Network analyses in systems pharmacology

Systems biology primer: the basic methods and approaches

Systems biology is an integrative discipline connecting the molecular components within a single biological scale and also among different scales (e.g. cells, tissues and organ systems) to physiological functions and organismal phenotypes through quantitative reasoning, computational models and high-throughput experimental technologies. Systems biology uses a wide range of quantitative experimental and computational methodologies to decode information flow from genes, proteins and other subcellular components of signaling, regulatory and functional pathways to control cell, tissue, organ and organismal level functions. The computational methods used in systems biology provide systems-level insights to understand interactions and dynamics at various scales, within cells, tissues, organs and organisms. In recent years, the systems biology framework has enabled research in quantitative and systems pharmacology and precision medicine for complex diseases. Here, we present a brief overview of current experimental and computational methods used in systems biology.

Context-enriched interactome powered by proteomics helps the identification of novel regulators of macrophage activation

The role of pro-inflammatory macrophage activation in cardiovascular disease (CVD) is a complex one amenable to network approaches. While an indispensible tool for elucidating the molecular underpinnings of complex diseases including CVD, the interactome is limited in its utility as it is not specific to any cell type, experimental condition or disease state. We introduced context-specificity to the interactome by combining it with co-abundance networks derived from unbiased proteomics measurements from activated macrophage-like cells. Each macrophage phenotype contributed to certain regions of the interactome. Using a network proximity-based prioritization method on the combined network, we predicted potential regulators of macrophage activation. Prediction performance significantly increased with the addition of co-abundance edges, and the prioritized candidates captured inflammation, immunity and CVD signatures. Integrating the novel network topology with transcriptomics and proteomics revealed top candidate drivers of inflammation. In vitro loss-of-function experiments demonstrated the regulatory role of these proteins in pro-inflammatory signaling.

When human cells or tissues are injured, the body triggers a response known as inflammation to repair the damage and protect itself from further harm. However, if the same issue keeps recurring, the tissues become inflamed for longer periods of time, which may ultimately lead to health problems. This is what could be happening in cardiovascular diseases, where long-term inflammation could damage the heart and blood vessels.

Many different proteins interact with each other to control inflammation; gaining an insight into the nature of these interactions could help to pinpoint the role of each molecular actor. Researchers have used a combination of unbiased, large-scale experimental and computational approaches to develop the interactome, a map of the known interactions between all proteins in humans. However, interactions between proteins can change between cell types, or during disease. Here, Halu et al. aimed to refine the human interactome and identify new proteins involved in inflammation, especially in the context of cardiovascular disease.

Cells called macrophages produce signals that trigger inflammation whey they detect damage in other cells or tissues. The experiments used a technique called proteomics to measure the amounts of all the proteins in human macrophages. Combining these data with the human interactome made it possible to predict new links between proteins known to have a role in inflammation and other proteins in the interactome. Further analysis using other sets of data from macrophages helped identify two new candidate proteins – GBP1 and WARS – that may promote inflammation. Halu et al. then used a genetic approach to deactivate the genes and decrease the levels of these two proteins in macrophages, which caused the signals that encourage inflammation to drop.

These findings suggest that GBP1 and WARS regulate the activity of macrophages to promote inflammation. The two proteins could therefore be used as drug targets to treat cardiovascular diseases and other disorders linked to inflammation, but further studies will be needed to precisely dissect how GBP1 and WARS work in humans.

Analyzing of Molecular Networks for Human Diseases and Drug Discovery

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

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.

A network pharmacology approach to explore the mechanisms of Erxian decoction in polycystic ovary syndrome

BACKGROUND

Polycystic ovary syndrome (PCOS) significantly affects women's health and well-being. To explore the pharmacological basis of the Erxian decoction (EXD) action in PCOS therapy, a network interaction analysis was conducted at the molecular level.

METHODS

The active elements of EXD were identified according to the oral bioavailability and drug-likeness filters from three databases: traditional Chinese medicine system pharmacology analysis platform, TCM@taiwan and TCMID, and their potential targets were also identified. Genes associated with PCOS and established protein-protein interaction networks were mined from the NCBI database. Finally, significant pathways and functions of these networks were identified using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to determine the mechanism of action of EXD.

RESULTS

Seventy active compounds were obtained from 981 ingredients present in the EXD decoction, corresponding to 247 targets. In addition, 262 genes were found to be closely related with PCOS, of which 50 overlapped with EXD and were thus considered therapeutically relevant. Pathway enrichment analysis identified PI3k-Akt, insulin resistance, Toll-like receptor, MAPK and AGE-RAGE from a total of 15 significant pathways in PCOS and its treatment.

CONCLUSIONS

EXD can effectively improve the symptoms of PCOS and our systemic pharmacological analysis lays the experimental foundation for further clinical applications of EXD.

Systems Pharmacological Approach to Investigate the Mechanism of Acori Tatarinowii Rhizoma for Alzheimer's Disease

In traditional Chinese medicine (TCM), Acori Tatarinowii Rhizoma (ATR) is widely used to treat memory and cognition dysfunction. This study aimed to confirm evidence regarding the potential therapeutic effect of ATR on Alzheimer's disease (AD) using a system network level based in silico approach. Study results showed that the compounds in ATR are highly connected to AD-related signaling pathways, biological processes, and organs. These findings were confirmed by compound-target network, target-organ location network, gene ontology analysis, and KEGG pathway enrichment analysis. Most compounds in ATR have been reported to have antifibrillar amyloid plaques, anti-tau phosphorylation, and anti-inflammatory effects. Our results indicated that compounds in ATR interact with multiple targets in a synergetic way. Furthermore, the mRNA expressions of genes targeted by ATR are elevated significantly in heart, brain, and liver. Our results suggest that the anti-inflammatory and immune system enhancing effects of ATR might contribute to its major therapeutic effects on Alzheimer's disease.

Overview of the Development of DprE1 Inhibitors for Combating the Menace of Tuberculosis

Decaprenylphosphoryl-β-d-ribose 2'-epimerase (DprE1), a vital enzyme for cell wall synthesis, plays a crucial role in the formation of lipoarabinomannan and arabinogalactan. It was first reported as a druggable target on the basis of inhibitors discovered in high throughput screening of a drug library. Since then, inhibitors with different types of chemical scaffolds have been reported for their activity against this enzyme. Formation of a covalent or noncovalent bond by the interacting ligand with the enzyme causes loss of its catalytic activity which ultimately leads to the death of the mycobacterium. This Perspective describes various DprE1 inhibitors as anti-TB agents reported to date.

