Illuminating drug action by network integration of disease genes: a case study of myocardial infarction

Method to link medicines to diseases using multiplex networks

The reuse of well-established medicines using computational modeling has gained a lot of attention due to its tremendous benefits. Based on this perspective, a new method for linking known medicines to diseases is proposed. The creation of a new treatment or medicine can be financially and temporally costly and the reuse of medicines is one possibility to accelerate this process efficiently. The main purpose of the reuse of medicines is to reduce some stages of the development of new medicines, motivating the proposition of several methods nowadays. In this work, a new method is developed aiming to connect known medicines to diseases based on available networks of protein interactions and available lists of medicines that affect protein action. The concepts of multiplex networks are used to connect subgraphs of vertices that represent medicines and proteins. The core of the procedure is determined by a weighting strategy constructed to define precisely the more relevant connections. The method was compared to other network link methods in the literature and a case study was presented and evaluated by the proposed method.

Machine learning-based integration of network features and chemical structure of compounds for SARS-CoV-2 drug effect analysis

High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS-CoV-2 infection. Drug repurposing and drug effect prediction are promising strategies that could shorten development time and reduce costs compared with de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds, and analyze their influence on the therapeutic effects for SARS-CoV-2 infection and on host cell cytotoxic effects. Random forest models trained on compounds with known experimental effects on SARS-CoV-2 infection and subsequent feature importance analysis based on Shapley values provided insights into the determinants of drug efficacy and cytotoxicity, which can be incorporated into novel drug discovery approaches. Given the complexity of molecular mechanisms of drug action and limited sample sizes, our models achieve a reasonable mean area under the receiver operating characteristic curve (ROC-AUC) of 0.73 on an unseen validation set. To our knowledge, this is the first work to incorporate a combination of network and physicochemical features of compounds into a machine learning model to predict drug effects on SARS-CoV-2 infection. Our systems pharmacology-based machine learning framework can be used to classify other existing drugs for SARS-CoV-2 infection and can easily be adapted to drug effect prediction for future viral outbreaks.

Repurposing Drugs for the Treatment of COVID-19 and Its Cardiovascular Manifestations

COVID-19 is an infectious disease caused by SARS-CoV-2 leading to the ongoing global pandemic. Infected patients developed a range of respiratory symptoms, including respiratory failure, as well as other extrapulmonary complications. Multiple comorbidities, including hypertension, diabetes, cardiovascular diseases, and chronic kidney diseases, are associated with the severity and increased mortality of COVID-19. SARS-CoV-2 infection also causes a range of cardiovascular complications, including myocarditis, myocardial injury, heart failure, arrhythmias, acute coronary syndrome, and venous thromboembolism. Although a variety of methods have been developed and many clinical trials have been launched for drug repositioning for COVID-19, treatments that consider cardiovascular manifestations and cardiovascular disease comorbidities specifically are limited. In this review, we summarize recent advances in drug repositioning for COVID-19, including experimental drug repositioning, high-throughput drug screening, omics data-based, and network medicine-based computational drug repositioning, with particular attention on those drug treatments that consider cardiovascular manifestations of COVID-19. We discuss prospective opportunities and potential methods for repurposing drugs to treat cardiovascular complications of COVID-19.

The mechanisms of Qizhu Tangshen formula in the treatment of diabetic kidney disease: Network pharmacology, machine learning, molecular docking and experimental assessment

BACKGROUND

Qizhu Tangshen Formula (QZTS) has been shown therapeutic effects on diabetic kidney disease (DKD). However, to date, the pharmacological mechanisms remain vague.

METHODS

To explore the underlying mechanisms of QZTS in treating DKD using network pharmacology, machine learning, molecular docking and experimental assessment.

