Systems pharmacology and genome medicine: a future perspective

Systematic Identification of Genomic Markers for Guiding Iron Oxide Nanoparticles in Cervical Cancer Based on Translational Bioinformatics

Purpose

Magnetic iron oxide nanoparticle (MNP) drug delivery system is a novel promising therapeutic option for cancer treatment. Material issues such as fabrication and functionalized modification have been investigated; however, pharmacologic mechanisms of bare MNPs inside cancer cells remain obscure. This study aimed to explore a systems pharmacology approach to understand the reaction of the whole cell to MNPs and suggest drug selection in MNP delivery systems to exert synergetic or additive anti-cancer effects.

Methods

HeLa and SiHa cell lines were used to estimate the properties of bare MNPs in cervical cancer through 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide (MTT) and enzyme activity assays and cellular fluorescence imaging. A systems pharmacology approach was utilized by combining bioinformatics data mining with clinical data analysis and without a predefined hypothesis. Key genes of the MNP onco-pharmacologic mechanism in cervical cancer were identified and further validated through transcriptome analysis with quantitative reverse transcription PCR (qRT-PCR).

Results

Low cytotoxic activity and cell internalization of MNP in HeLa and SiHa cells were observed. Lysosomal function was found to be impaired after MNP treatment. Protein tyrosine kinase 2 beta (PTK2B), liprin-alpha-4 (PPFIA4), mothers against decapentaplegic homolog 7 (SMAD7), and interleukin (IL) 1B were identified as key genes relevant for MNP pharmacology, clinical features, somatic mutation, and immune infiltration. The four key genes also exhibited significant correlations with the lysosome gene set. The qRT-PCR results showed significant alterations in the expression of the four key genes after MNP treatment in HeLa and SiHa cells.

Conclusion

Our research suggests that treatment of bare MNPs in HeLa and SiHa cells induced significant expression changes in PTK2B, PPFIA4, SMAD7, and IL1B, which play crucial roles in cervical cancer development and progression. Interactions of the key genes with specific anti-cancer drugs must be considered in the rational design of MNP drug delivery systems.

Osteoarthritis, Corticosteroids and Role of CYP Genes in COVID-19 Patients: A Mini Review

Objectives of this review is to evaluate the role of cytochrome P450 gene polymorphisms in COVID-19 infected patients with pre-existing OA on corticosteroids. The purpose of this review is to analyze whether polymorphisms of Cytochrome p450 isoforms (CYP2C9 and CYP3A4) affect the dosage of steroids in OA patients in COVID-19 infected patients. This review may provide more therapeutic options; suggest a few guidelines which may be useful in managing COVID-19 patients with pre-existing osteoarthritis. The important role of corticosteroids in treating patients infected with COVID-19 with preexisting osteoarthritis, its influence on incidence of mortality or morbidity may be highlighted. The influence of CYP enzymes and their polymorphisms suggest safety of treatments as well as the possible need for the dosage adjustment or their discontinuation.

From data to QSP models: a pipeline for using Boolean networks for hypothesis inference and dynamic model building

Quantitative Systems Pharmacology (QSP) models capture the physiological underpinnings driving the response to a drug and express those in a semi-mechanistic way, often involving ordinary differential equations (ODEs). The process of developing a QSP model generally starts with the definition of a set of reasonable hypotheses that would support a mechanistic interpretation of the expected response which are used to form a network of interacting elements. This is a hypothesis-driven and knowledge-driven approach, relying on prior information about the structure of the network. However, with recent advances in our ability to generate large datasets rapidly, often in a hypothesis-neutral manner, the opportunity emerges to explore data-driven approaches to establish the network topologies and models in a robust, repeatable manner. In this paper, we explore the possibility of developing complex network representations of physiological responses to pharmaceuticals using a logic-based analysis of available data and then convert the logic relations to dynamic ODE-based models. We discuss an integrated pipeline for converting data to QSP models. This pipeline includes using k-means clustering to binarize continuous data, inferring likely network relationships using a Best-Fit Extension method to create a Boolean network, and finally converting the Boolean network to a continuous ODE model. We utilized an existing QSP model for the dual-affinity re-targeting antibody flotetuzumab to demonstrate the robustness of the process. Key output variables from the QSP model were used to generate a continuous data set for use in the pipeline. This dataset was used to reconstruct a possible model. This reconstruction had no false-positive relationships, and the output of each of the species was similar to that of the original QSP model. This demonstrates the ability to accurately infer relationships in a hypothesis-neutral manner without prior knowledge of a system using this pipeline.

