Network-based approaches in drug discovery and early development

Network pharmacology-integrated molecular docking analysis of phytocompounds of Caesalpinia pulcherrima (peacock flower) as potential anti-metastatic agents

Caesalpinia pulcherrima, or peacock flower, has been a subject of cancer therapeutics research, showing promising anti-cancer and anti-metastatic properties. The present research aims to investigate the anti-metastatic potential of the flower, through bioinformatics approaches. Metastasis targets numbering 471 were identified through overlap analysis following NCBI gene, Gene Card and OMIM query. Phytocompounds of the flower were retrieved from PubChem and their protein interactions predicted using Super-PRED and TargetNet. The 28 targets that overlapped with the predicted proteins were used to generate STRING >0.7. Enrichment analysis revealed that C. pulcherrima may inhibit metastasis through angiogenesis-related and leukocyte migration-related pathways. HSP90AA1, ESR1, PIK3CA, ERBB2, KDR and MMP9 were identified as potential core targets while and 6 compounds (3-[(4-Hydroxyphenyl)methylidene]-7,8-dimethoxychromen-4-one (163076213), clotrimazole (2812), Isovouacapenol A (636673), [(4aR,5R,6aS,7R,11aS,11bR)-4a-hydroxy-4,4,7,11b-tetramethyl-9-oxo-1,2,3,5,6,6a,7,11a-octahydronaphtho[2,1-f][1]benzofuran-5-yl] benzoate (163104827), Stigmast-5-en-3beta-ol (86821) and 4,2'-dihydroxy-4'-methoxychalcone (592216)) were identified as potential core compounds. Molecular docking analysis and molecular dynamics simulations investigations revealed that ERBB2, HSP90AA1 and KDR, along with the newly discovered 163076213 compound to be the most significant metastasis targets and bioactive compound, respectively. These three core targets demonstrated interactions consistent with angiogenesis and leukocyte migration pathways. Furthermore, potentially novel interactions, such as KDR-MMP9, KDR-PIK3CA, ERBB2-HSP90AA1, ERBB2-ESR1, ERBB2-PIK3CA and ERBB2-MMP9 interactions were identified and may play a role in crosslinking the aforementioned metastatic pathways. Therefore, the present study revealed the main mechanisms behind the anti-metastatic effects of C. pulcherrima, paving the path for further research on these compounds and proteins to accelerate the research of cancer therapeutics and application of C. pulcherrima.Communicated by Ramaswamy H. Sarma.

In silico guided in vitro study of traditionally used medicinal plants reveal the alleviation of post-menopausal symptoms through ERβ binding and MAO-A inhibition

The slumping level of estrogen and serotonin in menopausal women is directly associated with the occurrence of menopausal symptoms where, estrogen receptor-β (ERβ) and monoamine oxidase-A (MAO-A) are directly involved. The present investigation aimed for validation of promising plants traditionally used to alleviate menopausal symptoms with ERβ mediated MAO-A inhibition potential through in silico disease-target network construction using Cytoscape plugins followed by molecular docking of phytomolecules through AutoDock vina. ADMET parameters of identified bioactive phytomolecules were analysed through swissADME and ProTox II. The efficacy of promising plant leads was further established through in vitro ERβ competitive binding, MAO-A inhibition, enzyme kinetics and free radical quenching assays. In silico analysis suggested glabrene (ΔG = -9.7 Kcal/mol) as most promising against ERβ in comparison to 17β-estradiol (ΔG = -11.4 Kcal/mol) whereas liquiritigenin (ΔG = -9.4 Kcal/mol) showed potential binding with MAO-A in comparison to standard harmine (ΔG = -8.8 Kcal/mol). In vitro analysis of promising plants segregated Glycyrrhiza glabra (IC50 = 0.052 ± 0.007 μg/ml) as most promising, followed by Hypericum perforatum (IC50 = 0.084 ± 0.01 μg/ml), Trifolium pratense (IC50 = 0.514 ± 0.01 μg/ml) and Rumex nepalensis (IC50 = 2.568 ± 0.11 μg/ml). The enzyme kinetics of promising plant leads showed reversible and competitive nature of inhibition against MAO-A. The potency of plant extracts in quenching free radicals was at par with ascorbic acid. The identified four potent medicinal plants with ERβ selective, MAO-A inhibitory and free radical quenching abilities could be used against menopausal symptoms however, finding needs to be validated further for menopausal symptoms in in vivo conditions for drug development.Communicated by Ramaswamy H. Sarma.

Biological Mechanism-based Neurology and Psychiatry: A BACE1/2 and Downstream Pathway Model

In oncology, comprehensive omics and functional enrichment studies have led to an extensive profiling of (epi)genetic and neurobiological alterations that can be mapped onto a single tumor's clinical phenotype and divergent clinical phenotypes expressing common pathophysiological pathways. Consequently, molecular pathway-based therapeutic interventions for different cancer typologies, namely tumor type- and site-agnostic treatments, have been developed, encouraging the real-world implementation of a paradigm shift in medicine. Given the breakthrough nature of the new-generation translational research and drug development in oncology, there is an increasing rationale to transfertilize this blueprint to other medical fields, including psychiatry and neurology. In order to illustrate the emerging paradigm shift in neuroscience, we provide a state-of-the-art review of translational studies on the β-site amyloid precursor protein cleaving enzyme (BACE) and its most studied downstream effector, neuregulin, which are molecular orchestrators of distinct biological pathways involved in several neurological and psychiatric diseases. This body of data aligns with the evidence of a shared genetic/biological architecture among Alzheimer's disease, schizoaffective disorder, and autism spectrum disorders. To facilitate a forward-looking discussion about a potential first step towards the adoption of biological pathway-based, clinical symptom-agnostic, categorization models in clinical neurology and psychiatry for precision medicine solutions, we engage in a speculative intellectual exercise gravitating around BACE-related science, which is used as a paradigmatic case here. We draw a perspective whereby pathway-based therapeutic strategies could be catalyzed by highthroughput techniques embedded in systems-scaled biology, neuroscience, and pharmacology approaches that will help overcome the constraints of traditional descriptive clinical symptom and syndrome-focused constructs in neurology and psychiatry.