Characterizing drug-related adverse events by joint analysis of biomedical and genomic data: A case study of drug-induced pulmonary fibrosis

Spontaneous reporting systems such as the FDA's adverse event reporting system (FAERS) present a great resource to mine for and analyze real-world medication usage. Our study is based on a central premise that FAERS captures unsuspected drug-related adverse events (AEs). Since drug-related AEs result for several reasons, no single approach will be able to predict the entire gamut of AEs. A fundamental premise of systems biology is that a full understanding of a biological process or phenotype (e.g., drug-related AE) requires that all the individual elements be studied in conjunction with one another. We therefore hypothesize that integrative analysis of FAERS-based drug-related AEs with the transcriptional signatures from disease models and drug treatments can lead to the generation of unbiased hypotheses for drug-induced AE-modulating mechanisms of action as well as drug combinations that may target those mechanisms. We test this hypothesis using drug-induced pulmonary fibrosis (DIPF) as a proof-of-concept study.

Systems Pharmacology Dissection of Traditional Chinese Medicine Wen-Dan Decoction for Treatment of Cardiovascular Diseases

Cardiovascular diseases (CVDs) have been recognized as first killer of human health. The underlying mechanisms of CVDs are extremely complicated and not fully revealed, leading to a challenge for CVDs treatment in modern medicine. Traditional Chinese medicine (TCM) characterized by multiple compounds and targets has shown its marked effects on CVDs therapy. However, system-level understanding of the molecular mechanisms is still ambiguous. In this study, a system pharmacology approach was developed to reveal the underlying molecular mechanisms of a clinically effective herb formula (Wen-Dan Decoction) in treating CVDs. 127 potential active compounds and their corresponding 283 direct targets were identified in Wen-Dan Decoction. The networks among active compounds, targets, and diseases were built to reveal the pharmacological mechanisms of Wen-Dan Decoction. A “CVDs pathway” consisted of several regulatory modules participating in therapeutic effects of Wen-Dan Decoction in CVDs. All the data demonstrates that Wen-Dan Decoction has multiscale beneficial activity in CVDs treatment, which provides a new way for uncovering the molecular mechanisms and new evidence for clinical application of Wen-Dan Decoction in cardiovascular disease.

A two-tiered unsupervised clustering approach for drug repositioning through heterogeneous data integration

Background

Drug repositioning is the process of identifying new uses for existing drugs. Computational drug repositioning methods can reduce the time, costs and risks of drug development by automating the analysis of the relationships in pharmacology networks. Pharmacology networks are large and heterogeneous. Clustering drugs into small groups can simplify large pharmacology networks, these subgroups can also be used as a starting point for repositioning drugs. In this paper, we propose a two-tiered drug-centric unsupervised clustering approach for drug repositioning, integrating heterogeneous drug data profiles: drug-chemical, drug-disease, drug-gene, drug-protein and drug-side effect relationships.

Results

The proposed drug repositioning approach is threefold; (i) clustering drugs based on their homogeneous profiles using the Growing Self Organizing Map (GSOM); (ii) clustering drugs based on drug-drug relation matrices based on the previous step, considering three state-of-the-art graph clustering methods; and (iii) inferring drug repositioning candidates and assigning a confidence value for each identified candidate. In this paper, we compare our two-tiered clustering approach against two existing heterogeneous data integration approaches with reference to the Anatomical Therapeutic Chemical (ATC) classification, using GSOM. Our approach yields Normalized Mutual Information (NMI) and Standardized Mutual Information (SMI) of 0.66 and 36.11, respectively, while the two existing methods yield NMI of 0.60 and 0.64 and SMI of 22.26 and 33.59. Moreover, the two existing approaches failed to produce useful cluster separations when using graph clustering algorithms while our approach is able to identify useful clusters for drug repositioning. Furthermore, we provide clinical evidence for four predicted results (Chlorthalidone, Indomethacin, Metformin and Thioridazine) to support that our proposed approach can be reliably used to infer ATC code and drug repositioning.

Conclusion

The proposed two-tiered unsupervised clustering approach is suitable for drug clustering and enables heterogeneous data integration. It also enables identifying reliable repositioning drug candidates with reference to ATC therapeutic classification. The repositioning drug candidates identified consistently by multiple clustering algorithms and with high confidence have a higher possibility of being effective repositioning candidates.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2123-4) contains supplementary material, which is available to authorized users.

In Silico Enhancing M. tuberculosis Protein Interaction Networks in STRING To Predict Drug-Resistance Pathways and Pharmacological Risks

Bacterial protein-protein interaction (PPI) networks are significant to reveal the machinery of signal transduction and drug resistance within bacterial cells. The database STRING has collected a large number of bacterial pathogen PPI networks, but most of the data are of low quality without being experimentally or computationally validated, thus restricting its further biomedical applications. We exploit the experimental data via four solutions to enhance the quality of M. tuberculosis H37Rv (MTB) PPI networks in STRING. Computational results show that the experimental data derived jointly by two-hybrid and copurification approaches are the most reliable to train an L₂-regularized logistic regression model for MTB PPI network validation. On the basis of the validated MTB PPI networks, we further study the three problems via breadth-first graph search algorithm: (1) discovery of MTB drug-resistance pathways through searching for the paths between known drug-target genes and drug-resistance genes, (2) choosing potential cotarget genes via searching for the critical genes located on multiple pathways, and (3) choosing essential drug-target genes via analysis of network degree distribution. In addition, we further combine the validated MTB PPI networks with human PPI networks to analyze the potential pharmacological risks of known and candidate drug-target genes from the point of view of system pharmacology. The evidence from protein structure alignment demonstrates that the drugs that act on MTB target genes could also adversely act on human signaling pathways.

Systems Pharmacology-Based Approach of Connecting Disease Genes in Genome-Wide Association Studies with Traditional Chinese Medicine

Traditional Chinese medicine (TCM) originated in ancient China has been practiced over thousands of years for treating various symptoms and diseases. However, the molecular mechanisms of TCM in treating these diseases remain unknown. In this study, we employ a systems pharmacology-based approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. We studied 102 TCM components and their target genes by analyzing microarray gene expression experiments. We constructed disease-gene networks from 2558 GWAS studies. We applied a systems pharmacology approach to prioritize disease-target genes. Using this bioinformatics approach, we analyzed 14,713 GWAS disease-TCM-target gene pairs and identified 115 disease-gene pairs with q value < 0.2. We validated several of these GWAS disease-TCM-target gene pairs with literature evidence, demonstrating that this computational approach could reveal novel indications for TCM. We also develop TCM-Disease web application to facilitate the traditional Chinese medicine drug repurposing efforts. Systems pharmacology is a promising approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. The computational approaches described in this study could be easily expandable to other disease-gene network analysis.

