Complex diseases require complex therapies

Artificial intelligence: A new tool in the pathologist's armamentarium for the diagnosis of IBD

Inflammatory bowel diseases (IBD) are classified into two entities, namely Crohn's disease (CD) and ulcerative colitis (UC), which differ in disease trajectories, genetics, epidemiological, clinical, endoscopic, and histopathological aspects. As no single golden standard modality for diagnosing IBD exists, the differential diagnosis among UC, CD, and non-IBD involves a multidisciplinary approach, considering professional groups that include gastroenterologists, endoscopists, radiologists, and pathologists. In this context, histological examination of endoscopic or surgical specimens plays a fundamental role. Nevertheless, in differentiating IBD from non-IBD colitis, the histopathological evaluation of the morphological lesions is limited by sampling and subjective human judgment, leading to potential diagnostic discrepancies. To overcome these limitations, artificial intelligence (AI) techniques are emerging to enable automated analysis of medical images with advantages in accuracy, precision, and speed of investigation, increasing interest in the histological analysis of gastrointestinal inflammation. This review aims to provide an overview of the most recent knowledge and advances in AI methods, summarizing its applications in the histopathological analysis of endoscopic biopsies from IBD patients, and discussing its strengths and limitations in daily clinical practice.

Inflammatory bowel disease genomics, transcriptomics, proteomics and metagenomics meet artificial intelligence

Various extrinsic and intrinsic factors such as drug exposures, antibiotic treatments, smoking, lifestyle, genetics, immune responses, and the gut microbiome characterize ulcerative colitis and Crohn's disease, collectively called inflammatory bowel disease (IBD). All these factors contribute to the complexity and heterogeneity of the disease etiology and pathogenesis leading to major challenges for the scientific community in improving management, medical treatments, genetic risk, and exposome impact. Understanding the interaction(s) among these factors and their effects on the immune system in IBD patients has prompted advances in multi-omics research, the development of new tools as part of system biology, and more recently, artificial intelligence (AI) approaches. These innovative approaches, supported by the availability of big data and large volumes of digital medical datasets, hold promise in better understanding the natural histories, predictors of disease development, severity, complications and treatment outcomes in complex diseases, providing decision support to doctors, and promising to bring us closer to the realization of the "precision medicine" paradigm. This review aims to provide an overview of current IBD omics based on both individual (genomics, transcriptomics, proteomics, metagenomics) and multi-omics levels, highlighting how AI can facilitate the integration of heterogeneous data to summarize our current understanding of the disease and to identify current gaps in knowledge to inform upcoming research in this field.

Focus on Achalasia in the Omics Era

Achalasia is a rare and complex esophageal disease of unknown etiology characterized by difficulty in swallowing due to the lack of opening of the lower esophageal sphincter and the absence of esophageal peristalsis. Recent advancements in technology for analyzing DNA, RNA and biomolecules in high-throughput techniques are offering new opportunities to better understand the etiology and the pathogenetic mechanisms underlying achalasia. Through this narrative review of the scientific literature, we aim to provide a comprehensive assessment of the state-of-the-art knowledge on omics of achalasia, with particular attention to those considered relevant to the pathogenesis of the disease. The notion and importance of the multi-omics approach, its limitations and future directions are also introduced, and it is highlighted how the integration of single omics data will lead to new insights into the development of achalasia and offer clinical tools which will allow early diagnosis and better patient management.

A combination treatment based on drug repurposing demonstrates mutation-agnostic efficacy in pre-clinical retinopathy models

Inherited retinopathies are devastating diseases that in most cases lack treatment options. Disease-modifying therapies that mitigate pathophysiology regardless of the underlying genetic lesion are desirable due to the diversity of mutations found in such diseases. We tested a systems pharmacology-based strategy that suppresses intracellular cAMP and Ca2+ activity via G protein-coupled receptor (GPCR) modulation using tamsulosin, metoprolol, and bromocriptine coadministration. The treatment improves cone photoreceptor function and slows degeneration in Pde6βrd10 and RhoP23H/WT retinitis pigmentosa mice. Cone degeneration is modestly mitigated after a 7-month-long drug infusion in PDE6A-/- dogs. The treatment also improves rod pathway function in an Rpe65-/- mouse model of Leber congenital amaurosis but does not protect from cone degeneration. RNA-sequencing analyses indicate improved metabolic function in drug-treated Rpe65-/- and rd10 mice. Our data show that catecholaminergic GPCR drug combinations that modify second messenger levels via multiple receptor actions provide a potential disease-modifying therapy against retinal degeneration.

Time-dependent ligand-receptor binding kinetics and functionality in a heterodimeric receptor model

GPCRs heteromerize both in CNS and non-CNS regions. The cell uses receptor heteromerization to modulate receptor functionality and to provide fine tuning of receptor signaling. In order for pharmacologists to explore these mechanisms for therapeutic purposes, quantitative receptor models are needed. We have developed a time-dependent model of the binding kinetics and functionality of a preformed heterodimeric receptor involving two drugs. Two cases were considered: both or only one of the drugs are in excess with respect to the total concentration of the receptor. The latter case can be applied to those situations in which a drug causes unwanted side effects that need to be reduced by decreasing its concentration. The required efficacy can be maintained by the allosteric effects mutually exerted by the two drugs in the two-drug combination system. We discuss this concept assuming that the drug causing unwanted side effects is an opioid and that analgesia is the therapeutic effect. As additional points, allosteric modulation by endogenous compounds and synthetic bivalent ligands was included in the study. Receptor heteromerization offers a mechanistic understanding and quantification of the pharmacological effects elicited by combinations of two drugs at different doses and with different efficacies and cooperativity effects, thus providing a conceptual framework for drug combination therapy.

Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine

Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severe major depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate the most adequate and effective treatment for each patient while minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of system biomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD.

