Connecting the dots: applications of network medicine in pharmacology and disease

Discovery of natural multi-targets neuraminidase inhibitor glycosides compounds against influenza A virus through network pharmacology, virtual screening, molecular dynamics simulation, and in vitro experiment

Influenza virus continually challenges both human and animal health. Moreover, influenza viruses are easy to mutate. In a certain degree, vaccines may not catch up with rapid mutant paces of viruses. Anti-influenza drugs NIs (neuraminidase inhibitors) are one of the best choices. Therefore, based on ADMET properties, eight optimal natural multi-targets NIs glycosides compounds (IC50  = 0.094-97.275 μM) are found from radix glycyrrhizae, flos sophorae, caulis spatholobi, radix astragali, radix glycyrrhizae, semen astragali complanati, and common fenugreek seed through network pharmacology, molecular docking, dynamics simulation, quantum chemistry, and in vitro experiment. Moreover, mechanism research illustrates these natural compounds treat influenza A virus through key targets TLR4, TNF, and IL6 (high fever, acute respiratory distress syndrome), MAPK1, and MAPK3 (MAPK signaling pathway, viral RNP export, and viral protein expression), IL1B (NOD-like receptor signaling pathway, suppressed maturation of pro-IL-1β and pro-IL-18), CASP3 (apoptosis), AKT1 (inhibited premature apoptosis), and EP300 (viral myocarditis, chemoattraction of monocytes and macrophages, T-cell activation antibody response).

Ferulic Acid and P-Coumaric Acid Synergistically Attenuate Non-Alcoholic Fatty Liver Disease through HDAC1/PPARG-Mediated Free Fatty Acid Uptake

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease and has become a growing public health concern worldwide. Polyphenols may improve high-fat diet (HFD)-related NAFLD. Our previous study found that ferulic acid (FA) and p-coumaric acid (p-CA) were the polyphenols with the highest content in foxtail millet. In this study, we investigated the mechanism underlying the impact of ferulic acid and p-coumaric acid (FA/p-CA) on non-alcoholic fatty liver (NAFLD). The association of FA and p-CA with fatty liver was first analyzed by network pharmacology. Synergistic ameliorating of NAFLD by FA and p-CA was verified in oleic acid (OA) and palmitic acid (PA) (FFA)-treated hepatocytes. Meanwhile, FA/p-CA suppressed final body weight and TG content and improved liver dysfunction in HFD-induced NAFLD mice. Mechanistically, our data indicated that FA and p-CA bind to histone deacetylase 1 (HDAC1) to inhibit its expression. The results showed that peroxisome proliferator activated receptor gamma (PPARG), which is positively related to HDAC1, was inhibited by FA/p-CA, and further suppressed fatty acid binding protein (FABP) and fatty acid translocase (CD36). It suggests that FA/p-CA ameliorate NAFLD by inhibiting free fatty acid uptake via the HDAC1/PPARG axis, which may provide potential dietary supplements and drugs for prevention of NAFLD.

Aucubin as a natural potential anti-acute hepatitis candidate: Inhibitory potency and hepatoprotective mechanism

BACKGROUND

Hepatic inflammation can substantially impact the development of acute hepatitis. It is a pressing need to identify and exploit novel therapeutic targets as well as effective drug therapies against acute hepatitis. Aucubin (AU) is one of the main active components extracted from the leaves of Eucommia ulmoides and possesses significant anti-inflammatory and antioxidant activities. However, the protective effect and mechanism of AU on acute hepatitis have not been reported yet.

PURPOSE

This study aims to investigate the protective effect of AU on LPS-induced acute hepatitis and the mechanism of action.

METHODS

The limma package was used to analyze differentially expressed genes (DEGs) between LPS-induced acute hepatitis and normal groups based on Gene Expression Omnibus (GEO) microarray data. Network pharmacology predicted targets for AU therapy against acute hepatitis, and Gene Ontology (GO) enrichment analysis of the biological processes involved in these targets. The key pathways were analyzed by protein-protein interaction, KEGG (Kyoto Encyclopedia of Genes and Genomes), and GSEA (Gene Set Enrichment Analysis) enrichment. The important interaction targets between AU and key pathways were evaluated by molecular simulation. The in silico predicted mechanism was verified based on in vitro and in vivo experiments.