RESULTS

First, we found that QZTS improved glycolipid metabolism disorder, decreased proteinuria and alleviated kidney tissue injury in DKD model KKAy mice. Then, by integrating multiple databases, a total of 96 targets of 74 active compounds in QZTS and 759 DKD-related genes were acquired. Next, we identified 13 hub targets of QZTS in DKD by three rank algorithms, including functional similarity, topological similarity and shortest path. Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses demonstrated that the pathways mainly centered on the processes of glycolipid metabolism disorder, inflammation and angiogenesis. Among them, VEGF signaling pathway was significantly enriched. Molecular docking showed that key active compounds of QZTS all had relatively good binding affinity with predicted hub targets. Finally, animal experiments found that QZTS significantly inhibited the secretion of plasma VEGF and downregulated the protein and mRNA expression levels of AKT, p38MAPK and VEGFR2.

CONCLUSION

Our results indicated that QZTS treated DKD via multiple targets and pathways and the VEGF signaling pathway may be highly involved in this process.

Molecular interaction networks and drug development: Novel approach to drug target identification and drug repositioning

Conventional drug discovery requires identifying a protein target believed to be important for disease mechanism and screening compounds for those that beneficially alter the target's function. While this approach has been an effective one for decades, recent data suggest that its continued success is limited largely owing to the highly prevalent irreducibility of biologically complex systems that govern disease phenotype to a single primary disease driver. Network medicine, a new discipline that applies network science and systems biology to the analysis of complex biological systems and disease, offers a novel approach to overcoming these limitations of conventional drug discovery. Using the comprehensive protein-protein interaction network (interactome) as the template through which subnetworks that govern specific diseases are identified, potential disease drivers are unveiled and the effect of novel or repurposed drugs, used alone or in combination, is studied. This approach to drug discovery offers new and exciting unbiased possibilities for advancing our knowledge of disease mechanisms and precision therapeutics.

Drug repurposing of dextromethorphan as a cellular target for the management of influenza

BACKGROUND

Influenza viruses are responsible for seasonal epidemics and sporadic pandemics of varying severity in humans, and additional treatment options are needed. High-throughput siRNA screens and a pre-clinical research model demonstrated that dextromethorphan (DM) has anti-viral activity as a cellular target for treatment of influenza. This study examined DM usage and hospitalization rates among patients with laboratory-confirmed influenza in a national cohort of United States veterans. We aimed to evaluate the potential drug repurposing of DM as a cellular target for the management of influenza utilizing a large, national claims and electronic health record database.

METHODS

This retrospective drug-disease association cohort study was conducted using data from the Veterans Affairs Informatics and Computing Infrastructure (VINCI). We used a cohort with laboratory-confirmed diagnosis of influenza and international classification of disease (ICD)-9/10 diagnosis codes of fever, cough, influenza, or acute upper respiratory infection in an outpatient setting. The study outcome is inpatient hospitalization (all-cause and respiratory) within 30 days of influenza diagnosis. We estimated the relative risk for all-cause and respiratory hospitalizations using Poisson generalized linear model (GLM) and a greedy nearest neighbor propensity score 1:1 matched sub-analysis for both hospitalization models.

FINDINGS

A total of 18,677 patients met the inclusion and exclusion criteria and were evaluated in our study. The cohorts consisted of 2801 patients dispensed DM and 15,876 untreated patients (no DM). The Poisson GLM adjusted for covariates demonstrated a relative risk reduction of 34% for all-cause hospitalizations (Relative Risk (RR) 0.66, 95% Confidence Interval (CI) 0.525-0.832) and 40% for respiratory hospitalizations (RR 0.597, 95% CI 0.423-0.843) in patients with influenza treated with DM.

CONCLUSION

Influenza viruses continue to emerge and cause infection (including pandemics) in humans, so there remains a critical need to advance the understanding of influenza treatment. Our results demonstrated reduced hospitalization rates for influenza patients treated with DM. Further research on cellular targets and/or DM is warranted for the treatment of influenza.