Single Cell Systems Analysis: Decision Geometry In Outliers

MOTIVATION

Anti-cancer therapeutics of the highest calibre currently focus on combinatorial targeting of specific oncoproteins and tumour suppressors. Clinical relapse depends upon intratumoral heterogeneity which serves as substrate variation during evolution of resistance to therapeutic regimens.

RESULTS

The present review advocates single cell systems biology as the optimal level of analysis for remediation of clinical relapse. Graph theory approaches to understanding decision-making in single cells may be abstracted one level further, to the geometry of decision-making in outlier cells, in order to define evolution-resistant cancer biomarkers. Systems biologists currently working with omics data are invited to consider phase portrait analysis as a mediator between graph theory and deep learning approaches. Perhaps counter-intuitively, the tangible clinical needs of cancer patients may depend upon the adoption of higher level mathematical abstractions of cancer biology.

SUPPLEMENTARY INFORMATION

supplementary data available at Bioinformatics online.

Development of an adverse drug event network to predict drug toxicity

Despite of their therapeutic effects, drug's exposure may have negative effects on human health such as adverse drug reaction (ADR) and side effects (SE). Adverse drug events (ADEs), that correspond to an event occurring during the drug treatment (i.e. ADR and SE), is not necessarily caused by the drug itself, as this is the case with medical errors and social factors. Due to the complexity of the biological systems, not all ADEs are known for marketed drugs. Therefore, new and effective methods are needed to determine potential risks, including the development of computational strategies. We present an ADE association network based on 90,827 drug-ADE associations between 930 unique drug and 6221 unique ADE, on which we implemented a scoring system based on a pull-down approach for prediction of drug-ADE combination. Based on our network, ADEs proposed for three drugs, safinamide, sonidegib, rufinamide are further discussed. The model was able to identify, already known drug-ADE associations that are supported by the literature and FDA reports, and also to predict uncharacterized associations such as dopamine dysregulation syndrome, or nicotinic acid deficiency for the drugs safinamide and sonidegib respectively, illustrating the power of such integrative toxicological approach.

Integrated use of bioinformatic resources reveals that co-targeting of histone deacetylases, IKBK and SRC inhibits epithelial-mesenchymal transition in cancer

With the advent of high-throughput technologies leading to big data generation, increasing number of gene signatures are being published to predict various features of diseases such as prognosis and patient survival. However, to use these signatures for identifying therapeutic targets, use of additional bioinformatic tools is indispensible part of research. Here, we have generated a pipeline comprised of nearly 15 bioinformatic tools and enrichment statistical methods to propose and validate a drug combination strategy from already approved drugs and present our approach using published pan-cancer epithelial-mesenchymal transition (EMT) signatures as a case study. We observed that histone deacetylases were critical targets to tune expression of multiple epithelial versus mesenchymal genes. Moreover, SRC and IKBK were the principal intracellular kinases regulating multiple signaling pathways. To confirm the anti-EMT efficacy of the proposed target combination in silico, we validated expression of targets in mesenchymal versus epithelial subtypes of ovarian cancer. Additionally, we inhibited the pinpointed proteins in vitro using an invasive lung cancer cell line. We found that whereas low-dose mono-therapy failed to limit cell dispersion from collagen spheroids in a microfluidic device as a metric of EMT, the combination fully inhibited dissociation and invasion of cancer cells toward cocultured endothelial cells. Given the approval status and safety profiles of the suggested drugs, the proposed combination set can be considered in clinical trials.

GWAS and Beyond: Using Omics Approaches to Interpret SNP Associations

PURPOSE OF REVIEW

Neurodegenerative diseases, neuropsychiatric disorders, and related traits have highly complex etiologies but are also highly heritable and identifying the causal genes and biological pathways underlying these traits may advance the development of treatments and preventive strategies. While many genome-wide association studies (GWAS) have successfully identified variants contributing to polygenic neurodegenerative and neuropsychiatric phenotypes including Alzheimer's disease (AD), schizophrenia (SCZ), and bipolar disorder (BPD) amongst others, interpreting the biological roles of significantly-associated variants in the genetic architecture of these traits remains a significant challenge. Here we review several 'omics' approaches which attempt to bridge the gap from associated genetic variants to phenotype by helping define the functional roles of GWAS loci in the development of neuropsychiatric disorders and traits.

RECENT FINDINGS

Several common 'omics' approaches have been applied to examine neuropsychiatric traits, such as nearest-gene mapping, trans-ethnic fine mapping, annotation enrichment analysis, transcriptomic analysis, and pathway analysis, and each of these approaches has strengths and limitations in providing insight into biological mechanisms. One popular emerging method is the examination of tissue-specific genetically-regulated gene expression (GReX), which aggregates the genetic variants' effects at the gene-level. Furthermore, proteomic, metabolomic, and microbiomic studies and phenome-wide association studies will further enhance our understanding of neuropsychiatric traits.

