Exploration of the Anti-Inflammatory Drug Space Through Network Pharmacology: Applications for Drug Repurposing

The future of pharmacology and therapeutics of the arachidonic acid cascade in the next decade: Innovative advancements in drug repurposing

Many drugs can act on multiple targets or disease pathways, regardless of their original purpose. Drug repurposing involves reevaluating existing compounds for new medical uses. This can include repositioning approved drugs, redeveloping unapproved drugs, or repurposing any chemical, nutraceutical, or biotherapeutic product for new applications. Traditional drug development is slow, expensive, and has high failure rates. Drug repurposing can speed up the process, costing less and saving time. This approach can save 6-7 years of early-stage research time. Drug repurposing benefits from existing compounds with optimized structures and approved for clinical use with associated structure-activity relationship publications, supporting the development of new effective compounds. Drug repurposes can now utilize advanced in silico screening enabled by artificial intelligence (AI) and sophisticated tissue and organ-level in vitro models. These models more accurately replicate human physiology and improve the selection of existing drugs for further pre-clinical testing and, eventually, clinical trials for new indications. This mini-review discusses some examples of drug repurposing and novel strategies for further development of compounds for targets of the arachidonic acid cascade. In particular, we will delve into the prospect of repurposing antiplatelet agents for cancer prevention and addressing the emerging noncanonical functionalities of 5-lipoxygenase, potentially for leukemia therapy.

Drug Repurposing Using FDA Adverse Event Reporting System (FAERS) Database

Drug repurposing is an emerging approach to reassigning existing pre-approved therapies for new indications. The FDA Adverse Event Reporting System (FAERS) is a large database of over 28 million adverse event reports submitted by medical providers, patients, and drug manufacturers and provides extensive drug safety signal data. In this review, four common drug repurposing strategies using FAERS are described, including inverse signal detection for a single disease, drug-drug interactions that mitigate a target ADE, identifying drug-ADE pairs with opposing gene perturbation signatures and identifying drug-drug pairs with congruent gene perturbation signatures. The purpose of this review is to provide an overview of these different approaches using existing successful applications in the literature. With the fast expansion of adverse drug event reports, FAERS-based drug repurposing represents a promising strategy for discovering new uses for existing therapies.

Drug repurposing for Basal breast cancer subpopulations using modular network signatures

Breast cancer is characterized as being a heterogeneous pathology with a broad phenotype variability. Breast cancer subtypes have been developed in order to capture some of this heterogeneity. Each of these breast cancer subtypes, in turns retains varied characteristic features impacting diagnostic, prognostic and therapeutics. Basal breast tumors, in particular have been challenging in these regards. Basal breast cancer is often more aggressive, of rapid evolution and no tailor-made targeted therapies are available yet to treat it. Arguably, epigenetic variability is behind some of these intricacies. It is possible to further classify basal breast tumor in groups based on their non-coding transcriptome and methylome profiles. It is expected that these groups will have differences in survival as well as in sensitivity to certain classes of drugs. With this in mind, we implemented a computational learning approach to infer different subpopulations of basal breast cancer (from TCGA multi-omic data) based on their epigenetic signatures. Such epigenomic signatures were associated with different survival profiles; we then identified their associated gene co-expression network structure, extracted a signature based on modules within these networks, and use these signatures to find and prioritize drugs (in the LINCS dataset) that may be used to target these types of cancer. In this way we are introducing the analytical workflow for an epigenomic signature-based drug repurposing structure.

The Effect and Mechanism of Corilagin from Euryale Ferox Salisb Shell on LPS-Induced Inflammation in Raw264.7 Cells

