injury in mice. Liver function test and histopathological analysis both suggested that the liver injury can be effectively attenuated by S. boulardii administration. In the meantime, S. boulardii induced dramatic changes in the gut microbial composition. At the phylum level, we found that S. boulardii significantly increased in the relative abundance of Bacteroidetes, and decreased the relative abundance of Firmicutes and Proteobacteria, which may explain the hepatic protective effects of S. boulardii. Taken together, our results demonstrated that S. boulardii administration could change the gut microbiota in mice and alleviate acute liver failure, indicating a potential protective and therapeutic role of S. boulardii.

Insights into the molecular mechanisms of Polygonum multiflorum Thunb-induced liver injury: a computational systems toxicology approach

An increasing number of cases of herb-induced liver injury (HILI) have been reported, presenting new clinical challenges. In this study, taking Polygonum multiflorum Thunb (PmT) as an example, we proposed a computational systems toxicology approach to explore the molecular mechanisms of HILI. First, the chemical components of PmT were extracted from 3 main TCM databases as well as the literature related to natural products. Then, the known targets were collected through data integration, and the potential compound-target interactions (CTIs) were predicted using our substructure-drug-target network-based inference (SDTNBI) method. After screening for hepatotoxicity-related genes by assessing the symptoms of HILI, a compound-target interaction network was constructed. A scoring function, namely, Ascore, was developed to estimate the toxicity of chemicals in the liver. We conducted network analysis to determine the possible mechanisms of the biphasic effects using the analysis tools, including BiNGO, pathway enrichment, organ distribution analysis and predictions of interactions with CYP450 enzymes. Among the chemical components of PmT, 54 components with good intestinal absorption were used for analysis, and 2939 CTIs were obtained. After analyzing the mRNA expression data in the BioGPS database, 1599 CTIs and 125 targets related to liver diseases were identified. In the top 15 compounds, seven with Ascore values >3000 (emodin, quercetin, apigenin, resveratrol, gallic acid, kaempferol and luteolin) were obviously associated with hepatotoxicity. The results from the pathway enrichment analysis suggest that multiple interactions between apoptosis and metabolism may underlie PmT-induced liver injury. Many of the pathways have been verified in specific compounds, such as glutathione metabolism, cytochrome P450 metabolism, and the p53 pathway, among others. Hepatitis symptoms, the perturbation of nine bile acids and yellow or tawny urine also had corresponding pathways, justifying our method. In conclusion, this computational systems toxicology method reveals possible toxic components and could be very helpful for understanding the mechanisms of HILI. In this way, the method might also facilitate the identification of novel hepatotoxic herbs.

Model Systems for Studying the Role of Canalicular Efflux Transporters in Drug-Induced Cholestatic Liver Disease

Bile formation is a key function of the liver. Disturbance of bile flow may lead to liver disease and is called cholestasis. Cholestasis may be inherited, for example, in progressive familial intrahepatic cholestasis or acquired, for example, by drug-mediated inhibition of bile salt export from hepatocytes into the canaliculi. The key transport system for exporting bile salts into the canaliculi is the bile salt export pump. Inhibition of the bile salt export pump by drugs is a well-established cause of drug-induced cholestasis. Investigation of the role of the multidrug resistance protein 3, essential for biliary phospholipid secretion, is emerging now. This overview summarizes current concepts and methods with an emphasis on in vitro model systems for the investigation of drug-induced cholestasis in the general context of drug-induced liver injury.