RESULTS

A total of 116 intersection targets between AU prediction targets and differentially expressed genes were identified. They were functionally involved in the imbalance of "inflammation-anti-inflammation" and "oxidation-antioxidation" systems in the process of LPS-induced cases. In vitro experiments revealed that AU reduced inflammation in LPS-induced HepG2 cells by reducing the inflammatory cytokines TNF-α, IL-6, as well as iNOS enzyme activity levels. In addition, LPS-induced oxidative stress can be alleviated by AU via adjusting the levels of superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), Malone dialdehyde (MDA) and reactive oxygen species (ROS). Protein-protein interaction and GSEA results showed that AU might exert anti-inflammatory effects mainly through the STAT3/NF-κB signal pathway. Molecular dynamics simulation as well as in vivo tests further demonstrated AU restrained nuclear transfer of NF-κB (P65), probably through reducing phosphorylation of STAT3. In addition, AU appears to reduce oxidative stress by upregulating NRF2/HO-1.

CONCLUSION

We explored potential targets and signal pathways of AU in inhibiting acute hepatitis. AU exerted anti-inflammatory and antioxidant activities and may be a useful candidate drug for the treatment of acute hepatitis.

Advancing drug safety science by integrating molecular knowledge with post-marketing adverse event reports

Promising drug development efforts may frequently fail due to unintended adverse reactions. Several methods have been developed to analyze such data, aiming to improve pharmacovigilance and drug safety. In this work, we provide a brief review of key directions to quantitatively analyzing adverse events and explore the potential of augmenting these methods using additional molecular data descriptors. We argue that molecular expansion of adverse event data may provide a path to improving the insights gained through more traditional pharmacovigilance approaches. Examples include the ability to assess statistical relevance with respect to underlying biomolecular mechanisms, the ability to generate plausible causative hypotheses and/or confirmation where possible, the ability to computationally study potential clinical trial designs and/or results, as well as the further provision of advanced features incorporated in innovative methods, such as machine learning. In summary, molecular data expansion provides an elegant way to extend mechanistic modeling, systems pharmacology, and patient‐centered approaches for the assessment of drug safety. We anticipate that such advances in real‐world data informatics and outcome analytics will help to better inform public health, via the improved ability to prospectively understand and predict various types of drug‐induced molecular perturbations and adverse events.

Exploration in the Therapeutic and Multi-Target Mechanism of Ketamine on Cerebral Ischemia Based on Network Pharmacology and Molecular Docking

Background

Ketamine is famous for its dissociative anesthetic properties. It is also analgesic, anti-inflammatory and anti-depressant, and even has a cerebral protective effect. We searched the evidence of the correlation between ketamine target and clinical efficacy and utilized network pharmacology to gather information about the multi-target mechanism of ketamine against cerebral ischemia (CI). We found that ketamine’s clinical significance may be more extensive than previously thought.

Methods

The drug target of ketamine and CI-related genes were predicted by SwissTargetPrediction, DrugBank, PubChem, GeneCards and DisGeNET databases. The intersection of ketamine’s drug-targets and CI-related genes was analyzed by using GO and KEGG. We predicted the molecular docking between the potential target and ketamine.

Results

The results indicated that the effect of ketamine on CI was primarily associated with the target of α-synuclein (SNCA), muscarinic acetylcholine receptor M1 (CHRM1) and nitric oxide synthase 1 (NOS1). It principally regulates the signal pathways of circadian transmission, calcium signaling pathway, dopaminergic synapse, cholinergic synapse and glutamatergic synapse. Molecular docking analysis exhibited that hydrogen bond and Pi-Pi interaction were the predominant modes of interaction.

Conclusion

There are protein targets affected by ketamine in the treatment of CI. Three pivotal targets involving 298 proteins, SNCA, CHRM1 and NOS1, have emerged as multi-target mechanisms for ketamine in CI therapy. Similarly, this study also provides a new idea for introducing network pharmacology into the evaluation of multi-targeted drugs for CI and cerebral protection.

