Challenges and opportunities of drug repositioning

Komatsu

The Journal of Immunology

et al. 2015

Mucin overproduction is a hallmark of otitis media (OM). Streptococcus pneumoniae is one of the most common bacterial pathogens causing OM. Mucin MUC5AC plays an important role in mucociliary clearance of bacterial pathogens. However, if uncontrolled, excessive mucus contributes significantly to conductive hearing loss. Currently, there is a lack of effective therapeutic agents that suppress mucus overproduction. In this study, we show that a currently existing antistroke drug, vinpocetine, a derivative of the alkaloid vincamine, inhibited S. pneumoniae–induced mucin MUC5AC upregulation in cultured middle ear epithelial cells and in the middle ear of mice. Moreover, vinpocetine inhibited MUC5AC upregulation by inhibiting the MAPK ERK pathway in an MKP-1–dependent manner. Importantly, ototopical administration of vinpocetine postinfection inhibited MUC5AC expression and middle ear inflammation induced by S. pneumoniae and reduced hearing loss and pneumococcal loads in a well-established mouse model of OM. Thus, these studies identified vinpocetine as a potential therapeutic agent for inhibiting mucus production in the pathogenesis of OM.

Section: Discussionmentioning

confidence: 99%

Vinpocetine Inhibits Streptococcus pneumoniae–Induced Upregulation of Mucin MUC5AC Expression via Induction of MKP-1 Phosphatase in the Pathogenesis of Otitis Media

Komatsu

The Journal of Immunology

et al. 2015

“…Firstly, the row-normalized matrices (1) and (2) are calculated from the drug similarity matrices subsim and GIP, , respectively. Secondly, according to the -nearest neighbors (KNN) method, the resultant matrices (1) and (2) are obtained from (1) and (2) by the following equation [44]:…”

Section: Similarity Network Fusionmentioning

confidence: 99%

“…Secondly, according to the -nearest neighbors (KNN) method, the resultant matrices (1) and (2) are obtained from (1) and (2) by the following equation [44]:…”

Section: Similarity Network Fusionmentioning

confidence: 99%

See 1 more Smart Citation

SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

Cheng

Lan

et al. 2017

Complexity

It is well known that drug discovery for complex diseases via biological experiments is a time-consuming and expensive process. Alternatively, the computational methods provide a low-cost and high-efficiency way for predicting drug-target interactions (DTIs) from biomolecular networks. However, the current computational methods mainly deal with DTI predictions of known drugs; there are few methods for large-scale prediction of failed drugs and new chemical entities that are currently stored in some biological databases may be effective for other diseases compared with their originally targeted diseases. In this study, we propose a method (called SDTRLS) which predicts DTIs through RLS-Kron model with chemical substructure similarity fusion and Gaussian Interaction Profile (GIP) kernels. SDTRLS can be an effective predictor for targets of old drugs, failed drugs, and new chemical entities from large-scale biomolecular network databases. Our computational experiments show that SDTRLS outperforms the state-of-the-art SDTNBI method; specifically, in the G protein-coupled receptors (GPCRs) external validation, the maximum and the average AUC values of SDTRLS are 0.842 and 0.826, respectively, which are superior to those of SDTNBI, which are 0.797 and 0.766, respectively. This study provides an important basis for new drug development and drug repositioning based on biomolecular networks.

“…This apparent inefficiency, however, masks enormous opportunities to repurpose existing drugs to treat diseases for which they were not originally intended, especially diseases with limited therapeutic options (1,2). Therapies for inflammatory diseases are an attractive choice for drug repositioning because of the complexity of the immune response, the hit-or-miss nature of treatment (which is often by trial and error), and the number of traditional drugs and new biologics for inflammatory diseases.…”

Section: Introductionmentioning

confidence: 99%

Therapeutic drug repositioning using personalized proteomics of liquid biopsies

Vélez¹,

Bassuk²,

Colgan³

et al. 2017

JCI Insight

IntroductionAfter spending several billion dollars and a decade or more in development and clinical trial, only approximately 10% of new drugs show efficacy and reach the market for patient use. This apparent inefficiency, however, masks enormous opportunities to repurpose existing drugs to treat diseases for which they were not originally intended, especially diseases with limited therapeutic options (1, 2). Therapies for inflammatory diseases are an attractive choice for drug repositioning because of the complexity of the immune response, the hit-or-miss nature of treatment (which is often by trial and error), and the number of traditional drugs and new biologics for inflammatory diseases.Determining which drugs to reposition, however, is not trivial. Among a heterogeneous patient population that seems affected by the same autoimmune condition, one cannot be sure if the disease is in fact the same, or at what progressive stage a patient is presenting. Currently, most predictions of patients that would benefit from drug repositioning are drawn from retrospective computational methods, genomic analyses, BACKGROUND. In patients with limited response to conventional therapeutics, repositioning of already approved drugs can bring new, more effective options. Current drug repositioning methods, however, frequently rely on retrospective computational analyses and genetic testing -time consuming methods that delay application of repositioned drugs. Here, we show how proteomic analysis of liquid biopsies successfully guided treatment of neovascular inflammatory vitreoretinopathy (NIV), an inherited autoinflammatory disease with otherwise poor clinical outcomes.