Molecular characterization of allergic constitution based on network pharmacology and multi-omics analysis methods

The objective of this study was to identify critical pathways associated with allergic constitution. Shared genes among allergic rhinitis (AR), asthma (AA), and atopic dermatitis (AD) were extracted from the GWAS catalog. RNA-seq data of AR, AA, and AD from gene expression omnibus (GEO) database were preprocessed and subjected to differential gene expression analysis. The differentially expressed genes (DEGs) were merged using the Robust Rank Aggregation (RRA) algorithm. Weighted gene co-expression network analysis (WGCNA) was performed to identify modules associated with allergies. Components of Guominkang (GMK) were obtained from 6 databases and activate components were identified by SwissADME website. Utilizing the SwissTarget Prediction, PharmMapper, SymMap, and HERB, the targets of GMK were predicted and subsequently validated using gene chip data from our team previous study. Differentially expressed proteins (DEPs) related to the allergic constitution were also extracted based on a previous study. Pathway enrichment analysis was performed using KOBAS-i on the GWAS, RRA, WGCNA modules, DEPs, and GMK targets. P values from multi-omics datasets were combined by meta-analysis, and Bonferroni correction was applied. The significant pathways were further validated using Gene Set Enrichment Analysis (GSEA) with intervention data of GMK. The GWAS results yielded 172 genes. Four datasets AR1, AA1, AD1, and AD2 were acquired from GSE75011, GSE125916, and GSE184237. The RRA algorithm identified 19 upregulated and 20 downregulated genes. WGCNA identified 5 significant modules, with the blue and turquoise modules displaying a moderate correlation with allergies. By performing network pharmacology analysis, we identified 127 active ingredients of GMK and predicted 618 targets. Validation using gene chip data confirmed 107 GMK targets. Single-omics pathway analysis was conducted using KOBAS-i, and 39 significant pathways were identified across multiple omics datasets. GSEA analysis using GMK intervention data identified 11 of 39 significant pathways as the final key pathways associated with the allergic constitution. Through multi-omics integrated pathway analysis, we identified 11 critical pathways of allergic constitution, including TH1 and TH2 cell differentiation, TLR cascade, and TH17 cell differentiation. Identifying these pathways suggests that the observed alterations at the pathway level may play significant roles in the molecular characteristics of the allergic constitution.

Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases

Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in-silico drug interventions. Here, we introduce UNAGI, a deep generative neural network tailored to analyze time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modeling and discovery. When applied to a dataset from patients with Idiopathic Pulmonary Fibrosis (IPF), UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation via proteomics reveals the accuracy of UNAGI's cellular dynamics analyses, and the use of the Fibrotic Cocktail treated human Precision-cut Lung Slices confirms UNAGI's predictions that Nifedipine, an antihypertensive drug, may have antifibrotic effects on human tissues. UNAGI's versatility extends to other diseases, including a COVID dataset, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond IPF, amplifying its utility in the quest for therapeutic solutions across diverse pathological landscapes.

Omics and Multi-Omics in IBD: No Integration, No Breakthroughs

The recent advent of sophisticated technologies like sequencing and mass spectroscopy platforms combined with artificial intelligence-powered analytic tools has initiated a new era of "big data" research in various complex diseases of still-undetermined cause and mechanisms. The investigation of these diseases was, until recently, limited to traditional in vitro and in vivo biological experimentation, but a clear switch to in silico methodologies is now under way. This review tries to provide a comprehensive assessment of state-of-the-art knowledge on omes, omics and multi-omics in inflammatory bowel disease (IBD). The notion and importance of omes, omics and multi-omics in both health and complex diseases like IBD is introduced, followed by a discussion of the various omics believed to be relevant to IBD pathogenesis, and how multi-omics "big data" can generate new insights translatable into useful clinical tools in IBD such as biomarker identification, prediction of remission and relapse, response to therapy, and precision medicine. The pitfalls and limitations of current IBD multi-omics studies are critically analyzed, revealing that, regardless of the types of omes being analyzed, the majority of current reports are still based on simple associations of descriptive retrospective data from cross-sectional patient cohorts rather than more powerful longitudinally collected prospective datasets. Given this limitation, some suggestions are provided on how IBD multi-omics data may be optimized for greater clinical and therapeutic benefit. The review concludes by forecasting the upcoming incorporation of multi-omics analyses in the routine management of IBD.

Compartmentalized Polyampholyte Microgels by Depletion Flocculation and Coacervation of Nanogels in Emulsion Droplets

In pH-responsive drug carriers, the distribution of charges has been proven to affect delivery efficiency but is difficult to control and verify. Herein, we fabricate polyampholyte nanogel-in-microgel colloids (NiM-C) and show that the arrangement of the nanogels (NG) can easily be manipulated by adapting synthesis conditions. Positively and negatively charged pH-responsive NG are synthesized by precipitation polymerization and labelled with different fluorescent dyes. The obtained NG are integrated into microgel (MG) networks by subsequent inverse emulsion polymerization in droplet-based microfluidics. By confocal laser scanning microscopy (CLSM), we verify that depending on NG concentration, pH value and ionic strength, NiM-C with different NG arrangements are obtained, including Janus-like phase-separation of NG, statistical distribution of NG, and core-shell arrangements. Our approach is a major step towards uptake and release of oppositely charged (drug) molecules.

RRx-001: a chimeric triple action NLRP3 inhibitor, Nrf2 inducer, and nitric oxide superagonist

RRx-001 is a shape shifting small molecule with Fast Track designation for the prevention/amelioration of chemoradiation-induced severe oral mucositis (SOM) in newly diagnosed Head and Neck cancer. It has been intentionally developed or "engineered" as a chimeric single molecular entity that targets multiple redox-based mechanisms. Like an antibody drug conjugate (ADC), RRx-001 contains, at one end a "targeting" moiety, which binds to the NLRP3 inflammasome and inhibits it as well as Kelch-like ECH-associated protein 1 (KEAP1), the negative regulator of Nrf2, and, at the other end, a conformationally constrained, dinitro containing 4 membered ring, which fragments under conditions of hypoxia and reduction to release therapeutically active metabolites i.e., the payload. This "payload", which is delivered specifically to hypoperfused and inflamed areas, includes nitric oxide, nitric oxide related species and carbon-centered radicals. As observed with ADCs, RRx-001 contains a backbone amide "linker" attached to a binding site, which correlates with the F_ab region of an antibody, and to the dinitroazetidine payload, which is microenvironmentally activated. However, unlike ADCs, whose large size impacts their pharmacokinetic properties, RRx-001 is a nonpolar small molecule that easily crosses cell membranes and the blood brain barrier (BBB) and distributes systemically. This short review is organized around the de novo design and in vivo pro-oxidant/pro-inflammatory and antioxidant/anti-inflammatory activity of RRx-001, which, in turn, depends on the reduced to oxidized glutathione ratio and the oxygenation status of tissues.

