Unveiling the role of network and systems biology in drug discovery

Some Aspects and Convergence of Human and Veterinary Drug Repositioning

Drug innovation traditionally follows a de novo approach with new molecules through a complex preclinical and clinical pathway. In addition to this strategy, drug repositioning has also become an important complementary approach, which can be shorter, cheaper, and less risky. This review provides an overview of drug innovation in both human and veterinary medicine, with a focus on drug repositioning. The evolution of drug repositioning and the effectiveness of this approach are presented, including the growing role of data science and computational modeling methods in identifying drugs with potential for repositioning. Certain business aspects of drug innovation, especially the relevant factors of market exclusivity, are also discussed. Despite the promising potential of drug repositioning for innovation, it remains underutilized, especially in veterinary applications. To change this landscape for mutual benefits of human and veterinary drug innovation, further exploitation of the potency of drug repositioning is necessary through closer cooperation between all stakeholders, academia, industry, pharmaceutical authorities, and innovation policy makers, and the integration of human and veterinary repositioning into a unified innovation space. For this purpose, the establishment of the conceptually new "One Health Drug Repositioning Platform" is proposed. Oncology is one of the disease areas where this platform can significantly support the development of new drugs for human and dog (or other companion animals) anticancer therapies. As an example of the utilization of human and veterinary drugs for veterinary repositioning, the use of COX inhibitors to treat dog cancers is reviewed.

Vitamin D analog calcitriol for breast cancer therapy; an integrated drug discovery approach

As breast cancer remains leading cause of cancer death globally, it is essential to develop an affordable breast cancer therapy in underdeveloped countries. Drug repurposing offers potential to address gaps in breast cancer treatment. Molecular networking studies were performed for drug repurposing approach by using heterogeneous data. The PPI networks were built to select the target genes from the EGFR overexpression signaling pathway and its associated family members. The selected genes EGFR, ErbB2, ErbB4 and ErbB3 were allowed to interact with 2637 drugs, leads to PDI network construction of 78, 61, 15 and 19 drugs, respectively. As drugs approved for treating non cancer-related diseases or disorders are clinically safe, effective, and affordable, these drugs were given considerable attention. Calcitriol had shown significant binding affinities with all four receptors than standard neratinib. The RMSD, RMSF, and H-bond analysis of protein-ligand complexes from molecular dynamics simulation (100 ns), confirmed the stable binding of calcitriol with ErbB2 and EGFR receptors. In addition, MMGBSA and MMP BSA also affirmed the docking results. These in-silico results were validated with in-vitro cytotoxicity studies in SK-BR-3 and Vero cells. The IC50 value of calcitriol (43.07 mg/ml) was found to be lower than neratinib (61.50 mg/ml) in SK-BR-3 cells. In Vero cells the IC50 value of calcitriol (431.05 mg/ml) was higher than neratinib (404.95 mg/ml). It demonstrates that calcitriol suggestively downregulated the SK-BR-3 cell viability in a dose-dependent manner. These implications revealed calcitriol has shown better cytotoxicity and decreased the proliferation rate of breast cancer cells than neratinib.Communicated by Ramaswamy H. Sarma.

Depigmenting topical therapy based on a synergistic combination of compounds targeting the key pathways involved in melasma pathophysiology

Melasma has a complex pathophysiology with different cell types and signalling pathways involved. Paracrine factors secreted by keratinocytes, fibroblasts and endothelial cells act on melanocytes and stimulate melanogenesis. These paracrine factors are involved in the oxidative stress, inflammatory, vascular and hormonal pathways, among others. Damage of the dermoepidermal barrier also occurs and facilitates melanin deposition in the dermis, also known as dermal or mixed melasma. We used artificial intelligence tools to define the best combination of compounds for skin pigmentation inhibition. Mathematical models suggested the combination of retinol, diosmin and ferulic acid to be the most effective one. In vitro cellular tyrosinase activity assay proved that this combination had a synergistic depigmenting effect. Further assays proved that the combination could inhibit key pathways involved in melasma by downregulating ET-1 and COX-2 gene expression and IBMX-induced dendricity in human melanocytes, and upregulated the gene expression of IL-1b, TIMP3 and several endogenous antioxidant enzymes. The combination also reduced melanin levels in a phototype VI 3D epidermis model. These results indicate that the combination of retinol, diosmin and ferulic acid is an effective synergistic complex for the treatment of melasma by regulating the key molecular pathways involved in skin hyperpigmentation pathophysiology.

Systems biology and artificial intelligence analysis highlights the pleiotropic effect of IVIg therapy in autoimmune diseases with a predominant role on B cells and complement system

Intravenous immunoglobulin (IVIg) is used as treatment for several autoimmune and inflammatory conditions, but its specific mechanisms are not fully understood. Herein, we aimed to evaluate, using systems biology and artificial intelligence techniques, the differences in the pathophysiological pathways of autoimmune and inflammatory conditions that show diverse responses to IVIg treatment. We also intended to determine the targets of IVIg involved in the best treatment response of the evaluated diseases. Our selection and classification of diseases was based on a previously published systematic review, and we performed the disease characterization through manual curation of the literature. Furthermore, we undertook the mechanistic evaluation with artificial neural networks and pathway enrichment analyses. A set of 26 diseases was selected, classified, and compared. Our results indicated that diseases clearly benefiting from IVIg treatment were mainly characterized by deregulated processes in B cells and the complement system. Indeed, our results show that proteins related to B-cell and complement system pathways, which are targeted by IVIg, are involved in the clinical response. In addition, targets related to other immune processes may also play an important role in the IVIg response, supporting its wide range of actions through several mechanisms. Although B-cell responses and complement system have a key role in diseases benefiting from IVIg, protein targets involved in such processes are not necessarily the same in those diseases. Therefore, IVIg appeared to have a pleiotropic effect that may involve the collaborative participation of several proteins. This broad spectrum of targets and 'non-specificity' of IVIg could be key to its efficacy in very different diseases.

Drug repositioning: A bibliometric analysis

Drug repurposing has become an effective approach to drug discovery, as it offers a new way to explore drugs. Based on the Science Citation Index Expanded (SCI-E) and Social Sciences Citation Index (SSCI) databases of the Web of Science core collection, this study presents a bibliometric analysis of drug repurposing publications from 2010 to 2020. Data were cleaned, mined, and visualized using Derwent Data Analyzer (DDA) software. An overview of the history and development trend of the number of publications, major journals, major countries, major institutions, author keywords, major contributors, and major research fields is provided. There were 2,978 publications included in the study. The findings show that the United States leads in this area of research, followed by China, the United Kingdom, and India. The Chinese Academy of Science published the most research studies, and NIH ranked first on the h-index. The Icahn School of Medicine at Mt Sinai leads in the average number of citations per study. Sci Rep, Drug Discov. Today, and Brief. Bioinform. are the three most productive journals evaluated from three separate perspectives, and pharmacology and pharmacy are unquestionably the most commonly used subject categories. Cheng, FX; Mucke, HAM; and Butte, AJ are the top 20 most prolific and influential authors. Keyword analysis shows that in recent years, most research has focused on drug discovery/drug development, COVID-19/SARS-CoV-2/coronavirus, molecular docking, virtual screening, cancer, and other research areas. The hotspots have changed in recent years, with COVID-19/SARS-CoV-2/coronavirus being the most popular topic for current drug repurposing research.