Digital biomarkers and sex impacts in Alzheimer's disease management - potential utility for innovative 3P medicine approach

Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices. Their use has revolutionized clinical research by enabling high-frequency, longitudinal, and sensitive measurements. In the field of neurodegenerative diseases, an example of a digital biomarker-based technology is instrumental activities of daily living (iADL) digital medical application, a predictive biomarker of conversion from mild cognitive impairment (MCI) due to Alzheimer's disease (AD) to dementia due to AD in individuals aged 55 + . Digital biomarkers show promise to transform clinical practice. Nevertheless, their use may be affected by variables such as demographics, genetics, and phenotype. Among these factors, sex is particularly important in Alzheimer's, where men and women present with different symptoms and progression patterns that impact diagnosis. In this study, we explore sex differences in Altoida's digital medical application in a sample of 568 subjects consisting of a clinical dataset (MCI and dementia due to AD) and a healthy population. We found that a biological sex-classifier, built on digital biomarker features captured using Altoida's application, achieved a 75% ROC-AUC (receiver operating characteristic - area under curve) performance in predicting biological sex in healthy individuals, indicating significant differences in neurocognitive performance signatures between males and females. The performance dropped when we applied this classifier to more advanced stages on the AD continuum, including MCI and dementia, suggesting that sex differences might be disease-stage dependent. Our results indicate that neurocognitive performance signatures built on data from digital biomarker features are different between men and women. These results stress the need to integrate traditional approaches to dementia research with digital biomarker technologies and personalized medicine perspectives to achieve more precise predictive diagnostics, targeted prevention, and customized treatment of cognitive decline.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00284-3.

Exploration on Molecular Mechanism of Reversal Effect of Compound Danshen Tablets on Hepatic Fibrosis Based on Network Pharmacology

Objective

To research the molecular mechanism of compound Danshen tablets in the treatment of hepatic fibrosis through network pharmacology.

Methods

Traditional Chinese medicine systems pharmacology (TCMSP) and online Mendelian inheritance in man (OMIM) databases were searched for compound Danshen tablets' active ingredients o and hepatic fibrosis-related genes. The network enrichment of the targets of "herb-compound-target" was visualized and analyzed using Cytoscape software. Then, the screened target genes were used to construct a protein-protein interaction network. The DAVID enrichment database (the database for annotation, visualization, and integrated discovery) was adopted for GO (Gene Ontology) enrichment and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment of vital nodes.

Results

The results yielded 234 targets of compound Danshen tablets; ten important targets (TNF, IL-10, TGF-β1, EGF, CXCL16, CCL21, SERPINB5, SERPINA1, SOD2, and PPIG) for reversing hepatic fibrosis; and four core targets (TNF, IL-10, TGF-1, and EGF). In addition, KEGG enrichment analysis showed that compound Danshen tablets mainly involved FoxO and MAPK signaling pathways, as the key signaling pathways in the treatment of hepatic fibrosis.

Conclusion

TNF, IL-10, TGF-1, and EGF and FOXO and MAPK signaling pathways play a key role in the pathogenesis of hepatic fibrosis. Based on the results of this study, the mechanism of action of compound Danshen tablets in the treatment of hepatic fibrosis may be associated with the regulation of FoxO and MAPK signaling pathways and inhibition of TNF, IL-10, TGF-1, and EGF.

Development of a Multi-Target Strategy for the Treatment of Vitiligo via Machine Learning and Network Analysis Methods

Vitiligo is a complex disorder characterized by the loss of pigment in the skin. The current therapeutic strategies are limited. The identification of novel drug targets and candidates is highly challenging for vitiligo. Here we proposed a systematic framework to discover potential therapeutic targets, and further explore the underlying mechanism of kaempferide, one of major ingredients from Vernonia anthelmintica (L.) willd, for vitiligo. By collecting transcriptome and protein-protein interactome data, the combination of random forest (RF) and greedy articulation points removal (GAPR) methods was used to discover potential therapeutic targets for vitiligo. The results showed that the RF model performed well with AUC (area under the receiver operating characteristic curve) = 0.926, and led to prioritization of 722 important transcriptomic features. Then, network analysis revealed that 44 articulation proteins in vitiligo network were considered as potential therapeutic targets by the GAPR method. Finally, through integrating the above results and proteomic profiling of kaempferide, the multi-target strategy for vitiligo was dissected, including 1) the suppression of the p38 MAPK signaling pathway by inhibiting CDK1 and PBK, and 2) the modulation of cellular redox homeostasis, especially the TXN and GSH antioxidant systems, for the purpose of melanogenesis. Meanwhile, this strategy may offer a novel perspective to discover drug candidates for vitiligo. Thus, the framework would be a useful tool to discover potential therapeutic strategies and drug candidates for complex diseases.