(1) Background: Euryale ferox Salisb is a large aquatic plant of the water lily family and an edible economic crop with medicinal value. The annual output of Euryale ferox Salisb shell in China is higher than 1000 tons, often as waste or used as fuel, resulting in waste of resources and environmental pollution. We isolated and identified the corilagin monomer from Euryale ferox Salisb shell and discovered its potential anti-inflammatory effects. This study aimed to investigate the anti-inflammatory effect of corilagin isolated from Euryale ferox Salisb shell. (2) Methods: We predict the anti-inflammatory mechanism by pharmacology. LPS was added to 264.7 cell medium to induce an inflammatory state, and the safe action range of corilagin was screened using CCK-8. The Griess method was used to determine NO content. The presence of TNF-α, IL-6, IL-1β, and IL-10 was determined by ELISA to evaluate the effect of corilagin on the secretion of inflammatory factors, while that of reactive oxygen species was detected by flow cytometry. The gene expression levels of TNF-α, IL-6, COX-2, and iNOS were determined using qRT-PCR. qRT-PCR and Western blot were used to detect the mRNA and expression of target genes in the network pharmacologic prediction pathway. (3) Results: Network pharmacology analysis revealed that the anti-inflammatory effect of corilagin may be related to MAPK and TOLL-like receptor signaling pathways. The results demonstrated the presence of an anti-inflammatory effect, as indicated by the reduction in the level of NO, TNF-α, IL-6, IL-1β, IL-10, and ROS in Raw264.7 cells induced by LPS. The results suggest that corilagin reduced the expression of TNF-α, IL-6, COX-2, and iNOS genes in Raw264.7 cells induced by LPS. The downregulation of the phosphorylation of IκB-α protein related to the toll-like receptor signaling pathway and upregulation of the phosphorylation of key proteins in the MAPK signaling pathway, P65 and JNK, resulted in reduced tolerance toward lipopolysaccharide, allowing for the exertion of the immune response. (4) Conclusions: The results demonstrate the significant anti-inflammatory effect of corilagin from Euryale ferox Salisb shell. This compound regulates the tolerance state of macrophages toward lipopolysaccharide through the NF-κB signaling pathway and plays an immunoregulatory role. The compound also regulates the expression of iNOS through the MAPK signaling pathway, thereby alleviating the cell damage caused by excessive NO release.

Intra-articular treatment of temporomandibular joint osteoarthritis by injecting actively-loaded meloxicam liposomes with dual-functions of anti-inflammation and lubrication

Temporomandibular joint (TMJ) osteoarthritis is a common osteochondral degenerative disease which can severely affect patient's mouth opening and mastication. Meloxicam (MLX), one of the most widely used non-steroidal anti-inflammatory drugs, is the main clinical therapy for the treatment of TMJ osteoarthritis. However, the clinical effect is greatly compromised because of its poor water solubility and high lipophilicity. In the present study, we developed an actively-loaded liposomal formulation, namely MLX-Ca(AC)₂Lipo, using meglumine to enhance aqueous solubility and divalent metal (Ca²⁺) solution to improve encapsulation efficiency. By the formation of the nano-bowl shaped MLX-Ca precipitates inside the liposomes, MLX-Ca(AC)₂Lipo successfully achieved an optimal encapsulation efficiency as high as 98.4% compared with previous passive loading method (60.6%). Additionally, MLX-Ca(AC)₂Lipo maintained stable, and the slow drug release not only prolonged the duration of drug efficacy but also improved bioavailability. It was shown in the in vitro and in vivo tests that MLX-Ca(AC)₂Lipo downregulated the synthesis of the inflammatory factors (such as prostaglandin-E2) and as a consequence reduced chondrocytes apoptosis and extracellular matrix degeneration. Furthermore, the intra-articular injection of MLX-Ca(AC)₂Lipo enhanced bioinspired lubrication of TMJ, protecting the cartilage from progressive wear. In summary, MLX-Ca(AC)₂Lipo with dual-functions of anti-inflammation and lubrication is a promising nanomedicine for the treatment of TMJ osteoarthritis by intra-articular injection.

Alternative strategies in cardiac preclinical research and new clinical trial formats

An efficient and safe drug development process is crucial for the establishment of new drugs on the market aiming to increase quality of life and life-span of our patients. Despite technological advances in the past decade, successful launches of drug candidates per year remain low. We here give an overview about some of these advances and suggest improvements for implementation to boost preclinical and clinical drug development with a focus on the cardiovascular field. We highlight advantages and disadvantages of animal experimentation and thoroughly review alternatives in the field of three-dimensional cell culture as well as preclinical use of spheroids and organoids. Microfluidic devices and their potential as organ-on-a-chip systems, as well as the use of living animal and human cardiac tissues are additionally introduced. In the second part, we examine recent gold standard randomized clinical trials and present possible modifications to increase lead candidate throughput: adaptive designs, master protocols, and drug repurposing. In silico and N-of-1 trials have the potential to redefine clinical drug candidate evaluation. Finally, we briefly discuss clinical trial designs during pandemic times.