Disulfiram in glioma: Literature review of drug repurposing

Gliomas are the most common malignant brain tumors. High-grade gliomas, represented by glioblastoma multiforme (GBM), have a poor prognosis and are prone to recurrence. The standard treatment strategy is tumor removal combined with radiotherapy and chemotherapy, such as temozolomide (TMZ). However, even after conventional treatment, they still have a high recurrence rate, resulting in an increasing demand for effective anti-glioma drugs. Drug repurposing is a method of reusing drugs that have already been widely approved for new indication. It has the advantages of reduced research cost, safety, and increased efficiency. Disulfiram (DSF), originally approved for alcohol dependence, has been repurposed for adjuvant chemotherapy in glioma. This article reviews the drug repurposing method and the progress of research on disulfiram reuse for glioma treatment.

The Potential Anti-remodeling Effect of Paroxetine After Myocardial Infarction May Be Blunted by Beta-Blockers

Background

Left ventricular (LV) remodeling consists in maladaptive changes in cardiac geometry and function following an insult such as ST-segment elevation myocardial infarction (STEMI). Interventions able to prevent LV remodeling after a STEMI are expected to improve the outcome of this condition. Paroxetine has inhibitory effects on GRK2, also known as beta-adrenergic receptor kinase 1 (ADRBK1). This drug does not yield beneficial effects on LV remodeling in patients with STEMI and LV ejection fraction ≤ 45%.

Methods

We compared the molecular effects of paroxetine and drugs for neurohormonal antagonism (beta-blockers, angiotensin converting enzyme inhibitors/angiotensin receptor blockers, mineralocorticoid receptor antagonists), using a bioinformatic approach integrating transcriptomic data in a swine model of post-MI and available evidence from the literature and massive public databases.

Results

Among standard therapies for MI, beta-blockers are the only ones acting directly upon GKR2, but the mechanism of action overlaps with angiotensin-converting enzyme inhibitors/angiotensin receptor blockers with respect to the AT2R-mediated anti-hypertensive response. Moreover, beta-blockers could have anti-fibrotic and anti-inflammatory effects through the regulation of myocyte-specific enhancer factors, endothelins and chemokines.

Conclusion

The additive benefit of paroxetine on the background of the standard therapy for STEMI, which includes beta-blockers, is expected to be limited. Nonetheless, paroxetine becomes particularly interesting when a beta-blocker is contraindicated (for example, in hypotensive individuals) or poorly tolerated.

Drug repurposing: An emerging strategy in alleviating skin cancer

Skin cancer is one of the most common forms of cancer. Several million people are estimated to have affected with this condition worldwide. Skin cancer generally includes melanoma and non-melanoma with the former being the most dangerous. Chemotherapy has been one of the key therapeutic strategies employed in the treatment of skin cancer, especially in advanced stages of the disease. It could be also used as an adjuvant with other treatment modalities depending on the type of skin cancer. However, there are several shortfalls associated with the use of chemotherapy such as non-selectivity, tumour resistance, life-threatening toxicities, and the exorbitant cost of medicines. Furthermore, new drug discovery is a lengthy and costly process with minimal likelihood of success. Thus, drug repurposing (DR) has emerged as a new avenue where the drug approved formerly for the treatment of one disease can be used for the treatment of another disease like cancer. This approach is greatly beneficial over the de novo approach in terms of time and cost. Moreover, there is minimal risk of failure of repurposed therapeutics in clinical trials. There are a considerable number of studies that have reported on drugs repurposed for the treatment of skin cancer. Thus, the present manuscript offers a comprehensive overview of drugs that have been investigated as repurposing candidates for the efficient treatment of skin cancers mainly melanoma and its oncogenic subtypes, and non-melanoma. The prospects of repurposing phytochemicals against skin cancer are also discussed. Furthermore, repurposed drug delivery via topical route and repurposed drugs in clinical trials are briefed. Based on the findings from the reported studies discussed in this manuscript, drug repurposing emerges to be a promising approach and thus is expected to offer efficient treatment at a reasonable cost in devitalizing skin cancer.

Overcoming Sparseness of Biomedical Networks to Identify Drug Repositioning Candidates

Modeling complex biological systems is necessary to understand biochemical interactions behind pharmacological effects of drugs. Successful in silico drug repurposing relies on exploration of diverse biochemical concepts and their relationships, including drug's adverse reactions, drug targets, disease symptoms, as well as disease associated genes and their pathways, to name a few. We present a computational method for inferring drug-disease associations from complex but incomplete and biased biological networks. Our method employs matrix completion to overcome the sparseness of biomedical data and to enrich the set of relationships between different biomedical entities. We present a strategy for identifying network paths supportive of drug efficacy as well as a computational procedure capable of combining different network patterns to better distinguish treatments from non-treatments. The algorithms is available at http://bioinfo.cs.uni.edu/AEONET.html.

Cardiac protection by pirfenidone after myocardial infarction: a bioinformatic analysis

Left ventricular (LV) remodeling after myocardial infarction (MI) is promoted by an intense fibrotic response, which could be targeted by the anti-fibrotic drug pirfenidone. We explored the relationship between protein modulation by pirfenidone and post-MI remodeling, based on molecular information and transcriptomic data from a swine model of MI. We identified 6 causative motives of post-MI remodeling (cardiomyocyte cell death, impaired myocyte contractility, extracellular matrix remodeling and fibrosis, hypertrophy, renin–angiotensin–aldosterone system activation, and inflammation), 4 pirfenidone targets and 21 bioflags (indirect effectors). Pirfenidone had a more widespread action than gold-standard drugs, encompassing all 6 motives, with prominent effects on p38γ-MAPK12, the TGFβ1-SMAD2/3 pathway and other effector proteins such as matrix metalloproteases 2 and 14, PDGFA/B, and IGF1. A bioinformatic approach allowed to identify several possible mechanisms of action of pirfenidone with beneficial effects in the post-MI LV remodeling, and suggests additional effects over guideline-recommended therapies.

Ceruletide and Alpha-1 Antitrypsin as a Novel Combination Therapy for Ischemic Stroke

Ischemic stroke is a primary cause of morbidity and mortality worldwide. Beyond the approved thrombolytic therapies, there is no effective treatment to mitigate its progression. Drug repositioning combinational therapies are becoming promising approaches to identify new uses of existing drugs to synergically target multiple disease-response mechanisms underlying complex pathologies. Here, we used a systems biology-based approach based on artificial intelligence and pattern recognition tools to generate in silico mathematical models mimicking the ischemic stroke pathology. Combinational treatments were acquired by screening these models with more than 5 million two-by-two combinations of drugs. A drug combination (CA) formed by ceruletide and alpha-1 antitrypsin showing a predicted value of neuroprotection of 92% was evaluated for their synergic neuroprotective effects in a mouse pre-clinical stroke model. The administration of both drugs in combination was safe and effective in reducing by 39.42% the infarct volume 24 h after cerebral ischemia. This neuroprotection was not observed when drugs were given individually. Importantly, potential incompatibilities of the drug combination with tPA thrombolysis were discarded in vitro and in vivo by using a mouse thromboembolic stroke model with t-PA-induced reperfusion, revealing an improvement in the forepaw strength 72 h after stroke in CA-treated mice. Finally, we identified the predicted mechanisms of action of ceruletide and alpha-1 antitrypsin and we demonstrated that CA modulates EGFR and ANGPT-1 levels in circulation within the acute phase after stroke. In conclusion, we have identified a promising combinational treatment with neuroprotective effects for the treatment of ischemic stroke.