A computational network analysis based on targets of antipsychotic agents

Currently, numerous antipsychotic agents have been developed in the area of pharmacological treatment of schizophrenia. However, the molecular mechanism underlying multi targets of antipsychotics were yet to be explored. In this study we performed a computational network analysis based on targets of antipsychotic agents. We retrieved a total of 96 targets from 56 antipsychotic agents. By expression enrichment analysis, we identified that the expressions of antipsychotic target genes were significantly enriched in liver, brain, blood and corpus striatum. By protein-protein interaction (PPI) network analysis, a PPI network with 77 significantly interconnected target genes was generated. By historeceptomics analysis, significant brain region specific target-drug interactions were identified in targets of dopamine receptors (DRD1-Olanzapine in caudate nucleus and pons (P-value<0.005), DRD2-Bifeprunox in caudate nucleus and pituitary (P-value<0.0005), DRD4-Loxapine in Pineal (P-value<0.00001)) and 5-hydroxytryptamine receptor (HTR2A-Risperidone in occipital lobe, prefrontal cortex and subthalamic nucleus (P-value<0.0001)). By pathway grouped network analysis, 34 significant pathways were identified and significantly grouped into 6 sub networks related with drug metabolism, Calcium signaling, GABA receptors, dopamine receptors, Bile secretion and Gap junction. Our results may provide biological explanation for antipsychotic targets and insights for molecular mechanism of antipsychotic agents.

Functional Categorization of Disease Genes Based on Spectral Graph Theory and Integrated Biological Knowledge

Interaction of multiple genetic variants is a major challenge in the development of effective treatment strategies for complex disorders. Identifying the most promising genes enhances the understanding of the underlying mechanisms of the disease, which, in turn leads to better diagnostic and therapeutic predictions. Categorizing the disease genes into meaningful groups even helps in analyzing the correlated phenotypes which will further improve the power of detecting disease-associated variants. Since experimental approaches are time consuming and expensive, computational methods offer an accurate and efficient alternative for analyzing gene-disease associations from vast amount of publicly available genomic information. Integration of biological knowledge encoded in genes are necessary for identifying significant groups of functionally similar genes and for the sufficient biological elucidation of patterns classified by these clusters. The aim of the work is to identify gene clusters by utilizing diverse genomic information instead of using a single class of biological data in isolation and using efficient feature selection methods and edge pruning techniques for performance improvement. An optimized and streamlined procedure is proposed based on spectral clustering for automatic detection of gene communities through a combination of weighted knowledge fusion, threshold-based edge detection and entropy-based eigenvector subset selection. The proposed approach is applied to produce communities of genes related to Autism Spectrum Disorder and is compared with standard clustering solutions.

Boolean network modeling in systems pharmacology

Quantitative systems pharmacology (QSP) is an emerging discipline that aims to discover how drugs modulate the dynamics of biological components in molecular and cellular networks and the impact of those perturbations on human pathophysiology. The integration of systems-based experimental and computational approaches is required to facilitate the advancement of this field. QSP models typically consist of a series of ordinary differential equations (ODE). However, this mathematical framework requires extensive knowledge of parameters pertaining to biological processes, which is often unavailable. An alternative framework that does not require knowledge of system-specific parameters, such as Boolean network modeling, could serve as an initial foundation prior to the development of an ODE-based model. Boolean network models have been shown to efficiently describe, in a qualitative manner, the complex behavior of signal transduction and gene/protein regulatory processes. In addition to providing a starting point prior to quantitative modeling, Boolean network models can also be utilized to discover novel therapeutic targets and combinatorial treatment strategies. Identifying drug targets using a network-based approach could supplement current drug discovery methodologies and help to fill the innovation gap across the pharmaceutical industry. In this review, we discuss the process of developing Boolean network models and the various analyses that can be performed to identify novel drug targets and combinatorial approaches. An example for each of these analyses is provided using a previously developed Boolean network of signaling pathways in multiple myeloma. Selected examples of Boolean network models of human (patho-)physiological systems are also reviewed in brief.

Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology

The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.

Systematic studies on the in vivo substance basis and the pharmacological mechanism of Acanthopanax Senticosus Harms leaves by UPLC-Q-TOF-MS coupled with a target-network method

The leaves of Acanthopanax Senticosus Harms (ASL) can be used as a food ingredient and also as raw materials for making tea and wine.

Next generation of network medicine: interdisciplinary signaling approaches

In the last decade, network approaches have transformed our understanding of biological systems. Network analyses and visualizations have allowed us to identify essential molecules and modules in biological systems, and improved our understanding of how changes in cellular processes can lead to complex diseases, such as cancer, infectious and neurodegenerative diseases. "Network medicine" involves unbiased large-scale network-based analyses of diverse data describing interactions between genes, diseases, phenotypes, drug targets, drug transport, drug side-effects, disease trajectories and more. In terms of drug discovery, network medicine exploits our understanding of the network connectivity and signaling system dynamics to help identify optimal, often novel, drug targets. Contrary to initial expectations, however, network approaches have not yet delivered a revolution in molecular medicine. In this review, we propose that a key reason for the limited impact, so far, of network medicine is a lack of quantitative multi-disciplinary studies involving scientists from different backgrounds. To support this argument, we present existing approaches from structural biology, 'omics' technologies (e.g., genomics, proteomics, lipidomics) and computational modeling that point towards how multi-disciplinary efforts allow for important new insights. We also highlight some breakthrough studies as examples of the potential of these approaches, and suggest ways to make greater use of the power of interdisciplinarity. This review reflects discussions held at an interdisciplinary signaling workshop which facilitated knowledge exchange from experts from several different fields, including in silico modelers, computational biologists, biochemists, geneticists, molecular and cell biologists as well as cancer biologists and pharmacologists.

Drug repurposing for the treatment of glioblastoma multiforme

Background

Glioblastoma Multiforme is the deadliest type of brain tumor and is characterized by very poor prognosis with a limited overall survival. Current optimal therapeutic approach has essentially remained unchanged for more than a decade, consisting in maximal surgical resection followed by radiotherapy plus temozolomide.

