Network analytics for drug repurposing in COVID-19

N, Siminea; Popescu, Víctor; Martin, Jose Angel Sanchez; D, Florea; Gavril, Georgiana; Gheorghe, Ana-Maria; Iţcuş, Corina; Kanhaiya, Krishna; Pacioglu, Octavian; Popa, Luis Ovidiu; Trandafir, Romică; Tusa, Iris; Sidoroff, Manuela; Păun, Mihaela; Czeizler, Eugen; Păun, Andrei; Petre, Ion

doi:10.1093/bib/bbab490

Cited by 20 publications

(8 citation statements)

References 89 publications

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“…Moreover, epidemiologic and worldwide cohort studies showed that caffeine can reduce the risk of depression, suicide, Parkinson’s disease, liver fibrosis, cirrhosis, and several cancer types (e.g., melanoma and nonmelanoma skin cancer, breast and prostate cancer, and hepatocellular carcinoma) [ 24 ]. Recently, following a drug repurposing approach, caffeine was proposed for treatment of COVID-19 infections [ 25 , 26 ] and was identified with therapeutic effects on hypertension biomarkers [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

New Life of an Old Drug: Caffeine as a Modulator of Antibacterial Activity of Commonly Used Antibiotics

et al. 2022

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With the rapid and continuous emergence of antimicrobial resistance, bacterial infections became a significant global healthcare concern. One of the proposed strategies to combat multidrug-resistant pathogens is to use additional compounds, such as natural biologically active substances, as adjuvants for existing antibiotics. In this study, we investigated the potential of caffeine, the widely consumed alkaloid, to modulate the antibacterial effects of antibiotics commonly used in clinical practice. We used disc diffusion assay to evaluate the effects of caffeine on 40 antibiotics in two Staphylococcus aureus strains (methicillin-resistant and methicillin-sensitive). Based on the results of this step, we selected five antibiotics for which the greatest caffeine-induced improvements in antibacterial activity were observed, and further analyzed their interactions with caffeine using a checkerboard approach. Caffeine at concentrations of 250 µg/mL or higher halved the MIC values of ticarcillin, cefepime, gentamycin, azithromycin, and novobiocin for all gram-negative species investigated (Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii). At the highest caffeine concentrations tested (up to 16 mg/mL), decreases in MIC values were 8- to 16-fold. The obtained results prove that caffeine modulates the activity of structurally diverse antibiotics, with the most promising synergistic effects observed for cefepime and azithromycin toward gram-negative pathogens.

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Section: Introductionmentioning

confidence: 99%

New Life of an Old Drug: Caffeine as a Modulator of Antibacterial Activity of Commonly Used Antibiotics

et al. 2022

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“…Several greedy-based approximations of the optimal solutions have been proposed for different variants of the problem 5 , 14 – 16 , also recent solutions based on linear integer programming 20 . In our experience 6 , 22 , the greedy algorithm tends to select few preferred input nodes in each solution. This is understandable since its search is based on consecutive edge selections that may lead it away from the preferred input nodes.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the standard experimental approach, recent advances in computational methods and the availability of high-quality big data enabled the evolution of efficient and promising computational approaches. Moreover, the urgency of the current COVID-19 pandemic has brought additional interest in the potential of computational drug repurposing 22 , 25 , 26 . Multiple computational methods have been successfully applied to the identification of drug repurposing candidates: machine learning (neural networks 27 , deep learning 28 , support vector machines and random forests 29 ), data mining (text mining 30 , semantic inference 31 ), and network analysis (clustering 32 , centrality measures 33 , controllability 26 ).…”

Section: Introductionmentioning

confidence: 99%

Network controllability solutions for computational drug repurposing using genetic algorithms

Popescu

Kanhaiya

Nastac

et al. 2022

Sci Rep

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Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We demonstrate our algorithm on several cancer networks and on several random networks with their edges distributed according to the Erdős–Rényi, the Scale-Free, and the Small World properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.

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“…Moreover, Food and Drug Administration (FDA) granted emergency use authorization (EUA) of Paxlovid™ (nirmatrelvir/ritonavir tablets) against SAR-COV-2 [ 7 ]. In addition, many control strategies have emerged since 2020, including vaccination, and some drugs such as remdesivir, dexamethasone, plasma therapy and monoclonal antibodies have shown effective results in clinical trials [ 4 , 8 ]. Although the field of research is actively continuing to understand the molecular basis of this virus to a clear and effective targeted treatment protocol [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Drug repurposing against main protease and RNA-dependent RNA polymerase of SARS-CoV-2 using molecular docking, MM-GBSA calculations and molecular dynamics

et al. 2022

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A virus called severe acute respiratory distress syndrome coronavirus type 2 (SARS‐CoV‐2) is the causing organism of coronavirus disease 2019 (COVID-19), which has severely affected human life and threatened public health. The pandemic took millions of lives worldwide and caused serious negative effects on human society and the economy. SARS-CoV-2 main protease (Mpro) and RNA-dependent RNA polymerase (RdRp) are interesting targets due to their crucial role in viral replication and growth. Since there is only one approved therapy for COVID-19, drug repurposing is a promising approach to finding molecules with potential activity against COVID-19 in a short time and at minimal cost. In this study, virtual screening was performed on the ChEMBL library containing 9923 FDA-approved drugs, using various docking filters with different accuracy. The best drugs with the highest docking scores were further examined for molecular dynamics (MD) studies and MM-GBSA calculations. The results of this study suggest that nadide, cangrelor and denufosol are promising potential candidates against COVID-19. Further in vitro, preclinical and clinical studies of these candidates would help to discover safe and effective anti-COVID-19 drugs.

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Network analytics for drug repurposing in COVID-19

Cited by 20 publications

References 89 publications

New Life of an Old Drug: Caffeine as a Modulator of Antibacterial Activity of Commonly Used Antibiotics

New Life of an Old Drug: Caffeine as a Modulator of Antibacterial Activity of Commonly Used Antibiotics

Network controllability solutions for computational drug repurposing using genetic algorithms

Drug repurposing against main protease and RNA-dependent RNA polymerase of SARS-CoV-2 using molecular docking, MM-GBSA calculations and molecular dynamics

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