Exopolysaccharide Isolated from Lactobacillus plantarum L-14 Has Anti-Inflammatory Effects via the Toll-Like Receptor 4 Pathway in LPS-Induced RAW 264.7 Cells

Inflammation is a biological response of the immune system to defend the body from negative stimulation. However, the excessive inflammatory response can damage host tissues and pose serious threats. Exopolysaccharide (EPS), one of the postbiotics, is secreted from lactic acid bacteria. Although many studies have described the beneficial effects of EPS, such as its anti-inflammatory and anti-oxidant effects, its underlying mechanisms have remained to be poorly understood. Thus, we identified that EPS obtained from Lactobacillus plantarum L-14 was a homogeneous polysaccharide primarily comprised of glucose. To examine these anti-inflammatory effects, an inflammatory response was induced by lipopolysaccharide (LPS) administration to mouse macrophage RAW 264.7 cells that were pretreated with EPS. The anti-inflammatory effects of EPS were identified by analyzing the changes within inflammatory markers at the molecular level. We demonstrate here that EPS suppressed proinflammatory mediators, such as cyclooxygenase-2, interleukin-6, tumor necrosis factor-α, and interleukin-1β, and downregulated the expression of an inducible nitric oxide synthase known to lead to oxidative stress. It was also confirmed that EPS had anti-inflammatory effects by blocking the interaction of LPS with Toll-like receptor 4 (TLR4), as demonstrated by using the known TLR4 inhibitor TAK-242. In addition, we found that EPS itself could suppress the expression of TLR4. Consequently, our data suggest that EPS can be a potential target for the development of natural product-derived medicine for treating inflammatory diseases related to TLR4.

The Interplay between Diabetes and Alzheimer's Disease-In the Hunt for Biomarkers

The brain is an organ in which energy metabolism occurs most intensively and glucose is an essential and dominant energy substrate. There have been many studies in recent years suggesting a close relationship between type 2 diabetes mellitus (T2DM) and Alzheimer’s disease (AD) as they have many pathophysiological features in common. The condition of hyperglycemia exposes brain cells to the detrimental effects of glucose, increasing protein glycation and is the cause of different non-psychiatric complications. Numerous observational studies show that not only hyperglycemia but also blood glucose levels near lower fasting limits (72 to 99 mg/dL) increase the incidence of AD, regardless of whether T2DM will develop in the future. As the comorbidity of these diseases and earlier development of AD in T2DM sufferers exist, new AD biomarkers are being sought for etiopathogenetic changes associated with early neurodegenerative processes as a result of carbohydrate disorders. The S100B protein seem to be interesting in this respect as it may be a potential candidate, especially important in early diagnostics of these diseases, given that it plays a role in both carbohydrate metabolism disorders and neurodegenerative processes. It is therefore necessary to clarify the relationship between the concentration of the S100B protein and glucose and insulin levels. This paper draws attention to a valuable research objective that may in the future contribute to a better diagnosis of early neurodegenerative changes, in particular in subjects with T2DM and may be a good basis for planning experiments related to this issue as well as a more detailed explanation of the relationship between the neuropathological disturbances and changes of glucose and insulin concentrations in the brain.

Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases

The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by 'borrowing' treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different 'waves' of repurposing. Early drug repurposing occurred in academia and was based on serendipitous observations or perceived disease similarity, often driven by the availability and popularity of drug classes. Since the 1990s, most biologic therapies have been developed for one or several RAIDs and then tested among the others, with varying levels of success. The past two decades have seen data-driven repurposing characterized by signature-based approaches that rely on molecular biology and genomics. Additionally, many data-driven strategies employ computational modelling and machine learning to integrate multiple sources of data. Together, these repurposing periods have led to advances in the treatment for many RAIDs.

A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome

Metabolic syndrome is a pathological condition characterized by obesity, hyperglycemia, hypertension, elevated levels of triglycerides and low levels of high-density lipoprotein cholesterol that increase cardiovascular disease risk and type 2 diabetes. Although numerous predisposing genetic risk factors have been identified, the biological mechanisms underlying this complex phenotype are not fully elucidated. Here we introduce a systems biology approach based on network analysis to investigate deregulated biological processes and subsequently identify drug repurposing candidates. A proximity score describing the interaction between drugs and pathways is defined by combining topological and functional similarities. The results of this computational framework highlight a prominent role of the immune system in metabolic syndrome and suggest a potential use of the BTK inhibitor ibrutinib as a novel pharmacological treatment. An experimental validation using a high fat diet-induced obesity model in zebrafish larvae shows the effectiveness of ibrutinib in lowering the inflammatory load due to macrophage accumulation.