Network-Based Systems Analysis Explains Sequence-Dependent Synergism of Bortezomib and Vorinostat in Multiple Myeloma

Bortezomib and vorinostat exhibit synergistic effects in multiple myeloma (MM) cells when given in sequence, and the purpose of this study was to evaluate the molecular determinants of the interaction using a systems pharmacology approach. A Boolean network model consisting of 79 proteins and 225 connections was developed using literature information characterizing mechanisms of drug action and intracellular signal transduction. Network visualization and structural analysis were conducted, and model simulations were compared with experimental data. Critical biomarkers, such as pNFκB, p53, cellular stress, and p21, were identified using measures of network centrality and model reduction. U266 cells were then exposed to bortezomib (3 nM) and vorinostat (2 μM) as single agents or in simultaneous and sequential (bortezomib for first 24 h, followed by addition of vorinostat for another 24 h) combinations. Temporal changes for nine of the critical proteins in the reduced Boolean model were measured over 48 h, and cellular proliferation was measured over 96 h. A mechanism-based systems model was developed that captured the biological basis of a bortezomib and vorinostat sequence-dependent pharmacodynamic interaction. The model was further extended in vivo by linking in vitro parameter values and dynamics of p21, caspase-3, and pAKT biomarkers to tumor growth in xenograft mice reported in the literature. Network-based methodologies and pharmacodynamic principles were integrated successfully to evaluate bortezomib and vorinostat interactions in a mechanistic and quantitative manner. The model can be potentially applied to evaluate their combination regimens and explore in vivo dosing regimens.

Diclofenac Administration after Physical Training Blunts Adaptations of Peripheral Systems and Leads to Losses in Exercise Performance: In Vivo and In Silico Analyses

Recovery in athletes is hampered by soreness and fatigue. Consequently, nonsteroidal anti-inflammatory drugs are used as an effective strategy to maintain high performance. However, impact of these drugs on adaptations induced by training remains unknown. This study assessed the effects of diclofenac administration (10 mg/kg/day) on rats subjected to an exhaustive test, after six weeks of swimming training. Over the course of 10 days, three repeated swimming bouts were performed, and diclofenac or saline were administered once a day. Trained animals exhibited higher muscle citrate synthase and lower plasma creatinine kinase activities as compared to sedentary animals, wherein diclofenac had no impact. Training increased time to exhaustion, however, diclofenac blunted this effect. It also impaired the increase in plasma and liver interleukin-6 levels. The trained group exhibited augmented catalase, glutathione peroxidase, and glutathione reductase activities, and a higher ratio of reduced-to-oxidized glutathione in the liver. However, diclofenac treatment blunted all these effects. Systems biology analysis revealed a close relationship between diclofenac and liver catalase. These results confirmed that regular exercise induces inflammation and oxidative stress, which are crucial for tissue adaptations. Altogether, diclofenac treatment might be helpful in preventing pain and inflammation, but its use severely affects performance and tissue adaptation.

Artificial intelligence guided discovery of a barrier-protective therapy in inflammatory bowel disease

Modeling human diseases as networks simplify complex multi-cellular processes, helps understand patterns in noisy data that humans cannot find, and thereby improves precision in prediction. Using Inflammatory Bowel Disease (IBD) as an example, here we outline an unbiased AI-assisted approach for target identification and validation. A network was built in which clusters of genes are connected by directed edges that highlight asymmetric Boolean relationships. Using machine-learning, a path of continuum states was pinpointed, which most effectively predicted disease outcome. This path was enriched in gene-clusters that maintain the integrity of the gut epithelial barrier. We exploit this insight to prioritize one target, choose appropriate pre-clinical murine models for target validation and design patient-derived organoid models. Potential for treatment efficacy is confirmed in patient-derived organoids using multivariate analyses. This AI-assisted approach identifies a first-in-class gut barrier-protective agent in IBD and predicted Phase-III success of candidate agents.

Traditional drug discovery process use differential, Bayesian and other network based approaches. We developed a Boolean approach for building disease maps and prioritizing pre-clinical models to discover a first-in-class therapy to restore and protect the leaky gut barrier in inflammatory bowel disease.

System Genetics Including Causal Inference Identify Immune Targets for Coronary Artery Disease and the Lifespan

BACKGROUND

Randomized clinical trials indicate that the immune response plays a significant role in coronary artery disease (CAD), a disorder impacting the lifespan potential. However, the identification of targets critical to the immune response in atheroma is still hampered by a lack of solid inference.

METHODS

Herein, we implemented a system genetics approach to identify causally associated immune targets implicated in atheroma. We leveraged genome-wide association studies to perform mapping and Mendelian randomization to assess causal associations between gene expression in blood cells with CAD and the lifespan. Expressed genes (eGenes) were prioritized in network and in single-cell expression derived from plaque immune cells.

RESULTS

Among 840 CAD-associated blood eGenes, 37 were predicted causally associated with CAD and 6 were also associated with the parental lifespan in Mendelian randomization. In multivariable Mendelian randomization, the impact of eGenes on the lifespan potential was mediated by the CAD risk. Predicted causal eGenes were central in network. FLT1 and CCR5 were identified as targets of approved drugs, whereas 22 eGenes were deemed tractable for the development of small molecules and antibodies. Analyses of plaque immune single-cell expression identified predicted causal eGenes enriched in macrophages (GPX1, C4orf3) and involved in ligand-receptor interactions (CCR5).