An Investigation into the Temporal Reproducibility of Tryptophan Metabolite Networks Among Healthy Adolescents

Tryptophan and its bioactive metabolites are associated with health conditions such as systemic inflammation, cardiometabolic diseases, and neurodegenerative disorders. There are dynamic interactions among metabolites of tryptophan. The interactions between metabolites, particularly those that are strong and temporally reproducible could be of pathophysiological relevance. Using a targeted metabolomics approach, the concentration levels of tryptophan and 18 of its metabolites across multiple pathways was quantified in 24-hours urine samples at 2 time-points, age 17 years (baseline) and 18 years (follow-up) from 132 (52% female) apparently healthy adolescent participants of the DOrtmund Nutritional and Anthropometric Longitudinally Designed (DONALD) Study. In sex-specific analyses, we applied 2 network approaches, the Gaussian graphical model and Bayesian network to (1) explore the network structure for both time-points, (2) retrieve strongly related metabolites, and (3) determine whether the strongly related metabolites were temporally reproducible. Independent of selected covariates, the 2 network approaches revealed 5 associations that were strong and temporally reproducible. These were novel relationships, between kynurenic acid and indole-3-acetic acid in females and between kynurenic acid and xanthurenic acid in males, as well as known relationships between kynurenine and 3-hydroxykynurenine, and between 3-hydroxykynurenine and 3-hydroxyanthranilic acid in females and between tryptophan and kynurenine in males. Overall, this epidemiological study using network-based approaches shed new light into tryptophan metabolism, particularly the interaction of host and microbial metabolites. The 5 observed relationships suggested the existence of a temporally stable pattern of tryptophan and 6 metabolites in healthy adolescent, which could be further investigated in search of fingerprints of specific physiological states. The metabolites in these relationships may represent a multi-biomarker panel that could be informative for health outcomes.

In Vitro and In Vivo Pipeline for Validation of Disease-Modifying Effects of Systems Biology-Derived Network Treatments for Traumatic Brain Injury-Lessons Learned

We developed a pipeline for the discovery of transcriptomics-derived disease-modifying therapies and used it to validate treatments in vitro and in vivo that could be repurposed for TBI treatment. Desmethylclomipramine, ionomycin, sirolimus and trimipramine, identified by in silico LINCS analysis as candidate treatments modulating the TBI-induced transcriptomics networks, were tested in neuron-BV2 microglial co-cultures, using tumour necrosis factor α as a monitoring biomarker for neuroinflammation, nitrite for nitric oxide-mediated neurotoxicity and microtubule associated protein 2-based immunostaining for neuronal survival. Based on (a) therapeutic time window in silico, (b) blood-brain barrier penetration and water solubility, (c) anti-inflammatory and neuroprotective effects in vitro (p < 0.05) and (d) target engagement of Nrf2 target genes (p < 0.05), desmethylclomipramine was validated in a lateral fluid-percussion model of TBI in rats. Despite the favourable in silico and in vitro outcomes, in vivo assessment of clomipramine, which metabolizes to desmethylclomipramine, failed to demonstrate favourable effects on motor and memory tests. In fact, clomipramine treatment worsened the composite neuroscore (p < 0.05). Weight loss (p < 0.05) and prolonged upregulation of plasma cytokines (p < 0.05) may have contributed to the worsened somatomotor outcome. Our pipeline provides a rational stepwise procedure for evaluating favourable and unfavourable effects of systems-biology discovered compounds that modulate post-TBI transcriptomics.

The Next Generation of Drug Safety Science: Coupling Detection, Corroboration, and Validation to Discover Novel Drug Effects and Drug-Drug Interactions

Rare adverse drug reactions and drug-drug interactions (DDIs) are difficult to detect in randomized trials and impossible to prove using observational studies. We must ascribe to a new way of conducting research that has the efficiency of a retrospective analysis and the rigor of a prospective trial. This can be achieved by integrating observational data from humans with laboratory experiments in model systems. The former establishes clinical significance and the latter supports causality.

From gene networks to drugs: systems pharmacology approaches for AUD

The alcohol research field has amassed an impressive number of gene expression datasets spanning key brain areas for addiction, species (humans as well as multiple animal models), and stages in the addiction cycle (binge/intoxication, withdrawal/negative effect, and preoccupation/anticipation). These data have improved our understanding of the molecular adaptations that eventually lead to dysregulation of brain function and the chronic, relapsing disorder of addiction. Identification of new medications to treat alcohol use disorder (AUD) will likely benefit from the integration of genetic, genomic, and behavioral information included in these important datasets. Systems pharmacology considers drug effects as the outcome of the complex network of interactions a drug has rather than a single drug-molecule interaction. Computational strategies based on this principle that integrate gene expression signatures of pharmaceuticals and disease states have shown promise for identifying treatments that ameliorate disease symptoms (called in silico gene mapping or connectivity mapping). In this review, we suggest that gene expression profiling for in silico mapping is critical to improve drug repurposing and discovery for AUD and other psychiatric illnesses. We highlight studies that successfully apply gene mapping computational approaches to identify or repurpose pharmaceutical treatments for psychiatric illnesses. Furthermore, we address important challenges that must be overcome to maximize the potential of these strategies to translate to the clinic and improve healthcare outcomes.

