Network-based technologies for early drug discovery

Although the traditional drug discovery approach has led to the development of many successful drugs, the attrition rates remain high. Recent advances in systems-oriented approaches (systems-biology and/or pharmacology) and 'omics technologies has led to a plethora of new computational tools that promise to enable a more-informed and successful implementation of the reductionist, one drug for one target for one disease, approach. These tools, based on biomolecular pathways and interaction networks, offer a systematic approach to unravel the mechanism(s) of a disease and link them to the chemical space and network footprint of a drug. Drug discovery can draw upon this holistic approach to identify the most-promising targets and compounds during the early phases of development.

Compensatory interactions to stabilize multiple steady states or mitigate the effects of multiple deregulations in biological networks

Complex diseases can be modeled as damage to intracellular networks that results in abnormal cell behaviors. Network-based dynamic models such as Boolean models have been employed to model a variety of biological systems including those corresponding to disease. Previous work designed compensatory interactions to stabilize an attractor of a Boolean network after single node damage. We generalize this method to a multinode damage scenario and to the simultaneous stabilization of multiple steady state attractors. We classify the emergent situations, with a special focus on combinatorial effects, and characterize each class through simulation. We explore how the structural and functional properties of the network affect its resilience and its possible repair scenarios. We demonstrate the method's applicability to two intracellular network models relevant to cancer. This work has implications in designing prevention strategies for complex disease.

Active fragments-guided drug discovery and design of selective tropane alkaloids using ultra-high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry coupled with virtual calculation and biological evaluation

Tropane alkaloids (TAs), rich in the plant of Physochlaina infundibularis Kuang, which is named Huashanshen (HSS) in China, showed good effects on types of spasms. However, no data were collected to explore the relationship between the specificity for muscarinic receptor subtypes and the structures of these TAs. To address this issue, an extracted ion chromatogram (EIC) strategy using ultra-high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UPLC-Q/TOF-MS) based on the fragmentation behavior of the TA standards was established to rapidly capture the varied TAs from HSS. Based on the provided structural information of diagnostic ions or neutral loss, 29 TAs were efficiently profiled, especially some trace ingredients. In additional, via virtual validation combined with molecular dynamic simulation, approximately a dozen alkaloids were found with high selectivity for muscarinic receptors. In additional, N-acetyl convolicine was chosen for selectivity evaluation of M₂ or M₃ receptors through the use of a dual-luciferase reporter assay system at the cellular level and an ACh-induced constricted strip test in vitro. After summarizing the active fragments and the structure-activity relationship (SAR) information, a new modified TA that takes advantage of both the high affinity and high selectivity for M₃ receptors was proposed and evaluated successfully. This study provided an effective approach for the discovery and design of natural products based on highly selective drugs by UPLC-Q/TOF-MS coupled with virtual calculation and biological evaluation. Graphical Abstract Active fragments-guided strategy for selective inhibitors from HSS.

A Glimpse to Background and Characteristics of Major Molecular Biological Networks

Recently, biology has become a data intensive science because of huge data sets produced by high throughput molecular biological experiments in diverse areas including the fields of genomics, transcriptomics, proteomics, and metabolomics. These huge datasets have paved the way for system-level analysis of the processes and subprocesses of the cell. For system-level understanding, initially the elements of a system are connected based on their mutual relations and a network is formed. Among omics researchers, construction and analysis of biological networks have become highly popular. In this review, we briefly discuss both the biological background and topological properties of major types of omics networks to facilitate a comprehensive understanding and to conceptualize the foundation of network biology.

Excess costs of comorbidities in chronic obstructive pulmonary disease: a systematic review

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. Comorbidities are often reported in patients with COPD and may influence the cost of care. Yet, the extent by which comorbidities affect costs remains to be determined.

OBJECTIVES

To review, quantify and evaluate excess costs of comorbidities in COPD.

METHODS

Using a systematic review approach, Pubmed and Embase were searched for studies analyzing excess costs of comorbidities in COPD. Resulting studies were evaluated according to study characteristics, comorbidity measurement and cost indicators. Mark-up factors were calculated for respective excess costs. Furthermore, a checklist of quality criteria was applied.

RESULTS

Twelve studies were included. Nine evaluated comorbidity specific costs; three examined index-based results. Pneumonia, cardiovascular disease and diabetes were associated with the highest excess costs. The mark-up factors for respective excess costs ranged between 1.5 and 2.5 in the majority of cases. On average the factors constituted a doubling of respective costs in the comorbid case. The main cost driver, among all studies, was inpatient cost. Indirect costs were not accounted for by the majority of studies. Study heterogeneity was high.

CONCLUSIONS

The reviewed studies clearly show that comorbidities are associated with significant excess costs in COPD. The inclusion of comorbid costs and effects in future health economic evaluations of preventive or therapeutic COPD interventions seems highly advisable.

A network approach to clinical intervention in neurodegenerative diseases

Network biology has become a powerful tool to dissect the molecular mechanisms triggering neurodegeneration. Recent developments in network biology have led to the discovery of disease-causing genes, diagnostic biomarkers, and therapeutic targets for several neurodegenerative diseases including Alzheimer's, Parkinson's, and Huntington's diseases. Network-based approaches have provided the molecular rationale for the relationship among cancer, diabetes, and neurodegenerative diseases, and have uncovered unexpected links between apparently unrelated diseases. Here, we summarize the recent advances in network biology to untangle the molecular underpinnings giving rise to the most prevalent neurodegenerative diseases. We propose that network analysis provides a feasible and practical tool for identifying biologically meaningful biomarkers and potential therapeutic targets for clinical intervention in neurodegenerative diseases.