CONCLUSIONS

We identified 37 blood eGenes predicted causally associated with CAD. The predicted expression for 6 eGenes impacted the lifespan potential through the risk of CAD. Prioritization based on network, annotations, and single-cell expression identified targets deemed tractable for the development of drugs and for drug repurposing.

Machine and deep learning approaches for cancer drug repurposing

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.

Engineering of fluorescent or photoactive Trojan probes for detection and eradication of β-Amyloids

Trojan horse technology institutes a potentially promising strategy to bring together a diagnostic or cell-based drug design and a delivery platform. It provides the opportunity to re-engineer a novel multimodal, neurovascular detection probe, or medicine to fuse with blood-brain barrier (BBB) molecular Trojan horse. In Alzheimer's disease (AD) this could allow the targeted delivery of detection or therapeutic probes across the BBB to the sites of plaques and tangles development to image or decrease amyloid load, enhance perivascular Aβ clearance, and improve cerebral blood flow, owing principally to the significantly improved cerebral permeation. A Trojan horse can also be equipped with photosensitizers, nanoparticles, quantum dots, or fluorescent molecules to function as multiple targeting theranostic compounds that could be activated following changes in disease-specific processes of the diseased tissue such as pH and protease activity, or exogenous stimuli such as, light. This concept review theorizes the use of receptor-mediated transport-based platforms to transform such novel ideas to engineer systemic and smart Trojan detection or therapeutic probes to advance the neurodegenerative field.

Drug-target-ADR Network and Possible Implications of Structural Variants in Adverse Events

Adverse drug reactions (ADRs) are of major concern in drug safety. However, due to the biological complexity of human systems, understanding the underlying mechanisms involved in development of ADRs remains a challenging task. Here, we applied network sciences to analyze a tripartite network between 1000 drugs, 1407 targets, and 6164 ADRs. It allowed us to suggest drug targets susceptible to be associated to ADRs and organs, based on the system organ class (SOC). Furthermore, a score was developed to determine the contribution of a set of proteins to ADRs. Finally, we identified proteins that might increase the susceptibility of genes to ADRs, on the basis of knowledge about genomic structural variation in genes encoding proteins targeted by drugs. Such analysis should pave the way to individualize drug therapy and precision medicine.

Computational/in silico methods in drug target and lead prediction

Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.

Future avenues for Alzheimer's disease detection and therapy: liquid biopsy, intracellular signaling modulation, systems pharmacology drug discovery

When Alzheimer's disease (AD) disease-modifying therapies will be available, global healthcare systems will be challenged by a large-scale demand for clinical and biological screening. Validation and qualification of globally accessible, minimally-invasive, and time-, cost-saving blood-based biomarkers need to be advanced. Novel pathophysiological mechanisms (and related candidate biomarkers) - including neuroinflammation pathways (TREM2 and YKL-40), axonal degeneration (neurofilament light chain protein), synaptic dysfunction (neurogranin, synaptotagmin, α-synuclein, and SNAP-25) - may be integrated into an expanding pathophysiological and biomarker matrix and, ultimately, integrated into a comprehensive blood-based liquid biopsy, aligned with the evolving ATN + classification system and the precision medicine paradigm. Liquid biopsy-based diagnostic and therapeutic algorithms are increasingly employed in Oncology disease-modifying therapies and medical practice, showing an enormous potential for AD and other brain diseases as well. For AD and other neurodegenerative diseases, newly identified aberrant molecular pathways have been identified as suitable therapeutic targets and are currently investigated by academia/industry-led R&D programs, including the nerve-growth factor pathway in basal forebrain cholinergic neurons, the sigma1 receptor, and the GTPases of the Rho family. Evidence for a clinical long-term effect on cognitive function and brain health span of cholinergic compounds, drug candidates for repositioning programs, and non-pharmacological multidomain interventions (nutrition, cognitive training, and physical activity) is developing as well. Ultimately, novel pharmacological paradigms, such as quantitative systems pharmacology-based integrative/explorative approaches, are gaining momentum to optimize drug discovery and accomplish effective pathway-based strategies for precision medicine. This article is part of the special issue on 'The Quest for Disease-Modifying Therapies for Neurodegenerative Disorders'.

Biological Network Approaches and Applications in Rare Disease Studies

Network biology has the capability to integrate, represent, interpret, and model complex biological systems by collectively accommodating biological omics data, biological interactions and associations, graph theory, statistical measures, and visualizations. Biological networks have recently been shown to be very useful for studies that decipher biological mechanisms and disease etiologies and for studies that predict therapeutic responses, at both the molecular and system levels. In this review, we briefly summarize the general framework of biological network studies, including data resources, network construction methods, statistical measures, network topological properties, and visualization tools. We also introduce several recent biological network applications and methods for the studies of rare diseases.

Design of Gallinamide A Analogs as Potent Inhibitors of the Cysteine Proteases Human Cathepsin L and Trypanosoma cruzi Cruzain

Gallinamide A, originally isolated with a modest antimalarial activity, was subsequently reisolated and characterized as a potent, selective, and irreversible inhibitor of the human cysteine protease cathepsin L. Molecular docking identified potential modifications to improve binding, which were synthesized as a suite of analogs. Resultingly, this current study produced the most potent gallinamide analog yet tested against cathepsin L (10, K_i = 0.0937 ± 0.01 nM and k_inact/K_i = 8 730 000). From a protein structure and substrate preference perspective, cruzain, an essential Trypanosoma cruzi cysteine protease, is highly homologous. Our investigations revealed that gallinamide and its analogs potently inhibit cruzain and are exquisitely toxic toward T. cruzi in the intracellular amastigote stage. The most active compound, 5, had an IC₅₀ = 5.1 ± 1.4 nM, but was relatively inactive to both the epimastigote (insect stage) and the host cell, and thus represents a new candidate for the treatment of Chagas disease.