Main body

Such a dismal patient outcome represents a compelling need for innovative and effective therapeutic approaches. Given the development of new drugs is a process presently characterized by an immense increase in costs and development time, drug repositioning, finding new uses for existing approved drugs or drug repurposing, re-use of old drugs when novel molecular findings make them attractive again, are gaining significance in clinical pharmacology, since it allows faster and less expensive delivery of potentially useful drugs from the bench to the bedside. This is quite evident in glioblastoma, where a number of old drugs is now considered for clinical use, often in association with the first-line therapeutic intervention. Interestingly, most of these medications are, or have been, widely employed for decades in non-neoplastic pathologies without relevant side effects. Now, the refinement of their molecular mechanism(s) of action through up-to-date technologies is paving the way for their use in the therapeutic approach of glioblastoma as well as other cancer types.

Short conclusion

The spiraling costs of new antineoplastic drugs and the long time required for them to reach the market demands a profoundly different approach to keep lifesaving therapies affordable for cancer patients. In this context, repurposing can represent a relatively inexpensive, safe and fast approach to glioblastoma treatment. To this end, pros and cons must be accurately considered.

Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway

Background and Purpose

An ever‐growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single‐drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway.

Experimental Approach

Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimization to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment.

Key Results

We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi‐drug treatments with high efficacy and minimal off‐target effects.

Conclusion and Implications

We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology workflow for the refinement of data and the generation of future therapeutic hypotheses.

Predictive model identifies key network regulators of cardiomyocyte mechano-signaling

Mechanical strain is a potent stimulus for growth and remodeling in cells. Although many pathways have been implicated in stretch-induced remodeling, the control structures by which signals from distinct mechano-sensors are integrated to modulate hypertrophy and gene expression in cardiomyocytes remain unclear. Here, we constructed and validated a predictive computational model of the cardiac mechano-signaling network in order to elucidate the mechanisms underlying signal integration. The model identifies calcium, actin, Ras, Raf1, PI3K, and JAK as key regulators of cardiac mechano-signaling and characterizes crosstalk logic imparting differential control of transcription by AT1R, integrins, and calcium channels. We find that while these regulators maintain mostly independent control over distinct groups of transcription factors, synergy between multiple pathways is necessary to activate all the transcription factors necessary for gene transcription and hypertrophy. We also identify a PKG-dependent mechanism by which valsartan/sacubitril, a combination drug recently approved for treating heart failure, inhibits stretch-induced hypertrophy, and predict further efficacious pairs of drug targets in the network through a network-wide combinatorial search.

Common stresses such as high blood pressure or heart attack can lead to heart failure, which afflicts over 25 million people worldwide. These stresses cause cardiomyocytes to grow and remodel, which may initially be beneficial but ultimately worsen heart function. Current heart failure drugs such as beta-blockers counteract biochemical cues prompting cardiomyocyte growth, yet mechanical cues to cardiomyocytes such as stretch are just as important in driving cardiac dysfunction. However, no pharmacological treatments have yet been approved that specifically target mechano-signaling, in part because it is not clear how cardiomyocytes integrate signals from multiple mechano-responsive sensors and pathways into their decision to grow. To address this challenge, we built a systems-level computational model that represents 125 interactions between 94 stretch-responsive signaling molecules. The model correctly predicts 134 of 172 previous independent experimental observations, and identifies the key regulators of stretch-induced cardiomyocyte remodeling. Although cardiomyocytes have many mechano-signaling pathways that function largely independently, we find that cooperation between them is necessary to cause growth and remodeling. We identify mechanisms by which a recently approved heart failure drug pair affects mechano-signaling, and we further predict additional pairs of drug targets that could be used to help reverse heart failure.

Systems Pharmacological Approach to the Effect of Bulsu-san Promoting Parturition

Bulsu-san (BSS) has been commonly used in oriental medicine for pregnant women in East Asia. The purpose of this research was to elucidate the effect of BSS on ease of parturition using a systems-level in silico analytic approach. Research results show that BSS is highly connected to the parturition related pathways, biological processes, and organs. There were numerous interactions between most compounds of BSS and multiple target genes, and this was confirmed using herb-compound-target network, target-pathway network, and gene ontology analysis. Furthermore, the mRNA expression of relevant target genes of BSS was elevated significantly in related organ tissues, such as those of the uterus, placenta, fetus, hypothalamus, and pituitary gland. This study used a network analytical approach to demonstrate that Bulsu-san (BSS) is closely related to the parturition related pathways, biological processes, and organs. It is meaningful that this systems-level network analysis result strengthens the basis of clinical applications of BSS on ease of parturition.

Cancer Transcriptome Dataset Analysis: Comparing Methods of Pathway and Gene Regulatory Network-Based Cluster Identification

Cancer transcriptome analysis is one of the leading areas of Big Data science, biomarker, and pharmaceutical discovery, not to forget personalized medicine. Yet, cancer transcriptomics and postgenomic medicine require innovation in bioinformatics as well as comparison of the performance of available algorithms. In this data analytics context, the value of network generation and algorithms has been widely underscored for addressing the salient questions in cancer pathogenesis. Analysis of cancer trancriptome often results in complicated networks where identification of network modularity remains critical, for example, in delineating the "druggable" molecular targets. Network clustering is useful, but depends on the network topology in and of itself. Notably, the performance of different network-generating tools for network cluster (NC) identification has been little investigated to date. Hence, using gastric cancer (GC) transcriptomic datasets, we compared two algorithms for generating pathway versus gene regulatory network-based NCs, showing that the pathway-based approach better agrees with a reference set of cancer-functional contexts. Finally, by applying pathway-based NC identification to GC transcriptome datasets, we describe cancer NCs that associate with candidate therapeutic targets and biomarkers in GC. These observations collectively inform future research on cancer transcriptomics, drug discovery, and rational development of new analysis tools for optimal harnessing of omics data.

Statistical Profiling of One Promiscuous Protein Binding Site: Illustrated by Urokinase Catalytic Domain

While recent literature focuses on drug promiscuity, the characterization of promiscuous binding sites (ability to bind several ligands) remains to be explored. Here, we present a proteochemometric modeling approach to analyze diverse ligands and corresponding multiple binding sub-pockets associated with one promiscuous binding site to characterize protein-ligand recognition. We analyze both geometrical and physicochemical profile correspondences. This approach was applied to examine the well-studied druggable urokinase catalytic domain inhibitor binding site, which results in a large number of complex structures bound to various ligands. This approach emphasizes the importance of jointly characterizing pocket and ligand spaces to explore the impact of ligand diversity on sub-pocket properties and to establish their main profile correspondences. This work supports an interest in mining available 3D holo structures associated with a promiscuous binding site to explore its main protein-ligand recognition tendency.

Network-based Approaches in Pharmacology

In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example.