A protein network descriptor server and its use in studying protein, disease, metabolic and drug targeted networks

The genetic, proteomic, disease and pharmacological studies have generated rich data in protein interaction, disease regulation and drug activities useful for systems-level study of the biological, disease and drug therapeutic processes. These studies are facilitated by the established and the emerging computational methods. More recently, the network descriptors developed in other disciplines have become more increasingly used for studying the protein-protein, gene regulation, metabolic, disease networks. There is an inadequate coverage of these useful network features in the public web servers. We therefore introduced upto 313 literature-reported network descriptors in PROFEAT web server, for describing the topological, connectivity and complexity characteristics of undirected unweighted (uniform binding constants and molecular levels), undirected edge-weighted (varying binding constants), undirected node-weighted (varying molecular levels), undirected edge-node-weighted (varying binding constants and molecular levels) and directed unweighted (oriented process) networks. The usefulness of the PROFEAT computed network descriptors is illustrated by their literature-reported applications in studying the protein-protein, gene regulatory, gene co-expression, protein-drug and metabolic networks. PROFEAT is accessible free of charge at http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi.

Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks

Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 108 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.

Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases

Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services-tranSMART, a Galaxy Server, and a MINERVA platform-are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data.

NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities

The sequential chain of interactions altering the binary state of a biomolecule represents the ‘information flow’ within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein–protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes—network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets.

Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms

Small molecules are indispensable to modern medical therapy. However, their use may lead to unintended, negative medical outcomes commonly referred to as adverse drug reactions (ADRs). These effects vary widely in mechanism, severity, and populations affected, making ADR prediction and identification important public health concerns. Current methods rely on clinical trials and postmarket surveillance programs to find novel ADRs; however, clinical trials are limited by small sample size, whereas postmarket surveillance methods may be biased and inherently leave patients at risk until sufficient clinical evidence has been gathered. Systems pharmacology, an emerging interdisciplinary field combining network and chemical biology, provides important tools to uncover and understand ADRs and may mitigate the drawbacks of traditional methods. In particular, network analysis allows researchers to integrate heterogeneous data sources and quantify the interactions between biological and chemical entities. Recent work in this area has combined chemical, biological, and large-scale observational health data to predict ADRs in both individual patients and global populations. In this review, we explore the rapid expansion of systems pharmacology in the study of ADRs. We enumerate the existing methods and strategies and illustrate progress in the field with a model framework that incorporates crucial data elements, such as diet and comorbidities, known to modulate ADR risk. Using this framework, we highlight avenues of research that may currently be underexplored, representing opportunities for future work.

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

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

Connectivity Homology Enables Inter-Species Network Models of Synthetic Lethality

Synthetic lethality is a genetic interaction wherein two otherwise nonessential genes cause cellular inviability when knocked out simultaneously. Drugs can mimic genetic knock-out effects; therefore, our understanding of promiscuous drugs, polypharmacology-related adverse drug reactions, and multi-drug therapies, especially cancer combination therapy, may be informed by a deeper understanding of synthetic lethality. However, the colossal experimental burden in humans necessitates in silico methods to guide the identification of synthetic lethal pairs. Here, we present SINaTRA (Species-INdependent TRAnslation), a network-based methodology that discovers genome-wide synthetic lethality in translation between species. SINaTRA uses connectivity homology, defined as biological connectivity patterns that persist across species, to identify synthetic lethal pairs. Importantly, our approach does not rely on genetic homology or structural and functional similarity, and it significantly outperforms models utilizing these data. We validate SINaTRA by predicting synthetic lethality in S. pombe using S. cerevisiae data, then identify over one million putative human synthetic lethal pairs to guide experimental approaches. We highlight the translational applications of our algorithm for drug discovery by identifying clusters of genes significantly enriched for single- and multi-drug cancer therapies.

Synthetic lethality is a genetic interaction that has promising implications for informing novel cancer therapies. Over 200 million pairwise tests would be required to identify all pairwise synthetic lethal interactions in humans–currently, an impossibly large experimental burden. To simplify the process, we have developed a method to predict human synthetic lethal pairs in translation from a well-studied species to one in which synthetic lethality is understudied using both species’ protein-protein interaction networks. Here, we explore the model’s success in translation from S. cerevisiae to S. pombe. We then predict human synthetic lethality and suggest novel areas of inquiry for cancer therapies.