A Network Pharmacology Approach for the Identification of Common Mechanisms of Drug-Induced Peripheral Neuropathy

Drug-induced peripheral neuropathy is a side effect of a variety of therapeutic agents that can affect therapeutic adherence and lead to regimen modifications, impacting patient quality of life. The molecular mechanisms involved in the development of this condition have yet to be completely described in the literature. We used a computational network pharmacology approach to explore the Connectivity Map, a large collection of transcriptional profiles from drug perturbation experiments to identify common genes affected by peripheral neuropathy-inducing drugs. Consensus profiles for 98 of these drugs were used to construct a drug-gene perturbation network. We identified 27 genes significantly associated with neuropathy-inducing drugs. These genes may have a potential role in the action of neuropathy-inducing drugs. Our results suggest that molecular mechanisms, including alterations in mitochondrial function, microtubule and cytoskeleton function, ion channels, transcriptional regulation including epigenetic mechanisms, signal transduction, and wound healing, may play a critical role in drug-induced peripheral neuropathy.

Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes

Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for BRAF-mutant melanoma that was top ranked by our approach, vemurafenib (BRAF inhibitor) + tretinoin (retinoic acid receptor agonist), using both in vitro and in vivo models of BRAF-mutant melanoma and RNA-sequencing analysis of drug-treated melanoma cells to validate the predicted mechanisms. Our approach is applicable to a wide range of disease domains, and, importantly, can model disease-relevant protein subnetworks in precision medicine contexts.

In silico drug repositioning based on drug-miRNA associations

Drug repositioning has become a prevailing tactic as this strategy is efficient, economical and low risk for drug discovery. Meanwhile, recent studies have confirmed that small-molecule drugs can modulate the expression of disease-related miRNAs, which indicates that miRNAs are promising therapeutic targets for complex diseases. In this study, we put forward and verified the hypothesis that drugs with similar miRNA profiles may share similar therapeutic properties. Furthermore, a comprehensive drug–drug interaction network was constructed based on curated drug-miRNA associations. Through random network comparison, topological structure analysis and network module extraction, we found that the closely linked drugs in the network tend to treat the same diseases. Additionally, the curated drug–disease relationships (from the CTD) and random walk with restarts algorithm were utilized on the drug–drug interaction network to identify the potential drugs for a given disease. Both internal validation (leave-one-out cross-validation) and external validation (independent drug–disease data set from the ChEMBL) demonstrated the effectiveness of the proposed approach. Finally, by integrating drug-miRNA and miRNA-disease information, we also explain the modes of action of drugs in the view of miRNA regulation. In summary, our work could determine novel and credible drug indications and offer novel insights and valuable perspectives for drug repositioning.

Differential Coexpression Network Analysis for Gene Expression Data

Gene expression profiling by microarray has been used to uncover molecular variations in many areas. The traditional analysis method to gene expression profiling just focuses on the individual genes, and the interactions among genes are ignored, while genes play their roles not by isolations but by interactions with each other. Consequently, gene-to-gene coexpression analysis emerged as a powerful approach to solve the above problems. Then complementary to the conventional differential expression analysis, the differential coexpression analysis can identify gene markers from the systematic level. There are three aspects for differential coexpression network analysis including the network global topological comparison, differential coexpression module identification, and differential coexpression genes and gene pairs identification. To date, the coexpression network and differential coexpression analysis are widely used in a variety of areas in response to environmental stresses, genetic differences, or disease changes. In this chapter, we reviewed the existing methods for differential coexpression network analysis and discussed the applications to cancer research.

dbDEPC 3.0: the database of differentially expressed proteins in human cancer with multi-level annotation and drug indication

Proteins are major effectors of biological functions, and differentially expressed proteins (DEPs) are widely reported as biomarkers in pathological mechanism, prognosis prediction as well as treatment targeting in cancer research. High-throughput technology of mass spectrometry (MS) has identified large amounts of DEPs in human cancers. Through mining published researches with detailed experiment information, dbDEPC was the first database aimed to provide a systematic resource for the storage and query of the DEPs generated by MS in cancer research. It was updated to dbDEPC 2.0 in 2012. Here, we provide another updated version of dbDEPC, with improvement of database contents and enhanced web interface. The current version of dbDEPC 3.0 contains 11 669 unique DEPs in 26 different cancer types. Multi-level annotations of DEPs have been firstly introduced this time, including cancer-related peptide amino acid variations, post-translational modifications and drug information. Moreover, these multi-level annotations can be displayed in the biological networks, which can benefit integrative analysis. Finally, an online enrichment analysis tool has been developed, to support a KEGG enrichment analysis and to browse the relationship among interested protein list and known DEPs in KEGG pathways. In summary, dbDEPC 3.0 provides a comprehensive resource for accessing integrated and highly annotated DEPs in human cancer.