A Holistic Perspective on How Photobiomodulation May Influence Fatigue, Pain, and Depression in Inflammatory Bowel Disease: Beyond Molecular Mechanisms

BACKGROUND

Numerous mechanisms, mostly molecular, have been tested and proposed for photobiomodulation. Photobiomodulation is finding a niche in the treatment of conditions that have no gold-standard treatment or only partially effective pharmacological treatment. Many chronic conditions are characterised by symptoms for which there is no cure or control and for which pharmaceuticals may add to the disease burden through side effects. To add quality to life, alternate methods of symptom management need to be identified.

OBJECTIVE

To demonstrate how photobiomodulation, through its numerous mechanisms, may offer an adjunctive therapy in inflammatory bowel disease. Rather than considering only molecular mechanisms, we take an overarching biopsychosocial approach to propose how existing evidence gleaned from other studies may underpin a treatment strategy of potential benefit to people with Crohn's disease and ulcerative colitis.

MAIN FINDINGS

In this paper, the authors have proposed the perspective that photobiomodulation, through an integrated effect on the neuroimmune and microbiome-gut-brain axis, has the potential to be effective in managing the fatigue, pain, and depressive symptoms of people with inflammatory bowel disease.

Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications

De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.

Multidrug-Loaded Lipid Nanoemulsions for the Combinatorial Treatment of Cerebral Cavernous Malformation Disease

Cerebral cavernous malformation (CCM) or cavernoma is a major vascular disease of genetic origin, whose main phenotypes occur in the central nervous system, and is currently devoid of pharmacological therapeutic strategies. Cavernomas can remain asymptomatic during a lifetime or manifest with a wide range of symptoms, including recurrent headaches, seizures, strokes, and intracerebral hemorrhages. Loss-of-function mutations in KRIT1/CCM1 are responsible for more than 50% of all familial cases, and have been clearly shown to affect cellular junctions, redox homeostasis, inflammatory responses, and angiogenesis. In this study, we investigated the therapeutic effects of multidrug-loaded lipid nanoemulsions in rescuing the pathological phenotype of CCM disease. The pro-autophagic rapamycin, antioxidant avenanthramide, and antiangiogenic bevacizumab were loaded into nanoemulsions, with the aim of reducing the major molecular dysfunctions associated with cavernomas. Through Western blot analysis of biomarkers in an in vitro CCM model, we demonstrated that drug-loaded lipid nanoemulsions rescue antioxidant responses, reactivate autophagy, and reduce the effect of pro-angiogenic factors better than the free drugs. Our results show the importance of developing a combinatorial preventive and therapeutic approach to reduce the risk of lesion formation and inhibit or completely revert the multiple hallmarks that characterize the pathogenesis and progression of cavernomas.

Computer-aided Evaluation of Polyvalent Medications' Pharmacological Potential. Multiphytoadaptogen as a Case Study

Many human diseases including cancer, degenerative and autoimmune disorders, diabetes and others are multifactorial. Pharmaceutical agents acting on a single target do not provide their efficient curation. Multitargeted drugs exhibiting pleiotropic pharmacological effects have certain advantages due to the normalization of the complex pathological processes of different etiology. Extracts of medicinal plants (EMP) containing multiple phytocomponents are widely used in traditional medicines for multifactorial disorders' treatment. Experimental studies of pharmacological potential for multicomponent compositions are quite expensive and time-consuming. In silico evaluation of EMP the pharmacological potential may provide the basis for selecting the most promising directions of testing and for identifying potential additive/synergistic effects. Multiphytoadaptogen (MPhA) containing 70 major phytocomponents of different chemical classes from 40 medicinal plant extracts has been studied in vitro, in vivo and in clinical researches. Antiproliferative and anti-tumor activities have been shown against some tumors as well as evidence-based therapeutic effects against age-related pathologies. In addition, the neuroprotective, antioxidant, antimutagenic, radioprotective, and immunomodulatory effects of MPhA were confirmed. Analysis of the PASS profiles of the biological activity of MPhA phytocomponents showed that most of the predicted anti-tumor and anti-metastatic effects were consistent with the results of laboratory and clinical studies. Antimutagenic, immunomodulatory, radioprotective, neuroprotective and anti-Parkinsonian effects were also predicted for most of the phytocomponents. Effects associated with positive effects on the male and female reproductive systems have been identified too. Thus, PASS and PharmaExpert can be used to evaluate the pharmacological potential of complex pharmaceutical compositions containing natural products.

Salvia miltiorrhiza in Breast Cancer Treatment: A Review of Its Phytochemistry, Derivatives, Nanoparticles, and Potential Mechanisms

Breast cancer is one of the most deadly malignancies in women worldwide. Salvia miltiorrhiza, a perennial plant that belongs to the genus Salvia, has long been used in the management of cardiovascular and cerebrovascular diseases. The main anti-breast cancer constituents in S. miltiorrhiza are liposoluble tanshinones including dihydrotanshinone I, tanshinone I, tanshinone IIA, and cryptotanshinone, and water-soluble phenolic acids represented by salvianolic acid A, salvianolic acid B, salvianolic acid C, and rosmarinic acid. These active components have potent efficacy on breast cancer in vitro and in vivo. The mechanisms mainly include induction of apoptosis, autophagy and cell cycle arrest, anti-metastasis, formation of cancer stem cells, and potentiation of antitumor immunity. This review summarized the main bioactive constituents of S. miltiorrhiza and their derivatives or nanoparticles that possess anti-breast cancer activity. Besides, the synergistic combination with other drugs and the underlying molecular mechanisms were also summarized to provide a reference for future research on S. miltiorrhiza for breast cancer treatment.

