High-throughput identification of off-targets for the mechanistic study of severe adverse drug reactions induced by analgesics

Systems Approach to Integrating Preclinical Apolipoprotein E-Knockout Investigations Reveals Novel Etiologic Pathways and Master Atherosclerosis Network in Humans

OBJECTIVE

Animal models of atherosclerosis are used extensively to interrogate molecular mechanisms in serial fashion. We tested whether a novel systems biology approach to integration of preclinical data identifies novel pathways and regulators in human disease. Approach and Results: Of 716 articles published in ATVB from 1995 to 2019 using the apolipoprotein E knockout mouse to study atherosclerosis, data were extracted from 360 unique studies in which a gene was experimentally perturbed to impact plaque size or composition and analyzed using Ingenuity Pathway Analysis software. TREM1 (triggering receptor expressed on myeloid cells) signaling and LXR/RXR (liver X receptor/retinoid X receptor) activation were identified as the top atherosclerosis-associated pathways in mice (both P<1.93×10-4, TREM1 implicated early and LXR/RXR in late atherogenesis). The top upstream regulatory network in mice (sc-58125, a COX2 inhibitor) linked 64.0% of the genes into a single network. The pathways and networks identified in mice were interrogated by testing for associations between the genetically predicted gene expression of each mouse pathway-identified human homolog with clinical atherosclerosis in a cohort of 88 660 human subjects. Homologous human pathways and networks were significantly enriched for gene-atherosclerosis associations (empirical P<0.01 for TREM1 and LXR/RXR pathways and COX2 network). This included 12(60.0%) TREM1 pathway genes, 15(53.6%) LXR/RXR pathway genes, and 67(49.3%) COX2 network genes. Mouse analyses predicted, and human study validated, the strong association of COX2 expression (PTGS2) with increased likelihood of atherosclerosis (odds ratio, 1.68 per SD of genetically predicted gene expression; P=1.07×10-6).

CONCLUSIONS

PRESCIANT (Preclinical Science Integration and Translation) leverages published preclinical investigations to identify high-confidence pathways, networks, and regulators of human disease.

Prioritizing Pain-Associated Targets with Machine Learning

While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model to predict new targets for 17 categories of pain. The model utilizes features from transcriptomics, proteomics, and gene ontology to prioritize targets for modulating pain. We focused on identifying novel G-protein-coupled receptors (GPCRs), ion channels, and protein kinases because these proteins represent the most successful drug target families. The performance of the model to predict novel pain targets is 0.839 on average based on AUROC, while the predictions for arthritis had the highest accuracy (AUROC = 0.929). The model predicts hundreds of novel targets for pain; for example, GPR132 and GPR109B are highly ranked GPCRs for rheumatoid arthritis. Overall, gene-pain association predictions cluster into three groups that are enriched for cytokine, calcium, and GABA-related cell signaling pathways. These predictions can serve as a foundation for future experimental exploration to advance the development of safer and more effective analgesics.

Structural investigation on the selective COX-2 inhibitors mediated cardiotoxicity: A review

Initially, the selective COX-2 inhibitors were developed as safer alternatives to the conventional NSAIDs, but later on, most of them were withdrawn from the market due to the risk of heart attack and stroke. Celecoxib, the first selective COX-2 inhibitor, was approved by the Food and Drug Administration (FDA) in December 1998 and was taken back from the market in 2004. Since then, many coxibs have been discontinued one by one due to adverse cardiovascular events. United States (US), Australian and European authorities related to Therapeutic Goods Administration (TGA) implemented the requirements to carry the "Black box" warning on the labels of COX-2 drugs highlighting the risks of serious cardiovascular events. These facts encouraged the researchers to explore them well and find out the biochemical basis behind the cardiotoxicity. From the last few decades, the molecular mechanisms behind the coxibs have regained the attention, especially the specific structural features of the selective COX-2 inhibitors that are associated with cardiotoxicity. This review discusses the key structural features of the selective COX-2 inhibitors and underlying mechanisms that are responsible for the cardiotoxicity. This report also unfolds different strategies that have been reported in the last 10 years to combat the problem of selective COX-2 inhibitors mediated cardiotoxicity.

Broad-Spectrum Profiling of Drug Safety via Learning Complex Network

Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this study, we developed an advanced machine learning model for de novo drug safety assessment by solving the multilayer drug‐gene‐adverse drug reaction (ADR) interaction network. For the first time, the drug safety was assessed in a broad landscape of 1,156 distinct ADRs. We also designed a parameter ToxicityScore to quantify the overall drug safety. Moreover, we determined association strength for every 3,807,631 gene‐ADR interactions, which clues mechanistic exploration of ADRs. For convenience, we deployed the model as a web service ADRAlert‐gene at http://www.bio-add.org/ADRAlert/. In summary, this study offers insights into prioritizing safe drug therapy. It helps reduce the attrition rate of new drug discovery by providing a reliable ADR profile in the early preclinical stage.