Database URL: https://www.scbit.org/dbdepc3/index.php

Cognitive Impairment in Chronic Obstructive Pulmonary Disease (COPD): Possible Utility of Marine Bioactive Compounds

Chronic obstructive pulmonary disease (COPD) is characterized by long-term airflow limitation. Early-onset COPD in non-smoker subjects is ≥60 years and in the elderly is often associated with different comorbidities. Cognitive impairment is one of the most common feature in patients with COPD, and is associated with COPD severity and comorbidities. Cognitive impairment in COPD enhances the assistance requirement in different aspects of daily living, treatment adherence, and effectual self-management.This review describes various bioactive compounds of natural marine sources that modulate different targets shared by both COPD and cognitive impairment and hypothesizes a possible link between these two syndromes.

Precision medicine and drug development in Alzheimer's disease: the importance of sexual dimorphism and patient stratification

Neurodegenerative diseases (ND) are among the leading causes of disability and mortality. Considerable sex differences exist in the occurrence of the various manifestations leading to cognitive decline. Alzheimer's disease (AD) exhibits substantial sexual dimorphisms and disproportionately affects women. Women have a higher life expectancy compared to men and, consequently, have more lifespan to develop AD. The emerging precision medicine and pharmacology concepts - taking into account the individual genetic and biological variability relevant for disease risk, prevention, detection, diagnosis, and treatment - are expected to substantially enhance our knowledge and management of AD. Stratifying the affected individuals by sex and gender is an important basic step towards personalization of scientific research, drug development, and care. We hypothesize that sex and gender differences, extending from genetic to psychosocial domains, are highly relevant for the understanding of AD pathophysiology, and for the conceptualization of basic/translational research and for clinical therapy trial design.

Network-Based Analysis of Bortezomib Pharmacodynamic Heterogeneity in Multiple Myeloma Cells

The objective of this study is to evaluate the heterogeneity in pharmacodynamic response in four in vitro multiple myeloma cell lines to treatment with bortezomib, and to assess whether such differences are associated with drug-induced intracellular signaling protein dynamics identified via a logic-based network modeling approach. The in vitro pharmacodynamic-efficacy of bortezomib was evaluated through concentration-effect and cell proliferation dynamical studies in U266, RPMI8226, MM.1S, and NCI-H929 myeloma cell lines. A Boolean logic-based network model incorporating intracellular protein signaling pathways relevant to myeloma cell growth, proliferation, and apoptosis was developed based on information available in the literature and used to identify key proteins regulating bortezomib pharmacodynamics. The time-course of network-identified proteins was measured using the MAGPIX protein assay system. Traditional pharmacodynamic modeling endpoints revealed variable responses of the cell lines to bortezomib treatment, classifying cell lines as more sensitive (MM.1S and NCI-H929) and less sensitive (U266 and RPMI8226). Network centrality and model reduction identified key proteins (e.g., phosphorylated nuclear factor-κB, phosphorylated protein kinase B, phosphorylated mechanistic target of rapamycin, Bcl-2, phosphorylated c-Jun N-terminal kinase, phosphorylated p53, p21, phosphorylated Bcl-2-associated death promoter, caspase 8, and caspase 9) that govern bortezomib pharmacodynamics. The corresponding relative expression (normalized to 0-hour untreated-control cells) of proteins demonstrated a greater magnitude and earlier onset of stimulation/inhibition in cells more sensitive (MM.1S and NCI-H929) to bortezomib-induced cell death at 20 nM, relative to the less sensitive cells (U266 and RPMI8226). Overall, differences in intracellular signaling appear to be associated with bortezomib pharmacodynamic heterogeneity, and key proteins may be potential biomarkers to evaluate bortezomib responses.

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

The quintessential biological response to disease is inflammation. It is a driver and an important element in a wide range of pathological states. Pharmacological management of inflammation is therefore central in the clinical setting. Anti-inflammatory drugs modulate specific molecules involved in the inflammatory response; these drugs are traditionally classified as steroidal and non-steroidal drugs. However, the effects of these drugs are rarely limited to their canonical targets, affecting other molecules and altering biological functions with system-wide effects that can lead to the emergence of secondary therapeutic applications or adverse drug reactions (ADRs). In this study, relationships among anti-inflammatory drugs, functional pathways, and ADRs were explored through network models. We integrated structural drug information, experimental anti-inflammatory drug perturbation gene expression profiles obtained from the Connectivity Map and Library of Integrated Network-Based Cellular Signatures, functional pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases, as well as adverse reaction information from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The network models comprise nodes representing anti-inflammatory drugs, functional pathways, and adverse effects. We identified structural and gene perturbation similarities linking anti-inflammatory drugs. Functional pathways were connected to drugs by implementing Gene Set Enrichment Analysis (GSEA). Drugs and adverse effects were connected based on the proportional reporting ratio (PRR) of an adverse effect in response to a given drug. Through these network models, relationships among anti-inflammatory drugs, their functional effects at the pathway level, and their adverse effects were explored. These networks comprise 70 different anti-inflammatory drugs, 462 functional pathways, and 1,175 ADRs. Network-based properties, such as degree, clustering coefficient, and node strength, were used to identify new therapeutic applications within and beyond the anti-inflammatory context, as well as ADR risk for these drugs, helping to select better repurposing candidates. Based on these parameters, we identified naproxen, meloxicam, etodolac, tenoxicam, flufenamic acid, fenoprofen, and nabumetone as candidates for drug repurposing with lower ADR risk. This network-based analysis pipeline provides a novel way to explore the effects of drugs in a therapeutic space.

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.

