Computational drug repositioning for peripheral arterial disease: prediction of anti-inflammatory and pro-angiogenic therapeutics

Autophagy-related genes analysis reveals potential biomarkers for prediction of the impaired walking capacity of peripheral arterial disease

BACKGROUND

The role of autophagy and autophagy-related genes in peripheral arterial disease (PAD) remains unknown and may be of diagnostic and prognostic value. The aim of this study is to investigate the relationship between autophagy and PAD, and identify potential diagnostic or prognostic biomarkers for medical practice.

METHODS

Differentially expressed autophagy-related genes in PAD were explored from GSE57691 and validated in our WalkByLab registry participants by quantitative real-time polymerase chain reaction (qRT-PCR). The level of autophagy in peripheral blood mononuclear cells (PBMCs) of WalkByLab participants was assessed by analyzing autophagic marker proteins (beclin-1, P62, LC3B). Single sample gene set enrichment analysis (ssGSEA) was used to evaluate the immune microenvironment within the artery wall of PAD patients and healthy persons. Chemokine antibody array and enzyme-linked immunosorbent assay were used to assess the chemokines in participants' plasma. Treadmill testing with Gardner protocol was used to evaluate participants' walking capacity. Pain-free walking distance, maximum walking distance, and walking time were recorded. Finally, a nomogram model based on logistic regression was built to predict impaired walking performance.

RESULTS

A total of 20 relevant autophagy-related genes were identified, and these genes were confirmed to be expressed at low levels in our PAD participants. Western blotting demonstrated that the expression of autophagic marker proteins beclin-1 and LC3BII were significantly reduced in PAD patients' PBMCs. ssGSEA revealed that most of the autophagy-related genes were strongly correlated with immune function, with the largest number of associated genes showing interaction between cytokine-and-cytokine receptors (CCR). In this context, the chemokines growth-related oncogene (GRO) and neutrophil activating protein2 (NAP2) are highly expressed in the plasma of WalkByLab PAD patients and were significantly negatively correlated with the walking distance assessed by Gardner treadmill testing. Finally, the plasma NAP2 level (AUC: 0.743) and derived nomogram model (AUC: 0.860) has a strong predictive potential to identify a poor walking capacity.

CONCLUSIONS

Overall, these data highlight both the important role of autophagy and autophagy-related genes in PAD and link them to vascular inflammation (expression of chemokines). In particular, chemokine NAP2 emerged as a novel biomarker that can be used to predict the impaired walking capacity in PAD patients.

A public-private collaboration model for clinical innovation

Launched in May 2012 as part of the New Therapeutic Uses program, NCATS' NIH-Industry Partnerships initiative fostered collaboration between pharmaceutical companies and the biomedical research community to advance therapeutic development. Over the 10-year life of the initiative, the industry partners included: AstraZeneca; AbbVie (formerly Abbott); Bristol-Myers Squibb; Eli Lilly and Company; GlaxoSmithKline; Janssen Pharmaceutical Research & Development, L.L.C.; Pfizer; Sanofi; and Mereo (out licensed assets). The initiative provided researchers at academic medical centers with a rare opportunity to propose clinical trials to test ideas for new therapeutic uses for a selection of clinic-ready and often previously proprietary experimental pharmaceutical assets that were provided by industry partners. Here we describe the process by which collaborations between pharmaceutical companies with viable experimental assets and academic researchers with ideas for new uses of those assets were established; and how NCATS/NIH funding supported not only phase 1 and 2 clinical trials as well as any non-clinical studies needed before testing in a new patient population, it also provided an opportunity for testing innovative outcome measures for proof of concept trials. While the program did not demonstrate improved success rates for phase 2 clinical trials, this collaboration model leverages the strengths of each party and with a focus toward evaluating innovative outcome measure, could be used to reduce patient burden and trial costs, and improve patient engagement.

Systems biology of angiogenesis signaling: Computational models and omics

Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases.

This article is categorized under:

Cardiovascular Diseases > Computational Models

Cancer > Computational Models

The application of proteomics in the diagnosis and treatment of bronchial asthma

Bronchial asthma is a common chronic inflammatory disease of the airways. Although its pathogenic mechanism remains unknown, it is influenced by both genetic and environmental factors. The emergence and application of proteomic technologies can help to facilitate analysis of the changes in transcription factors, inflammatory mediators, chemokines, cytokines, and cell apoptosis-and proliferation-related proteins in the pathological processes of asthma. Proteomic technologies can unearth prospects and theoretical bases for improved understanding of the biological mechanism of asthma and effective identification of diagnostic and therapeutic targets.

A Network-Biology Informed Computational Drug Repositioning Strategy to Target Disease Risk Trajectories and Comorbidities of Peripheral Artery Disease

Currently, drug discovery approaches focus on the design of therapies that alleviate an index symptom by reengineering the underlying biological mechanism in agonistic or antagonistic fashion. For example, medicines are routinely developed to target an essential gene that drives the disease mechanism. Therapeutic overloading where patients get multiple medications to reduce the primary and secondary side effect burden is standard practice. This single-symptom based approach may not be scalable, as we understand that diseases are more connected than random and molecular interactions drive disease comorbidities. In this work, we present a proof-of-concept drug discovery strategy by combining network biology, disease comorbidity estimates, and computational drug repositioning, by targeting the risk factors and comorbidities of peripheral artery disease, a vascular disease associated with high morbidity and mortality. Individualized risk estimation and recommending disease sequelae based therapies may help to lower the mortality and morbidity of peripheral artery disease.

A network based approach to drug repositioning identifies plausible candidates for breast cancer and prostate cancer

BACKGROUND

The high cost and the long time required to bring drugs into commerce is driving efforts to repurpose FDA approved drugs-to find new uses for which they weren't intended, and to thereby reduce the overall cost of commercialization, and shorten the lag between drug discovery and availability. We report on the development, testing and application of a promising new approach to repositioning.

METHODS

Our approach is based on mining a human functional linkage network for inversely correlated modules of drug and disease gene targets. The method takes account of multiple information sources, including gene mutation, gene expression, and functional connectivity and proximity of within module genes.

RESULTS

The method was used to identify candidates for treating breast and prostate cancer. We found that (i) the recall rate for FDA approved drugs for breast (prostate) cancer is 20/20 (10/11), while the rates for drugs in clinical trials were 131/154 and 82/106; (ii) the ROC/AUC performance substantially exceeds that of comparable methods; (iii) preliminary in vitro studies indicate that 5/5 candidates have therapeutic indices superior to that of Doxorubicin in MCF7 and SUM149 cancer cell lines. We briefly discuss the biological plausibility of the candidates at a molecular level in the context of the biological processes that they mediate.

CONCLUSIONS

Our method appears to offer promise for the identification of multi-targeted drug candidates that can correct aberrant cellular functions. In particular the computational performance exceeded that of other CMap-based methods, and in vitro experiments indicate that 5/5 candidates have therapeutic indices superior to that of Doxorubicin in MCF7 and SUM149 cancer cell lines. The approach has the potential to provide a more efficient drug discovery pipeline.