Integrated Systems Pharmacology Research Articles

Systems Pharmacology-Based Discovery of Natural Products for Precision Oncology Through Targeting Cancer Mutated Genes.

Massive cancer genomics data have facilitated the rapid revolution of a novel oncology drug discovery paradigm through targeting clinically relevant driver genes or mutations for the development of precision oncology. Natural products with polypharmacological profiles have been demonstrated as promising agents for the development of novel cancer therapies. In this study, we developed an integrated systems pharmacology framework that facilitated identifying potential natural products that target mutated genes across 15 cancer types or subtypes in the realm of precision medicine. High performance was achieved for our systems pharmacology framework. In case studies, we computationally identified novel anticancer indications for several US Food and Drug Administration‐approved or clinically investigational natural products (e.g., resveratrol, quercetin, genistein, and fisetin) through targeting significantly mutated genes in multiple cancer types. In summary, this study provides a powerful tool for the development of molecularly targeted cancer therapies through targeting the clinically actionable alterations by exploiting the systems pharmacology of natural products.

A systems pharmacology perspective to decipher the mechanism of action of Parangichakkai chooranam, a Siddha formulation for the treatment of psoriasis.

Psoriasis is a chronic relapsing immune mediated disorder of the skin. The disease presents itself with well featured clinical and histological characteristics however the aetiology of the disease still remains obscure. The current systemic therapies aim to eliminate the symptoms of disease rather than offering a complete cure. Parangichakkai chooranam (PC), a Siddha oral herbal formulation has been widely prescribed for the treatment of psoriasis. Though the medication is highly prescribed by the Siddha healers the mechanism of PC for the treatment of psoriasis remains to be elucidated. The current study utilizes an integrated systems pharmacology approach to decipher the mechanism of action of PC. The comprehensive network pharmacological approach resulted in the construction of a Compound-Target network which encloses 155 compounds and 583 protein targets. A Disease-Target network was constructed by assembling disease proteins and their partners. When the compound targets were mapped to the network their involvement as controllers of the disease and triggers of disease associated comorbidities were identified. A Target-Pathway network raised from the pathway enrichment analysis not only identified disease specific pathways but also the pathways mediating secondary complications such as skin hemostasis, wound healing, desquamation and itch. The present work sheds light on the mechanism of action of PC in treating psoriasis. This work not only highlights the pharmacological action of the formulation but also emphasis on safe herbal remedies offered by the Siddha medicinal system.

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

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

DrugGenEx-Net: a novel computational platform for systems pharmacology and gene expression-based drug repurposing.

BackgroundThe targeting of disease-related proteins is important for drug discovery, and yet target-based discovery has not been fruitful. Contextualizing overall biological processes is critical to formulating successful drug-disease hypotheses. Network pharmacology helps to overcome target-based bottlenecks through systems biology analytics, such as protein-protein interaction (PPI) networks and pathway regulation.ResultsWe present a systems polypharmacology platform entitled DrugGenEx-Net (DGE-NET). DGE-NET predicts empirical drug-target (DT) interactions, integrates interaction pairs into a multi-tiered network analysis, and ultimately predicts disease-specific drug polypharmacology through systems-based gene expression analysis. Incorporation of established biological network annotations for protein target-disease, −signaling pathway, −molecular function, and protein-protein interactions enhances predicted DT effects on disease pathophysiology. Over 50 drug-disease and 100 drug-pathway predictions are validated. For example, the predicted systems pharmacology of the cholesterol-lowering agent ezetimibe corroborates its potential carcinogenicity.When disease-specific gene expression analysis is integrated, DGE-NET prioritizes known therapeutics/experimental drugs as well as their contra-indications. Proof-of-concept is established for immune-related rheumatoid arthritis and inflammatory bowel disease, as well as neuro-degenerative Alzheimer’s and Parkinson’s diseases.ConclusionsDGE-NET is a novel computational method that predicting drug therapeutic and counter-therapeutic indications by uniquely integrating systems pharmacology with gene expression analysis. DGE-NET correctly predicts various drug-disease indications by linking the biological activity of drugs and diseases at multiple tiers of biological action, and is therefore a useful approach to identifying drug candidates for re-purposing.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1065-y) contains supplementary material, which is available to authorized users.