Systematic identification of latent disease-gene associations from PubMed articles

Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research.

Biological network border detection

Complex networks have been widely used to model biological systems. The concept of accessibility has been proposed recently as a means to organize the nodes of complex networks as belonging to its border or center. Such an approach paves the way to investigating how the functional and structural properties of nodes vary with their respective position in the networks. In this work, we approach such a problem in a biological context applying border detection to Protein-Protein Interaction networks from four organisms of the Mycoplasma genus. We found evidence that the borderness of proteins bears a relation with their spatial organization and molecular function specificity.

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.

PhID: An Open-Access Integrated Pharmacology Interactions Database for Drugs, Targets, Diseases, Genes, Side-Effects, and Pathways

The current network pharmacology study encountered a bottleneck with a lot of public data scattered in different databases. There is a lack of an open-access and consolidated platform that integrates this information for systemic research. To address this issue, we have developed PhID, an integrated pharmacology database which integrates >400 000 pharmacology elements (drug, target, disease, gene, side-effect, and pathway) and >200 000 element interactions in branches of public databases. PhID has three major applications: (1) assisting scientists searching through the overwhelming amount of pharmacology element interaction data by names, public IDs, molecule structures, or molecular substructures; (2) helping visualizing pharmacology elements and their interactions with a web-based network graph; and (3) providing prediction of drug-target interactions through two modules: PreDPI-ki and FIM, by which users can predict drug-target interactions of PhID entities or some drug-target pairs of their own interest. To get a systems-level understanding of drug action and disease complexity, PhID as a network pharmacology tool was established from the perspective of data layer, visualization layer, and prediction model layer to present information untapped by current databases.

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.

Cooperative Targets of Combined mTOR/HDAC Inhibition Promote MYC Degradation

Cancer treatments often require combinations of molecularly targeted agents to be effective. mTORi (rapamycin) and HDACi (MS-275/entinostat) inhibitors have been shown to be effective in limiting tumor growth, and here we define part of the cooperative action of this drug combination. More than 60 human cancer cell lines responded synergistically (CI<1) when treated with this drug combination compared with single agents. In addition, a breast cancer patient-derived xenograft, and a BCL-XL plasmacytoma mouse model both showed enhanced responses to the combination compared with single agents. Mice bearing plasma cell tumors lived an average of 70 days longer on combination treatment compared with single agents. A set of 37 genes cooperatively affected (34 downregulated; 3 upregulated) by the combination responded pharmacodynamically in human myeloma cell lines, xenografts, and a P493 model, and were both enriched in tumors, and correlated with prognostic markers in myeloma patient datasets. Genes downregulated by the combination were overexpressed in several untreated cancers (breast, lung, colon, sarcoma, head and neck, myeloma) compared with normal tissues. The MYC/E2F axis, identified by upstream regulator analyses and validated by immunoblots, was significantly inhibited by the drug combination in several myeloma cell lines. Furthermore, 88% of the 34 genes downregulated have MYC-binding sites in their promoters, and the drug combination cooperatively reduced MYC half-life by 55% and increased degradation. Cells with MYC mutations were refractory to the combination. Thus, integrative approaches to understand drug synergy identified a clinically actionable strategy to inhibit MYC/E2F activity and tumor cell growth in vivoMol Cancer Ther; 16(9); 2008-21. ©2017 AACR.

Finding melanoma drugs through a probabilistic knowledge graph

Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing treatments but no known cure. The omics data explosion has created many possible drug candidates; however, filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an application programming interface or web interface, and has generated 25 high-quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.

Combination therapeutics in complex diseases

The biological redundancies in molecular networks of complex diseases limit the efficacy of many single drug therapies. Combination therapeutics, as a common therapeutic method, involve pharmacological intervention using several drugs that interact with multiple targets in the molecular networks of diseases and may achieve better efficacy and/or less toxicity than monotherapy in practice. The development of combination therapeutics is complicated by several critical issues, including identifying multiple targets, targeting strategies and the drug combination. This review summarizes the current achievements in combination therapeutics, with a particular emphasis on the efforts to develop combination therapeutics for complex diseases.

A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine

Drug candidates exhibiting well-defined pharmacokinetic and pharmacodynamic profiles that are otherwise safe often fail to demonstrate proof-of-concept in phase II and III trials. Innovation in drug discovery and development has been identified as a critical need for improving the efficiency of drug discovery, especially through collaborations between academia, government agencies, and industry. To address the innovation challenge, we describe a comprehensive, unbiased, integrated, and iterative quantitative systems pharmacology (QSP)-driven drug discovery and development strategy and platform that we have implemented at the University of Pittsburgh Drug Discovery Institute. Intrinsic to QSP is its integrated use of multiscale experimental and computational methods to identify mechanisms of disease progression and to test predicted therapeutic strategies likely to achieve clinical validation for appropriate subpopulations of patients. The QSP platform can address biological heterogeneity and anticipate the evolution of resistance mechanisms, which are major challenges for drug development. The implementation of this platform is dedicated to gaining an understanding of mechanism(s) of disease progression to enable the identification of novel therapeutic strategies as well as repurposing drugs. The QSP platform will help promote the paradigm shift from reactive population-based medicine to proactive personalized medicine by focusing on the patient as the starting and the end point.