Advanced Boolean modeling of biological networks applied to systems pharmacology

Motivation

Literature on complex diseases is abundant but not always quantitative. Many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. Tools for analysis of discrete networks are useful to capture the available information in the literature but have not been efficiently integrated by the pharmaceutical industry. We propose an expansion of the usual analysis of discrete networks that facilitates the identification/validation of therapeutic targets.

Results

In this article, we propose a methodology to perform Boolean modeling of Systems Biology/Pharmacology networks by using SPIDDOR (Systems Pharmacology for effIcient Drug Development On R) R package. The resulting models can be used to analyze the dynamics of signaling networks associated to diseases to predict the pathogenesis mechanisms and identify potential therapeutic targets.

Availability and Implementation

The source code is available at https://github.com/SPIDDOR/SPIDDOR .

Contact

itzirurzun@alumni.unav.es , itroconiz@unav.es.

Supplementary information

Supplementary data are available at Bioinformatics online.

Systems Pharmacological Approach of Pulsatillae Radix on Treating Crohn's Disease

In East Asian traditional medicine, Pulsatillae Radix (PR) is widely used to treat amoebic dysentery and renowned for its anti-inflammatory effects. This study aimed to confirm evidence regarding the potential therapeutic effect of PR on Crohn's disease using a system network level based in silico approach. Study results showed that the compounds in PR are highly connected to Crohn's disease related pathways, biological processes, and organs, and these findings were confirmed by compound-target network, target-pathway network, and gene ontology analysis. Most compounds in PR have been reported to possess anti-inflammatory, anticancer, and antioxidant effects, and we found that these compounds interact with multiple targets in a synergetic way. Furthermore, the mRNA expressions of genes targeted by PR are elevated significantly in immunity-related organ tissues, small intestine, and colon. Our results suggest that the anti-inflammatory and repair and immune system enhancing effects of PR might have therapeutic impact on Crohn's disease.

Enhancing Drug Efficacy and Therapeutic Index through Cheminformatics-Based Selection of Small Molecule Binary Weapons That Improve Transporter-Mediated Targeting: A Cytotoxicity System Based on Gemcitabine

The transport of drug molecules is mainly determined by the distribution of influx and efflux transporters for which they are substrates. To enable tissue targeting, we sought to develop the idea that we might affect the transporter-mediated disposition of small-molecule drugs via the addition of a second small molecule that of itself had no inhibitory pharmacological effect but that influenced the expression of transporters for the primary drug. We refer to this as a “binary weapon” strategy. The experimental system tested the ability of a molecule that on its own had no cytotoxic effect to increase the toxicity of the nucleoside analog gemcitabine to Panc1 pancreatic cancer cells. An initial phenotypic screen of a 500-member polar drug (fragment) library yielded three “hits.” The structures of 20 of the other 2,000 members of this library suite had a Tanimoto similarity greater than 0.7 to those of the initial hits, and each was itself a hit (the cheminformatics thus providing for a massive enrichment). We chose the top six representatives for further study. They fell into three clusters whose members bore reasonable structural similarities to each other (two were in fact isomers), lending strength to the self-consistency of both our conceptual and experimental strategies. Existing literature had suggested that indole-3-carbinol might play a similar role to that of our fragments, but in our hands it was without effect; nor was it structurally similar to any of our hits. As there was no evidence that the fragments could affect toxicity directly, we looked for effects on transporter transcript levels. In our hands, only the ENT1-3 uptake and ABCC2,3,4,5, and 10 efflux transporters displayed measurable transcripts in Panc1 cultures, along with a ribonucleoside reductase RRM1 known to affect gemcitabine toxicity. Very strikingly, the addition of gemcitabine alone increased the expression of the transcript for ABCC2 (MRP2) by more than 12-fold, and that of RRM1 by more than fourfold, and each of the fragment “hits” served to reverse this. However, an inhibitor of ABCC2 was without significant effect, implying that RRM1 was possibly the more significant player. These effects were somewhat selective for Panc cells. It seems, therefore, that while the effects we measured were here mediated more by efflux than influx transporters, and potentially by other means, the binary weapon idea is hereby fully confirmed: it is indeed possible to find molecules that manipulate the expression of transporters that are involved in the bioactivity of a pharmaceutical drug. This opens up an entirely new area, that of chemical genomics-based drug targeting.

Active learning for computational chemogenomics

Aim: Computational chemogenomics models the compound–protein interaction space, typically for drug discovery, where existing methods predominantly either incorporate increasing numbers of bioactivity samples or focus on specific subfamilies of proteins and ligands. As an alternative to modeling entire large datasets at once, active learning adaptively incorporates a minimum of informative examples for modeling, yielding compact but high quality models. Results/methodology: We assessed active learning for protein/target family-wide chemogenomic modeling by replicate experiment. Results demonstrate that small yet highly predictive models can be extracted from only 10–25% of large bioactivity datasets, irrespective of molecule descriptors used. Conclusion: Chemogenomic active learning identifies small subsets of ligand–target interactions in a large screening database that lead to knowledge discovery and highly predictive models.

Herb pair Danggui-Honghua: mechanisms underlying blood stasis syndrome by system pharmacology approach

Herb pair Danggui-Honghua has been frequently used for treatment of blood stasis syndrome (BSS) in China, one of the most common clinical pathological syndromes in traditional Chinese medicine (TCM). However, its therapeutic mechanism has not been clearly elucidated. In the present study, a feasible system pharmacology model based on chemical, pharmacokinetic and pharmacological data was developed via network construction approach to clarify the mechanisms of this herb pair. Thirty-one active ingredients of Danggui-Honghua possessing favorable pharmacokinetic profiles and biological activities were selected, interacting with 42 BSS-related targets to provide potential synergistic therapeutic actions. Systematic analysis of the constructed networks revealed that these targets such as HMOX1, NOS2, NOS3, HIF1A and PTGS2 were mainly involved in TNF signaling pathway, HIF-1 signaling pathway, estrogen signaling pathway and neurotrophin signaling pathway. The contribution index of every active ingredient also indicated six compounds, including hydroxysafflor yellow A, safflor yellow A, safflor yellow B, Z-ligustilide, ferulic acid, and Z-butylidenephthalide, as the principal components of this herb pair. These results successfully explained the polypharmcological mechanisms underlying the efficiency of Danggui-Honghua for BSS treatment, and also probed into the potential novel therapeutic strategies for BSS in TCM.

Network pharmacology-based virtual screening of natural products from Clerodendrum species for identification of novel anti-cancer therapeutics

In the present work, latest network pharmacological approach has been used for the screening of natural anticancer compounds from Clerodendrum species.