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

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

The dili-sim initiative. Integrated systems pharmacology modeling to explain and predict drug hepatotoxicity

Drugs-induced liver injury (DILI) can occur through a variety of adverse outcome pathways. Understanding and predicting the effects of multiple toxicity pathways as a function of time and exposure are difficult without systematic organization. Quantitative systems modeling can combine multiple drug effects to address this challenge. The DILIsim Initiative is a public-private partnership involving scientists from 14 major pharmaceutical companies and the FDA; it is now entering its fourth year. In addition to financial support, companies provide often unpublished data and perform in kind research to fill gaps in knowledge. The software produced by the initiative, DILIsym®, is a highly specified, mechanistic, hepatic model that utilizes extensive kinetic information among interrelated biological processes to explore the hepatotoxic underpinnings via simulations. Mechanisms currently included in the model are oxidative stress, mitochondrial dysfunction, bile acid transporter inhibition, and lipotoxicity. The Dilisym® software was originally developed to explain and predict interspecies differences in dose dependent hepatotoxicity to help inform first in man dosing. However, the modeling effort has expanded to improve interpretation of traditional and mechanistic serum biomarkers including miR122, CK18 and its caspase-cleaved fragment, and HMGB1. It is now possible to utilize DILIsym® to predict the range of percent hepatocyte loss through necrosis or apoptosis from measurements of these biomarkers in serial serum samples archived from clinical trials. By varying parameters within Dilisym®, it is possible to create simulated patient populations that mimic selected clinic populations in terms of susceptibility to DILI. This approach successfully recently predicted the latency and incidence of serum ALT elevations that were observed in the clinical trials of troglitazone (ClinPharmacolTher. 96(5):589–598, 2014). Systems modeling tools such as DILIsym® will increasingly be used to support decision making throughout the life cycle of new drug candidates. Drugs-induced liver injury (DILI) can occur through a variety of adverse outcome pathways. Understanding and predicting the effects of multiple toxicity pathways as a function of time and exposure are difficult without systematic organization. Quantitative systems modeling can combine multiple drug effects to address this challenge. The DILIsim Initiative is a public-private partnership involving scientists from 14 major pharmaceutical companies and the FDA; it is now entering its fourth year. In addition to financial support, companies provide often unpublished data and perform in kind research to fill gaps in knowledge. The software produced by the initiative, DILIsym®, is a highly specified, mechanistic, hepatic model that utilizes extensive kinetic information among interrelated biological processes to explore the hepatotoxic underpinnings via simulations. Mechanisms currently included in the model are oxidative stress, mitochondrial dysfunction, bile acid transporter inhibition, and lipotoxicity. The Dilisym® software was originally developed to explain and predict interspecies differences in dose dependent hepatotoxicity to help inform first in man dosing. However, the modeling effort has expanded to improve interpretation of traditional and mechanistic serum biomarkers including miR122, CK18 and its caspase-cleaved fragment, and HMGB1. It is now possible to utilize DILIsym® to predict the range of percent hepatocyte loss through necrosis or apoptosis from measurements of these biomarkers in serial serum samples archived from clinical trials. By varying parameters within Dilisym®, it is possible to create simulated patient populations that mimic selected clinic populations in terms of susceptibility to DILI. This approach successfully recently predicted the latency and incidence of serum ALT elevations that were observed in the clinical trials of troglitazone (ClinPharmacolTher. 96(5):589–598, 2014). Systems modeling tools such as DILIsym® will increasingly be used to support decision making throughout the life cycle of new drug candidates.

Systems pharmacology dissection of the anti-inflammatory mechanism for the medicinal herb Folium eriobotryae.

Inflammation is a hallmark of many diseases like diabetes, cancers, atherosclerosis and arthritis. Thus, lots of concerns have been raised toward developing novel anti-inflammatory agents. Many alternative herbal medicines possess excellent anti-inflammatory properties, yet their precise mechanisms of action are yet to be elucidated. Here, a novel systems pharmacology approach based on a large number of chemical, biological and pharmacological data was developed and exemplified by a probe herb Folium Eriobotryae, a widely used clinical anti-inflammatory botanic drug. The results show that 11 ingredients of this herb with favorable pharmacokinetic properties are predicted as active compounds for anti-inflammatory treatment. In addition, via systematic network analyses, their targets are identified to be 43 inflammation-associated proteins including especially COX2, ALOX5, PPARG, TNF and RELA that are mainly involved in the mitogen-activated protein kinase (MAPK) signaling pathway, the rheumatoid arthritis pathway and NF-κB signaling pathway. All these demonstrate that the integrated systems pharmacology method provides not only an effective tool to illustrate the anti-inflammatory mechanisms of herbs, but also a new systems-based approach for drug discovery from, but not limited to, herbs, especially when combined with further experimental validations.