SUMMARY

GWAS has been applied to neuropsychiatric traits for a decade, but our understanding about the biological function of identified variants remains limited. Today, technological advancements have created analytical approaches for integrating transcriptomics, metabolomics, proteomics, pharmacology and toxicology as tools for understanding the functional roles of genetics variants. These data, as well as the broader clinical information provided by electronic health records, can provide additional insight and complement genomic analyses.

In Silico Target Prediction for Small Molecules

Drugs modulate disease states through their actions on targets in the body. Determining these targets aids the focused development of new treatments, and helps to better characterize those already employed. One means of accomplishing this is through the deployment of in silico methodologies, harnessing computational analytical and predictive power to produce educated hypotheses for experimental verification. Here, we provide an overview of the current state of the art, describe some of the well-established methods in detail, and reflect on how they, and emerging technologies promoting the incorporation of complex and heterogeneous data-sets, can be employed to improve our understanding of (poly)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.

Key Players of Cisplatin Resistance: Towards a Systems Pharmacology Approach

The major obstacle in the clinical use of the antitumor drug cisplatin is inherent and acquired resistance. Typically, cisplatin resistance is not restricted to a single mechanism demanding for a systems pharmacology approach to understand a whole cell's reaction to the drug. In this study, the cellular transcriptome of untreated and cisplatin-treated A549 non-small cell lung cancer cells and their cisplatin-resistant sub-line A549^rCDDP²⁰⁰⁰ was screened with a whole genome array for relevant gene candidates. By combining statistical methods with available gene annotations and without a previously defined hypothesis HRas, MAPK14 (p38), CCL2, DOK1 and PTK2B were identified as genes possibly relevant for cisplatin resistance. These and related genes were further validated on transcriptome (qRT-PCR) and proteome (Western blot) level to select candidates contributing to resistance. HRas, p38, CCL2, DOK1, PTK2B and JNK3 were integrated into a model of resistance-associated signalling alterations describing differential gene and protein expression between cisplatin-sensitive and -resistant cells in reaction to cisplatin exposure.

A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy

Background

In recent years, the integration of ‘omics’ technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc.

Results

In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds’ treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound’s efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound’s treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds.

Conclusions

In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

Electronic supplementary material

The online version of this article (10.1186/s12918-017-0501-6) contains supplementary material, which is available to authorized users.

Predicting new indications of compounds with a network pharmacology approach: Liuwei Dihuang Wan as a case study

With the ever increasing cost and time required for drug development, new strategies for drug development are highly demanded, whereas repurposing old drugs has attracted much attention in drug discovery. In this paper, we introduce a new network pharmacology approach, namely PINA, to predict potential novel indications of old drugs based on the molecular networks affected by drugs and associated with diseases. Benchmark results on FDA approved drugs have shown the superiority of PINA over traditional computational approaches in identifying new indications of old drugs. We further extend PINA to predict the novel indications of Traditional Chinese Medicines (TCMs) with Liuwei Dihuang Wan (LDW) as a case study. The predicted indications, including immune system disorders and tumor, are validated by expert knowledge and evidences from literature, demonstrating the effectiveness of our proposed computational approach.

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism

Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.

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.

Integrating Multiscale Modeling with Drug Effects for Cancer Treatment

In this paper, we review multiscale modeling for cancer treatment with the incorporation of drug effects from an applied system's pharmacology perspective. Both the classical pharmacology and systems biology are inherently quantitative; however, systems biology focuses more on networks and multi factorial controls over biological processes rather than on drugs and targets in isolation, whereas systems pharmacology has a strong focus on studying drugs with regard to the pharmacokinetic (PK) and pharmacodynamic (PD) relations accompanying drug interactions with multiscale physiology as well as the prediction of dosage-exposure responses and economic potentials of drugs. Thus, it requires multiscale methods to address the need for integrating models from the molecular levels to the cellular, tissue, and organism levels. It is a common belief that tumorigenesis and tumor growth can be best understood and tackled by employing and integrating a multifaceted approach that includes in vivo and in vitro experiments, in silico models, multiscale tumor modeling, continuous/discrete modeling, agent-based modeling, and multiscale modeling with PK/PD drug effect inputs. We provide an example application of multiscale modeling employing stochastic hybrid system for a colon cancer cell line HCT-116 with the application of Lapatinib drug. It is observed that the simulation results are similar to those observed from the setup of the wet-lab experiments at the Translational Genomics Research Institute.