A physarum-inspired prize-collecting steiner tree approach to identify subnetworks for drug repositioning

Background

Drug repositioning can reduce the time, costs and risks of drug development by identifying new therapeutic effects for known drugs. It is challenging to reposition drugs as pharmacological data is large and complex. Subnetwork identification has already been used to simplify the visualization and interpretation of biological data, but it has not been applied to drug repositioning so far. In this paper, we fill this gap by proposing a new Physarum-inspired Prize-Collecting Steiner Tree algorithm to identify subnetworks for drug repositioning.

Results

Drug Similarity Networks (DSN) are generated using the chemical, therapeutic, protein, and phenotype features of drugs. In DSNs, vertex prizes and edge costs represent the similarities and dissimilarities between drugs respectively, and terminals represent drugs in the cardiovascular class, as defined in the Anatomical Therapeutic Chemical classification system. A new Physarum-inspired Prize-Collecting Steiner Tree algorithm is proposed in this paper to identify subnetworks. We apply both the proposed algorithm and the widely-used GW algorithm to identify subnetworks in our 18 generated DSNs. In these DSNs, our proposed algorithm identifies subnetworks with an average Rand Index of 81.1%, while the GW algorithm can only identify subnetworks with an average Rand Index of 64.1%. We select 9 subnetworks with high Rand Index to find drug repositioning opportunities. 10 frequently occurring drugs in these subnetworks are identified as candidates to be repositioned for cardiovascular diseases.

Conclusions

We find evidence to support previous discoveries that nitroglycerin, theophylline and acarbose may be able to be repositioned for cardiovascular diseases. Moreover, we identify seven previously unknown drug candidates that also may interact with the biological cardiovascular system. These discoveries show our proposed Prize-Collecting Steiner Tree approach as a promising strategy for drug repositioning.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0371-3) contains supplementary material, which is available to authorized users.

Quality by design (QbD) approach of pharmacogenomics in drug designing and formulation development for optimization of drug delivery systems

Conventional approaches of drug discovery are very complex, costly and time consuming. But after the completion of human genome project, applications of pharmacogenomics in this area completely revolutionize the drug discovery and development process to produce a quality by design (QbD) approach based products. The applications of two areas of pharmacogenomics i.e. structural and functional pharmacogenomics excel the drug discovery process by employing genomic data in drug target identification and evaluation, lead optimization via high throughput screening, evaluation of drug metabolizing enzymes, drug transporters and drug receptors using computer aided technique and bioinformatics library data base. Pharmacogenomics also provides an important and reliable basis for evaluation and optimization of the dosage forms as well as repositioning of failed drugs for the treatment of new disease. Various dosage forms of category of drugs such as anticancer drugs, vaccines, gene and DNA delivery systems and immunological agents can be easily evaluated based on the genetic markers of the related disease. The effect of different formulation polymers on pharmacokinetic and pharmacodynamic properties of drugs can be assessed easily and therefore it plays an important role in formulation optimization. However, current applications of pharmacogenomics in drug discovery and formulation optimization are very limited because of costly and non accessible techniques for everyone, but in future, with the advancement in the technology; the application of genomic data in drug discovery will provide us with innovative, safer and more efficacious medicines.

A network-based phenotype mapping approach to identify genes that modulate drug response phenotypes

To better address the problem of drug resistance during cancer chemotherapy and explore the possibility of manipulating drug response phenotypes, we developed a network-based phenotype mapping approach (P-Map) to identify gene candidates that upon perturbed can alter sensitivity to drugs. We used basal transcriptomics data from a panel of human lymphoblastoid cell lines (LCL) to infer drug response networks (DRNs) that are responsible for conferring response phenotypes for anthracycline and taxane, two common anticancer agents use in clinics. We further tested selected gene candidates that interact with phenotypic differentially expressed genes (PDEGs), which are up-regulated genes in LCL for a given class of drug response phenotype in triple-negative breast cancer (TNBC) cells. Our results indicate that it is possible to manipulate a drug response phenotype, from resistant to sensitive or vice versa, by perturbing gene candidates in DRNs and suggest plausible mechanisms regulating directionality of drug response sensitivity. More important, the current work highlights a new way to formulate systems-based therapeutic design: supplementing therapeutics that aim to target disease culprits with phenotypic modulators capable of altering DRN properties with the goal to re-sensitize resistant phenotypes.

Network motifs modulate druggability of cellular targets

Druggability refers to the capacity of a cellular target to be modulated by a small-molecule drug. To date, druggability is mainly studied by focusing on direct binding interactions between a drug and its target. However, druggability is impacted by cellular networks connected to a drug target. Here, we use computational approaches to reveal basic principles of network motifs that modulate druggability. Through quantitative analysis, we find that inhibiting self-positive feedback loop is a more robust and effective treatment strategy than inhibiting other regulations, and adding direct regulations to a drug-target generally reduces its druggability. The findings are explained through analytical solution of the motifs. Furthermore, we find that a consensus topology of highly druggable motifs consists of a negative feedback loop without any positive feedback loops, and consensus motifs with low druggability have multiple positive direct regulations and positive feedback loops. Based on the discovered principles, we predict potential genetic targets in Escherichia coli that have either high or low druggability based on their network context. Our work establishes the foundation toward identifying and predicting druggable targets based on their network topology.

Yin-Chen-Hao Tang Attenuates Severe Acute Pancreatitis in Rat: An Experimental Verification of In silico Network Target Prediction

Yin-Chen-Hao Tang (YCHT) is a classical Chinese medicine compound that has a long history of clinical use in China for the treatment of inflammatory diseases. However, the efficacy and mechanisms of YCHT for the treatment of severe acute pancreatitis (SAP) are not known. The current study investigated the pharmacological properties of YCHT against SAP and its underlying mechanisms. A computational prediction of potential targets of YCHT was initially established based on a network pharmacology simulation. The model suggested that YCHT attenuated SAP progress by apoptosis inducement, anti-inflammation, anti-oxidation and blood lipid regulation. These effects were validated in SAP rats. YCHT administration produced the following results: (1) significantly inhibited the secretion of pancreatic enzymes and protected pancreatic tissue; (2) obviously increased the number of in situ terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL)-positive cells and induced apoptosis; (3) markedly inhibited neutrophil infiltration to the impaired pancreas and reduced the inflammatory reaction; (4) notably enhanced the activities of antioxidant enzymes and decreased the nitric oxide synthase levels; (5) significantly reduced the levels of triglycerides, total cholesterol and low-density lipoprotein and increased high-density lipoprotein; and (6) significantly up-regulated peroxisome proliferator-activated receptor-γ (PPARγ) and down-regulated nuclear factor-kappa B (NF-κB). In summary, these results demonstrated that YCHT attenuated SAP progress by inducing apoptosis, repressing inflammation, alleviating oxidative stress and regulating lipid metabolism partially via regulation of the NF-κB/PPARγ signal pathway.