Therapeutic implications of statins in heart failure with reduced ejection fraction and heart failure with preserved ejection fraction: a review of current literature

Statins are one of the standard treatments to prevent cardiovascular events such as coronary artery disease and heart failure (HF). However, data on the use of statins to improve clinical outcomes in patients with established HF remains controversial. We summarized available clinical studies which investigated the effects of statins on clinical outcomes in patients with HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF). Statins possess many pleiotropic effects in addition to lipid-lowering properties that positively affect the pathophysiology of HF. In HFrEF, data from two large randomized placebo-controlled trials did not show benefits of statins on mortality of patients with HFrEF. However, more recent prospective cohort studies and meta-analyses have shown decreased risk of mortality as well as cardiovascular hospitalization with statins treatment. In HFpEF, most prospective and retrospective cohort studies as well as meta analyses have consistently reported positive effects of statins, including reducing mortality and improving other clinical outcomes. Current evidence also suggests better outcomes with lipophilic statins in patients with HF. In summary, statins might be effective in improving survival and other clinical outcomes in patients with HF, especially for patients with HFpEF. Lipophilic statins might also be more beneficial for HF patients. Based on current evidence, statins did not cause harm and should be continued in HF patients who are already taking the medication. Further randomized controlled trials are needed to clarify the benefits of statins in HF patients.

Network medicine in Cardiovascular Research

The ability to generate multi-omics data coupled with deeply characterizing the clinical phenotype of individual patients promises to improve understanding of complex cardiovascular pathobiology. There remains an important disconnection between the magnitude and granularity of these data and our ability to improve phenotype-genotype correlations for complex cardiovascular diseases. This shortcoming may be due to limitations associated with traditional reductionist analytical methods, which tend to emphasize a single molecular event in the pathogenesis of diseases more aptly characterized by crosstalk between overlapping molecular pathways. Network medicine is a rapidly growing discipline that considers diseases as the consequences of perturbed interactions between multiple interconnected biological components. This powerful integrative approach has enabled a number of important discoveries in complex disease mechanisms. In this review, we introduce the basic concepts of network medicine and highlight specific examples by which this approach has accelerated cardiovascular research. We also review how network medicine is well-positioned to promote rational drug design for patients with cardiovascular diseases, with particular emphasis on advancing precision medicine.

Recent advances in network medicine: From disease mechanisms to new treatment strategies

Conventional reductionist approaches have guided most of our understanding in disease diagnostic and treatment. However, most diseases are not consequence of perturbations in a single protein or metabolite, but rather of the effect that these perturbations have in their cellular context. The emerging field of network medicine offers a set of tools to explore molecular networks and to retrieve insights about mechanisms of different diseases. The study of the protein interactome, the map of physical interactions among human proteins, revealed that disease proteins tend to interact with each other, linking diseases to well-defined interactome neighborhoods. These disease-associated neighborhoods have been defined as disease modules, and they can uncover the biological significance of genes identified by genetic studies, reveal molecular mechanisms that connect different phenotypes, and help identify new pharmacological strategies for disease treatment. Therefore, network medicine offers a framework in which the complexity of different aspects of multiple sclerosis can be explored in an integrative fashion, which can ultimately provide insights about disease mechanisms and treatment.

Rationalization of a traditional liver medicine using systems biology approach and its evaluation in preclinical trial

'Bottom-up', i.e., molecule to medicine strategy for the discovery of new drugs takes enormous time and cost. In most of the cases, inherent toxicity and undesired side effects of the developed drug hinder its way beyond the early stages of development. In this regard, the systems pharmacology can play an excellent role by reducing the cost and time of drug development through rationalization and/or repurposing of traditional drugs with known side effects. In the present study, our aim was to develop an integrated systems biology method for the prediction of active ingredients of a traditional medicine and their potential targets inside the body. Further, we evaluated the predictive capacity of the developed method in a preclinical animal model. Here, we have prepared a formulation (SKP17LIV01) from an extract of eight medicinal plants traditionally used as liver medicine and identified the constituents using UHPLC-MS technique. Using systems biology approach, we have rationalized the components of the formulation for potential use in the treatment of heavy metal-induced hepatotoxicity. The active ingredients and potential therapeutic targets were also predicted. A detailed biochemical, histopathological and molecular study on the mice model of lead toxicity confirms the efficacy of the formulation as per prediction by the systems pharmacology approach. The study may open a new frontier for re-discovery of drugs that are already used in traditional medicine.

