An Integrative <i>in silico</i> Drug Repurposing Approach for Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease

Djoković, Nemanja; Djikić, Teodora; Cvijić, Sandra; Ignjatović, Jelisaveta; Ibrić, Svetlana; Baralić, Katarina; Djordjević, Aleksandra Buha; Ćurčić, Marijana; Djukic-Cosic, Danijela; Nikolic, Katarina

doi:10.1002/minf.202000187

Cited by 7 publications

(4 citation statements)

References 82 publications

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“…In path a, revefenacin was a drug for chronic obstructive pulmonary disease (COPD), which can help relax the lung muscles and help relieve cough and shortness of breath [ 55 ], while COPD has many potential negative interactions with COVID-19 [ 56 ] and abnormal expression of angiotensin converting enzyme 2 (ACE2) plays an important role in both COPD and COVID-19 [ 57 , 58 ]. Since revefenacin and COVID-19 did not appear together in all literature abstracts, we did a full-text search review and found that Djokovic et al [ 59 ] used structure-based molecular modeling and physiological-based pharmacokinetic modeling for drug repurposing, and the full text mentions revefenacin as a candidate with potential activity on the SARS-CoV-2 main protease. In path b, we focused the second node of the disease on a specific symptom (gastrointestinal symptom), and rabeprazole and omeprazole have been used to treat gastrointestinal diseases and have the same type of efficacy [ 60 , 61 ], while omeprazole has been used to treat COVID-19 [ 62 ].…”

Section: Resultsmentioning

confidence: 99%

Large-Scale Biomedical Relation Extraction Across Diverse Relation Types: Model Development and Usability Study on COVID-19

Zhang,

Fang,

et al. 2023

J Med Internet Res

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Background Biomedical relation extraction (RE) is of great importance for researchers to conduct systematic biomedical studies. It not only helps knowledge mining, such as knowledge graphs and novel knowledge discovery, but also promotes translational applications, such as clinical diagnosis, decision-making, and precision medicine. However, the relations between biomedical entities are complex and diverse, and comprehensive biomedical RE is not yet well established. Objective We aimed to investigate and improve large-scale RE with diverse relation types and conduct usability studies with application scenarios to optimize biomedical text mining. Methods Data sets containing 125 relation types with different entity semantic levels were constructed to evaluate the impact of entity semantic information on RE, and performance analysis was conducted on different model architectures and domain models. This study also proposed a continued pretraining strategy and integrated models with scripts into a tool. Furthermore, this study applied RE to the COVID-19 corpus with article topics and application scenarios of clinical interest to assess and demonstrate its biological interpretability and usability. Results The performance analysis revealed that RE achieves the best performance when the detailed semantic type is provided. For a single model, PubMedBERT with continued pretraining performed the best, with an F1-score of 0.8998. Usability studies on COVID-19 demonstrated the interpretability and usability of RE, and a relation graph database was constructed, which was used to reveal existing and novel drug paths with edge explanations. The models (including pretrained and fine-tuned models), integrated tool (Docker), and generated data (including the COVID-19 relation graph database and drug paths) have been made publicly available to the biomedical text mining community and clinical researchers. Conclusions This study provided a comprehensive analysis of RE with diverse relation types. Optimized RE models and tools for diverse relation types were developed, which can be widely used in biomedical text mining. Our usability studies provided a proof-of-concept demonstration of how large-scale RE can be leveraged to facilitate novel research.

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Section: Resultsmentioning

confidence: 99%

Large-Scale Biomedical Relation Extraction Across Diverse Relation Types: Model Development and Usability Study on COVID-19

Zhang,

Fang,

et al. 2023

J Med Internet Res

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“…coli [ 48 ], the HIV-1 integrase strand transfer complex [ 49 ], dihydroorotase in S . aureus [ 50 ], and most recently there has been a significant effort to target the main protease (M pro ) of SARS-CoV-2 [ 51 – 53 ].…”

Section: Discussionmentioning

confidence: 99%

Targeting the BspC-vimentin interaction to develop anti-virulence therapies during Group B streptococcal meningitis