Construction and contextualization approaches for protein-protein interaction networks

Protein-protein interaction network (PPIN) analysis is a widely used method to study the contextual role of proteins of interest, to predict novel disease genes, disease or functional modules, and to identify novel drug targets. PPIN-based analysis uses both generic and context-specific networks. Multiple contextualization methodologies have been described, such as shortest-path algorithms, neighborhood-based methods, and diffusion/propagation algorithms. This review discusses these methods, provides intuitive representations of PPIN contextualization, and also examines how the quality of such context-specific networks could be improved by considering additional sources of evidence. As a heuristic, we observe that tasks such as identifying disease genes, drug targets, and protein complexes should consider local neighborhoods, while uncovering disease mechanisms and discovering disease-pathways would gain from diffusion-based construction.

Targeting disorders in unstructured and structured proteins in various diseases

Intrinsically disordered proteins (IDPs) and intrinsically disordered protein regions (IDPRs) are proteins and protein segments that usually do not acquire well-defined folded structures even under physiological conditions. They are abundantly present and challenge the "one sequence-one structure-one function" theory due to a lack of stable secondary and/or tertiary structure. Due to conformational flexibility, IDPs/IDPRs can bind with multiple interacting partners with high-specificity and low-affinity and perform essential biological functions associated with signalling, recognition and regulation. Mis-functioning and mis-regulation of IDPs and IDPRs causes disorder in disordered proteins and disordered protein segments which results in numerous human diseases, such as cancer, Parkinson's disease (PD), Alzheimer's disease (AD), diabetes, metabolic disorders, systemic disorders and so on. Due to the strong connection of IDPs/IDPRs with human diseases they are considered potentential targets for drug therapy. Since they disobey the "one sequence-one structure-one function" concept, IDPs/IDPRs are complex systems for drug targeting. This review summarises various protein disorder diseases and different methods for therapeutic targeting of disordered proteins/segments. Targeting IDPs/IDPRs for diseases will open up a new era of rational drug design and drug discovery.

Protein-protein interactions: Methods, databases, and applications in virus-host study

Almost all the cellular processes in a living system are controlled by proteins: They regulate gene expression, catalyze chemical reactions, transport small molecules across membranes, and transmit signal across membranes. Even, a viral infection is often initiated through virus-host protein interactions. Protein-protein interactions (PPIs) are the physical contacts between two or more proteins and they represent complex biological functions. Nowadays, PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins. Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets. In this review, we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies. Here, we present a short but comprehensive review on PPIs, including the experimental and computational methods of finding PPIs, the databases dedicated to virus-host PPIs, and the associated various applications in protein interaction networks of some lethal viruses with their hosts.

Effects and Components of Herb Pair Huanglian-Banxia on Diabetic Gastroparesis by Network Pharmacology

Diabetic gastroparesis (DGP) is a serious and chronic complication of long-standing diabetes mellitus, which brings a heavy burden to individuals and society. Traditional Chinese medicine (TCM) is considered a complementary and alternative therapy for DGP patients. Huanglian (Coptidis Rhizoma, HL) and Banxia (Pinelliae Rhizoma, BX) combined as herb pair have been frequently used in TCM prescriptions, which can effectively treat DGP in China. In this article, a practical application of TCM network pharmacological approach was used for the research on herb pair HL-BX in the treatment of DGP. Firstly, twenty-seven potential active components of HL-BX were screened from the TCMSP database, and their potential targets were also retrieved. Then, the compound-target network and PPI network were constructed from predicted common targets, and several key targets were found based on the degree of the network. Next, GO and KEGG enrichment analyses were conducted to obtain several significantly enriched terms. Finally, the experimental verification was made. The results demonstrated that network pharmacological approach was a powerful means for identifying bioactive ingredients and mechanisms of action for TCM. Network pharmacology provided an effective strategy for TCM modern research.

Benchmark Concentrations for Untargeted Metabolomics Versus Transcriptomics for Liver Injury Compounds in In Vitro Liver Models

Interpretation of untargeted metabolomics data from both in vivo and physiologically relevant in vitro model systems continues to be a significant challenge for toxicology research. Potency-based modeling of toxicological responses has served as a pillar of interpretive context and translation of testing data. In this study, we leverage the resolving power of concentration-response modeling through benchmark concentration (BMC) analysis to interpret untargeted metabolomics data from differentiated cultures of HepaRG cells exposed to a panel of reference compounds and integrate data in a potency-aligned framework with matched transcriptomic data. For this work, we characterized biological responses to classical human liver injury compounds and comparator compounds, known to not cause liver injury in humans, at 10 exposure concentrations in spent culture media by untargeted liquid chromatography-mass spectrometry analysis. The analyte features observed (with limited metabolites identified) were analyzed using BMC modeling to derive compound-induced points of departure. The results revealed liver injury compounds produced concentration-related increases in metabolomic response compared to those rarely associated with liver injury (ie, sucrose, potassium chloride). Moreover, the distributions of altered metabolomic features were largely comparable with those observed using high throughput transcriptomics, which were further extended to investigate the potential for in vitro observed biological responses to be observed in humans with exposures at therapeutic doses. These results demonstrate the utility of BMC modeling of untargeted metabolomics data as a sensitive and quantitative indicator of human liver injury potential.

Consolidation of network and experimental pharmacology to divulge the antidiabetic action of Ficus benghalensis L. bark

A total of 21 different bioactives were identified from F. benghalensis in which 3 molecules, i.e., apigenin, 3',4',5,7-tetrahydroxy-3-methoxyflavone, and kaempferol were predicted to target the highest number of proteins involved in diabetic pathogenesis in which protein tyrosine phosphatase 1b was primarily targeted. Similarly, a docking study identified ursolic acid to have the highest binding affinity with protein tyrosine phosphatase 1b. The combined synergic network analysis identified PI3K/Akt signaling pathway to be primarily modulated followed by the calcium signaling pathway. Similarly, in oral glucose tolerance test, we observed the efficacy of hydroalcoholic extract of F. benghalensis to lower the total area under the curve of glucose and increase total area under curve of insulin for 2 hours. Likewise, hydroalcoholic extract reversed the altered homeostatic hepatic enzymes after 28 days of treatments. Similarly, the extract also enhanced the antioxidant enzymes level like catalase and superoxide dismutase in liver homogenate. In summary, hydroalcoholic extract of F. benghalensis bark may act as an antidiabetic agent by enhancing the glycolysis, decreasing gluconeogenesis, promoting glucose uptake, enhancing insulin secretion, and maintaining pancreatic β-cell mass via PI3K/Akt signaling pathway and downregulating the function of  protein tyrosine phosphatase 1b.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02788-7.