Systems pharmacology-based drug discovery for marine resources: an example using sea cucumber (Holothurians)

ETHNOPHARMACOLOGICAL RELEVANCE

Sea cucumber, a kind of marine animal, have long been utilized as tonic and traditional remedies in the Middle East and Asia because of its effectiveness against hypertension, asthma, rheumatism, cuts and burns, impotence, and constipation. In this study, an overall study performed on sea cucumber was used as an example to show drug discovery from marine resource by using systems pharmacology model. The value of marine natural resources has been extensively considered because these resources can be potentially used to treat and prevent human diseases. However, the discovery of drugs from oceans is difficult, because of complex environments in terms of composition and active mechanisms. Thus, a comprehensive systems approach which could discover active constituents and their targets from marine resource, understand the biological basis for their pharmacological properties is necessary.

MATERIALS AND METHODS

In this study, a feasible pharmacological model based on systems pharmacology was established to investigate marine medicine by incorporating active compound screening, target identification, and network and pathway analysis.

RESULTS

As a result, 106 candidate components of sea cucumber and 26 potential targets were identified. Furthermore, the functions of sea cucumber in health improvement and disease treatment were elucidated in a holistic way based on the established compound-target and target-disease networks, and incorporated pathways.

CONCLUSIONS

This study established a novel strategy that could be used to explore specific active mechanisms and discover new drugs from marine sources.

Cytoprotection of human endothelial cells from oxidant stress with CDDO derivatives: network analysis of genes responsible for cytoprotection

AIM

To identify drugs that may reduce the impact of oxidant stress on cell viability.

METHODS

Human umbilical vein endothelial cells were treated with 200 nmol/l CDDO-Im (imidazole) and CDDO-Me (methyl) after exposure to menadione and compared to vehicle-treated cells. Cell viability and cytotoxicity were assessed, and gene expression profiling was performed.

RESULTS

CDDO-Im was significantly more cytoprotective and less cytotoxic (p < 0.001) than CDDO-Me. Although both provided cytoprotection by induction of gene transcription, CDDO-Im induced more genes. In addition to a higher induction of the key cytoprotective gene heme oxygenase-1 (38.9-fold increase for CDDO-Im and 26.5-fold increase for CDDO-Me), CDDO-Im also induced greater expression of heat shock proteins (5.5-fold increase compared to 2.8-fold for CDDO-Me).

CONCLUSIONS

Both compounds showed good induction of heme oxygenase, which largely accounted for their cytoprotective effect. Differences were detected in cytotoxicity at higher doses, indicating that CDDO-Me was more cytotoxic than CDDO-Im. Significant differences were detected in the ability of CDDO-Im and CDDO-Me to affect differential gene transcription. CDDO-Im induced more genes than did CDDO-Me. The source of the differences in gene expression patterns between CDDO-Im and CDDO-Me was not determined but may be important in long-term use of this class of drugs.

Systems medicine: evolution of systems biology from bench to bedside

High-throughput experimental techniques for generating genomes, transcriptomes, proteomes, metabolomes, and interactomes have provided unprecedented opportunities to interrogate biological systems and human diseases on a global level. Systems biology integrates the mass of heterogeneous high-throughput data and predictive computational modeling to understand biological functions as system-level properties. Most human diseases are biological states caused by multiple components of perturbed pathways and regulatory networks rather than individual failing components. Systems biology not only facilitates basic biological research but also provides new avenues through which to understand human diseases, identify diagnostic biomarkers, and develop disease treatments. At the same time, systems biology seeks to assist in drug discovery, drug optimization, drug combinations, and drug repositioning by investigating the molecular mechanisms of action of drugs at a system's level. Indeed, systems biology is evolving to systems medicine as a new discipline that aims to offer new approaches for addressing the diagnosis and treatment of major human diseases uniquely, effectively, and with personalized precision.

Systems Pharmacology Links GPCRs with Retinal Degenerative Disorders

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

Harnessing pharmacological knowledge for personalized medicine and pharmacotyping: Challenges and lessons learned

The contribution of the genetic make-up to an individual’s capacity has long been recognized in modern pharmacology as a crucial factor leading to therapy inefficiency and toxicity, negatively impacting the economic burden of healthcare and restricting the monitoring of diseases. In practical terms, and in order for drug prescription to be improved toward meeting the personalized medicine concept in drug delivery, the maximum clinical outcome for most, if not all, patients must be achieved, i.e., pharmacotyping. Such a direction although promising and of high expectation from the society, it is however hardly to be afforded for healthcare worldwide. To overcome any existed hurdles, this means that practical clinical utility of personalized medicine decisions have to be documented and validated in the clinical setting. The latter implies for drug delivery the efficient implementation of previously gained in vivo pharmacology experience with pharmacogenomics knowledge. As an approach to work faster and in a more productive way, the elaboration of advanced physiologically based pharmacokinetics models is discussed. And in better clarifying this topic, the example of tamoxifen is thoroughly presented. Overall, pharmacotyping represents a major challenge in modern therapeutics for which pharmacologists need to work in successfully fulfilling this task.