Anti-nerve growth factor therapy increases spontaneous day/night activity in mice with orthopedic surgery-induced pain

Total knee arthroplasty (TKA) and total hip arthroplasty (THA) are 2 of the most common and successful surgical interventions to relieve osteoarthritis pain. Control of postoperative pain is critical for patients to fully participate in the required physical therapy which is the most influential factor in effective postoperative knee rehabilitation. Currently, opiates are a mainstay for managing postoperative orthopedic surgery pain including TKA or THA pain. Recently, issues including efficacy, dependence, overdose, and death from opiates have made clinicians and researchers more critical of use of opioids for treating nonmalignant skeletal pain. In the present report, a nonopiate therapy using a monoclonal antibody raised against nerve growth factor (anti-NGF) was assessed for its ability to increase the spontaneous activity of the operated knee joint in a mouse model of orthopedic surgery pain-induced by drilling and coring the trochlear groove of the mouse femur. Horizontal activity and velocity and vertical rearing were continually assessed over a 20 hours day/night period using automated activity boxes in an effort to reduce observer bias and capture night activity when the mice are most active. At days 1 and 3, after orthopedic surgery, there was a marked reduction in spontaneous activity and vertical rearing; anti-NGF significantly attenuated this decline. The present data suggest that anti-NGF improves limb use in a rodent model of joint/orthopedic surgery and as such anti-NGF may be useful in controlling pain after orthopedic surgeries such as TKA or THA.

Synthesis and biological evaluations of chalcones, flavones and chromenes as farnesoid x receptor (FXR) antagonists

Farnesoid X receptor (FXR), a nuclear receptor mainly distributed in liver and intestine, has been regarded as a potential target for the treatment of various metabolic diseases, cancer and infectious diseases related to liver. Starting from two previously identified chalcone-based FXR antagonists, we tried to increase the activity through the design and synthesis of a library containing chalcones, flavones and chromenes, based on substitution manipulation and conformation (ring closure) restriction strategy. Many chalcones and four chromenes were identified as microM potent FXR antagonists, among which chromene 11c significantly decreased the plasma and hepatic triglyceride level in KKay mice.

In silico prediction of chemical mechanism of action via an improved network-based inference method

BACKGROUND AND PURPOSE

Deciphering chemical mechanism of action (MoA) enables the development of novel therapeutics (e.g. drug repositioning) and evaluation of drug side effects. Development of novel computational methods for chemical MoA assessment under a systems pharmacology framework would accelerate drug discovery and development with greater efficiency and low cost.

EXPERIMENTAL APPROACH

In this study, we proposed an improved network-based inference method, balanced substructure-drug-target network-based inference (bSDTNBI), to predict MoA for old drugs, clinically failed drugs and new chemical entities. Specifically, three parameters were introduced into network-based resource diffusion processes to adjust the initial resource allocation of different node types, the weighted values of different edge types and the influence of hub nodes. The performance of the method was systematically validated by benchmark datasets and bioassays.

KEY RESULTS

High performance was yielded for bSDTNBI in both 10-fold and leave-one-out cross validations. A global drug-target network was built to explore MoA of anticancer drugs and repurpose old drugs for 15 cancer types/subtypes. In a case study, 27 predicted candidates among 56 commercially available compounds were experimentally validated to have binding affinities on oestrogen receptor α with IC₅₀ or EC₅₀ values ≤10 μM. Furthermore, two dual ligands with both agonistic and antagonistic activities ≤1 μM would provide potential lead compounds for the development of novel targeted therapy in breast cancer or osteoporosis.

CONCLUSION AND IMPLICATIONS

In summary, bSDTNBI would provide a powerful tool for the MoA assessment on both old drugs and novel compounds in drug discovery and development.

Nuclear Receptors as Therapeutic Targets in Liver Disease: Are We There Yet?

Nuclear receptors (NR) are ligand-modulated transcription factors that play diverse roles in cell differentiation, development, proliferation, and metabolism and are associated with numerous liver pathologies such as cancer, steatosis, inflammation, fibrosis, cholestasis, and xenobiotic/drug-induced liver injury. The network of target proteins associated with NRs is extremely complex, comprising coregulators, small noncoding microRNAs, and long noncoding RNAs. The importance of NRs as targets of liver disease is exemplified by the number of NR ligands that are currently used in the clinics or in clinical trials with promising results. Understanding the regulation by NR during pathophysiological conditions, and identifying ligands for orphan NR, points to a potential therapeutic approach for patients with liver diseases. An overview of complex NR metabolic networks and their pharmacological implications in liver disease is presented here.

FXR: Big fish or small fry for drug-induced liver injury?