Integrated systems pharmacology analysis of clinical drug-induced peripheral neuropathy.

A systems pharmacology approach was undertaken to define and identify the proteins/genes significantly associated with clinical incidence and severity of drug-induced peripheral neuropathy (DIPN). Pharmacological networks of 234 DIPN drugs, their known targets (both intended and unintended), and the intermediator proteins/genes interacting with these drugs via their known targets were examined. A permutation test identified 230 DIPN-associated intermediators that were enriched with apoptosis and stress response genes. Neuropathy incidence and severity were curated from drug labels and literature and were used to build a predictive model of DIPN using a regression tree algorithm, based on the drug targets and their intermediators. DIPN drugs whose targets interacted with both v-myc avian myelocytomatosis viral oncogene homolog (MYC) and proliferating cell nuclear antigen-associated factor (PAF15) were associated with a neuropathy incidence of 38.1%, whereas drugs interacting only with MYC had an incidence of 2.9%. These results warrant further investigation in order to develop a predictive tool for the DIPN potential of a new drug.

A Systems Pharmacology Perspective on the Clinical Development of Fatty Acid Amide Hydrolase Inhibitors for Pain

The level of the endocannabinoid anandamide is controlled by fatty acid amide hydrolase (FAAH). In 2011, PF-04457845, an irreversible inhibitor of FAAH, was progressed to phase II clinical trials for osteoarthritic pain. This article discusses a prospective, integrated systems pharmacology model evaluation of FAAH as a target for pain in humans, using physiologically based pharmacokinetic and systems biology approaches. The model integrated physiological compartments; endocannabinoid production, degradation, and disposition data; PF-04457845 pharmacokinetics and pharmacodynamics, and cannabinoid receptor CB1-binding kinetics. The modeling identified clear gaps in our understanding and highlighted key risks going forward, in particular relating to whether methods are in place to demonstrate target engagement and pharmacological effect. The value of this modeling exercise will be discussed in detail and in the context of the clinical phase II data, together with recommendations to enable optimal future evaluation of FAAH inhibitors.

Systems pharmacology strategies for drug discovery and combination with applications to cardiovascular diseases

Ethnopharmacological relevanceMulti-target therapeutics is a promising paradigm for drug discovery which is expected to produce greater levels of efficacy with fewer adverse effects and toxicity than monotherapies. Medical herbs featuring multi-components and multi-targets may serve as valuable resources for network-based multi-target drug discovery. Materials and methodsIn this study, we report an integrated systems pharmacology platform for drug discovery and combination, with a typical example applied to herbal medicines in the treatment of cardiovascular diseases. ResultsFirst, a disease-specific drug–target network was constructed and examined at systems level to capture the key disease-relevant biology for discovery of multi-targeted agents. Second, considering an integration of disease complexity and multilevel connectivity, a comprehensive database of literature-reported associations, chemicals and pharmacology for herbal medicines was designed. Third, a large-scale systematic analysis combining pharmacokinetics, chemogenomics, pharmacology and systems biology data through computational methods was performed and validated experimentally, which results in a superior output of information for systematic drug design strategies for complex diseases. ConclusionsThis strategy integrating different types of technologies is expected to help create new opportunities for drug discovery and combination.

Systems Pharmacology to Predict Drug Toxicity: Integration Across Levels of Biological Organization

To achieve sensitive and specific mechanism-based prediction of drug toxicity, the tools of systems pharmacology will be integrated using structured ontological approaches, analytics, mathematics, and statistics. Success of this effort is based on the assumption that a systems network that consists of drug-induced perturbations of physiological functions can be characterized. This network spans the hierarchy of biological organization, from gene to mRNA to protein to intracellular organelle to cell to organ to organism. It is populated with data from each of these levels of biological organization. These data, from disparate sources, include the published literature, drug development archives of all approved drugs and drug candidates that did not complete development, and various toxicity databases and adverse event reporting systems. The network contains interrelated genomics, transcriptomics, and metabolomics data, as well as organ and physiological functional data that are derived from the universe of information that describes and analyzes drug toxicity. Here we describe advances in bioinformatics, computer sciences, next-generation sequencing, and systems biology that create the opportunity for integrated systems pharmacology-based prediction of drug safety.