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Network-based in silico drug efficacy screening.

ABSTRACT: The increasing cost of drug development together with a significant drop in the number of new drug approvals raises the need for innovative approaches for target identification and efficacy prediction. Here, we take advantage of our increasing understanding of the network-based origins of diseases to introduce a drug-disease proximity measure that quantifies the interplay between drugs targets and diseases. By correcting for the known biases of the interactome, proximity helps us uncover the therapeutic effect of drugs, as well as to distinguish palliative from effective treatments. Our analysis of 238 drugs used in 78 diseases indicates that the therapeutic effect of drugs is localized in a small network neighborhood of the disease genes and highlights efficacy issues for drugs used in Parkinson and several inflammatory disorders. Finally, network-based proximity allows us to predict novel drug-disease associations that offer unprecedented opportunities for drug repurposing and the detection of adverse effects.

SUBMITTER: Guney E

PROVIDER: S-EPMC4740350 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Network-based in silico drug efficacy screening.

Guney Emre E Menche Jörg J Vidal Marc M Barábasi Albert-László AL

Nature communications 20160201

The increasing cost of drug development together with a significant drop in the number of new drug approvals raises the need for innovative approaches for target identification and efficacy prediction. Here, we take advantage of our increasing understanding of the network-based origins of diseases to introduce a drug-disease proximity measure that quantifies the interplay between drugs targets and diseases. By correcting for the known biases of the interactome, proximity helps us uncover the the ...[more]

PMID: 26831545

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Project description:Onychomycosis is a prominent fungal infection that causes discoloration, thickening, and mutilation leading to the separation of the nail from the nail bed. Treatment modalities for onychomycosis may include oral, topical, or combination therapy with antifungals and at times may require chemical or surgical intervention. The burden of side effects of antifungals is enormous, and therefore using molecular docking-based drug selection in context with the target keratin protein would ensure better disease management. Ciclopirox, Amorolfine HCl, Efinaconazole, Tioconazole, and Tavaborole were submitted for assessment, revealing that Amorolfine HCl is the best fit. Consequently, two formulations (Nail lacquer and nanoemulgel) were developed from Amorolfine HCl to validate the in silico screening outcomes. The formulations were further fortified with over-the-counter ingredients vis-a-vis with vitamin E in nail lacquer and undecylenic acid in nanoemulgel for their prominent roles in improving nail health. Both the formulations were systematically designed, optimized, and characterized. Amorolfine HCl containing nanoemulgel (NEG) was developed using undecylenic acid as an oil phase and thioglycolic acid as a penetration enhancer. The quality parameters evaluated were particle size, the zeta potential for nanoemulsion (NE) (78.04 ± 4.724 nm and -0.7mV, respectively), in vitro cumulative drug release (96.74% for NE and 88.54% for NEG), and transungual permeation (about 73.49% for NEG and 54.81% for NE). Nail lacquer was evaluated for the drying time, non-volatile content, and blush test. In vitro cumulative drug release of the developed nail lacquer and comparator marketed formulations were around 81.5% and 75%, respectively. Similarly, the transungual drug permeation was 6.32 μg/cm2 and 5.89 μg/cm2, respectively, in 24 h. The in silico guided preparation of both formulations containing Amorolfine HCl and over the counter ingredients is amenable for therapeutic use against onychomycosis and will be evaluated in the in vivo model.

Project description:BackgroundSystem medicine approaches have played a pivotal role in identifying novel disease networks especially in miRNA research. It is no wonder that miRNAs are implicated in multiple clinical conditions, allowing us to establish the hubs and nodes for network models of Alzheimer's Disease (AD). AD is an age-related, progressive, irreversible, and multifactorial neurodegenerative disorder characterized by cognitive and memory impairment and is the most common cause of dementia in older adults. Worldwide, around 50 million people have dementia, and there are nearly 10 million new cases every year. δ-secretase, also known as asparagine endopeptidase (AEP) or legumain (LGMN), is a lysosomal cysteine protease that cleaves peptide bonds C-terminally to asparagine residues in both amyloid precursor protein (APP) and tau, mediating the amyloid-β and tau pathology in AD. The patient's miRNA expression was found to be deregulated in the brain, extracellular fluid, blood plasma, and serum.MethodsProtein-Protein Interaction (PPI) networks of LGMN or δ-secretase were constructed using the Genemania database. Network Analyzer, a Cytoscape plugin, analyzed the network topological properties of LGMN. miRNAs related to Alzheimer's were extracted from the HMDD (Human microRNA Disease Database) and experimentally verified miRNA-gene interaction was obtained by searching miRWalk. Starbase v2.0 and miRanda were used for screening miRNA of LGMN genes. Moreover, to understand the regulatory mechanism in AD, we have screened major transcription factors of LGMN targeted genes using the Network Analyst 3.0, TRRUST (v2.0) server, and ENCODE. The Genotype-Tissue Expression (GTEx) and BEST tool were used to investigate the expression pattern of the LGMN gene. In parallel, we performed in-silico drug designing of the novel inhibitor scaffold of δ-secretase as powerful therapeutic targets by using the concept of scaffolds and frameworks. In this context, this study also aimed at identifying effective small molecule inhibitors targeting δ-secretase.ResultsAmong the 16 experimentally verified miRNAs, Network analysis of the LGMN and its associated miRNA identify novel hsa-miRNA-106a-5p and hsa-miRNA-34a-5p being more expressed in the brain. Our in silico high throughput screening, followed by XP docking revealed Oprea1 as the lead. Molecular dynamic simulations of the δ-secretase-docked complex have been carried out for a time period of 200 ns and revealed that Root Mean Square Deviation (RMSD) of the protein Cα-backbone with respect to its starting position increased to 1.20 Å for the first 25 ns of the trajectory and then became stable around 0.6 Å in the last 170 ns course of the simulation. The radius of gyration (RGYR) reveals that compactness was maintained till the end of simulations.ConclusionNetwork analysis of LGMN associated miRNAs lead to the identification of two novel miRNAs, being highly expressed in the brain. This study also lead to the identification and expression of 10 Transcription factors associated with LGMN. Expression Heatmap results show high and continuous expression of LGMN in most of the regions of the brain, especially in the frontal cortex. Further, in silico drug analysis led us to the identification of Oprea1 which could be taken for further investigation to explore its potential for AD therapy.

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Network-based in silico drug efficacy screening.

Publications

Network-based in silico drug efficacy screening.

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