Dynamic networks in systems medicine

Exploring Mechanisms of Houshiheisan in Treating Ischemic Stroke with Network Pharmacology and Independent Cascade Model

BACKGROUND

Houshiheisan (HSHS) has been effective in the treatment of ischemic stroke (IS) for centuries. However, its mechanisms are still underexplored.

OBJECTIVE

The objective of this study is to identify the active ingredients and mechanisms of HSHS in treating IS.

METHODS

We searched the main active compounds in HSHS and their potential targets, and key targets related to IS. Based on the common targets of HSHS and IS, we further expanded genes by KEGG database to obtain target genes and related genes, as well as gene interactions in the form of A→B, and then constructed a directed network including traditional Chinese medicines (TCMs), active compounds and genes. Finally, based on enrichment analysis, independent cascade (IC) model, and molecular docking, we explored the mechanisms of HSHS in treating IS.

RESULTS

A directed network with 6,348 nodes and 64,996 edges was constructed. The enrichment analysis suggested that the AGE pathway, glucose metabolic pathway, lipid metabolic pathway, and inflammation pathway played critical roles in the treatment of IS by HSHS. Furthermore, the gene ontologies (GOs) of three monarch drugs in HSHS mainly involved cellular response to chemical stress, blood coagulation, hemostasis, positive regulation of MAPK cascade, and regulation of inflammatory response. Several candidate drug molecules were identified by molecular docking.

CONCLUSION

This study advocated potential drug development with targets in the AGE signaling pathway, with emphasis on neuroprotective, anti-inflammatory, and anti-apoptotic functions. The molecular docking simulation indicated that the ligand-target combination selection method based on the IC model was effective and reliable.

Advancing Biomarker Development Through Convergent Engagement: Summary Report of the 2nd International Danube Symposium on Biomarker Development, Molecular Imaging and Applied Diagnostics; March 14-16, 2018; Vienna, Austria

Here, we report on the outcome of the 2nd International Danube Symposium on advanced biomarker development that was held in Vienna, Austria, in early 2018. During the meeting, cross-speciality participants assessed critical aspects of non-invasive, quantitative biomarker development in view of the need to expand our understanding of disease mechanisms and the definition of appropriate strategies both for molecular diagnostics and personalised therapies. More specifically, panelists addressed the main topics, including the current status of disease characterisation by means of non-invasive imaging, histopathology and liquid biopsies as well as strategies of gaining new understanding of disease formation, modulation and plasticity to large-scale molecular imaging as well as integrative multi-platform approaches. Highlights of the 2018 meeting included dedicated sessions on non-invasive disease characterisation, development of disease and therapeutic tailored biomarkers, standardisation and quality measures in biospecimens, new therapeutic approaches and socio-economic challenges of biomarker developments. The scientific programme was accompanied by a roundtable discussion on identification and implementation of sustainable strategies to address the educational needs in the rapidly evolving field of molecular diagnostics. The central theme that emanated from the 2nd Donau Symposium was the importance of the conceptualisation and implementation of a convergent approach towards a disease characterisation beyond lesion-counting "lumpology" for a cost-effective and patient-centric diagnosis, therapy planning, guidance and monitoring. This involves a judicious choice of diagnostic means, the adoption of clinical decision support systems and, above all, a new way of communication involving all stakeholders across modalities and specialities. Moreover, complex diseases require a comprehensive diagnosis by converging parameters from different disciplines, which will finally yield to a precise therapeutic guidance and outcome prediction. While it is attractive to focus on technical advances alone, it is important to develop a patient-centric approach, thus asking "What can we do with our expertise to help patients?"

General Practitioners Records Are Epidemiological Predictors of Comorbidities: An Analytical Cross-Sectional 10-Year Retrospective Study

Background. Comorbidity represents the co-occurrence of pathological conditions in the same individual, and presents with very complex patterns. In most cases, reference data for the study of various types of comorbidities linked to complex diseases are those of hospitalized patients. Such patients may likely require cure due to acute conditions. We consider the emerging role of EHR (Electronic Healthcare Records), and study comorbidity patterns in a general population, focusing on diabetic and non-diabetic patients. Methods. We propose a cross-sectional 10-year retrospective study of 14,958 patients and 1,728,736 prescriptions obtained from family doctors, and thus refer to these data as General Practitioner Records (GPR). We then choose networks as the tools to analyze the diabetes comorbidity patterns, distinguished by both prescription type and main patient characteristics (age, gender). Results. As expected, comorbidity increases with patients’ age, and the network representations allow the assessment of associations between morbidity groups. The specific morbidities present in the diabetic population justify the higher comorbidity patterns observed in the target group compared to the non-diabetic population. Conclusions. GPR are usually combined with other data types in EHR studies, but we have shown that prescription data have value as standalone predictive tools, useful to anticipate trends observed at epidemiological level on large populations. This study is thus relevant to policy makers seeking inference tools for an efficient use of massive administrative database resources, and suggests a strategy for detecting comorbidities and investigating their evolution.

Clinical decision-making and secondary findings in systems medicine

BACKGROUND

Systems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology (especially systems biology); "big data" statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working to generate systems medicine knowledge and clinicians working to apply it.

DISCUSSION

This article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through "big data" approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the "death computer," to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their "quantified self." This paper examines possible ethical challenges that are likely to be raised as systems medicine to be translated into clinical medicine. These include the epistemological challenges for clinical decision-making, the use of scoring systems optimized by big data techniques and the risk that incidental and secondary findings will significantly increase. While some ethical implications remain still hypothetical we should use the opportunity to prospectively identify challenges to avoid making foreseeable mistakes when systems medicine inevitably arrives in routine care.

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.