Randomised clinical trial: Efficacy and safety of Qing-Chang-Hua-Shi granules in a multicenter, randomized, and double-blind clinical trial of patients with moderately active ulcerative colitis

Qing-Chang-Hua-Shi (QCHS) is a Chinese herbal formula, which is composed of 11 herbs. Studies have also shown that QCHS granules can alleviate colitis in animal models by preventing inflammatory responses and suppressing apoptosis through the MEK/ERK signaling pathway. To determine the efficacy and safety of QCHS granules in patients with moderately active UC. We performed a multicenter, randomized, placebo-controlled, double-blind study of patients with moderately active UC who did not respond to 4 weeks of mesalazine therapy at the maximum dose. Patients were randomly assigned to groups and administered QCHS granules (125 g/day, n = 59) or an identical placebo, which was similar to the QCHS granules in color and taste (125 g/day, n = 60), with continued 5-ASA 4 g/d therapy for 12 weeks. The primary outcome was the rate of clinical response and clinical remission at week 12. The secondary outcomes were health-related quality of life, endoscopic response rate, and mucosal healing rate. Any changes in mucus/bloody stool and diarrhea were recorded. Out of the 119 enrolled patients at 10 different centers in China, 102 patients completed the trial. Clinical remission and clinical response were seen in 31.48% and 92.59% of QCHS-treated patients, and 12.50% and 72.92% of placebo-treated patients, respectively. There was a significant difference between the two treatment groups. More patients receiving QCHS granules vs. placebo achieved remission of mucus/bloody stool (70.37% vs. 47.92%, P = 0.0361). Adverse event rates were similar (QCHS granules 38.33%; placebo 25.42%). In conclusion, QCHS granules were superior to the placebo in introducing clinical remission and mucosal healing, as well as in relieving mucus/blood stool in patients with moderately active and 5-ASA-refractory UC.

Computational study of zebrafish immune-targeted microarray data for prediction of preventive drug candidates

Viral hemorrhagic septicemia virus (VHSV) is a rhabdovirus reported to cause economic loss in fish farms. Because of the lack of adequate preventative treatments, the identification of multipath genes involved in VHS infection might be an alternative to explore the possibility of using drugs for the seasonal prevention of this fish disease. We propose labeling a category of drug molecules by further classification and interpretation of the Drug Gene Interaction Database using gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment scores. The study investigated disease networks of up-and down-regulated genes to find those with high interaction as substantial genes in pathways among the different disease networks. We prioritized these genes based on their relationship to those associated with VHS infection in the context of human protein-protein interaction networks and disease pathways. Among the 29 genes as potential drug targets, nine were selected as promising druggable genes (ERBB2, FGFR3, ITGA2B, MAP2K1, NGF, NTRK1, PDGFRA, SCN2B, and SERPINC1). PDGFRA is the most important druggable up-and down-regulated gene and is considered an important gene in the IMATINIB pathway. This study findings indicate a promising approach for drug target prediction for VHS treatment, which might be useful for disease therapeutics.

Drug Repurposing for Rare Diseases

Currently, there are about 7000 identified rare diseases, together affecting 10% of the population. However, fewer than 6% of all rare diseases have an approved treatment option, highlighting their tremendous unmet needs in drug development. The process of repurposing drugs for new indications, compared with the development of novel orphan drugs, is a time-saving and cost-efficient method resulting in higher success rates, which can therefore drastically reduce the risk of drug development for rare diseases. Although drug repurposing is not novel, new strategies have been developed in recent years to do it in a systematic and rational way. Here, we review applied methodologies, recent accomplished progress, and the challenges associated in drug repurposing for rare diseases.

A Mechanistic Model of NMDA and AMPA Receptor-Mediated Synaptic Transmission in Individual Hippocampal CA3-CA1 Synapses: A Computational Multiscale Approach

Inside hippocampal circuits, neuroplasticity events that individual cells may undergo during synaptic transmissions occur in the form of Long-Term Potentiation (LTP) and Long-Term Depression (LTD). The high density of NMDA receptors expressed on the surface of the dendritic CA1 spines confers to hippocampal CA3-CA1 synapses the ability to easily undergo NMDA-mediated LTP and LTD, which is essential for some forms of explicit learning in mammals. Providing a comprehensive kinetic model that can be used for running computer simulations of the synaptic transmission process is currently a major challenge. Here, we propose a compartmentalized kinetic model for CA3-CA1 synaptic transmission. Our major goal was to tune our model in order to predict the functional impact caused by disease associated variants of NMDA receptors related to severe cognitive impairment. Indeed, for variants Glu413Gly and Cys461Phe, our model predicts negative shifts in the glutamate affinity and changes in the kinetic behavior, consistent with experimental data. These results point to the predictive power of this multiscale viewpoint, which aims to integrate the quantitative kinetic description of large interaction networks typical of system biology approaches with a focus on the quality of a few, key, molecular interactions typical of structural biology ones.

Systems pharmacology approach uncovers Ligustilide attenuates experimental colitis in mice by inhibiting PPARγ-mediated inflammation pathways

Inflammatory bowel disease (IBD) is a chronic idiopathic disorder causing inflammation in the gastro-intestinal tract, which is lack of effective drug targets and medications. To identify novel therapeutic agents against consistent targets, we exploited a systems pharmacology-driven framework that incorporates drug-target networks of natural product and IBD disease genes. Our in silico approach found that Ligustilide (LIG), one of the major active components of Angelica acutiloba and Cnidium Officinale, potently attenuated IBD. The following in vivo and in vitro results demonstrated that LIG prevented experimental mice colitis induced by dextran sulfate sodium (DSS) via suppressing inflammatory cell infiltration, the activity of MPO and iNOS, and the expression and production of IL-1β, IL-6, and TNF-α. Subsequently, the network analysis helped to validate that LIG alleviated colitis by inhibiting NF-κB and MAPK/AP-1 pathway through activating PPARγ, which were further confirmed in RAW 264.7 cells and bone marrow-derived macrophages in vitro. In summary, this study reveals that LIG activated PPARγ to inhibit the activation of NF-κB and AP-1 signaling thus eventually alleviated DSS-induced colitis, which has promising activities and may serve as a candidate for the treatment of IBD.Graphical abstract This study suggested novel computational and experimental pharmacology approaches to identify potential IBD therapeutic agents by exploiting polypharmacology of natural products. We demonstrated that LIG could attenuate inflammation in IBD by inhibiting NF-κB and AP-1 pathways via PPARγ activation to reduce the expression of pro-inflammatory cytokines in macrophages. These findings offer comprehensive pre-clinical evidence that LIG may serve as a promising candidate for IBD therapy in the future. Graphical headlights: 1. Systems pharmacology uncovered Ligustilide attenuates experimental colitis in mice. 2. Network-based analysis predicted the mechanism of Ligustilide against IBD, which was validated by inhibiting PPARγ-mediated inflammation pathways. 3. Ligustilide activated PPARγ to inhibit NF-κB and AP-1 activation thus eventually alleviated DSS-induced colitis.4. Ligustilide has promising activities and may serve as a candidate for the treatment of IBD.

Magic Shotgun Nature with Scattergun Approach of Curcumin Repurposing in Obsessive-compulsive Disorder: A Novel Metaphysician of Drug Discovery

BACKGROUND

Obsessive-Compulsive Disorder (OCD) is an intricate, debilitating neuropsychiatric disorder. Exclusively, Selective Serotonin Reuptake Inhibitors (SSRIs) are effective agents used for the treatment of OCD. However, SSRIs are not a magic pill-they do not respond adequately to everybody. In this consideration, a single drug target (magic bullet) is only a slightly superior option for all patients with a lot of pathognomonic signs.