Network-Based Assessment of Adverse Drug Reaction Risk in Polypharmacy Using High-Throughput Screening Data

The risk of adverse drug reactions increases in a polypharmacology setting. High-throughput drug screening with transcriptomics applied to human cells has shown that drugs have effects on several molecular pathways, and these affected pathways may be predictive proxy for adverse drug reactions. Depending on the way that different drugs may contribute to adverse drug reactions, different options may exist in the clinical setting. Here, we formulate a network framework to integrate the relationships between drugs, biological functions, and adverse drug reactions based on the high-throughput drug perturbation data from the Library of Integrated Network-Based Cellular Signatures (LINCS) project. We present network-based parameters that indicate whether a given reaction may be related to the effect of a single drug or to the combination of several drugs, as well as the relative risk of adverse drug reaction manifestation given a certain drug combination.

A Network Pharmacology-Based Analysis of Multi-Target, Multi-Pathway, Multi-Compound Treatment for Ovarian Serous Cystadenocarcinoma

BACKGROUND AND OBJECTIVES

Pharmacological control against ovarian serous cystadenocarcinoma has received increasing attention. The purpose of this study was to investigate multi-drug treatments as synergetic therapy for ovarian serous cystadenocarcinoma and to explore their mechanisms of action by the network pharmacology method.

METHODS

Genes acting on ovarian serous cystadenocarcinoma were first collected from GEPIA and DisGeNET. Gene Ontology annotation, Kyoto Encyclopedia of Genes and Genomes pathway, Reactome pathway, and Disease Ontology analyses were then conducted. A connectivity map analysis was employed to identify compounds as treatment options for ovarian serous cystadenocarcinoma. Targets of these compounds were obtained from the Search Tool for Interacting Chemicals (STITCH). The intersections between the ovarian serous cystadenocarcinoma-related genes and the compound targets were identified. Finally, the Kyoto Encyclopedia of Genes and Genomes and Reactome pathways in which the overlapped genes participated were selected, and a correspondence compound-target pathway network was constructed.

RESULTS

A total of 541 ovarian serous cystadenocarcinoma-related genes were identified. The functional enrichment and pathway analyses indicated that these genes were associated with critical tumor-related pathways. Based on the connectivity map analysis, five compounds (resveratrol, MG-132, puromycin, 15-delta prostaglandin J2, and valproic acid) were determined as treatment agents for ovarian serous cystadenocarcinoma. Next, 48 targets of the five compounds were collected. Following mapping of the 48 targets to the 541 ovarian serous cystadenocarcinoma-related genes, we identified six targets (PTGS1, FOS, HMOX1, CASP9, PPARG, and ABCB1) as therapeutic targets for ovarian serous cystadenocarcinoma by the five compounds. By analysis of the compound-target pathway network, we found the synergistic anti-ovarian serous cystadenocarcinoma potential and the underlying mechanisms of action of the five compounds.

CONCLUSION

In summary, latent drugs against ovarian serous cystadenocarcinoma were acquired and their target actions and pathways were determined by the network pharmacology strategy, which provides a new prospect for medicamentous therapy for ovarian serous cystadenocarcinoma. However, further in-depth studies are indispensable to increase the validity of this study.

Nonredundant, Highly Connected MicroRNAs Control Functionality in Breast Cancer Networks

Alterations to transcriptional regulation are an important factor in breast cancer. Noncoding RNA, such as microRNA (miR), have very influential roles in the transcriptional regulation of genes. Transcriptional regulation can be successfully modeled and analyzed using complex network theory. Particularly, interactions between two distinct classes of biological elements, such as miR and genes, can be approached through the bipartite network formalism. Based on bipartite network properties, it is possible to identify highly influential miRs in the network, such as those that have a large number of connections indicating regulation of a large set of genes. Some miRs in a network are nonredundant, which indicates that they are solely responsible of the regulation of a particular set of genes, which in turn may be associated to a particular biological process. We hypothesize that highly influential, nonredundant miRs, which we call Commodore miRs (Cdre-miRs), have an important role on the control of biological functions through transcriptional networks. In this work, we analyze the regulation of gene expression by miRs in healthy and cancerous breast tissue using bipartite miR-gene networks inferred from the Cancer Genome Atlas (TCGA) expression data. We observe differences in the degree, clustering coefficient and redundancy distributions for miRs and genes in the network, indicating differences in the way that these elements interact with each other. Furthermore, we identify a small set of five Cdre-miRs in the breast cancer network: miR-190b, miR-let7i, miR-292-b, miR-511, and miR-141. The neighborhood of genes controlled by each of these miRs is involved in particular biological functions such as dynein structure-associated processes, immune response, angiogenesis, cytokine activity, and cell motility. We propose that these Cdre-miRs are important control elements of biological functions deregulated in breast cancer.