Results of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD-What, Why, and How

Many diseases that affect modern humans fall in the category of complex diseases, thus called because they result from a combination of multiple aetiological and pathogenic factors. Regardless of the organ or system affected, complex diseases present major challenges in diagnosis, classification, and management. Current forms of therapy are usually applied in an indiscriminate fashion based on clinical information, but even the most advanced drugs only benefit a limited number of patients and to a variable and unpredictable degree. This 'one measure does not fit all' situation has spurred the notion that therapy for complex disease should be tailored to individual patients or groups of patients, giving rise to the notion of 'precision medicine' [PM]. Inflammatory bowel disease [IBD] is a prototypical complex disease where the need for PM has become increasingly clear. This prompted the European Crohn's and Colitis Organisation to focus the Seventh Scientific Workshop on this emerging theme. The articles in this special issue of the Journal address the various complementary aspects of PM in IBD, including what PM is; why it is needed and how it can be used; how PM can contribute to prediction and prevention of IBD; how IBD PM can aid in prognosis and improve response to therapy; and the challenges and future directions of PM in IBD. This first article of this series is structured on three simple concepts [what, why, and how] and addresses the definition of PM, discusses the rationale for the need of PM in IBD, and outlines the methodology required to implement PM in IBD in a correct and clinically meaningful way.

IBD Systems Biology Is Here to Stay

BACKGROUND

Systems biology is a rapidly advancing field of science that allows us to look into disease mechanisms, patient diagnosis and stratification, and drug development in a completely new light. It is based on the utilization of unbiased computational systems free of the traditional experimental approaches based on personal choices of what is important and what select experiments should be performed to obtain the expected results.

METHODS

Systems biology can be applied to inflammatory bowel disease (IBD) by learning basic concepts of omes and omics and how omics-derived "big data" can be integrated to discover the biological networks underlying highly complex diseases like IBD. Once these biological networks (interactomes) are identified, then the molecules controlling the disease network can be singled out and specific blockers developed.

RESULTS

The field of systems biology in IBD is just emerging, and there is still limited information on how to best utilize its power to advance our understanding of Crohn disease and ulcerative colitis to develop novel therapeutic strategies. Few centers have embraced systems biology in IBD, but the creation of international consortia and large biobanks will make biosamples available to basic and clinical IBD investigators for further research studies.

CONCLUSIONS

The implementation of systems biology is indispensable and unavoidable, and the patient and medical communities will both benefit immensely from what it will offer in the near future.

Towards precision nanomedicine for cerebrovascular diseases with emphasis on Cerebral Cavernous Malformation (CCM)

Introduction: Cerebrovascular diseases encompass various disorders of the brain vasculature, such as ischemic/hemorrhagic strokes, aneurysms, and vascular malformations, also affecting the central nervous system leading to a large variety of transient or permanent neurological disorders. They represent major causes of mortality and long-term disability worldwide, and some of them can be inherited, including Cerebral Cavernous Malformation (CCM), an autosomal dominant cerebrovascular disease linked to mutations in CCM1/KRIT1, CCM2, or CCM3/PDCD10 genes.Areas covered: Besides marked clinical and etiological heterogeneity, some commonalities are emerging among distinct cerebrovascular diseases, including key pathogenetic roles of oxidative stress and inflammation, which are increasingly recognized as major disease hallmarks and therapeutic targets. This review provides a comprehensive overview of the different clinical features and common pathogenetic determinants of cerebrovascular diseases, highlighting major challenges, including the pressing need for new diagnostic and therapeutic strategies, and focusing on emerging innovative features and promising benefits of nanomedicine strategies for early detection and targeted treatment of such diseases.Expert opinion: Specifically, we describe and discuss the multiple physico-chemical features and unique biological advantages of nanosystems, including nanodiagnostics, nanotherapeutics, and nanotheranostics, that may help improving diagnosis and treatment of cerebrovascular diseases and neurological comorbidities, with an emphasis on CCM disease.

IL-17A expressed on neutrophil extracellular traps promotes mesenchymal stem cell differentiation toward bone-forming cells in ankylosing spondylitis

Ankylosing spondylitis (AS) is an inflammatory disease characterized by excessive bone formation. We investigated the presence of neutrophil extracellular traps (NETs) in AS and how they are involved in the osteogenic capacity of bone marrow mesenchymal stem cells (MSCs) through interleukin-17A (IL-17A). Peripheral neutrophils and sera were obtained from patients with active AS and healthy controls. NET formation and neutrophil/NET-associated proteins were studied using immunofluorescence, immunoblotting, qPCR, and ELISA. In vitro co-culture systems of AS NET structures and MSCs isolated from controls were deployed to examine the role of NETs in the differentiation of MSCs toward osteogenic cells. Analysis was performed using specific staining and qPCR. Neutrophils from patients with AS were characterized by enhanced formation of NETs carrying bioactive IL-17A and IL-1β. IL-17A-enriched AS NETs mediated the differentiation of MSCs toward bone-forming cells. The neutrophil expression of IL-17A was positively regulated by IL-1β. Blocking IL-1β signaling on neutrophils with anakinra or dismantling NETs using DNase-I disrupted osteogenesis driven by IL-17A-bearing NETs. These findings propose a novel role of neutrophils in AS-related inflammation, linking IL-17A-decorated NETs with the differentiation of MSCs toward bone-forming cells. Moreover, IL-1β triggers the expression of IL-17A on NETs offering an additional therapeutic target in AS.

Design of Dual Inhibitors of Histone Deacetylase 6 and Heat Shock Protein 90

Histone deacetylase 6 (HDAC6) and heat shock protein 90 (Hsp90) are widely investigated anticancer drug targets. Importantly, several lines of evidence indicate that their regulation and activity are intimately linked, and that their combined inhibition may lead to impressive therapeutic benefits. In this study, we developed and applied an integrated computational strategy to design dual inhibitors of HDAC6 and Hsp90. Although the two targets share very little homology, an integrated ligand-based and structure-based virtual screening approach indicated a subset of compounds possessing the key structural requirements for binding at both targets. In vitro tests demonstrated that some of the selected candidates are able to selectively inhibit HDAC6 over HDAC1, to increase the acetylation levels of tubulin on cell assays and to reduce cell proliferation. The discovered compounds represent valuable starting points for further hit optimization.

