Uncovering New Drug Properties in Target-Based Drug–Drug Similarity Networks

Udrescu, Lucreţia; Bogdan, Paul; Chiș, Aimee Rodica; Sîrbu, Ioan Ovidiu; Topîrceanu, Alexandru; Văruţ, Renata-Maria; Udrescu, Mihai

doi:10.3390/pharmaceutics12090879

Cited by 15 publications

(15 citation statements)

References 89 publications

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“…In Udrescu et al . [ 76 ], the authors built a drug–drug interaction network, where the weighted edges denoted a link between two drugs if they shared both a target and mode of action (agonist/antagonist). The drug–drug similarity networks formed were subjected to community clustering, and betweenness to degree ratio was determined.…”

Section: How Are Network/centrality Measures Applied For Drug Repositioningmentioning

confidence: 99%

Topological network measures for drug repositioning

Badkas

Landtsheer

Sauter

2020

Briefings in Bioinformatics

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Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a key role. Various structural (topological) network measures have thereby contributed to uncovering unintuitive functional relationships and repositioning candidates in drug-disease and other networks. This review gives a broad overview of the topic, and offers perspectives on the application of topological measures for network analysis. It also discusses unexplored measures, and draws attention to a wider scope of application efforts, especially in drug repositioning.

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Section: How Are Network/centrality Measures Applied For Drug Repositioningmentioning

confidence: 99%

Topological network measures for drug repositioning

Badkas

Landtsheer

Sauter

2020

Briefings in Bioinformatics

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“…It is one of the most widely used virtual screening tools, particularly when the three-dimensional structure of the target protein is available. Docking enables the prediction of both ligand–target binding affinity and the structure of the protein–ligand complex, which are useful for optimizing the lead [ 33 , 34 ].…”

Section: Resultsmentioning

confidence: 99%

Biocomputational Prediction Approach Targeting FimH by Natural SGLT2 Inhibitors: A Possible Way to Overcome the Uropathogenic Effect of SGLT2 Inhibitor Drugs

et al. 2021

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The Food and Drug Administration (FDA) approved a new class of anti-diabetic medication (a sodium–glucose co-transporter 2 (SGLT2) inhibitor) in 2013. However, SGLT2 inhibitor drugs are under evaluation due to their associative side effects, such as urinary tract and genital infection, urinary discomfort, diabetic ketosis, and kidney problems. Even clinicians have difficulty in recommending it to diabetic patients due to the increased probability of urinary tract infection. In our study, we selected natural SGLT2 inhibitors, namely acerogenin B, formononetin, (−)-kurarinone, (+)-pteryxin, and quinidine, to explore their potential against an emerging uropathogenic bacterial therapeutic target, i.e., FimH. FimH plays a critical role in the colonization of uropathogenic bacteria on the urinary tract surface. Thus, FimH antagonists show promising effects against uropathogenic bacterial strains via their targeting of FimH’s adherence mechanism with less chance of resistance. The molecular docking results showed that, among natural SGLT2 inhibitors, formononetin, (+)-pteryxin, and quinidine have a strong interaction with FimH proteins, with binding energy (∆G) and inhibition constant (ki) values of −5.65 kcal/mol and 71.95 µM, −5.50 kcal/mol and 92.97 µM, and −5.70 kcal/mol and 66.40 µM, respectively. These interactions were better than those of the positive control heptyl α-d-mannopyranoside and far better than those of the SGLT2 inhibitor drug canagliflozin. Furthermore, a 50 ns molecular dynamics simulation was conducted to optimize the interaction, and the resulting complexes were found to be stable. Physicochemical property assessments predicted little toxicity and good drug-likeness properties for these three compounds. Therefore, formononetin, (+)-pteryxin, and quinidine can be proposed as promising SGLT2 inhibitors drugs, with add-on FimH inhibition potential that might reduce the probability of uropathogenic side effects.

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“…Repositioning drug candidates can be identified using drug-drug interaction networks [ 60 ]; the method even allows the ranking of compounds into simple and complex multi-pathology therapies [ 61 ]. Unsupervised machine learning approaches can be used to establish dug–drug similarity networks based on drug–target interactions, which also lead to the identification of repositioning candidates [ 62 ]. Other approaches in drug repositioning, as well as limitations and recommendations, are presented in [ 58 ].…”

Section: Discussionmentioning

confidence: 99%

3D-ALMOND-QSAR Models to Predict the Antidepressant Effect of Some Natural Compounds

et al. 2021

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The current treatment of depression involves antidepressant synthetic drugs that have a variety of side effects. In searching for alternatives, natural compounds could represent a solution, as many studies reported that such compounds modulate the nervous system and exhibit antidepressant effects. We used bioinformatics methods to predict the antidepressant effect of ten natural compounds with neuroleptic activity, reported in the literature. For all compounds we computed their drug-likeness, absorption, distribution, metabolism, excretion (ADME), and toxicity profiles. Their antidepressant and neuroleptic activities were predicted by 3D-ALMOND-QSAR models built by considering three important targets, namely serotonin transporter (SERT), 5-hydroxytryptamine receptor 1A (5-HT1A), and dopamine D2 receptor. For our QSAR models we have used the following molecular descriptors: hydrophobicity, electrostatic, and hydrogen bond donor/acceptor. Our results showed that all compounds present drug-likeness features as well as promising ADME features and no toxicity. Most compounds appear to modulate SERT, and fewer appear as ligands for 5-HT1A and D2 receptors. From our prediction, linalyl acetate appears as the only ligand for all three targets, neryl acetate appears as a ligand for SERT and D2 receptors, while 1,8-cineole appears as a ligand for 5-HT1A and D2 receptors.

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Uncovering New Drug Properties in Target-Based Drug–Drug Similarity Networks

Cited by 15 publications

References 89 publications

Topological network measures for drug repositioning

Topological network measures for drug repositioning

Biocomputational Prediction Approach Targeting FimH by Natural SGLT2 Inhibitors: A Possible Way to Overcome the Uropathogenic Effect of SGLT2 Inhibitor Drugs

3D-ALMOND-QSAR Models to Predict the Antidepressant Effect of Some Natural Compounds

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