In silico  drug repurposing of FDA-approved drugs to predict new inhibitors for drug resistant T315I mutant and wild-type BCR-ABL1: A virtual screening and molecular dynamics study

Sohraby, Farzin; Bagheri, Milad; Aliyar, Masoud; Aryapour, Hassan

doi:10.1016/j.jmgm.2017.04.005

Cited by 21 publications

(12 citation statements)

References 28 publications

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“…Regarding study design, we sought to investigate if the publications fit into a broad categorization of the in silico strategies. We found that most studies solely used computational methods without further testing their findings in other scientific settings (46.64%) ( Shah et al, 2012 ; Abazeed et al, 2013 ; Ain et al, 2013 ; Eichhorn et al, 2013 ; Emig et al, 2013 ; Leung et al, 2013 ; Liu et al, 2013 , 2020 , 2021 ; Menden et al, 2013 ; Naresh et al, 2013 ; Omotuyi et al, 2013 ; Singh et al, 2013 ; Chen, 2014 ; Dean et al, 2014 ; Xu and Wang, 2014 ; Chuang et al, 2015 ; Dunna et al, 2015 ; Gustafson et al, 2015 ; Hammad and Azam, 2015 ; Kaur et al, 2015 ; Paula et al, 2015 ; Rubio-Perez et al, 2015 ; Sarvagalla et al, 2015 ; Zhang et al, 2015 ; Brown et al, 2016 ; Gayvert et al, 2016 ; Lee et al, 2016 ; Shafique et al, 2016 ; Tan, 2016 ; Verma et al, 2016 ; Spiliotopoulos et al, 2017a , 2017b ; Jadhav and Karuppayil, 2017 ; Kabir et al, 2017 , 2018 , 2019 ; Khanam et al, 2017 ; Salentin et al, 2017 ; Sohraby et al, 2017 ; Ulfa et al, 2017 ; Arora and Singh, 2018 ; Barua et al, 2018 ; Fröhlich et al, 2018 ; Jubie et al, 2018 ; Juritz et al, 2018 ; Karube et al, 2018 ; Konidala et al, 2018 ; Lagarde et al, 2018 ; Mady et al, 2018 ; Montes-Grajales et al, 2018 ; Shi et al, 2018 ; Shin et al, 2018 ;…”

Section: Resultsmentioning

confidence: 99%

In silico approaches for drug repurposing in oncology: a scoping review

Cavalcante,

Freitas,

Siquara da Rocha

et al. 2024

Front. Pharmacol.

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Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology.Methods: The scoping review was conducted according to the Arksey and O’Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021.Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies.Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.

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Section: Resultsmentioning

confidence: 99%

In silico approaches for drug repurposing in oncology: a scoping review

Cavalcante,

Freitas,

Siquara da Rocha

et al. 2024

Front. Pharmacol.

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“…The discovery of these drugs is based on (i) clinical trials results, (ii) random observations, (iii) the biological background of a disease, or (iv) in vitro and (v) in silico high-throughput screenings [ 107 ]. There are also many studies investigating drug repurposing in different subtypes of hematological malignancies [ 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 ]. Concerning CLL, 2816 compounds were studied in vitro and 102 of them influenced the lymphocytes of all six CLL patients tested.…”

Section: Drug Repurposing In Cllmentioning

confidence: 99%

Proteomics and Drug Repurposing in CLL towards Precision Medicine

Mavridou

Psatha

Aivaliotis

2021

Cancers

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CLL is a hematological malignancy considered as the most frequent lymphoproliferative disease in the western world. It is characterized by high molecular heterogeneity and despite the available therapeutic options, there are many patient subgroups showing the insufficient effectiveness of disease treatment. The challenge is to investigate the individual molecular characteristics and heterogeneity of these patients. Proteomics analysis is a powerful approach that monitors the constant state of flux operators of genetic information and can unravel the proteome heterogeneity and rewiring into protein pathways in CLL patients. This review essences all the available proteomics studies in CLL and suggests the way these studies can be exploited to find effective therapeutic options combined with drug repurposing approaches. Drug repurposing utilizes all the existing knowledge of the safety and efficacy of FDA-approved or investigational drugs and anticipates drug alignment to crucial CLL therapeutic targets, leading to a better disease outcome. The drug repurposing studies in CLL are also discussed in this review. The next goal involves the integration of proteomics-based drug repurposing in precision medicine, as well as the application of this procedure into clinical practice to predict the most appropriate drugs combination that could ensure therapy and the long-term survival of each CLL patient.

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“…In silico systems pharmacology has been recognized as one such method to identify drugs. Structural modeling [3][4][5][6] , metabolic network modeling 7,8 , and unbiased machine learning approaches that leverage large proteomic or expression datasets [9][10][11] have all been used to filter through long lists of chemicals to identify putative therapeutics with a higher likelihood of being useful against cancer or microbial infections. There is great potential for systems pharmacology approaches in development of treatments against heart failure and cardiac injury [12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

Network model‐based screen for FDA‐approved drugs affecting cardiac fibrosis

Zeigler

Chandrabhatla

Christiansen

et al. 2021

CPT Pharmacom & Syst Pharma

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Cardiac fibrosis is a significant component of pathological heart remodeling, yet it is not directly targeted by existing drugs. Systems pharmacology approaches have the potential to provide mechanistic frameworks with which to predict and understand how drugs modulate biological systems. Here, we combine network modeling of the fibroblast signaling network with 36 unique drug-target interactions from DrugBank to predict drugs that modulate fibroblast phenotype and fibrosis. Galunisertib was predicted to decrease collagen and αsmooth muscle actin expression, which we validated in human cardiac fibroblasts. In vivo fibrosis data from the literature validated predictions for 10 drugs. Further, the model was used to identify network mechanisms by which these drugs work. Arsenic trioxide was predicted to induce fibrosis by AP1-driven TGFβ expression and MMP2-driven TGFβ activation. Entresto (valsartan/sacubitril) was predicted to suppress fibrosis by valsartan suppression of ERK signaling and sacubitril enhancement of PKG activity, both of which decreased Smad3 activity. Overall, this study provides a framework for integrating drugtarget mechanisms with logic-based network models, which can drive further studies both in cardiac fibrosis and other conditions.

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In silico drug repurposing of FDA-approved drugs to predict new inhibitors for drug resistant T315I mutant and wild-type BCR-ABL1: A virtual screening and molecular dynamics study

Cited by 21 publications

References 28 publications

In silico approaches for drug repurposing in oncology: a scoping review

In silico approaches for drug repurposing in oncology: a scoping review

Proteomics and Drug Repurposing in CLL towards Precision Medicine

Network model‐based screen for FDA‐approved drugs affecting cardiac fibrosis

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