What's new in IBD therapy: An "omics network" approach

The industrial revolution that began in the late 1800s has resulted in dramatic changes in the environment, human lifestyle, dietary habits, social structure, and so on. Almost certainly because this rapid evolution has outpaced the ability of the body to adapt to a number of environmental and behavioral changes, there has been a parallel emergence of several chronic inflammatory diseases, among which are inflammatory bowel diseases (IBD), primarily ulcerative colitis and Crohn's disease. The ability to treat these conditions has progressively improved in the last 50 years, particularly in the last couple of decades with the introduction of biological therapy targeting primarily soluble mediators produced by inflammatory cells. A large number of biologics are now available, but all of them induce similarly unsatisfactory (<50%) rates of clinical response and remission, and most of them lose efficacy over time, requiring dose escalation or switching from one biologic to another. So, treatment of IBD still needs improvement that will occur only if different approaches are taken. A reason why even the most recent forms of IBD therapy are unsatisfactory is because they target only selected components of an exceedingly complex pathophysiological process, a reality that must be honestly considered if better IBD therapies are to be achieved. Brand new approaches must integrate all relevant factors in their totality - the "omes" - and identify the key controllers of biological responses. This can be accomplished by using systems biology-based approaches and advanced bioinformatics tools, which together represent the essence of network medicine. This review looks at the past and the present of IBD pathogenesis and therapy, and discusses how to develop new therapies based on a network medicine approach.

Pharmacological intervention of histone deacetylase enzymes in the neurodegenerative disorders

Reversal of aging symptoms and related disorders are the challenging task where epigenetic is a crucial player that includes DNA methylation, histone modification; chromatin remodeling and regulation that are linked to the progression of various neurodegenerative disorders (NDDs). Overexpression of various histone deacetylase (HDACs) can activate Glycogen synthase kinase 3 which promotes the hyperphosphorylation of tau and inhibits its degradation. While HDAC is important for maintaining the neuronal morphology and brain homeostasis, at the same time, these enzymes are promoting neurodegeneration, if it is deregulated. Different experimental models have also confirmed the neuroprotective effects caused by HDAC enzymes through the regulation of neuronal apoptosis, inflammatory response, DNA damage, cell cycle regulation, and metabolic dysfunction. Apart from transcriptional regulation, protein-protein interaction, histone post-translational modifications, deacetylation mechanism of non-histone protein and direct association with disease proteins have been linked to neuronal imbalance. Histone deacetylases inhibitors (HDACi) can be able to alter gene expression and shown its efficacy on experimental models, and in clinical trials for NDD's and found to be a very promising therapeutic agent with certain limitation, for instance, non-specific target effect, isoform-selectivity, specificity, and limited number of predicted biomarkers. Herein, we discussed (i) the catalytic mechanism of the deacetylation process of various HDAC's in in vivo and in vitro experimental models, (ii) how HDACs are participating in neuroprotection as well as in neurodegeneration, (iii) a comprehensive role of HDACi in maintaining neuronal homeostasis and (iv) therapeutic role of biomolecules to modulate HDACs.

DeepBindRG: a deep learning based method for estimating effective protein-ligand affinity

Proteins interact with small molecules to modulate several important cellular functions. Many acute diseases were cured by small molecule binding in the active site of protein either by inhibition or activation. Currently, there are several docking programs to estimate the binding position and the binding orientation of protein–ligand complex. Many scoring functions were developed to estimate the binding strength and predict the effective protein–ligand binding. While the accuracy of current scoring function is limited by several aspects, the solvent effect, entropy effect, and multibody effect are largely ignored in traditional machine learning methods. In this paper, we proposed a new deep neural network-based model named DeepBindRG to predict the binding affinity of protein–ligand complex, which learns all the effects, binding mode, and specificity implicitly by learning protein–ligand interface contact information from a large protein–ligand dataset. During the initial data processing step, the critical interface information was preserved to make sure the input is suitable for the proposed deep learning model. While validating our model on three independent datasets, DeepBindRG achieves root mean squared error (RMSE) value of pKa (−logK_d or −logK_i) about 1.6–1.8 and R value around 0.5–0.6, which is better than the autodock vina whose RMSE value is about 2.2–2.4 and R value is 0.42–0.57. We also explored the detailed reasons for the performance of DeepBindRG, especially for several failed cases by vina. Furthermore, DeepBindRG performed better for four challenging datasets from DUD.E database with no experimental protein–ligand complexes. The better performance of DeepBindRG than autodock vina in predicting protein–ligand binding affinity indicates that deep learning approach can greatly help with the drug discovery process. We also compare the performance of DeepBindRG with a 4D based deep learning method “pafnucy”, the advantage and limitation of both methods have provided clues for improving the deep learning based protein–ligand prediction model in the future.

Multifunctional Platinum@BSA-Rapamycin Nanocarriers for the Combinatorial Therapy of Cerebral Cavernous Malformation

Platinum nanoparticles (PtNPs) are antioxidant enzyme-mimetic nanomaterials with significant potential for the treatment of complex diseases related to oxidative stress. Among such diseases, Cerebral Cavernous Malformation (CCM) is a major cerebrovascular disorder of genetic origin, which affects at least 0.5% of the general population. Accumulated evidence indicates that loss-of-function mutations of the three known CCM genes predispose endothelial cells to oxidative stress-mediated dysfunctions by affecting distinct redox-sensitive signaling pathways and mechanisms, including pro-oxidant and antioxidant pathways and autophagy. A multitargeted combinatorial therapy might thereby represent a promising strategy for the effective treatment of this disease. Herein, we developed a multifunctional nanocarrier by combining the radical scavenging activity of PtNPs with the autophagy-stimulating activity of rapamycin (Rapa). Our results show that the combinatorial targeting of redox signaling and autophagy dysfunctions is effective in rescuing major molecular and cellular hallmarks of CCM disease, suggesting its potential for the treatment of this and other oxidative stress-related diseases.