OBJECTIVE

The principal aim of the current study was to check the potential contribution of repurposing of magic shotgun nature of curcumin (rhizomes of Curcuma longa) with scattergun approach- proceeding a pioneer 'fine-tune' for obsessive-compulsive disorder.

METHOD

Swiss albino mice (male 20 to 25 gram) were grouped into different groups (n = 6) used for the MBB (marble-burying behaviour) and MA (motor activity) test as a model for evaluation of anti-compulsive activity (Anti-OCD). Ethanolic extract of Curcuma longa (EECL-10, 15, 25, 40 mg/kg), or SSRI (fluoxetine 5, 10, 15 mg/kg) followed by pre-treated with either sub effective dose of fluoxetine attenuated MBB without effected the MA, or neurotoxin p-chlorophenyl alanine induced compulsive behavior and specific 5-HT receptors agonists/ antagonist, intraperitoneally revealed neuromodulation.

RESULTS

EECL (40 mg/kg) significantly attenuated the MBB. Although, during treatments, none of the above had any critical impact on MA. p < 0.05 was considered significant in every case.

CONCLUSION

Multiple drug-target interactions with multifarious biogenic receptors, supervene unexpected side effects followed by the repurposing of wanted effects (scattergun effect) were evoked by curcumin treatment. Finally, the study shows that EECL (curcumin) has anti-compulsive activity, which is mediated by neuromodulation with 5-HT receptors.

Repurposing clinical drugs is a promising strategy to discover drugs against Zika virus infection

Zika virus (ZIKV) is an emerging pathogen associated with neurological complications, such as Guillain-Barré syndrome in adults and microcephaly in fetuses and newborns. This mosquito-borne flavivirus causes important social and sanitary problems owing to its rapid dissemination. However, the development of antivirals against ZIKV is lagging. Although various strategies have been used to study anti-ZIKV agents, approved drugs or vaccines for the treatment (or prevention) of ZIKV infections are currently unavailable. Repurposing clinically approved drugs could be an effective approach to quickly respond to an emergency outbreak of ZIKV infections. The well-established safety profiles and optimal dosage of these clinically approved drugs could provide an economical, safe, and efficacious approach to address ZIKV infections. This review focuses on the recent research and development of agents against ZIKV infection by repurposing clinical drugs. Their characteristics, targets, and potential use in anti-ZIKV therapy are presented. This review provides an update and some successful strategies in the search for anti-ZIKV agents are given.

Systems biology drug screening identifies statins as enhancers of current therapies in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is a B lymphoid malignancy highly dependent on the microenvironment. Despite new targeted therapies such as ibrutinib and venetoclax, disease progression and relapse remain an issue. CLL cell interactions with the supportive tissue microenvironment play a critical role in disease pathogenesis. We used a platform for drug discovery based on systems biology and artificial intelligence, to identify drugs targeting key proteins described to have a role in the microenvironment. The selected compounds were screened in CLL cell lines in the presence of stromal cells to mimic the microenvironment and validated the best candidates in primary CLL cells. Our results showed that the commercial drug simvastatin was the most effective and selective out of the tested compounds. Simvastatin decreased CLL cell survival and proliferation as well as cell adhesion. Importantly, this drug enhanced the antitumor effect of venetoclax and ibrutinib. We proposed that systems biology approaches combined with pharmacological screening could help to find new drugs for CLL treatment and to predict new combinations with current therapies. Our results highlight the possibility of repurposing widely used drugs such as statins to target the microenvironment and to improve the efficacy of ibrutinib or venetoclax in CLL cells.

In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm

From January 2020, COVID-19 is spreading around the world producing serious respiratory symptoms in infected patients that in some cases can be complicated by the severe acute respiratory syndrome, sepsis and septic shock, multiorgan failure, including acute kidney injury and cardiac injury. Cost and time efficient approaches to reduce the burthen of the disease are needed. To find potential COVID-19 treatments among the whole arsenal of existing drugs, we combined system biology and artificial intelligence-based approaches. The drug combination of pirfenidone and melatonin has been identified as a candidate treatment that may contribute to reduce the virus infection. Starting from different drug targets the effect of the drugs converges on human proteins with a known role in SARS-CoV-2 infection cycle. Simultaneously, GUILDify v2.0 web server has been used as an alternative method to corroborate the effect of pirfenidone and melatonin against the infection of SARS-CoV-2. We have also predicted a potential therapeutic effect of the drug combination over the respiratory associated pathology, thus tackling at the same time two important issues in COVID-19. These evidences, together with the fact that from a medical point of view both drugs are considered safe and can be combined with the current standard of care treatments for COVID-19 makes this combination very attractive for treating patients at stage II, non-severe symptomatic patients with the presence of virus and those patients who are at risk of developing severe pulmonary complications.

3-Quinuclidinyl-α-methoxydiphenylacetate: A multi-targeted ligand with antimuscarinic and antinicotinic effects designed for the treatment of anticholinesterase poisoning

Racemic 3-quinuclidinyl-α-methoxydiphenylacetate (MB266) was synthesised. Its activity at muscarinic acetylcholine receptors (mAChRs), and muscle and neuronal nicotinic acetylcholine receptors (nAChRs), was compared to that of atropine and racemic 3-quinucidinyl benzilate (QNB) using a functional assay based on agonist-induced elevation of intracellular calcium ion concentration in CN21, Chinese Hamster Ovary (CHO) and SHSY5Y human cell lines. MB266 acted as an antagonist at acetylcholine receptors, displaying 18-fold selectivity for mAChR versus nAChR (compared to the 15,200-fold selectivity observed for QNB). Thus O-methylation of QNB reduced the affinity for mAChR antagonism and increased the relative potency at both muscle and neuronal nAChRs. Despite MB266 having a pharmacological profile potentially useful for the treatment of anticholinesterase poisoning, its administration did not improve the neuromuscular function in a soman-poisoned guinea-pig diaphragm preparation pretreated with the organophosphorus nerve agent soman. Consideration should be given to exploring the potential of MB266 for possible anticonvulsant action in vitro as part of a multi-targeted ligand approach.

Overcoming trastuzumab resistance in HER2-positive breast cancer using combination therapy

Human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC) comprises around 20-30% of all BC subtypes and is correlated with poor prognosis. For many years, trastuzumab, a monoclonal antibody, has been used to inhibit the HER2 activity. Though, the main resistance to trastuzumab has challenged the use of this drug in the management of HER2-positive BC. Therefore, the determination of resistance mechanisms and the incorporation of new agents may lead to the development of a better blockade of the HER family receptor signaling. During the last few years, some therapeutic drugs have been developed for treating patients with trastuzumab-resistant HER2-positive BC that have more effective influences in the management of this condition. In this regard, the present study aimed at reviewing the mechanisms of trastuzumab resistance and the innovative therapies that have been investigated in trastuzumab-resistant HER2-positive BC subjects.