Large-Scale Target Identification of Herbal Medicine Using a Reverse Docking Approach

Herbal medicine has been used to countermine various diseases for centuries. However, most of the therapeutic targets underlying herbal therapy remain unclear, which largely slow down the novel drug discovery process from natural products. In this study, we developed a novel computational pipeline for assisting de novo identification of protein targets for herbal ingredients. The pipeline involves pharmacophore comparison and reverse ligand-protein docking simulation in a high throughput manner. We evaluated the pipeline using three traditional Chinese medicine ingredients such as acteoside, quercetin, and epigallocatechin gallate as examples. A majority of current known targets of these ingredients were successfully identified by the pipeline. Structural comparative analyses confirmed that the predicted ligand-target interactions used the same binding pockets and binding modes as those of known ligand-target interactions. Furthermore, we illustrated the mechanism of actions of the ingredients by constructing the pharmacological networks on the basis of the predicted target profiles. In summary, we proposed an efficient and economic option for large-scale target exploration in the herb study. This pipeline will be particularly valuable in aiding precise drug discovery and drug repurposing from natural products.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

Nutshell Extracts of Xanthoceras sorbifolia: A New Potential Source of Bioactive Phenolic Compounds as a Natural Antioxidant and Immunomodulator

The nutshell of Xanthoceras sorbifolia, a waste product in the production of edible oil, is rich in health-promoting phenolic acids. However, the individual constituents, bioactivities, and mechanism of action are largely unknown. In this study, 20 phenolic compounds were characterized in nutshell extract (NE) of X. sorbifolia by gas chromatography-mass spectrometry. Four established in vitro studies showed that NE has significant antioxidant potential. Results in vivo indicated that oral administration of NE effectively ameliorated clinical disease severity of experimental autoimmune encephalomyelitis (EAE) and reduced the neuroinflammation and the central nervous system (CNS) demyelination. The underlying mechanism of NE-induced effects involved decreased penetration of pathogenic immunocyte into the CNS, a reduced production of proinflammatory cytokines and factors, and suppressed differentiation of type 1 T helper and type 17 T helper cells through the Janus kinase/signal transducer and activator of transcription pathway. Taken together, our studies showed that X. sorbifolia nutshell, considered a waste material in the food industry, is a novel source of a natural antioxidant and immunomodulator.

Opioids: Modulators of angiogenesis in wound healing and cancer

Opioids are potent drugs that are widely used to control wound or cancer pain. Increasing evidence suggest that opioids mediate clinically relevant effects that go beyond their classical role as analgesics. Of note, opioids appear to modulate angiogenesis - a process that is critical in wound healing and cancer progression. In this review, we focus on pro- and anti-angiogenic facets of opioids that arise from the activation of individual opioid receptors and the usage of individual concentrations or application routes. We overview the still incompletely elucidated mechanisms of these angiogenic opioid actions. Moreover, we describe plausible opioids effects, which - although not primarily studied in the context of vessel formation - may be related to the opioid-driven processes of angiogenesis. Finally we discuss the use of opioids as an innovative therapeutic avenue for the treatment of chronic wounds and cancer.

Large-scale identification of adverse drug reaction-related proteins through a random walk model

Adverse drug reactions (ADRs) are responsible for drug failure in clinical trials and affect life quality of patients. The identification of ADRs during the early phases of drug development is an important task. Therefore, predicting potential protein targets eliciting ADRs is essential for understanding the pathogenesis of ADRs. In this study, we proposed a computational algorithm,Integrated Network for Protein-ADR relations (INPADR), to infer potential protein-ADR relations based on an integrated network. First, the integrated network was constructed by connecting the protein-protein interaction network and the ADR similarity network using known protein-ADR relations. Then, candidate protein-ADR relations were further prioritized by performing a random walk with restart on this integrated network. Leave-one-out cross validation was used to evaluate the ability of the INPADR. An AUC of 0.8486 was obtained, which was a significant improvement compared to previous methods. We also applied the INPADR to two ADRs to evaluate its accuracy. The results suggested that the INPADR is capable of finding novel protein-ADR relations. This study provides new insight to our understanding of ADRs. The predicted ADR-related proteins will provide a reference for preclinical safety pharmacology studies and facilitate the identification of ADRs during the early phases of drug development.

Adverse Drug Reactions for Medicines Newly Approved in Japan from 1999 to 2013: Hypertension and Hypotension

In this survey, the correlation between adverse drug reactions (ADRs) in human and animal toxicities was investigated for 393 medicines which were approved in Japan from September 1999 to March 2013. ADRs were collected from each Japanese package insert. Comparable animal toxicities with ADRs were collected by thorough investigation of common technical documents. The results of this survey show that hypertension and/or hypotension were mainly observed in medicines affecting the central nervous system. Hypertension was also observed in antipyretics, analgesics, anti-inflammatory agents, vasoconstrictors and agents using antibody. Concordance between human ADRs and animal toxicities was analysed. True-positive rate for hypertension and hypotension is 0.29 and 0.52, respectively. Positive likelihood ratio and inverse negative likelihood ratio are 1.98 and 1.21, respectively, in hypertension and 1.67 and 1.44, respectively, in hypotension. Concordance between human ADRs and animal toxicities is not so high in hypertension and hypotension. Identified mechanisms as on-target for hypertension and hypotension are 29.8% and 30.5%, respectively. More than half of the causative factors of hypertension and hypotension were unable to be elucidated. Our results show that the intake of medicines is often linked to blood pressure variations that are not predicted in animal toxicity studies. Improvement of drug development processes may be necessary to provide safer medicines because current animal toxicity studies are insufficient to predict all ADRs in human beings.

In silico assessment of adverse drug reactions and associated mechanisms

During recent years, various in silico approaches have been developed to estimate chemical and biological drug features, for example chemical fragments, protein targets, pathways, among others, that correlate with adverse drug reactions (ADRs) and explain the associated mechanisms. These features have also been used for the creation of predictive models that enable estimation of ADRs during the early stages of drug development. In this review, we discuss various in silico approaches to predict these features for a certain drug, estimate correlations with ADRs, establish causal relationships between selected features and ADR mechanisms and create corresponding predictive models.