Toward an integrated software platform for systems pharmacology

Understanding complex biological systems requires the extensive support of computational tools. This is particularly true for systems pharmacology, which aims to understand the action of drugs and their interactions in a systems context. Computational models play an important role as they can be viewed as an explicit representation of biological hypotheses to be tested. A series of software and data resources are used for model development, verification and exploration of the possible behaviors of biological systems using the model that may not be possible or not cost effective by experiments. Software platforms play a dominant role in creativity and productivity support and have transformed many industries, techniques that can be applied to biology as well. Establishing an integrated software platform will be the next important step in the field. © 2013 The Authors. Biopharmaceutics & Drug Disposition published by John Wiley & Sons, Ltd.

Towards structural systems pharmacology to study complex diseases and personalized medicine

Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.

Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles

Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However, to really capitalize on the polypharmacological effects of drugs, there is a critical need to better model and understand how the complex interactions between drugs and their cellular targets contribute to drug efficacy and possible side effects. Network graphs provide a convenient modeling framework for dealing with the fact that most drugs act on cellular systems through targeting multiple proteins both through on-target and off-target binding. Network pharmacology models aim at addressing questions such as how and where in the disease network should one target to inhibit disease phenotypes, such as cancer growth, ideally leading to therapies that are less vulnerable to drug resistance and side effects by means of attacking the disease network at the systems level through synergistic and synthetic lethal interactions. Since the exponentially increasing number of potential drug target combinations makes pure experimental approach quickly unfeasible, this review depicts a number of computational models and algorithms that can effectively reduce the search space for determining the most promising combinations for experimental evaluation. Such computational-experimental strategies are geared toward realizing the full potential of multi-target treatments in different disease phenotypes. Our specific focus is on system-level network approaches to polypharmacology designs in anticancer drug discovery, where we give representative examples of how network-centric modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies.

Identification of immunomodulatory signatures induced by american ginseng in murine immune cells

Background. American ginseng (Panax quinquefolius, AG) has been used for more than 300 years. Some of its claimed benefits can be attributed to the immunomodulatory activities, whose molecular mechanisms are largely unknown. Methods. Murine splenic cells from adult male C57BL/6 (B6) mice were isolated and divided into 4 groups to mimic 4 basic pathophysiological states: (1) normal naïve; (2) normal activated; (3) deficient naïve; (4) deficient activated. Then, different AG extracts were added to all groups for 24 h incubation. MTT proliferation assays were performed to evaluate the phenotypic features of cells. Finally, microarray assays were carried out to identify differentially expressed genes associated with AG exposure. Real-time PCR was performed to validate the expression of selected genes. Results. Microarray data showed that most of gene expression changes were identified in the deficient naïve group, suggesting that the pathophysiological state has major impacts on transcriptomic changes associated with AG exposure. Specifically, this study revealed downregulation of interferon-γ signaling pathway in the deficient group of cells. Conclusion. Our study demonstrated that only specific groups of immune cells responded to AG intervention and immunocompromised cells were more likely regulated by AG treatment.

The fall and rise of pharmacology--(re-)defining the discipline?

Pharmacology is an integrative discipline that originated from activities, now nearly 7000 years old, to identify therapeutics from natural product sources. Research in the 19th Century that focused on the Law of Mass Action (LMA) demonstrated that compound effects were dose-/concentration-dependent eventually leading to the receptor concept, now a century old, that remains the key to understanding disease causality and drug action. As pharmacology evolved in the 20th Century through successive biochemical, molecular and genomic eras, the precision in understanding receptor function at the molecular level increased and while providing important insights, led to an overtly reductionistic emphasis. This resulted in the generation of data lacking physiological context that ignored the LMA and was not integrated at the tissue/whole organism level. As reductionism became a primary focus in biomedical research, it led to the fall of pharmacology. However, concerns regarding the disconnect between basic research efforts and the approval of new drugs to treat 21st Century disease tsunamis, e.g., neurodegeneration, metabolic syndrome, etc. has led to the reemergence of pharmacology, its rise, often in the semantic guise of systems biology. Against a background of limited training in pharmacology, this has resulted in issues in experimental replication with a bioinformatics emphasis that often has a limited relationship to reality. The integration of newer technologies within a pharmacological context where research is driven by testable hypotheses rather than technology, together with renewed efforts in teaching pharmacology, is anticipated to improve the focus and relevance of biomedical research and lead to novel therapeutics that will contain health care costs.

Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening and knowledge of transporters: where drug discovery went wrong and how to fix it

Despite the sequencing of the human genome, the rate of innovative and successful drug discovery in the pharmaceutical industry has continued to decrease. Leaving aside regulatory matters, the fundamental and interlinked intellectual issues proposed to be largely responsible for this are: (a) the move from 'function-first' to 'target-first' methods of screening and drug discovery; (b) the belief that successful drugs should and do interact solely with single, individual targets, despite natural evolution's selection for biochemical networks that are robust to individual parameter changes; (c) an over-reliance on the rule-of-5 to constrain biophysical and chemical properties of drug libraries; (d) the general abandoning of natural products that do not obey the rule-of-5; (e) an incorrect belief that drugs diffuse passively into (and presumably out of) cells across the bilayers portions of membranes, according to their lipophilicity; (f) a widespread failure to recognize the overwhelmingly important role of proteinaceous transporters, as well as their expression profiles, in determining drug distribution in and between different tissues and individual patients; and (g) the general failure to use engineering principles to model biology in parallel with performing 'wet' experiments, such that 'what if?' experiments can be performed in silico to assess the likely success of any strategy. These facts/ideas are illustrated with a reasonably extensive literature review. Success in turning round drug discovery consequently requires: (a) decent systems biology models of human biochemical networks; (b) the use of these (iteratively with experiments) to model how drugs need to interact with multiple targets to have substantive effects on the phenotype; (c) the adoption of polypharmacology and/or cocktails of drugs as a desirable goal in itself; (d) the incorporation of drug transporters into systems biology models, en route to full and multiscale systems biology models that incorporate drug absorption, distribution, metabolism and excretion; (e) a return to 'function-first' or phenotypic screening; and (f) novel methods for inferring modes of action by measuring the properties on system variables at all levels of the 'omes. Such a strategy offers the opportunity of achieving a state where we can hope to predict biological processes and the effect of pharmaceutical agents upon them. Consequently, this should both lower attrition rates and raise the rates of discovery of effective drugs substantially.

The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so

A recent paper in this journal sought to counter evidence for the role of transport proteins in effecting drug uptake into cells, and questions that transporters can recognize drug molecules in addition to their endogenous substrates. However, there is abundant evidence that both drugs and proteins are highly promiscuous. Most proteins bind to many drugs and most drugs bind to multiple proteins (on average more than six), including transporters (mutations in these can determine resistance); most drugs are known to recognise at least one transporter. In this response, we alert readers to the relevant evidence that exists or is required. This needs to be acquired in cells that contain the relevant proteins, and we highlight an experimental system for simultaneous genome-wide assessment of carrier-mediated uptake in a eukaryotic cell (yeast).

CPT: Pharmacometrics and Systems Pharmacology

Welcome to the first issue of CPT: Pharmacometrics and Systems Pharmacology (CPT:PSP), a new journal from the American Society for Clinical Pharmacology and Therapeutics. CPT:PSP is a cross-disciplinary journal devoted to publishing advances in quantitative, model-based approaches as applied in pharmacology, (patho)physiology, and disease to aid the discovery, development, and utilization of human therapeutics. The emphasis of CPT:PSP will be on the application of modeling and simulation and the impact of Pharmacometrics and Systems Pharmacology on the discovery and development of innovative therapies.

Reducing systems biology to practice in pharmaceutical company research; selected case studies

Reviews of the productivity of the pharmaceutical industry have concluded that the current business model is unsustainable. Various remedies for this have been proposed, however, arguably these do not directly address the fundamental issue; namely, that it is the knowledge required to enable good decisions in the process of delivering a drug that is largely absent; in turn, this leads to a disconnect between our intuition of what the right drug target is and the reality of pharmacological intervention in a system such as a human disease state. As this system is highly complex, modelling will be required to elucidate emergent properties together with the data necessary to construct such models. Currently, however, both the models and data available are limited. The ultimate solution to the problem of pharmaceutical productivity may be the virtual human, however, it is likely to be many years, if at all, before this goal is realised. The current challenge is, therefore, whether systems modelling can contribute to improving productivity in the pharmaceutical industry in the interim and help to guide the optimal route to the virtual human. In this context, this chapter discusses the emergence of systems pharmacology in drug discovery from the interface of pharmacokinetic-pharmacodynamic modelling and systems biology. Examples of applications to the identification of optimal drug targets in given pathways, selecting drug modalities and defining biomarkers are discussed, together with future directions.