Systems Pharmacology Dissection of the Integrated Treatment for Cardiovascular and Gastrointestinal Disorders by Traditional Chinese Medicine

Though cardiovascular diseases (CVDs) and gastrointestinal disorders (GIDs) are different diseases associated with different organs, they are highly correlated clinically. Importantly, in Traditional Chinese Medicine (TCM), similar treatment strategies have been applied in both diseases. However, the etiological mechanisms underlying them remain unclear. Here, an integrated systems pharmacology approach is presented for illustrating the molecular correlations between CVDs and GIDs. Firstly, we identified pairs of genes that are associated with CVDs and GIDs and found that these genes are functionally related. Then, the association between 115 heart meridian (HM) herbs and 163 stomach meridian (SM) herbs and their combination application in Chinese patent medicine was investigated, implying that both CVDs and GIDs can be treated by the same strategy. Exemplified by a classical formula Sanhe Decoration (SHD) treating chronic gastritis, we applied systems-based analysis to introduce a drug-target-pathway-organ network that clarifies mechanisms of different diseases being treated by the same strategy. The results indicate that SHD regulated several pathological processes involved in both CVDs and GIDs. We experimentally confirmed the predictions implied by the effect of SHD for myocardial ischemia. The systems pharmacology suggests a novel integrated strategy for rational drug development for complex associated diseases.

Systematic understanding the mechanisms of vitiligo pathogenesis and its treatment by Qubaibabuqi formula

ETHNOPHARMACOLOGICAL RELEVANCE

Vitiligo is a depigmentation disorder, which results in substantial cosmetic disfigurement and poses a detriment to patients' physical as well as mental. Now the molecular pathogenesis of vitiligo still remains unclear, which leads to a daunting challenge for vitiligo therapy in modern medicine. Herbal medicines, characterized by multi-compound and multi-target, have long been shown effective in treating vitiligo, but their molecular mechanisms of action also remain ambiguous.

MATERIALS AND METHODS

Here we proposed a systems pharmacology approach using a clinically effective herb formula as a tool to detect the molecular pathogenesis of vitiligo. This study provided an integrative analysis of active chemicals, drug targets and interacting pathways of the Uygur medicine Qubaibabuqi formula for curing Vitiligo.

RESULTS

The results show that 56 active ingredients of Qubaibabuqi interacting with 83 therapeutic proteins were identified. And Qubaibabuqi probably participate in immunomodulation, neuromodulation and keratinocytes apoptosis inhibition in treatment of vitiligo by a synergistic/cooperative way.

CONCLUSIONS

The drug-target network-based analysis and pathway-based analysis can provide a new approach for understanding the pathogenesis of vitiligo and uncovering the molecular mechanisms of Qubaibabuqi, which will also facilitate the application of traditional Chinese herbs in modern medicine.

Investigating core genetic-and-epigenetic cell cycle networks for stemness and carcinogenic mechanisms, and cancer drug design using big database mining and genome-wide next-generation sequencing data

Recent studies have demonstrated that cell cycle plays a central role in development and carcinogenesis. Thus, the use of big databases and genome-wide high-throughput data to unravel the genetic and epigenetic mechanisms underlying cell cycle progression in stem cells and cancer cells is a matter of considerable interest.

Real genetic-and-epigenetic cell cycle networks (GECNs) of embryonic stem cells (ESCs) and HeLa cancer cells were constructed by applying system modeling, system identification, and big database mining to genome-wide next-generation sequencing data. Real GECNs were then reduced to core GECNs of HeLa cells and ESCs by applying principal genome-wide network projection. In this study, we investigated potential carcinogenic and stemness mechanisms for systems cancer drug design by identifying common core and specific GECNs between HeLa cells and ESCs. Integrating drug database information with the specific GECNs of HeLa cells could lead to identification of multiple drugs for cervical cancer treatment with minimal side-effects on the genes in the common core. We found that dysregulation of miR-29C, miR-34A, miR-98, and miR-215; and methylation of ANKRD1, ARID5B, CDCA2, PIF1, STAMBPL1, TROAP, ZNF165, and HIST1H2AJ in HeLa cells could result in cell proliferation and anti-apoptosis through NFκB, TGF-β, and PI3K pathways. We also identified 3 drugs, methotrexate, quercetin, and mimosine, which repressed the activated cell cycle genes, ARID5B, STK17B, and CCL2, in HeLa cells with minimal side-effects.

Network analysis of genes and their association with diseases

A plethora of network-based approaches within the Systems Biology universe have been applied, to date, to investigate the underlying molecular mechanisms of various human diseases. In the present study, we perform a bipartite, topological and clustering graph analysis in order to gain a better understanding of the relationships between human genetic diseases and the relationships between the genes that are implicated in them. For this purpose, disease-disease and gene-gene networks were constructed from combined gene-disease association networks. The latter, were created by collecting and integrating data from three diverse resources, each one with different content covering from rare monogenic disorders to common complex diseases. This data pluralism enabled us to uncover important associations between diseases with unrelated phenotypic manifestations but with common genetic origin. For our analysis, the topological attributes and the functional implications of the individual networks were taken into account and are shortly discussed. We believe that some observations of this study could advance our understanding regarding the etiology of a disease with distinct pathological manifestations, and simultaneously provide the springboard for the development of preventive and therapeutic strategies and its underlying genetic mechanisms.

Systems Pharmacology Uncovers the Multiple Mechanisms of Xijiao Dihuang Decoction for the Treatment of Viral Hemorrhagic Fever

Background. Viral hemorrhagic fevers (VHF) are a group of systemic diseases characterized by fever and bleeding, which have posed a formidable potential threat to public health with high morbidity and mortality. Traditional Chinese Medicine (TCM) formulas have been acknowledged with striking effects in treatment of hemorrhagic fever syndromes in China's history. Nevertheless, their accurate mechanisms of action are still confusing. Objective. To systematically dissect the mechanisms of action of Chinese medicinal formula Xijiao Dihuang (XJDH) decoction as an effective treatment for VHF. Methods. In this study, a systems pharmacology method integrating absorption, distribution, metabolism, and excretion (ADME) screening, drug targeting, network, and pathway analysis was developed. Results. 23 active compounds of XJDH were obtained and 118 VHF-related targets were identified to have interactions with them. Moreover, systematic analysis of drug-target network and the integrated VHF pathway indicate that XJDH probably acts through multiple mechanisms to benefit VHF patients, which can be classified as boosting immune system, restraining inflammatory responses, repairing the vascular system, and blocking virus spread. Conclusions. The integrated systems pharmacology method provides precise probe to illuminate the molecular mechanisms of XJDH for VHF, which will also facilitate the application of traditional medicine in modern medicine.