Metabolomics as a Challenging Approach for Medicinal Chemistry and Personalized Medicine

"Omics" sciences have been developed to provide a holistic point of view of biology and to better understand the complexity of an organism as a whole. These systems biology approaches can be examined at different levels, starting from the most fundamental, i.e., the genome, and finishing with the most functional, i.e., the metabolome. Similar to how genomics is applied to the exploration of DNA, metabolomics is the qualitative and quantitative study of metabolites. This emerging field is clearly linked to genomics, transcriptomics, and proteomics. In addition, metabolomics provides a unique and direct vision of the functional outcome of an organism's activities that are required for it to survive, grow, and respond to internal and external stimuli or stress, e.g., pathologies and drugs. The links between metabolic changes, patient phenotype, physiological and/or pathological status, and treatment are now well established and have opened a new area for the application of metabolomics in the drug discovery process and in personalized medicine.

Process Pharmacology: A Pharmacological Data Science Approach to Drug Development and Therapy

A novel functional‐genomics based concept of pharmacology that uses artificial intelligence techniques for mining and knowledge discovery in “big data” providing comprehensive information about the drugs’ targets and their functional genomics is proposed. In “process pharmacology”, drugs are associated with biological processes. This puts the disease, regarded as alterations in the activity in one or several cellular processes, in the focus of drug therapy. In this setting, the molecular drug targets are merely intermediates. The identification of drugs for therapeutic or repurposing is based on similarities in the high‐dimensional space of the biological processes that a drug influences. Applying this principle to data associated with lymphoblastic leukemia identified a short list of candidate drugs, including one that was recently proposed as novel rescue medication for lymphocytic leukemia. The pharmacological data science approach provides successful selections of drug candidates within development and repurposing tasks.

A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases

Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 10⁵ compounds and several functional relations among 1.67 10⁵ proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature.

Neglected tropical diseases are human infectious diseases that are often associated with poverty. Historically, lack of interest from the pharmaceutical industry resulted in the lack of good drugs to combat the majority of the pathogens that cause these diseases. Recently, the availability of open chemical information has increased with the advent of public domain chemical resources and the release of data from high throughput screening assays. Our aim in this work was to make use of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to prioritize and identify candidate drug targets in neglected pathogen proteomes, and drug-like bioactive molecules to foster drug development against neglected diseases. Our approach to the problem relied on applying bioinformatics and computational biology strategies to model large datasets spanning complete proteomes and extensive chemical information from publicly available sources. As a result, we were able to prioritize drug targets and identify potential targets for orphan bioactive drugs.

Saffron'omics': The challenges of integrating omic technologies

Saffron is one of the highly exotic spices known for traditional values and antiquity. It is used for home décor besides serving as a colorant flavor and is widely known for medicinal value. Over the last few years, saffron has garnered a lot of interest due to its anti-cancer, anti-mutagenic, anti-oxidant and immunomodulatory properties. Integration of systems biology approaches with wide applications of saffron remains a growing challenge as new techniques and methods advance. Keeping in view of the dearth of a review summarizing the omics and systems biology of saffron, we bring an outline on advancements in integrating omic technologies, the medicinal plant has seen in recent times.

Novel Holistic Approaches for Overcoming Therapy Resistance in Pancreatic and Colon Cancers

Gastrointestinal (GI) cancers, such as of the colon and pancreas, are highly resistant to both standard and targeted therapeutics. Therapy-resistant and heterogeneous GI cancers harbor highly complex signaling networks (the resistome) that resist apoptotic programming. Commonly used gemcitabine or platinum-based regimens fail to induce meaningful (i.e. disease-reversing) perturbations in the resistome, resulting in high rates of treatment failure. The GI cancer resistance networks are, in part, due to interactions between parallel signaling and aberrantly expressed microRNAs (miRNAs) that collectively promote the development and survival of drug-resistant cancer stem cells with epithelial-to-mesenchymal transition (EMT) characteristics. The lack of understanding of the resistance networks associated with this subpopulation of cells as well as reductionist, single protein-/pathway-targeted approaches have made 'effective drug design' a difficult task. We propose that the successful design of novel therapeutic regimens to target drug-resistant GI tumors is only possible if network-based drug avenues and agents, in particular 'natural agents' with no known toxicity, are correctly identified. Natural agents (dietary agents or their synthetic derivatives) can individually alter miRNA profiles, suppress EMT pathways and eliminate cancer stem-like cells that derive from pancreatic cancer and colon cancer, by partially targeting multiple yet meaningful networks within the GI cancer resistome. However, the efficacy of these agents as combinations (e.g. consumed in the diet) against this resistome has never been studied. This short review article provides an overview of the different challenges involved in the understanding of the GI resistome, and how novel computational biology can help in the design of effective therapies to overcome resistance.

Constellation pharmacology: a new paradigm for drug discovery

Constellation pharmacology is a cell-based high-content phenotypic-screening platform that utilizes subtype-selective pharmacological agents to elucidate the cell-specific combinations (constellations) of key signaling proteins that define specific cell types. Heterogeneous populations of native cells, in which the different individual cell types have been identified and characterized, are the foundation for this screening platform. Constellation pharmacology is useful for screening small molecules or for deconvoluting complex mixtures of biologically active natural products. This platform has been used to purify natural products and discover their molecular mechanisms. In the ongoing development of constellation pharmacology, there is a positive feedback loop between the pharmacological characterization of cell types and screening for new drug candidates. As constellation pharmacology is used to discover compounds with novel targeting-selectivity profiles, those new compounds then further help to elucidate the constellations of specific cell types, thereby increasing the content of this high-content platform.