Signalogs: orthology-based identification of novel signaling pathway components in three metazoans

BACKGROUND

Uncovering novel components of signal transduction pathways and their interactions within species is a central task in current biological research. Orthology alignment and functional genomics approaches allow the effective identification of signaling proteins by cross-species data integration. Recently, functional annotation of orthologs was transferred across organisms to predict novel roles for proteins. Despite the wide use of these methods, annotation of complete signaling pathways has not yet been transferred systematically between species.

PRINCIPAL FINDINGS

Here we introduce the concept of 'signalog' to describe potential novel signaling function of a protein on the basis of the known signaling role(s) of its ortholog(s). To identify signalogs on genomic scale, we systematically transferred signaling pathway annotations among three animal species, the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and humans. Using orthology data from InParanoid and signaling pathway information from the SignaLink database, we predict 88 worm, 92 fly, and 73 human novel signaling components. Furthermore, we developed an on-line tool and an interactive orthology network viewer to allow users to predict and visualize components of orthologous pathways. We verified the novelty of the predicted signalogs by literature search and comparison to known pathway annotations. In C. elegans, 6 out of the predicted novel Notch pathway members were validated experimentally. Our approach predicts signaling roles for 19 human orthodisease proteins and 5 known drug targets, and suggests 14 novel drug target candidates.

CONCLUSIONS

Orthology-based pathway membership prediction between species enables the identification of novel signaling pathway components that we referred to as signalogs. Signalogs can be used to build a comprehensive signaling network in a given species. Such networks may increase the biomedical utilization of C. elegans and D. melanogaster. In humans, signalogs may identify novel drug targets and new signaling mechanisms for approved drugs.

A network-based multi-target computational estimation scheme for anticoagulant activities of compounds

Background

Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target.

Methodology

We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery.

Conclusions

This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking.

Proof of concept: network and systems biology approaches aid in the discovery of potent anticancer drug combinations

Cancer therapies that target key molecules have not fulfilled expected promises for most common malignancies. Major challenges include the incomplete understanding and validation of these targets in patients, the multiplicity and complexity of genetic and epigenetic changes in the majority of cancers, and the redundancies and cross-talk found in key signaling pathways. Collectively, the uses of single-pathway targeted approaches are not effective therapies for human malignancies. To overcome these barriers, it is important to understand the molecular cross-talk among key signaling pathways and how they may be altered by targeted agents. Innovative approaches are needed, such as understanding the global physiologic environment of target proteins and the effects of modifying them without losing key molecular details. Such strategies will aid the design of novel therapeutics and their combinations against multifaceted diseases, in which efficacious combination therapies will focus on altering multiple pathways rather than single proteins. Integrated network modeling and systems biology have emerged as powerful tools benefiting our understanding of drug mechanisms of action in real time. This review highlights the significance of the network and systems biology-based strategy and presents a proof of concept recently validated in our laboratory using the example of a combination treatment of oxaliplatin and the MDM2 inhibitor MI-219 in genetically complex and incurable pancreatic adenocarcinoma.

Role of systems pharmacology in understanding drug adverse events

Systems pharmacology involves the application of systems biology approaches, combining large-scale experimental studies with computational analyses, to the study of drugs, drug targets, and drug effects. Many of these initial studies have focused on identifying new drug targets, new uses of known drugs, and systems-level properties of existing drugs. This review focuses on systems pharmacology studies that aim to better understand drug side effects and adverse events. By studying the drugs in the context of cellular networks, these studies provide insights into adverse events caused by off-targets of drugs as well as adverse events-mediated complex network responses. This allows rapid identification of biomarkers for side effect susceptibility. In this way, systems pharmacology will lead to not only newer and more effective therapies, but safer medications with fewer side effects.

Training in systems pharmacology: predoctoral program in pharmacology and systems biology at Mount Sinai School of Medicine

Our recently developed predoctoral training program in pharmacology and systems biology prepares students to become experts in systems-level models of disease that identify therapeutic targets and predict adverse effects or new uses of existing therapeutics. Multiple computational modeling modes are introduced throughout a curriculum that integrates basic cell and molecular sciences with the physiology and pathophysiology of disease states. Problem-based learning exercises enable students from different experimental and computational backgrounds to design experiments and interpret data quantitatively.