Systems Pharmacology: Defining the Interactions of Drug Combinations

The majority of diseases are associated with alterations in multiple molecular pathways and complex interactions at the cellular and organ levels. Single-target monotherapies therefore have intrinsic limitations with respect to their maximum therapeutic benefits. The potential of combination drug therapies has received interest for the treatment of many diseases and is well established in some areas, such as oncology. Combination drug treatments may allow us to identify synergistic drug effects, reduce adverse drug reactions, and address variability in disease characteristics between patients. Identification of combination therapies remains challenging. We discuss current state-of-the-art systems pharmacology approaches to enable rational identification of combination therapies. These approaches, which include characterization of mechanisms of disease and drug action at a systems level, can enable understanding of drug interactions at the molecular, cellular, physiological, and organismal levels. Such multiscale understanding can enable precision medicine by promoting the rational development of combination therapy at the level of individual patients for many diseases.

Autophagy in Neutrophils: From Granulopoiesis to Neutrophil Extracellular Traps

Autophagy is an evolutionarily conserved intracellular degradation system aiming to maintain cell homeostasis in response to cellular stress. At physiological states, basal or constitutive level of autophagy activity is usually low; however, it is markedly up-regulated in response to oxidative stress, nutrient starvation, and various immunological stimuli including pathogens. Many studies over the last years have indicated the implication of autophagy in a plethora of cell populations and functions. In this review, we focus on the role of autophagy in the biology of neutrophils. Early studies provided a link between autophagy and neutrophil cell death, a process essential for resolution of inflammation. Since then, several lines of evidence both in the human system and in murine models propose a critical role for autophagy in neutrophil-driven inflammation and defense against pathogens. Autophagy is essential for major neutrophil functions, including degranulation, reactive oxygen species production, and release of neutrophil extracellular traps. Going back to neutrophil generation in the bone marrow, autophagy plays a critical role in myelopoiesis, driving the differentiation of progenitor cells of the myeloid lineage toward neutrophils. Taken together, in this review we discuss the functional role of autophagy in neutrophils throughout their life, from their production in the bone marrow to inflammatory responses and NETotic cell death.

Delivering Precision Medicine and Patient-Centred Care Through a Multidisciplinary Approach

The current treatment strategy for patients with inflammatory bowel disease (IBD) aims to enable physicians to deliver optimal care and to improve the role that patients play in treatment decisions. The multidisciplinary team (MDT) approach integrates the patient’s perspective and sees the discussion of treatment options with both gastroenterologists and surgeons as early as possible. The MDT approach is also vital in managing the risk of IBD and cardiovascular-related comorbidities in patients with psoriasis (PsO) and psoriatic arthritis (PsA), where selection of appropriate medication may affect both the rheumatic condition and the associated comorbidity. Close interdisciplinary interactions between gastroenterologists, rheumatologists, and/or dermatologists are vital, and the ensuing knowledge transfer facilitates the provision of optimal patient care. Personalised medicine will have a profound impact on future treatment algorithms in IBD and other chronic inflammatory conditions. Owing to the complexity of these diseases, a novel approach is urgently needed that will aggregate data from multiple systems and integrate it into a so-called ‘IBD interactome’. This may help identify and target the key molecular components responsible for inflammation. Future treatment practices will also address the psychosocial aspects of IBD by empowering patients and integrating their perspective into the shared treatment decision-making process early on.

Network Medicine: A Mandatory Next Step for Inflammatory Bowel Disease

Despite unquestionable progress in the management of inflammatory bowel disease (IBD) and the much improved clinical results achievable today in Crohn's disease (CD) and ulcerative colitis (UC) patients, the overall therapeutic outcome remains far from optimal. The main reason of this partial success is that all current medications only block individual components of a highly complex disease process that results from the integration of multiple and incompletely identified pathogenic components. Thus, if further progress is to be achieved in IBD therapeutics and we want to move from the current success rate to nearly 100%, bold new ideas must be entertained and new approaches put into practice. Both are necessary because in IBD we are dealing with a prototypical complex disease superimposed to the background of the extreme biological diversity of humans in response to injury. An unresolved challenge mandates the adoption of new solutions specifically designed to address the unique features of that challenge. Translated to a disease condition, and IBD in particular, the unresolved challenges of CD and UC demand bold new thinking leading to the conception and implementation of totally innovative therapies. In this article, we propose that one such new thinking is the notion of network medicine for IBD, and that the development of brand new treatments should be based on the identification of the molecular structure of the IBD interactome with the purpose of targeting its controlling elements (central nodes or hubs). This specific targeting of the underlying molecular disease modules will lead to the disruption of the IBD interactome and foster the resolution of intestinal inflammatory process.

Human aging and disease: Lessons from age-related macular degeneration

Aging is the most significant risk factor associated with chronic disease in humans. The accumulation of genetic damage throughout life leads to a variety of biological aberrations, including disrupted protein homeostasis, metabolic dysfunction, and altered cellular signaling. Such changes ultimately result in cellular senescence, death, or transformation to uncontrolled proliferation, thereby compromising human health. Events contributing to age-dependent physiological decline also occur in the context of hormonal and metabolic changes, affecting interconnected cellular networks. This complexity often confounds the development of effective treatments for aging and age-related diseases. In contrast to monotherapy and polypharmacology, an innovative systems pharmacology approach can identify synergistic combinations of drugs that modulate distinct mechanistic nodes within a network, minimizing off-target side effects and enabling better therapeutic outcomes. G protein-coupled receptors (GPCRs) are particularly good targets for the application of systems pharmacology, because they activate different signal transduction pathways that can culminate in a common response. Here, we describe a systems pharmacology strategy for the treatment of age-related macular degeneration (AMD), a multifactorial chronic disease of the eye. By considering the retina as part of a large, interconnected network, systems pharmacology will enable the identification of combination therapies targeting GPCRs to help restore genomic, proteomic, and endocrine homeostasis. Such an approach can be advantageous in providing drug regimens for the treatment of AMD, while also having broader ramifications for ameliorating adverse effects of chronic, age-related disease in humans.