A Six-Stage Workflow for Robust Application of Systems Pharmacology

Quantitative and systems pharmacology (QSP) is increasingly being applied in pharmaceutical research and development. One factor critical to the ultimate success of QSP is the establishment of commonly accepted language, technical criteria, and workflows. We propose an integrated workflow that bridges conceptual objectives with underlying technical detail to support the execution, communication, and evaluation of QSP projects.

Quantitative Systems Pharmacology: A Framework for Context

Quantitative Systems Pharmacology (QSP) is receiving increased attention. As the momentum builds and the expectations grow it is important to (re)assess and formalize the basic concepts and approaches. In this short review, I argue that QSP, in addition to enabling the rational integration of data and development of complex models, maybe more importantly, provides the foundations for developing an integrated framework for the assessment of drugs and their impact on disease within a broader context expanding the envelope to account in great detail for physiology, environment and prior history. I articulate some of the critical enablers, major obstacles and exciting opportunities manifesting themselves along the way. Charting such overarching themes will enable practitioners to identify major and defining factors as the field progressively moves towards personalized and precision health care delivery.

Assessing Concordance of Drug-Induced Transcriptional Response in Rodent Liver and Cultured Hepatocytes

The effect of drugs, disease and other perturbations on mRNA levels are studied using gene expression microarrays or RNA-seq, with the goal of understanding molecular effects arising from the perturbation. Previous comparisons of reproducibility across laboratories have been limited in scale and focused on a single model. The use of model systems, such as cultured primary cells or cancer cell lines, assumes that mechanistic insights derived from the models would have been observed via in vivo studies. We examined the concordance of compound-induced transcriptional changes using data from several sources: rat liver and rat primary hepatocytes (RPH) from Drug Matrix (DM) and open TG-GATEs (TG), human primary hepatocytes (HPH) from TG, and mouse liver / HepG2 results from the Gene Expression Omnibus (GEO) repository. Gene expression changes for treatments were normalized to controls and analyzed with three methods: 1) gene level for 9071 high expression genes in rat liver, 2) gene set analysis (GSA) using canonical pathways and gene ontology sets, 3) weighted gene co-expression network analysis (WGCNA). Co-expression networks performed better than genes or GSA when comparing treatment effects within rat liver and rat vs. mouse liver. Genes and modules performed similarly at Connectivity Map-style analyses, where success at identifying similar treatments among a collection of reference profiles is the goal. Comparisons between rat liver and RPH, and those between RPH, HPH and HepG2 cells reveal lower concordance for all methods. We observe that the baseline state of untreated cultured cells relative to untreated rat liver shows striking similarity with toxicant-exposed cells in vivo, indicating that gross systems level perturbation in the underlying networks in culture may contribute to the low concordance.

Gene expression studies in model systems are widely used for understanding the mechanism of drugs and other perturbations in biological systems. Other researchers have examined the reproducibility of microarray studies between laboratories, or comparing microarrays and/or RNA sequencing. However, no large scale studies have compared results from protocols which differ in minor details, or results generated in vivo vs. in vitro culture systems thought to serve as useful models. The rat liver is by far the most extensively studied model evaluating effects of drugs and other perturbations, and existing data allowed us to assess the level of concordance between rat liver and rat primary hepatocytes cultured in collagen-coated plates (i.e. “flat” culture) for hundreds of drugs. We found that the mouse liver serves as a better model of the rat liver than do rat primary hepatocytes, even after allowing for differences due to pharmacokinetics. The low concordance observed between rat liver and rat hepatocytes suggests that validating the utility of ‘omics data generated on emerging cell culture approaches (e.g. “organ-on-a-chip”, 3D-printed tissues) using rat cells and comparison to the rat liver may be necessary in order to gain confidence these approaches substantially improve on traditional culture models of human cells.

Systems psychopharmacology: A network approach to developing novel therapies

The multifactorial origin of most chronic disorders of the brain, including schizophrenia, has been well accepted. Consequently, pharmacotherapy would require multi-targeted strategies. This contrasts to the majority of drug therapies used until now, addressing more or less specifically only one target molecule. Nevertheless, quite some searches for multiple molecular targets specific for mental disorders have been undertaken. For example, genome-wide association studies have been conducted to discover new target genes of disease. Unfortunately, these attempts have not fulfilled the great hopes they have started with. Polypharmacology and network pharmacology approaches of drug treatment endeavor to abandon the one-drug one-target thinking. To this end, most approaches set out to investigate network topologies searching for modules, endowed with "important" nodes, such as "hubs" or "bottlenecks", encompassing features of disease networks, and being useful as tentative targets of drug therapies. This kind of research appears to be very promising. However, blocking or inhibiting "important" targets may easily result in destruction of network integrity. Therefore, it is suggested here to study functions of nodes with lower centrality for more subtle impact on network behavior. Targeting multiple nodes with low impact on network integrity by drugs with multiple activities ("dirty drugs") or by several drugs, simultaneously, avoids to disrupt network integrity and may reset deviant dynamics of disease. Natural products typically display multi target functions and therefore could help to identify useful biological targets. Hence, future efforts should consider to combine drug-target networks with target-disease networks using mathematical (graph theoretical) tools, which could help to develop new therapeutic strategies in long-term psychiatric disorders.

Network-based in silico drug efficacy screening

The increasing cost of drug development together with a significant drop in the number of new drug approvals raises the need for innovative approaches for target identification and efficacy prediction. Here, we take advantage of our increasing understanding of the network-based origins of diseases to introduce a drug-disease proximity measure that quantifies the interplay between drugs targets and diseases. By correcting for the known biases of the interactome, proximity helps us uncover the therapeutic effect of drugs, as well as to distinguish palliative from effective treatments. Our analysis of 238 drugs used in 78 diseases indicates that the therapeutic effect of drugs is localized in a small network neighborhood of the disease genes and highlights efficacy issues for drugs used in Parkinson and several inflammatory disorders. Finally, network-based proximity allows us to predict novel drug-disease associations that offer unprecedented opportunities for drug repurposing and the detection of adverse effects.

Attempts to predict novel use for existing drugs rarely consider information on the impact on the genes perturbed in a given disease. Here, the authors present a novel network-based drug-disease proximity measure that provides insight on gene specific therapeutic effect of drugs and may facilitate drug repurposing.