Cardioprotective mechanisms of salvianic acid A sodium in rats with myocardial infarction based on proteome and transcriptome analysis

Ischemic heart diseases (IHDs) cause great morbidity and mortality worldwide, necessitating effective treatment. Salvianic acid A sodium (SAAS) is an active compound derived from the well-known herbal medicine Danshen, which has been widely used for clinical treatment of cardiovascular diseases in China. This study aimed to confirm the cardioprotective effects of SAAS in rats with myocardial infarction and to investigate the underlying molecular mechanisms based on proteome and transcriptome profiling of myocardial tissue. The results showed that SAAS effectively protected against myocardial injury and improved cardiac function. The differentially expressed proteins and genes included important structural molecules, receptors, transcription factors, and cofactors. Functional enrichment analysis indicated that SAAS participated in the regulation of actin cytoskeleton, phagosome, focal adhesion, tight junction, apoptosis, MAPK signaling, and Wnt signaling pathways, which are closely related to cardiovascular diseases. SAAS may exert its cardioprotective effect by targeting multiple pathways at both the proteome and transcriptome levels. This study has provided not only new insights into the pathogenesis of myocardial infarction but also a road map of the cardioprotective molecular mechanisms of SAAS, which may provide pharmacological evidence to aid in its clinical application.

Network Medicine in Pathobiology

The past decade has witnessed exponential growth in the generation of high-throughput human data across almost all known dimensions of biological systems. The discipline of network medicine has rapidly evolved in parallel, providing an unbiased, comprehensive biological framework through which to interrogate and integrate systematically these large-scale, multi-omic data to enhance our understanding of disease mechanisms and to design drugs that reflect a deep knowledge of molecular pathobiology. In this review, we discuss the key principles of network medicine and the human disease network and explore the latest applications of network medicine in this multi-omic era. We also highlight the current conceptual and technological challenges, which serve as exciting opportunities by which to improve and expand the network-based applications beyond the artificial boundaries of the current state of human pathobiology.

Network-based approach to prediction and population-based validation of in silico drug repurposing

Here we identify hundreds of new drug-disease associations for over 900 FDA-approved drugs by quantifying the network proximity of disease genes and drug targets in the human (protein–protein) interactome. We select four network-predicted associations to test their causal relationship using large healthcare databases with over 220 million patients and state-of-the-art pharmacoepidemiologic analyses. Using propensity score matching, two of four network-based predictions are validated in patient-level data: carbamazepine is associated with an increased risk of coronary artery disease (CAD) [hazard ratio (HR) 1.56, 95% confidence interval (CI) 1.12–2.18], and hydroxychloroquine is associated with a decreased risk of CAD (HR 0.76, 95% CI 0.59–0.97). In vitro experiments show that hydroxychloroquine attenuates pro-inflammatory cytokine-mediated activation in human aortic endothelial cells, supporting mechanistically its potential beneficial effect in CAD. In summary, we demonstrate that a unique integration of protein-protein interaction network proximity and large-scale patient-level longitudinal data complemented by mechanistic in vitro studies can facilitate drug repurposing.

Repurposing approved drugs could accelerate treatment options for various diseases. Here, the authors use network proximity of disease gene products and drug targets in the human protein interactome to identify drug-disease associations for cardiovascular disease, and validate these using longitudinal healthcare data.