The IBD interactome: an integrated view of aetiology, pathogenesis and therapy

Crohn's disease and ulcerative colitis are prototypical complex diseases characterized by chronic and heterogeneous manifestations, induced by interacting environmental, genomic, microbial and immunological factors. These interactions result in an overwhelming complexity that cannot be tackled by studying the totality of each pathological component (an '-ome') in isolation without consideration of the interaction among all relevant -omes that yield an overall 'network effect'. The outcome of this effect is the 'IBD interactome', defined as a disease network in which dysregulation of individual -omes causes intestinal inflammation mediated by dysfunctional molecular modules. To define the IBD interactome, new concepts and tools are needed to implement a systems approach; an unbiased data-driven integration strategy that reveals key players of the system, pinpoints the central drivers of inflammation and enables development of targeted therapies. Powerful bioinformatics tools able to query and integrate multiple -omes are available, enabling the integration of genomic, epigenomic, transcriptomic, proteomic, metabolomic and microbiome information to build a comprehensive molecular map of IBD. This approach will enable identification of IBD molecular subtypes, correlations with clinical phenotypes and elucidation of the central hubs of the IBD interactome that will aid discovery of compounds that can specifically target the hubs that control the disease.

A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation

Multiple physiological systems interact throughout the development of a complex disease. Knowledge of the dynamics and connectivity of interactions across physiological systems could facilitate the prevention or mitigation of organ damage underlying complex diseases, many of which are currently refractory to available therapeutics (e.g., hypertension). We studied the regulatory interactions operating within and across organs throughout disease development by integrating in vivo analysis of gene expression dynamics with a reverse engineering approach to infer data-driven dynamic network models of multi-organ gene regulatory influences. We obtained experimental data on the expression of 22 genes across five organs, over a time span that encompassed the development of autonomic nervous system dysfunction and hypertension. We pursued a unique approach for identification of continuous-time models that jointly described the dynamics and structure of multi-organ networks by estimating a sparse subset of ∼12,000 possible gene regulatory interactions. Our analyses revealed that an autonomic dysfunction-specific multi-organ sequence of gene expression activation patterns was associated with a distinct gene regulatory network. We analyzed the model structures for adaptation motifs, and identified disease-specific network motifs involving genes that exhibited aberrant temporal dynamics. Bioinformatic analyses identified disease-specific single nucleotide variants within or near transcription factor binding sites upstream of key genes implicated in maintaining physiological homeostasis. Our approach illustrates a novel framework for investigating the pathogenesis through model-based analysis of multi-organ system dynamics and network properties. Our results yielded novel candidate molecular targets driving the development of cardiovascular disease, metabolic syndrome, and immune dysfunction.

Complex diseases such as hypertension often involve maladaptive autonomic nervous system control over the cardiovascular, renal, hepatic, immune, and endocrine systems. We studied the pathogenesis of physiological homeostasis by examining the temporal dynamics of gene expression levels from multiple organs in an animal model of autonomic dysfunction characterized by cardiovascular disease, metabolic dysregulation, and immune system aberrations. We employed a data-driven modeling approach to jointly predict continuous gene expression dynamics and gene regulatory interactions across organs in the disease and control phenotypes. We combined our analyses of multi-organ gene regulatory network dynamics and connectivity with bioinformatic analyses of genetic mutations that could regulate gene expression. Our multi-organ modeling approach to investigate the mechanisms of complex disease pathogenesis revealed novel candidates for therapeutic interventions against the development and progression of complex diseases involving autonomic nervous system dysfunction.

Deciphering the Mechanism of Action of Wrightia tinctoria for Psoriasis Based on Systems Pharmacology Approach

OBJECTIVE

Psoriasis is a chronic immune-mediated disorder of the skin. The disease manifests itself with red or silvery scaly plaques distributing over the lower back, scalp, and extensor aspects of limbs. Several medications are available for the treatment of psoriasis; however, high rates of remission and side-effects still persist as a major concern. Siddha, one of the traditional systems of Indian medicine offers cure to many dermatological conditions, including psoriasis. The oil prepared from the leaves of Wrightia tinctoria is prescribed by many healers for the treatment of psoriasis. This work aims to decipher the mechanism of action of the W. tinctoria in curing psoriasis and its associated comorbidities.

DESIGN

The work integrates various pharmacology approaches such as drug-likeness evaluation, oral bioavailability predictions, and network pharmacology approaches to understand the roles of various bioactive components of the herb.

RESULTS

This work identified 67 compounds of W. tinctoria interacting with 238 protein targets. The compounds were found to act through synergistic mechanism in reviving the disrupted process in the diseased state.

CONCLUSION

The results of this work not only shed light on the pharmacological action of the herb but also validate the usage of safe herbal drugs.

Lessons learned from twins in autoimmune and chronic inflammatory diseases

Autoimmunity and chronic inflammation recognize numerous shared factors and, as a result, the resulting diseases frequently coexist in the same patients or respond to the same treatments. Among the convenient truths of autoimmune and chronic inflammatory diseases, there is now agreement that these are complex conditions in which the individual genetic predisposition provides a rate of heritability. The concordance rates in monozygotic and dizygotic twins allows to estimate the weight of the environment in determining disease susceptibility, despite recent data supporting that only a minority of immune markers depend on hereditary factors. Concordance rates in monozygotic and dizygotic twins should be evaluated over an observation period to minimize the risk of false negatives and this is well represented by type I diabetes mellitus. Further, concordance rates in monozygotic twins should be compared to those in dizygotic twins, which share 50% of their genes, as in regular siblings, but also young-age environmental factors. Twin studies have been extensively performed in several autoimmune conditions and cumulatively suggest that some diseases, i.e. celiac disease and psoriasis, are highly genetically determined, while rheumatoid arthritis or systemic sclerosis have a limited role for genetics. These observations are necessary to interpret data gathered by genome-wide association studies of polymorphisms and DNA methylation in MZ twins. New high-throughput technological platforms are awaited to provide new insights into the mechanisms of disease discordance in twins beyond strong associations such as those with HLA alleles.