Network Pharmacology-Based Dissection of the Anti-diabetic Mechanism of Lobelia chinensis

Diabetes mellitus (DM) is a chronic inflammatory disease, and the rapidly increasing DM is becoming a major problem of global public health. Traditional Chinese medicine (TCM) has a long history of treating diabetes. It has been developed and utilized because of its good efficacy and no toxic side effects. Lobelia chinensis is a traditional whole grass herbal. With the continuous deepening of pharmacological research on TCM, the active ingredients of L. chinensis are continuously revealed, which contained the alkaloids, flavonoids, flavonoid glycosides and amino acids that have the good effects of anti-inflammatory, anti-viral and anti-diabetic. In order to further explore the targets of active ingredients and its anti-diabetic mechanism, a feasible network pharmacology analysis model based on chemical, pharmacokinetic and pharmacological data was developed by network construction method to clarify the anti-diabetic mechanism of L. chinensis. The present study conducted by gas chromatography–mass spectrometer (GC/MS), which identified 208 metabolites of L. chinensis, of which 23 ingredients may have effective pharmacological effects after absorption, distribution, metabolism, and excretion (ADME) screening. Network pharmacological analysis on the active ingredients revealed that 5-hydroxymethylfurfural in L. chinensis affects the insulin resistance signaling pathway by acting on GSK3B, TNF, and MAPK1, acacetin affects the diabetic pathway by acting on INSR, DPP4, and GSK3B, that regulate type 2 diabetes, non-insulin-dependent DM, and inflammatory diseases. These results successfully indicated the potential anti-diabetic mechanism of the active ingredients of L. chinensis.

PTML Model of ChEMBL Compounds Assays for Vitamin Derivatives

Determining the biological activity of vitamin derivatives is needed given that organic synthesis of analogs of vitamins is an active field of interest for medicinal chemistry, pharmaceuticals, and food additives. Accordingly, scientists from different disciplines perform preclinical assays (n_ij) with a considerable combination of assay conditions (c_j). Indeed, the ChEMBL platform contains a database that includes results from 36 220 different biological activity bioassays of 21 240 different vitamins and vitamin derivatives. These assays present are heterogeneous in terms of assay combinations of c_j. They are focused on >500 different biological activity parameters (c₀), >340 different targets (c₁), >6200 types of cell (c₂), >120 organisms of assay (c₃), and >60 assay strains (c₄). It includes a total of >1850 niacin assays, >1580 tretinoin assays, >1580 retinol assays, 857 ascorbic acid assays, etc. Given the complexity of this combinatorial data in terms of being assimilated by researchers, we propose to build a model by combining perturbation theory (PT) and machine learning (ML). Through this study, we propose a PTML (PT + ML) combinatorial model for ChEMBL results on biological activity of vitamins and vitamins derivatives. The linear discriminant analysis (LDA) model presented the following results for training subset a: specificity (%) = 90.38, sensitivity (%) = 87.51, and accuracy (%) = 89.89. The model showed the following results for the external validation subset: specificity (%) = 90.58, sensitivity (%) = 87.72, and accuracy (%) = 90.09. Different types of linear and nonlinear PTML models, such as logistic regression (LR), classification tree (CT), näive Bayes (NB), and random Forest (RF), were applied to contrast the capacity of prediction. The PTML-LDA model predicts with more accuracy by applying combinatorial descriptors. In addition, a PCA experiment with chemical structure descriptors allowed us to characterize the high structural diversity of the chemical space studied. In any case, PTML models using chemical structure descriptors do not improve the performance of the PTML-LDA model based on ALOGP and PSA. We can conclude that the three variable PTML-LDA model is a simplified and adaptable tool for the prediction, for different experiment combinations, the biological activity of derivative vitamins.

A perspective on the discovery of selected compounds with anthelmintic activity against the barber's pole worm-Where to from here?

Parasitic roundworms (nematodes) cause substantial morbidity and mortality in animals worldwide. Anthelmintic treatment is central to controlling these worms, but widespread resistance to most of the commercially available anthelmintics for veterinary and agricultural use is compromising control, such that there is an urgency to discover new and effective drugs. The purpose of this article is to review information on parasitic nematodes, the treatment and control of parasitic nematode infections and aspects of discovering new anthelmintics in the context of anthelmintic resistance problems, and then to discuss some progress that our group has made in identifying selected compounds with activity against nematodes. The focus of our recent work has been on discovering new chemical entities and known drugs with anthelmintic activities against Haemonchus contortus as well as other socioeconomically important parasitic nematodes for subsequent development. Using whole worm-based phenotypic assays, we have been screening compound collections obtained via product-development-partnerships and/or collaborators, and active compounds have been assessed for their potential as anthelmintic candidates. Following the screening of 15,333 chemicals from five distinct compound collections against H. contortus, we have discovered one new chemical entity (designated SN00797439), two human kinase inhibitors (SNS-032 and AG-1295), 14 tetrahydroquinoxaline analogues, one insecticide (tolfenpyrad) and two tolfenpyrad (pyrazole-5-carboxamide) derivatives (a-15 and a-17) with anthelmintic activity in vitro. Some of these 20 'hit' compounds have selectivity against H. contortus in vitro when compared to particular human cell lines. In our opinion, some of these compounds could represent starting points for 'lead' development. Accordingly, the next research steps to be pursued include: (i) chemical optimisation of representative chemicals via structure-activity relationship (SAR) evaluations; (ii) assessment of the breadth of spectrum of anthelmintic activity on a range of other parasitic nematodes, such as strongyloids, ascaridoids, enoplids and filarioids; (iii) detailed investigations of the absorption, distribution, metabolism, excretion and toxicity (ADMET) of optimised chemicals with broad nematocidal or nematostatic activity; and (iv) establishment of the modes of action of lead candidates.

Identification of relevant hub genes for early intervention at gene coexpression modules with altered predicted expression in schizophrenia

Genetic risk for schizophrenia is due to the joint effect of multiple genes acting mainly at two different processes, prenatal/perinatal neurodevelopment and adolescence/early adulthood synapse maturation. Identification of important genes at the second process is of relevance for early intervention. The aim of this work was to identify gene co-expression modules with altered expression in schizophrenia during adolescence/early adulthood. To this goal, we predicted frontal cortex gene expression in one discovery sample, the largest GWAS of schizophrenia from the Psychiatric Genomics Consortium, using S-prediXcan, and in one target sample, consisting of 625 schizophrenic patients and 819 controls from Spain, using prediXcan. Prediction models were trained on GTEx frontal cortex expression dataset. In parallel, we identified brain co-expression modules from BrainSpan using WGCNA. Then, we estimated polygenic risk scores based on predicted expression (PE-PRS) for each co-expression module in the target sample, based on PE-PRS model from the discovery sample. This analysis led to the identification of a module with mainly adolescence/adulthood expression whose PE-PRS was significantly associated with schizophrenia. The module was significantly enriched in synaptic processes. Several hub genes at this module are drugabble, according to the drug-gene interaction database, and/or involved in synaptic transmission, such as the voltage-gated ion channels SCN2B and KCNAB2, the calcium calmodulin kinases CAMK2A and CAMK1G, or genes involved in synaptic vesicle cycle, such as DNM1, or SYNGR1. Therefore, identification of this module may be the first step in patient stratification based on biology, as well as in drug design and drug repurposing efforts.

