Network spectra for drug-target identification in complex diseases: new guns against old foes

The fundamental understanding of altered complex molecular interactions in a diseased condition is the key to its cure. The overall functioning of these molecules is kind of jugglers play in the cell orchestra and to anticipate these relationships among the molecules is one of the greatest challenges in modern biology and medicine. Network science turned out to be providing a successful and simple platform to understand complex interactions among healthy and diseased tissues. Furthermore, much information about the structure and dynamics of a network is concealed in the eigenvalues of its adjacency matrix. In this review, we illustrate rapid advancements in the field of network science in combination with spectral graph theory that enables us to uncover the complexities of various diseases. Interpretations laid by network science approach have solicited insights into molecular relationships and have reported novel drug targets and biomarkers in various complex diseases.

Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data

Tools from network science can be utilized to study relations between diseases. Different studies focus on different types of inter-disease linkages. One of them is the comorbidity patterns derived from large-scale longitudinal data of hospital discharge records. Researchers seek to describe comorbidity relations as a network to characterize pathways of disease progressions and to predict future risks. The first step in such studies is the construction of the network itself, which subsequent analyses rest upon. There are different ways to build such a network. In this paper, we provide an overview of several existing statistical approaches in network science applicable to weighted directed networks. We discuss the differences between the null models that these models assume and their applications. We apply these methods to the inpatient data of approximately one million people, spanning approximately 17 years, pertaining to the Montreal Census Metropolitan Area. We discuss the differences in the structure of the networks built by different methods, and different features of the comorbidity relations that they extract. We also present several example applications of these methods.

ANMCO/SIC Consensus Document: cardiology networks for outpatient heart failure care

Changing demographics and an increasing burden of multiple chronic comorbidities in Western countries dictate refocusing of heart failure (HF) services from acute in-hospital care to better support the long inter-critical out-of- hospital phases of HF. In Italy, as well as in other countries, needs of the HF population are not adequately addressed by current HF outpatient services, as documented by differences in age, gender, comorbidities and recommended therapies between patients discharged for acute hospitalized HF and those followed-up at HF clinics.

The Italian Working Group on Heart Failure has drafted a guidance document for the organisation of a national HF care network. Aims of the document are to describe tasks and requirements of the different health system points of contact for HF patients, and to define how diagnosis, management and care processes should be documented and shared among health-care professionals.

The document classifies HF outpatient clinics in three groups: (i) community HF clinics, devoted to management of stable patients in strict liaison with primary care, periodic re-evaluation of emerging clinical needs and prompt treatment of impending destabilizations, (ii) hospital HF clinics, that target both new onset and chronic HF patients for diagnostic assessment, treatment planning and early post-discharge follow-up. They act as main referral for general internal medicine units and community clinics, and (iii) advanced HF clinics, directed at patients with severe disease or persistent clinical instability, candidates to advanced treatment options such as heart transplant or mechanical circulatory support. Those different types of HF clinics are integrated in a dedicated network for management of HF patients on a regional basis, according to geographic features. By sharing predefined protocols and communication systems, these HF networks integrate multi-professional providers to ensure continuity of care and patient empowerment.

In conclusion, This guidance document details roles and interactions of cardiology specialists, so as to best exploit the added value of their input in the care of HF patients and is intended to promote a more efficient and effective organization of HF services.

Systems psychopharmacology: A network approach to developing novel therapies

The multifactorial origin of most chronic disorders of the brain, including schizophrenia, has been well accepted. Consequently, pharmacotherapy would require multi-targeted strategies. This contrasts to the majority of drug therapies used until now, addressing more or less specifically only one target molecule. Nevertheless, quite some searches for multiple molecular targets specific for mental disorders have been undertaken. For example, genome-wide association studies have been conducted to discover new target genes of disease. Unfortunately, these attempts have not fulfilled the great hopes they have started with. Polypharmacology and network pharmacology approaches of drug treatment endeavor to abandon the one-drug one-target thinking. To this end, most approaches set out to investigate network topologies searching for modules, endowed with “important” nodes, such as “hubs” or “bottlenecks”, encompassing features of disease networks, and being useful as tentative targets of drug therapies. This kind of research appears to be very promising. However, blocking or inhibiting “important” targets may easily result in destruction of network integrity. Therefore, it is suggested here to study functions of nodes with lower centrality for more subtle impact on network behavior. Targeting multiple nodes with low impact on network integrity by drugs with multiple activities (“dirty drugs”) or by several drugs, simultaneously, avoids to disrupt network integrity and may reset deviant dynamics of disease. Natural products typically display multi target functions and therefore could help to identify useful biological targets. Hence, future efforts should consider to combine drug-target networks with target-disease networks using mathematical (graph theoretical) tools, which could help to develop new therapeutic strategies in long-term psychiatric disorders.

DrugNet: network-based drug-disease prioritization by integrating heterogeneous data

OBJECTIVE

Computational drug repositioning can lead to a considerable reduction in cost and time in any drug development process. Recent approaches have addressed the network-based nature of biological information for performing complex prioritization tasks. In this work, we propose a new methodology based on heterogeneous network prioritization that can aid researchers in the drug repositioning process.

METHODS

We have developed DrugNet, a new methodology for drug-disease and disease-drug prioritization. Our approach is based on a network-based prioritization method called ProphNet which has recently been developed by the authors. ProphNet is able to integrate data from complex networks involving a wide range of types of elements and interactions. In this work, we built a network of interconnected drugs, proteins and diseases and applied DrugNet to different types of tests for drug repositioning.

RESULTS

We tested the performance of our approach on different validation tests, including cross validation and tests based on real clinical trials. DrugNet achieved a mean AUC value of 0.9552±0.0015 in 5-fold cross validation tests, and a mean AUC value of 0.8364 for tests based on recent clinical trials (phases 0-4) not present in our data. These results suggest that DrugNet could be very useful for discovering new drug uses. We also studied specific cases of particular interest, proving the benefits of heterogeneous data integration in this problem.

CONCLUSIONS

Our methodology suggests that new drugs can be repositioned by generating ranked lists of drugs based on a given disease query or vice versa. Our study shows that the simultaneous integration of information about diseases, drugs and targets can lead to a significant improvement in drug repositioning tasks. DrugNet is available as a web tool from http://genome2.ugr.es/drugnet/ (accessed 23.09.14). Matlab source code is also available on the website.