Network-based in silico drug efficacy screening

By Emre Guney, Jörg Menche, Marc Vidal, Albert-László Barabási. 2016

540 citations recorded by the source corpus.

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. Attempts to predict novel use for existing drugs rarely consider information on the impact on the genes perturbed in a given disease. Here, the authors present a novel network-based drug-disease proximity measure that provides insight on gene specific therapeutic effect of drugs and may facilitate drug repurposing.

Subfields: Cell Biology, Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible (2016, background): “…discovery power for noisy data sets by ‘network smoothing’ (10,11), help define drug efficiency by network-based ‘drug-disease proximity measures’ (12), help to interpret the results of genome-wide association screens (13–17) and enable the discovery of new molecular players through the ‘guilt by…”
Computational approaches streamlining drug discovery (2023, background): “Methods based on extensive ligand–target binding datasets, chemical similarity clustering and network-based approaches have also been suggested for drug repurposing 76,77 .”
SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping (2018, methodology): “To remove possible false positives, we used Fisher’s Exact Test (23) to obtain reliable associations with statistical significance, and Fisher’s exact test is effective and widely used for evaluating the reliability of biomedical associations (24).”
Systematic integration of biomedical knowledge prioritizes drugs for repurposing (2017, methodology): “However, several systematic studies report underwhelming performance of this approach ( Gottlieb et al., 2011 ; Cheng et al., 2014 ; Guney et al., 2016 ) — a finding supported by the low performance of the CuGdD and CdGuD metapaths in Project Rephetio.”
Graph Representation Learning in Biomedicine and Healthcare (2021, unknown): “Modern drug discovery requires elucidating the chemical structure of a candidate drug, identifying its drug targets, quantifying its efficacy and toxicity, and detecting its potential side effects 13,14,32,151 .”
Network medicine framework for identifying drug-repurposing opportunities for COVID-19 (2020, methodology): “(17), and network proximity (11) (Fig.”
Network-based machine learning and graph theory algorithms for precision oncology (2017, methodology): “43 The analysis of gene modules in the PPI of similar diseases has suggested associations between diseases gene functions or 43 When drug and disease genes are analyzed together in the PPI network, their proximities are for drug 6 The methods compared in Figs.”
CORUM: the comprehensive resource of mammalian protein complexes—2019 (2018, methodology): “Use cases are network-based in silico drug efficacy screening (14), pituitary hormone deficiency in inherited gingival fibromatosis (15) or the architecture of human protein communities and disease networks (16).”