In-silico simulated prototype-patients using TPMS technology to study a potential adverse effect of sacubitril and valsartan

Unveiling the mechanism of action of a drug is key to understand the benefits and adverse reactions of a medication in an organism. However, in complex diseases such as heart diseases there is not a unique mechanism of action but a wide range of different responses depending on the patient. Exploring this collection of mechanisms is one of the clues for a future personalized medicine. The Therapeutic Performance Mapping System (TPMS) is a Systems Biology approach that generates multiple models of the mechanism of action of a drug. Each molecular mechanism generated could be associated to particular individuals, here defined as prototype-patients, hence the generation of models using TPMS technology may be used for detecting adverse effects to specific patients. TPMS operates by (1) modelling the responses in humans with an accurate description of a protein network and (2) applying a Multilayer Perceptron-like and sampling strategy to find all plausible solutions. In the present study, TPMS is applied to explore the diversity of mechanisms of action of the drug combination sacubitril/valsartan. We use TPMS to generate a wide range of models explaining the relationship between sacubitril/valsartan and heart failure (the indication), as well as evaluating their association with macular degeneration (a potential adverse effect). Among the models generated, we identify a set of mechanisms of action associated to a better response in terms of heart failure treatment, which could also be associated to macular degeneration development. Finally, a set of 30 potential biomarkers are proposed to identify mechanisms (or prototype-patients) more prone of suffering macular degeneration when presenting good heart failure response. All prototype-patients models generated are completely theoretical and therefore they do not necessarily involve clinical effects in real patients. Data and accession to software are available at http://sbi.upf.edu/data/tpms/.

Turning liabilities into opportunities: Off-target based drug repurposing in cancer

Targeted drugs and precision medicine have transformed the landscape of cancer therapy and significantly improved patient outcomes in many cases. However, as therapies are becoming more and more tailored to smaller patient populations and acquired resistance is limiting the duration of clinical responses, there is an ever increasing demand for new drugs, which is not easily met considering steadily rising drug attrition rates and development costs. Considering these challenges drug repurposing is an attractive complementary approach to traditional drug discovery that can satisfy some of these needs. This is facilitated by the fact that most targeted drugs, despite their implicit connotation, are not singularly specific, but rather display a wide spectrum of target selectivity. Importantly, some of the unintended drug "off-targets" are known anticancer targets in their own right. Others are becoming recognized as such in the process of elucidating off-target mechanisms that in fact are responsible for a drug's anticancer activity, thereby revealing potentially new cancer vulnerabilities. Harnessing such beneficial off-target effects can therefore lead to novel and promising precision medicine approaches. Here, we will discuss experimental and computational methods that are employed to specifically develop single target and network-based off-target repurposing strategies, for instance with drug combinations or polypharmacology drugs. By illustrating concrete examples that have led to clinical translation we will furthermore examine the various scientific and non-scientific factors that cumulatively determine the success of these efforts and thus can inform the future development of new and potentially lifesaving off-target based drug repurposing strategies for cancers that constitute important unmet medical needs.

Bioinformatics for Renal and Urinary Proteomics: Call for Aggrandization

The clinical sampling of urine is noninvasive and unrestricted, whereby huge volumes can be easily obtained. This makes urine a valuable resource for the diagnoses of diseases. Urinary and renal proteomics have resulted in considerable progress in kidney-based disease diagnosis through biomarker discovery and treatment. This review summarizes the bioinformatics tools available for this area of proteomics and the milestones reached using these tools in clinical research. The scant research publications and the even more limited bioinformatic tool options available for urinary and renal proteomics are highlighted in this review. The need for more attention and input from bioinformaticians is highlighted, so that progressive achievements and releases can be made. With just a handful of existing tools for renal and urinary proteomic research available, this review identifies a gap worth targeting by protein chemists and bioinformaticians. The probable causes for the lack of enthusiasm in this area are also speculated upon in this review. This is the first review that consolidates the bioinformatics applications specifically for renal and urinary proteomics.

Towards reproducible computational drug discovery

The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.

Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work

Drug discovery and development are long and financially taxing processes. On an average it takes 12-15 years and costs 1.2 billion USD for successful drug discovery and approval for clinical use. Many lead molecules are not developed further and their potential is not tapped to the fullest due to lack of resources or time constraints. In order for a drug to be approved by FDA for clinical use, it must have excellent therapeutic potential in the desired area of target with minimal toxicities as supported by both pre-clinical and clinical studies. The targeted clinical evaluations fail to explore other potential therapeutic applications of the candidate drug. Drug repurposing or repositioning is a fast and relatively cheap alternative to the lengthy and expensive de novo drug discovery and development. Drug repositioning utilizes the already available clinical trials data for toxicity and adverse effects, at the same time explores the drug's therapeutic potential for a different disease. This review addresses recent developments and future scope of drug repositioning strategy.

Drug repurposing for breast cancer therapy: Old weapon for new battle

Despite tremendous resources being invested in prevention and treatment, breast cancer remains a leading cause of cancer deaths in women globally. The available treatment modalities are very costly and produces severe side effects. Drug repurposing that relate to new uses for old drugs has emerged as a novel approach for drug development. Repositioning of old, clinically approved, off patent non-cancer drugs with known targets, into newer indication is like using old weapons for new battle. The advances in genomics, proteomics and information computational biology has facilitated the process of drug repurposing. Repositioning approach not only fastens the process of drug development but also offers more effective, cheaper, safer drugs with lesser/known side effects. During the last decade, drugs such as alkylating agents, anthracyclins, antimetabolite, CDK4/6 inhibitor, aromatase inhibitor, mTOR inhibitor and mitotic inhibitors has been repositioned for breast cancer treatment. The repositioned drugs have been successfully used for the treatment of most aggressive triple negative breast cancer. The literature review suggest that serendipity plays a major role in the drug development. This article describes the comprehensive overview of the current scenario of drug repurposing for the breast cancer treatment. The strategies as well as several examples of repurposed drugs are provided. The challenges associated with drug repurposing are discussed.

MAP7 and MUCL1 Are Biomarkers of Vitamin D3-Induced Tolerogenic Dendritic Cells in Multiple Sclerosis Patients

The administration of autologous tolerogenic dendritic cells (tolDC) has become a promising alternative for the treatment of autoimmune diseases, such as multiple sclerosis (MS). Specifically, the use of vitamin D3 for the generation of tolDC (vitD3-tolDC) constitutes one of the most widely studied approaches, as it has evidenced significant immune regulatory properties, both in vitro and in vivo. In this article, we generated human vitD3-tolDC from monocytes from healthy donors and MS patients, characterized in both cases by a semi-mature phenotype, secretion of IL-10 and inhibition of allogeneic lymphocyte proliferation. Additionally, we studied their transcriptomic profile and selected a number of differentially expressed genes compared to control mature and immature dendritic cells for their analysis. Among them, qPCR results validated CYP24A1, MAP7 and MUCL1 genes as biomarkers of vitD3-tolDC in both healthy donors and MS patients. Furthermore, we constructed a network of protein interactions based on the literature, which manifested that MAP7 and MUCL1 genes are both closely connected between them and involved in immune-related functions. In conclusion, this study evidences that MAP7 and MUCL1 constitute robust and potentially functional biomarkers of the generation of vitD3-tolDC, opening the window for their use as quality controls in clinical trials for MS.

A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery

BACKGROUND

Host factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part controllability analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. In this context, controllability analyses aim to identify key regulating host factors of the infected cell's progression. This knowledge can be utilized in further biological analysis to understand disease dynamics and isolate proteins for study as drug target candidates.