Bridging islands of information to establish an integrated knowledge base of drugs and health outcomes of interest

The entire drug safety enterprise has a need to search, retrieve, evaluate, and synthesize scientific evidence more efficiently. This discovery and synthesis process would be greatly accelerated through access to a common framework that brings all relevant information sources together within a standardized structure. This presents an opportunity to establish an open-source community effort to develop a global knowledge base, one that brings together and standardizes all available information for all drugs and all health outcomes of interest (HOIs) from all electronic sources pertinent to drug safety. To make this vision a reality, we have established a workgroup within the Observational Health Data Sciences and Informatics (OHDSI, http://ohdsi.org) collaborative. The workgroup’s mission is to develop an open-source standardized knowledge base for the effects of medical products and an efficient procedure for maintaining and expanding it. The knowledge base will make it simpler for practitioners to access, retrieve, and synthesize evidence so that they can reach a rigorous and accurate assessment of causal relationships between a given drug and HOI. Development of the knowledge base will proceed with the measureable goal of supporting an efficient and thorough evidence-based assessment of the effects of 1,000 active ingredients across 100 HOIs. This non-trivial task will result in a high-quality and generally applicable drug safety knowledge base. It will also yield a reference standard of drug–HOI pairs that will enable more advanced methodological research that empirically evaluates the performance of drug safety analysis methods.

Systematic identification of signal integration by protein kinase A

Cellular processes and homeostasis control in eukaryotic cells is achieved by the action of regulatory proteins such as protein kinase A (PKA). Although the outbound signals from PKA directed to processes such as metabolism, growth, and aging have been well charted, what regulates this conserved regulator remains to be systematically identified to understand how it coordinates biological processes. Using a yeast PKA reporter assay, we identified genes that influence PKA activity by measuring protein-protein interactions between the regulatory and the two catalytic subunits of the PKA complex in 3,726 yeast genetic-deletion backgrounds grown on two carbon sources. Overall, nearly 500 genes were found to be connected directly or indirectly to PKA regulation, including 80 core regulators, denoting a wide diversity of signals regulating PKA, within and beyond the described upstream linear pathways. PKA regulators span multiple processes, including the antagonistic autophagy and methionine biosynthesis pathways. Our results converge toward mechanisms of PKA posttranslational regulation by lysine acetylation, which is conserved between yeast and humans and that, we show, regulates protein complex formation in mammals and carbohydrate storage and aging in yeast. Taken together, these results show that the extent of PKA input matches with its output, because this kinase receives information from upstream and downstream processes, and highlight how biological processes are interconnected and coordinated by PKA.

Systems pharmacology augments drug safety surveillance

Small molecule drugs are the foundation of modern medical practice yet their use is limited by the onset of unexpected and severe adverse events (AEs). Regulatory agencies rely on post-marketing surveillance to monitor safety once drugs are approved for clinical use. Despite advances in pharmacovigilance methods that address issues of confounding bias, clinical data of AEs are inherently noisy. Systems pharmacology– the integration of systems biology and chemical genomics – can illuminate drug mechanisms of action. We hypothesize that these data can improve drug safety surveillance by highlighting drugs with a mechanistic connection to the target phenotype (enriching true positives) and filtering those that do not (depleting false positives). We present an algorithm, the modular assembly of drug safety subnetworks (MADSS), to combine systems pharmacology and pharmacovigilance data and significantly improve drug safety monitoring for four clinically relevant adverse drug reactions.

A chemo-centric view of human health and disease

Efforts to compile the phenotypic effects of drugs and environmental chemicals offer the opportunity to adopt a chemo-centric view of human health that does not require detailed mechanistic information. Here, we consider thousands of chemicals and analyze the relationship of their structures with adverse and therapeutic responses. Our study includes molecules related to the etiology of 934 health threatening conditions and used to treat 835 diseases. We first identify chemical moieties that could be independently associated with each phenotypic effect. Using these fragments, we build accurate predictors for approximately 400 clinical phenotypes, finding many privileged and liable structures. Finally, we connect two diseases if they relate to similar chemical structures. The resulting networks of human conditions are able to predict disease comorbidities, as well as identifying potential drug side effects and opportunities for drug repositioning, and show a remarkable coincidence with clinical observations.