2022

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Bacterial infections are a major cause of morbidity and mortality worldwide and the rise of antibiotic resistance necessitates development of alternative treatments. Pathogen adhesins that bind to host cells initiate disease pathogenesis and represent potential therapeutic targets. We have shown previously that the BspC adhesin in Group B Streptococcus (GBS), the leading cause of bacterial neonatal meningitis, interacts with host vimentin to promote attachment to brain endothelium and disease development. Here we determined that the BspC variable (V-) domain contains the vimentin binding site and promotes GBS adherence to brain endothelium. Site directed mutagenesis identified a binding pocket necessary for GBS host cell interaction and development of meningitis. Using a virtual structure-based drug screen we identified compounds that targeted the V-domain binding pocket, which blocked GBS adherence and entry into the brain in vivo. These data indicate the utility of targeting the pathogen-host interface to develop anti-virulence therapeutics.

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“…Lately, several researchers have utilized the physiologically-based pharmacokinetic (PBPK) model for drug repurposing of antiviral agents [ 11 , 12 , 13 , 14 , 15 ]. The PBPK model is a mathematical model describing absorption, distribution, metabolism, and excretion (ADME) based on physiological, physicochemical, and biochemical parameters [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Minimal Physiologically-Based Pharmacokinetic Model to Simulate Lung and Trachea Exposure of Pyronaridine and Artesunate in Hamsters

Kang

Kim

et al. 2023

Pharmaceutics

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A fixed-dose combination of pyronaridine and artesunate, one of the artemisinin-based combination therapies, has been used as a potent antimalarial treatment regimen. Recently, several studies have reported the antiviral effects of both drugs against severe acute respiratory syndrome coronavirus two (SARS-CoV-2). However, there are limited data on the pharmacokinetics (PKs), lung, and trachea exposures that could be correlated with the antiviral effects of pyronaridine and artesunate. The purpose of this study was to evaluate the pharmacokinetics, lung, and trachea distribution of pyronaridine, artesunate, and dihydroartemisinin (an active metabolite of artesunate) using a minimal physiologically-based pharmacokinetic (PBPK) model. The major target tissues for evaluating dose metrics are blood, lung, and trachea, and the nontarget tissues were lumped together into the rest of the body. The predictive performance of the minimal PBPK model was evaluated using visual inspection between observations and model predictions, (average) fold error, and sensitivity analysis. The developed PBPK models were applied for the multiple-dosing simulation of daily oral pyronaridine and artesunate. A steady state was reached about three to four days after the first dosing of pyronaridine and an accumulation ratio was calculated to be 1.8. However, the accumulation ratio of artesunate and dihydroartemisinin could not be calculated since the steady state of both compounds was not achieved by daily multiple dosing. The elimination half-life of pyronaridine and artesunate was estimated to be 19.8 and 0.4 h, respectively. Pyronaridine was extensively distributed to the lung and trachea with the lung-to-blood and trachea-to-blood concentration ratios (=Cavg,tissue/Cavg,blood) of 25.83 and 12.41 at the steady state, respectively. Also, the lung-to-blood and trachea-to-blood AUC ratios for artesunate (dihydroartemisinin) were calculated to be 3.34 (1.51) and 0.34 (0.15). The results of this study could provide a scientific basis for interpreting the dose–exposure–response relationship of pyronaridine and artesunate for COVID-19 drug repurposing.

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An Integrative in silico Drug Repurposing Approach for Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease

Cited by 7 publications

References 82 publications

Large-Scale Biomedical Relation Extraction Across Diverse Relation Types: Model Development and Usability Study on COVID-19

Large-Scale Biomedical Relation Extraction Across Diverse Relation Types: Model Development and Usability Study on COVID-19

Targeting the BspC-vimentin interaction to develop anti-virulence therapies during Group B streptococcal meningitis

Application of Minimal Physiologically-Based Pharmacokinetic Model to Simulate Lung and Trachea Exposure of Pyronaridine and Artesunate in Hamsters

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