Systems approaches to polypharmacology and drug discovery

Systems biology uses experimental and computational approaches to characterize large sample populations systematically, process large datasets, examine and analyze regulatory networks, and model reactions to determine how components are joined to form functional systems. Systems biology technologies, data and knowledge are particularly useful in understanding disease processes and drug actions. An important area of integration between systems biology and drug discovery is the concept of polypharmacology: the treatment of diseases by modulating more than one target. Polypharmacology for complex diseases is likely to involve multiple drugs acting on distinct targets that are part of a network regulating physiological responses. This review discusses the current state of the systems-level understanding of diseases and both the therapeutic and adverse mechanisms of drug actions. Drug-target networks can be used to identify multiple targets and to determine suitable combinations of drug targets or drugs. Thus, the discovery of new drug therapies for complex diseases may be greatly aided by systems biology.

Minireview: global regulation and dynamics of ribonucleic Acid

Gene expression starts with transcription and is followed by multiple posttranscriptional processes that carry out the splicing, capping, polyadenylation, and export of each mRNA. Interest in posttranscriptional regulation has increased recently with explosive discoveries of large numbers of noncoding RNAs such as microRNAs, yet posttranscriptional processes depend largely on the functions of RNA-binding proteins as well. Glucocorticoid nuclear receptors are classical examples of environmentally reactive activators and repressors of transcription, but there has also been a significant increase in studies of the role of posttranscriptional regulation in endocrine responses, including insulin and insulin receptors, and parathyroid hormone as well as other hormonal responses, at the levels of RNA stability and translation. On the global level, the transcriptome is defined as the total RNA complement of the genome, and thereby, represents the accumulated levels of all expressed RNAs, because they are each being produced and eventually degraded in either the nucleus or the cytoplasm. In addition to RNA turnover, the many underlying posttranscriptional layers noted above that follow from the transcriptome function within a dynamic ribonucleoprotein (RNP) environment of global RNA-protein and RNA-RNA interactions. With the exception of the spliceosome and the ribosome, thousands of heterodispersed RNP complexes wherein RNAs are dynamically processed, trafficked, and exchanged are heterogeneous in size and composition, thus providing significant challenges to their investigation. Among the diverse RNPs that show dynamic features in the cytoplasm are processing bodies and stress granules as well as a large number of smaller heterogeneous RNPs distributed throughout the cell. Although the localization of functionally related RNAs within these RNPs are responsive to developmental and environmental signals, recent studies have begun to elucidate the global RNA components of RNPs that are dynamically coordinated in response to these signals. Among the factors that have been found to affect coordinated RNA regulation are developmental signals and treatments with small molecule drugs, hormones, and toxins, but this field is just beginning to understand the role of RNA dynamics in these responses.

'Principled' personalized medicine?

The advent of both population genomic studies and direct-to-consumer personal genetic testing raises ethical challenges for researchers and physicians alike. Quality and solidarity can now be added to traditional ethical principles, such as autonomy and privacy. There is no doubt that genetic information is going 'public'. Informatic technologies allow for greater accessibility and integration, but can researchers and physicians handle the challenges? Are ethics committees equipped to handle this shift towards greater openness and towards a conflation of research and traditional medical ethics?

Principles of modular tumor therapy

Nature is interwoven with communication and is represented and reproduced through communication acts. The central question is how may multimodal modularly acting and less toxic therapy approaches, defined as modular therapies, induce an objective response or even a continuous complete remission, although single stimulatory or inhibitingly acting drugs neither exert mono-activity in the respective metastatic tumor type nor are they directed to potentially ‘tumor-specific’ targets. Modularity in the present context is a formal pragmatic communicative systems concept, describing the degree to which systems objects (cells, pathways etc.) may be communicatively separated in a virtual continuum, and recombined and rededicated to alter validity and denotation of communication processes in the tumor. Intentional knowledge, discharging in reductionist therapies, disregards the risk-absorbing background knowledge of the tumor’s living world including the holistic communication processes, which we rely on in every therapy. At first, this knowledge constitutes the validity of informative intercellular processes, which is the prerequisite for therapeutic success. All communication-relevant steps, such as intentions, understandings, and the appreciation of messages, may be modulated simultaneously, even with a high grade of specificity. Thus, modular therapy approaches including risk-absorbing and validity-modifying background knowledge may overcome reductionist idealizations. Modular therapies show modular events assembled by the tumor’s living world as an additional evolution-constituting dimension. This way, modular knowledge may be acquired from the environment, either incidentally or constitutionally. The new communicatively defined modular coherency of environment, i.e. the tumor-associated microenvironment, and tumor cells open novel ways for the scientific community in ‘translational medicine’.