The pain interactome: connecting pain-specific protein interactions

Understanding the molecular mechanisms associated with disease is a central goal of modern medical research. As such, many thousands of experiments have been published that detail individual molecular events that contribute to a disease. Here we use a semi-automated text mining approach to accurately and exhaustively curate the primary literature for chronic pain states. In so doing, we create a comprehensive network of 1,002 contextualized protein-protein interactions (PPIs) specifically associated with pain. The PPIs form a highly interconnected and coherent structure, and the resulting network provides an alternative to those derived from connecting genes associated with pain using interactions that have not been shown to occur in a painful state. We exploit the contextual data associated with our interactions to analyse subnetworks specific to inflammatory and neuropathic pain, and to various anatomical regions. Here, we identify potential targets for further study and several drug-repurposing opportunities. Finally, the network provides a framework for the interpretation of new data within the field of pain.

The future of drug discovery: enabling technologies for enhancing lead characterization and profiling therapeutic potential

Technology often serves as a handmaiden and catalyst of invention. The discovery of safe, effective medications depends critically upon experimental approaches capable of providing high-impact information on the biological effects of drug candidates early in the discovery pipeline. This information can enable reliable lead identification, pharmacological compound differentiation and successful translation of research output into clinically useful therapeutics. The shallow preclinical profiling of candidate compounds promulgates a minimalistic understanding of their biological effects and undermines the level of value creation necessary for finding quality leads worth moving forward within the development pipeline with efficiency and prognostic reliability sufficient to help remediate the current pharma-industry productivity drought. Three specific technologies discussed herein, in addition to experimental areas intimately associated with contemporary drug discovery, appear to hold particular promise for strengthening the preclinical valuation of drug candidates by deepening lead characterization. These are: i) hydrogen-deuterium exchange mass spectrometry for characterizing structural and ligand-interaction dynamics of disease-relevant proteins; ii) activity-based chemoproteomics for profiling the functional diversity of mammalian proteomes; and iii) nuclease-mediated precision gene editing for developing more translatable cellular and in vivo models of human diseases. When applied in an informed manner congruent with the clinical understanding of disease processes, technologies such as these that span levels of biological organization can serve as valuable enablers of drug discovery and potentially contribute to reducing the current, unacceptably high rates of compound clinical failure.

Managing the innovation supply chain to maximize personalized medicine

Personalized medicine epitomizes an evolving model of care tailored to the individual patient. This emerging paradigm harnesses radical technological advances to define each patient's molecular characteristics and decipher his or her unique pathophysiological processes. Translated into individualized algorithms, personalized medicine aims to predict, prevent, and cure disease without producing therapeutic adverse events. Although the transformative power of personalized medicine is generally recognized by physicians, patients, and payers, the complexity of translating discoveries into new modalities that transform health care is less appreciated. We often consider the flow of innovation and technology along a continuum of discovery, development, regulation, and application bridging the bench with the bedside. However, this process also can be viewed through a complementary prism, as a necessary supply chain of services and providers, each making essential contributions to the development of the final product to maximize value to consumers. Considering personalized medicine in this context of supply chain management highlights essential points of vulnerability and/or scalability that can ultimately constrain translation of the biological revolution or potentiate it into individualized diagnostics and therapeutics for optimized value creation and delivery.

Molecular insights provide the critical path to disease mitigation

The revolution in scientific innovation, driven by the engines of enabling technologies, is increasingly capable of deconstructing complex disease processes for the express purpose of reconstructing patient-specific solutions. These revelations in biological mechanisms provide the pressure points of opportunity for radical discovery and development to advance modern health care. Principles of mechanism-based discovery and their translation into therapeutic algorithms will, however, be challenged in the near term by emerging global public health crises that currently have no immediate solutions: chronic diseases, obesity, antibiotic-resistant infections, dementia, depression. The threat of these pandemics (multiplied in an increasingly aging population), the global burden of disease they represent, and their worldwide assault on human capital underscore the importance of continued and accelerated investments in science-propelled practice advancement, converting new knowledge into delivery of tangible health solutions. In that context, this annual issue of CPT on therapeutics innovations highlights remarkable recent successes in the discovery-development paradigm translating molecular innovations into diagnostic and therapeutic realities that transform the management of disease, impacting global health.

Network medicine: finding the links to personalized therapy

Network medicine is a new approach that focuses on applying systems biology to pharmacology by "understanding the molecular system [and its perturbations] as a whole" so as to unravel the complex relationships among disease processes, genes, drugs, therapeutic indicators, and adverse effects.(1,2.)