By integrating network analysis and molecular modeling, a "system pharmacology" approach identified FXR as a potential off-target protein mediating non-steroidal anti-inflammatory drugs (NSAID)-induced liver injury. In vitro assays showed that NSAID are potent FXR antagonists that inhibit FXR transcriptional activity. Given the role of FXR in bile acid homeostasis, liver inflammation and cell proliferation, the data suggest that FXR antagonism could mediate, at least in part, NSAID-induced liver injury.

Biomarker-based drug safety assessment in the age of systems pharmacology: from foundational to regulatory science

Improved biomarker-based assessment of drug safety is needed in drug discovery and development as well as regulatory evaluation. However, identifying drug safety-related biomarkers such as genes, proteins, miRNA and single-nucleotide polymorphisms remains a big challenge. The advances of 'omics' and computational technologies such as genomics, transcriptomics, metabolomics, proteomics, systems biology, network biology and systems pharmacology enable us to explore drug actions at the organ and organismal levels. Computational and experimental systems pharmacology approaches could be utilized to facilitate biomarker-based drug safety assessment for drug discovery and development and to inform better regulatory decisions. In this article, we review the current status and advances of systems pharmacology approaches for the development of predictive models to identify biomarkers for drug safety assessment.

Predicting Pharmacodynamic Drug-Drug Interactions through Signaling Propagation Interference on Protein-Protein Interaction Networks

As pharmacodynamic drug-drug interactions (PD DDIs) could lead to severe adverse effects in patients, it is important to identify potential PD DDIs in drug development. The signaling starting from drug targets is propagated through protein-protein interaction (PPI) networks. PD DDIs could occur by close interference on the same targets or within the same pathways as well as distant interference through cross-talking pathways. However, most of the previous approaches have considered only close interference by measuring distances between drug targets or comparing target neighbors. We have applied a random walk with restart algorithm to simulate signaling propagation from drug targets in order to capture the possibility of their distant interference. Cross validation with DrugBank and Kyoto Encyclopedia of Genes and Genomes DRUG shows that the proposed method outperforms the previous methods significantly. We also provide a web service with which PD DDIs for drug pairs can be analyzed at http://biosoft.kaist.ac.kr/targetrw.

NSAIDs Ibuprofen, Indometacin, and Diclofenac do not interact with Farnesoid X Receptor

The nuclear farnesoid X receptor (FXR) is a ligand activated transcription factor and acts as cellular sensor for bile acids. In this role, FXR is a highly important liver protector and FXR inhibition by antagonists or knockout has shown several deleterious effects. A recent report characterized non-steroidal anti-rheumatic drugs (NSAIDs) such as ibuprofen or diclofenac as FXR antagonists and linked hepatotoxic effects of these drugs with antagonistic activity on FXR. Since this would guide a way to develop safer anti-inflammatory agents by sparing FXR, we intended to further characterize the reported antagonistic activity and intensively investigated ibuprofen, indometacin and diclofenac. However, we conclude that these agents do not interact with FXR and that the reported reduced FXR signaling induced by CDCA in presence of NSAIDs is merely a consequence than a cause of hepatotoxicity.

Molecular basis for nonspecificity of nonsteroidal anti-inflammatory drugs (NSAIDs)

Inhibition of the production of inflammatory mediators by the action of nonsteroidal anti-inflammatory drugs (NSAIDs) is highly accredited to their recognition of cyclooxygenase enzymes. Along with inflammation relief, however, NSAIDs also cause adverse effects. Although NSAIDs strongly inhibit enzymes of the prostaglandin synthesis pathways, several other proteins also serve as fairly potent targets for these drugs. Based on their recognition pattern, these receptors are categorised as enzymes modifying NSAIDs, noncatalytic proteins binding to NSAIDs and enzymes with catalytic functions that are inhibited by NSAIDs. The extensive binding of NSAIDs is responsible for their limited in vivo efficacy as well as the large spectrum of their effects. The biochemical nature of drugs binding to multiple protein targets and its implications on physiology are discussed.