Automated data-adaptive analytics for electronic healthcare data to study causal treatment effects

BACKGROUND

Decision makers in health care increasingly rely on nonrandomized database analyses to assess the effectiveness, safety, and value of medical products. Health care data scientists use data-adaptive approaches that automatically optimize confounding control to study causal treatment effects. This article summarizes relevant experiences and extensions.

METHODS

The literature was reviewed on the uses of high-dimensional propensity score (HDPS) and related approaches for health care database analyses, including methodological articles on their performance and improvement. Articles were grouped into applications, comparative performance studies, and statistical simulation experiments.

RESULTS

The HDPS algorithm has been referenced frequently with a variety of clinical applications and data sources from around the world. The appeal of HDPS for database research rests in 1) its superior performance in situations of unobserved confounding through proxy adjustment, 2) its predictable efficiency in extracting confounding information from a given data source, 3) its ability to automate estimation of causal treatment effects to the extent achievable in a given data source, and 4) its independence of data source and coding system. Extensions of the HDPS approach have focused on improving variable selection when exposure is sparse, using free text information and time-varying confounding adjustment.

CONCLUSION

Semiautomated and optimized confounding adjustment in health care database analyses has proven successful across a wide range of settings. Machine-learning extensions further automate its use in estimating causal treatment effects across a range of data scenarios.

Emerging Role of Precision Medicine in Cardiovascular Disease

Precision medicine is an integrative approach to cardiovascular disease prevention and treatment that considers an individual's genetics, lifestyle, and exposures as determinants of their cardiovascular health and disease phenotypes. This focus overcomes the limitations of reductionism in medicine, which presumes that all patients with the same signs of disease share a common pathophenotype and, therefore, should be treated similarly. Precision medicine incorporates standard clinical and health record data with advanced panomics (ie, transcriptomics, epigenomics, proteomics, metabolomics, and microbiomics) for deep phenotyping. These phenotypic data can then be analyzed within the framework of molecular interaction (interactome) networks to uncover previously unrecognized disease phenotypes and relationships between diseases, and to select pharmacotherapeutics or identify potential protein-drug or drug-drug interactions. In this review, we discuss the current spectrum of cardiovascular health and disease, population averages and the response of extreme phenotypes to interventions, and population-based versus high-risk treatment strategies as a pretext to understanding a precision medicine approach to cardiovascular disease prevention and therapeutic interventions. We also consider the search for resilience and Mendelian disease genes and argue against the theory of a single causal gene/gene product as a mediator of the cardiovascular disease phenotype, as well as an Erlichian magic bullet to solve cardiovascular disease. Finally, we detail the importance of deep phenotyping and interactome networks and the use of this information for rational polypharmacy. These topics highlight the urgent need for precise phenotyping to advance precision medicine as a strategy to improve cardiovascular health and prevent disease.

Systems biology: An emerging strategy for discovering novel pathogenetic mechanisms that promote cardiovascular disease

Reductionist theory proposes that analyzing complex systems according to their most fundamental components is required for problem resolution, and has served as the cornerstone of scientific methodology for more than four centuries. However, technological gains in the current scientific era now allow for the generation of large datasets that profile the proteomic, genomic, and metabolomic signatures of biological systems across a range of conditions. The accessibility of data on such a vast scale has, in turn, highlighted the limitations of reductionism, which is not conducive to analyses that consider multiple and contemporaneous interactions between intermediates within a pathway or across constructs. Systems biology has emerged as an alternative approach to analyze complex biological systems. This methodology is based on the generation of scale-free networks and, thus, provides a quantitative assessment of relationships between multiple intermediates, such as protein-protein interactions, within and between pathways of interest. In this way, systems biology is well positioned to identify novel targets implicated in the pathogenesis or treatment of diseases. In this review, the historical root and fundamental basis of systems biology, as well as the potential applications of this methodology are discussed with particular emphasis on integration of these concepts to further understanding of cardiovascular disorders such as coronary artery disease and pulmonary hypertension.