RESULTS

Both topological and controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in controllability, therefore playing important roles in cell behavior during infection. Functional analysis finds overlap of results with previous siRNA studies of host factors involved in influenza replication, NF-kB pathway and infection relevance, and roles as interferon regulating genes. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets.

CONCLUSIONS

Seasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year with a constant threat of pandemic infection. This research aims to increase the efficiency of antiviral drug target discovery using existing protein-protein interaction data and network analysis methods. These results are beneficial to future studies of influenza virus, both experimental and computational, and provide evidence that the combination of topology and controllability analyses may be valuable for future efforts in drug target discovery.

Idea2Data: Toward a New Paradigm for Drug Discovery

Increasing the success rate and throughput of drug discovery will require efficiency improvements throughout the process that is currently used in the pharmaceutical community, including the crucial step of identifying hit compounds to act as drivers for subsequent optimization. Hit identification can be carried out through large compound collection screening and often involves the generation and testing of many hypotheses based on available knowledge. In practice, hypothesis generation can involve the selection of promising chemical structures from compound collections using predictive models built from previous screening/assay results. Available physical collections, typically used during hit identification, are of the order of 10⁶ compounds but represent only a small fraction of the small molecule drug-like chemical space. In an effort to survey a larger portion of chemical space and eliminate inefficiencies during hit identification, we introduce a new process, termed Idea2Data (I2D) that tightly integrates computational and experimental components of the drug discovery process. I2D provides the ability to connect a vast virtual collection of compounds readily synthesizable on automated synthesis systems with computational predictive models for the identification of promising structures. This new paradigm enables researchers to process billions of virtual molecules and select structures that can be prepared on automated systems and made available for biological testing, allowing for timely hypothesis testing and follow-up. Since its introduction, I2D has positively impacted several portfolio efforts through identification of new chemical scaffolds and functionalization of existing scaffolds. In this Innovations paper, we describe the I2D process and present an application for the discovery of new ULK inhibitors.

Network-centric medicine for peripheral nerve injury: Treating the whole to boost endogenous mechanisms of neuroprotection and regeneration

Peripheral nerve injuries caused by accidents may lead to paralysis, sensory disturbances, anaesthesia, and lack of autonomic functions. Functional recovery after disconnection of the motoneuronal soma from target tissue with proximal rupture of axons is determined by several factors: motoneuronal soma viability, proper axonal sprouting across inhibitory zones and elongation toward specific muscle, effective synapse contact rebuilding, and prevention of muscle atrophy. Therapies, such as adjuvant drugs with pleiotropic effects, that promote functional recovery after peripheral nerve injury are needed. Toward this aim, we designed a drug discovery workflow based on a network-centric molecular vision using unbiased proteomic data and neural artificial computational tools. Our focus is on boosting intrinsic capabilities of neurons for neuroprotection; this is in contrast to the common approach based on suppression of a pathobiological pathway known to be associated with disease condition. Using our workflow, we discovered neuroheal, a combination of two repurposed drugs that promotes motoneuronal soma neuroprotection, is anti-inflammatory, enhances axonal regeneration after axotomy, and reduces muscle atrophy. This drug discovery workflow has thus yielded a therapy that is close to its clinical application.

Dynamical differential expression (DyDE) reveals the period control mechanisms of the Arabidopsis circadian oscillator

The circadian oscillator, an internal time-keeping device found in most organisms, enables timely regulation of daily biological activities by maintaining synchrony with the external environment. The mechanistic basis underlying the adjustment of circadian rhythms to changing external conditions, however, has yet to be clearly elucidated. We explored the mechanism of action of nicotinamide in Arabidopsis thaliana, a metabolite that lengthens the period of circadian rhythms, to understand the regulation of circadian period. To identify the key mechanisms involved in the circadian response to nicotinamide, we developed a systematic and practical modeling framework based on the identification and comparison of gene regulatory dynamics. Our mathematical predictions, confirmed by experimentation, identified key transcriptional regulatory mechanisms of circadian period and uncovered the role of blue light in the response of the circadian oscillator to nicotinamide. We suggest that our methodology could be adapted to predict mechanisms of drug action in complex biological systems.

Brief Survey of Biological Network Alignment and a Variant with Incorporation of Functional Annotations

Background:

Biological network alignment has been widely studied in the context of protein-protein interaction (PPI) networks, metabolic networks and others in bioinformatics. The topological structure of networks and genomic sequence are generally used by existing methods for achieving this task.

Objective and Method:

Here we briefly survey the methods generally used for this task and introduce a variant with incorporation of functional annotations based on similarity in Gene Ontology (GO). Making full use of GO information is beneficial to provide insights into precise biological network alignment.

Results and Conclusion:

We analyze the effect of incorporation of GO information to network alignment. Finally, we make a brief summary and discuss future directions about this topic.

A Network Pharmacology Approach to Uncover the Molecular Mechanisms of Herbal Formula Ban-Xia-Xie-Xin-Tang

Ban-Xia-Xie-Xin-Tang (BXXXT) is a classical formula from Shang-Han-Lun which is one of the earliest books of TCM clinical practice. In this work, we investigated the therapeutic mechanisms of BXXXT for the treatment of multiple diseases using a network pharmacology approach. Here three BXXXT representative diseases (colitis, diabetes mellitus, and gastric cancer) were discussed, and we focus on in silico methods that integrate drug-likeness screening, target prioritizing, and multilayer network extending. A total of 140 core targets and 72 representative compounds were finally identified to elucidate the pharmacology of BXXXT formula. After constructing multilayer networks, a good overlap between BXXXT nodes and disease nodes was observed at each level, and the network-based proximity analysis shows that the relevance between the formula targets and disease genes was significant according to the shortest path distance (SPD) and a random walk with restart (RWR) based scores for each disease. We found that there were 22 key pathways significantly associated with BXXXT, and the therapeutic effects of BXXXT were likely addressed by regulating a combination of targets in a modular pattern. Furthermore, the synergistic effects among BXXXT herbs were highlighted by elucidating the molecular mechanisms of individual herbs, and the traditional theory of "Jun-Chen-Zuo-Shi" of TCM formula was effectively interpreted from a network perspective. The proposed approach provides an effective strategy to uncover the mechanisms of action and combinatorial rules of BXXXT formula in a holistic manner.

Analyzing of Molecular Networks for Human Diseases and Drug Discovery

Molecular networks represent the interactions and relations of genes/proteins, and also encode molecular mechanisms of biological processes, development and diseases. Among the molecular networks, protein-protein Interaction Networks (PINs) have become effective platforms for uncovering the molecular mechanisms of diseases and drug discovery. PINs have been constructed for various organisms and utilized to solve many biological problems. In human, most proteins present their complex functions by interactions with other proteins, and the sum of these interactions represents the human protein interactome. Especially in the research on human disease and drugs, as an emerging tool, the PIN provides a platform to systematically explore the molecular complexities of specific diseases and the references for drug design. In this review, we summarized the commonly used approaches to aid disease research and drug discovery with PINs, including the network topological analysis, identification of novel pathways, drug targets and sub-network biomarkers for diseases. With the development of bioinformatic techniques and biological networks, PINs will play an increasingly important role in human disease research and drug discovery.