Modeling cytokine regulatory network dynamics driving neuroinflammation in central nervous system disorders

A central goal of pharmacological efforts to treat central nervous system (CNS) diseases is to develop systemic therapeutics that can restore CNS homeostasis. Achieving this goal requires a fundamental understanding of CNS function within the organismal context so as to leverage the mechanistic insights on the molecular basis of cellular and tissue functions towards novel drug target identification. The immune system constitutes a key link between the periphery and CNS, and many neurological disorders and neurodegenerative diseases are characterized by immune dysfunction. We review the salient opportunities for applying computational models to CNS disease research, and summarize relevant approaches from studies of immune function and neuroinflammation. While the accurate prediction of disease-related phenomena is often considered the central goal of modeling studies, we highlight the utility of computational modeling applications beyond making predictions, particularly for drawing counterintuitive insights from model-based analysis of multi-parametric and time series data sets.

Synergistically acting agonists and antagonists of G protein-coupled receptors prevent photoreceptor cell degeneration

Photoreceptor cell degeneration leads to visual impairment and blindness in several types of retinal disease. However, the discovery of safe and effective therapeutic strategies conferring photoreceptor cell protection remains challenging. Targeting distinct cellular pathways with low doses of different drugs that produce a functionally synergistic effect could provide a strategy for preventing or treating retinal dystrophies. We took a systems pharmacology approach to identify potential combination therapies using a mouse model of light-induced retinal degeneration. We showed that a combination of U.S. Food and Drug Administration-approved drugs that act on different G protein (heterotrimeric guanine nucleotide-binding protein)-coupled receptors (GPCRs) exhibited synergistic activity that protected retinas from light-induced degeneration even when each drug was administered at a low dose. In functional assays, the combined effects of these drugs were stimulation of Gi/o signaling by activating the dopamine receptors D2R and D4R, as well as inhibition of Gs and Gq signaling by antagonizing D1R and the α1A-adrenergic receptor ADRA1A, respectively. Moreover, transcriptome analyses demonstrated that such combined GPCR-targeted treatments preserved patterns of retinal gene expression that were more similar to those of the normal retina than did higher-dose monotherapy. Our study thus supports a systems pharmacology approach to identify treatments for retinopathies, an approach that could extend to other complex disorders.

Systems Pharmacology and Rational Polypharmacy: Nitric Oxide-Cyclic GMP Signaling Pathway as an Illustrative Example and Derivation of the General Case

Impaired nitric oxide (NO˙)-cyclic guanosine 3', 5'-monophosphate (cGMP) signaling has been observed in many cardiovascular disorders, including heart failure and pulmonary arterial hypertension. There are several enzymatic determinants of cGMP levels in this pathway, including soluble guanylyl cyclase (sGC) itself, the NO˙-activated form of sGC, and phosphodiesterase(s) (PDE). Therapies for some of these disorders with PDE inhibitors have been successful at increasing cGMP levels in both cardiac and vascular tissues. However, at the systems level, it is not clear whether perturbation of PDE alone, under oxidative stress, is the best approach for increasing cGMP levels as compared with perturbation of other potential pathway targets, either alone or in combination. Here, we develop a model-based approach to perturbing this pathway, focusing on single reactions, pairs of reactions, or trios of reactions as targets, then monitoring the theoretical effects of these interventions on cGMP levels. Single perturbations of all reaction steps within this pathway demonstrated that three reaction steps, including the oxidation of sGC, NO˙ dissociation from sGC, and cGMP degradation by PDE, exerted a dominant influence on cGMP accumulation relative to other reaction steps. Furthermore, among all possible single, paired, and triple perturbations of this pathway, the combined perturbations of these three reaction steps had the greatest impact on cGMP accumulation. These computational findings were confirmed in cell-based experiments. We conclude that a combined perturbation of the oxidatively-impaired NO˙-cGMP signaling pathway is a better approach to the restoration of cGMP levels as compared with corresponding individual perturbations. This approach may also yield improved therapeutic responses in other complex pharmacologically amenable pathways.

Developing drugs for a well-defined biochemical or molecular pathway has conventionally been approached by optimizing the inhibition (or activation) of a single target by a single pharmacologic agent. On occasion, drug combinations have been used that generally target multiple pathways affecting a common phenotype, again by optimizing the extent of inhibition of individual targets, semi-empirically adjusting their doses to minimize toxicities as they are manifest. Here, we present a computational approach for identifying optimal combinations of agents that can affect (inhibit) a well-defined biochemical pathway, doing so at minimal combined concentrations, thereby potentially minimizing dose-dependent toxicities. This approach is illustrated computationally and experimentally with a well-known pathway, the nitric oxide-cyclic GMP pathway, but is readily generalizable to rational polypharmacy.

Network and systems biology: essential steps in virtualising drug discovery and development

The biological processes that keep us healthy or cause disease, as well as the mechanisms of action of possible drugs are inherently complex. In the face of this complexity, attempts at discovering new drugs to treat diseases have alternated between trial-and-error (typically on experimental systems) and grand simplification, usually based on much too little information. We now have the chance to combine these strategies through establishment of 'virtual patient' models, centred on a detailed molecular characterisation of thousands or even, in the future, millions of patients. In doing so, we lay the foundations for truly personalised therapy, as well as a far-reaching virtualisation of drug discovery and development in oncology and other areas of medicine.

Systems Pharmacology Links GPCRs with Retinal Degenerative Disorders

In most biological systems, second messengers and their key regulatory and effector proteins form links between multiple cellular signaling pathways. Such signaling nodes can integrate the deleterious effects of genetic aberrations, environmental stressors, or both in complex diseases, leading to cell death by various mechanisms. Here we present a systems (network) pharmacology approach that, together with transcriptomics analyses, was used to identify different G protein-coupled receptors that experimentally protected against cellular stress and death caused by linked signaling mechanisms. We describe the application of this concept to degenerative and diabetic retinopathies in appropriate mouse models as an example. Systems pharmacology also provides an attractive framework for devising strategies to combat complex diseases by using (repurposing) US Food and Drug Administration-approved pharmacological agents.

Two diseases, one approach: multitarget drug discovery in Alzheimer's and neglected tropical diseases

Multitarget drug discovery may represent a promising therapeutic approach to treat Alzheimer's and neglected tropical diseases.