A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19

Ahmed, Faheem; Soomro, Afaque Manzoor; Salih, Abdul Rahim Chethikkattuveli; Samantasinghar, Anupama; Asif, Arun; Kang, In Suk; Choi, Kyung Hyun

doi:10.1016/j.biopha.2022.113350

Cited by 48 publications

(34 citation statements)

References 184 publications

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“…To validate the ranked drugs, literature search was done. The peer reviewed journals, databases, and patents were mainly focused [11] , [35] , [36] . To avoid the bias, evidence was extended to positive and negative results for a given potential drug.…”

Section: Methodsmentioning

confidence: 99%

Drug repurposing in psoriasis, performed by reversal of disease-associated gene expression profiles

Ahmed

Ho²,

Samantasinghar

et al. 2022

Computational and Structural Biotechnology Journal

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Section: Methodsmentioning

confidence: 99%

Drug repurposing in psoriasis, performed by reversal of disease-associated gene expression profiles

Ahmed

Ho²,

Samantasinghar

et al. 2022

Computational and Structural Biotechnology Journal

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“…In this section ML, DL, and other approaches-based drug repurposing approaches 24,[171][172][173] are mentioned along with some examples.…”

Section: And DL Approaches For Drug Repurposing In Viral Cancersmentioning

confidence: 99%

“…12,13 With the advent of next generation sequencing (NGS) technology [14][15][16][17][18] such as single cell RNA [19][20][21][22] sequencing the ability to model explore the complex phenomenon such as cancer heterogeneity, viral cancers, drug resistance and etiologies have become easily accessible. Additionally, the unprecedented progress in computational power 23 and computational strategies of machine learning (ML) 4,24,25 and deep learning (DL) [26][27][28][29][30] in combination with NGS single-cell RNA technologies have started to yield better results. Development of databases related to cancer, drugs, and gene expression profiles [31][32][33] have proved key components to the implementation of ML and DL in drug discovery and drug repurposing for cancer.…”

mentioning

confidence: 99%

Drug repurposing for viral cancers: A paradigm of machine learning, deep learning, and virtual screening‐based approaches

Ahmed

Kang

Kim

et al. 2023

Journal of Medical Virology

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Cancer management is major concern of health organizations and viral cancers account for approximately 15.4% of all known human cancers. Due to large number of patients, efficient treatments for viral cancers are needed. De novo drug discovery is time consuming and expensive process with high failure rate in clinical stages. To address this problem and provide treatments to patients suffering from viral cancers faster, drug repurposing emerges as an effective alternative which aims to find the other indications of the Food and Drug Administration approved drugs. Applied to viral cancers, drug repurposing studies following the niche have tried to find if already existing drugs could be used to treat viral cancers. Multiple drug repurposing approaches till date have been introduced with successful results in viral cancers and many drugs have been successfully repurposed various viral cancers.Here in this study, a critical review of viral cancer related databases, tools, and different machine learning, deep learning and virtual screening-based drug repurposing studies focusing on viral cancers is provided. Additionally, the mechanism of viral cancers is presented along with drug repurposing case study specific to each viral cancer. Finally, the limitations and challenges of various approaches along with possible solutions are provided.antihepatitis B virus antivirals, antiviral agents, artificial intelligence, drug repurposing, rational drug design | INTRODUCTIONDrug repurposing (also referred to as drug repositioning, drug reprofiling, drug redirecting, drug rescue and more 1 ) is a method for finding new indications beyond the scope of original indications of already approved drugs. 2 A conventional method of drug discovery is quite time taking and expensive task. Time required for a de novo drug is 12-17 years with an estimated expense of $2-$3 billion whereas the success rate is less than 10%. 3 This renders de novo drug discovery a risky process. Drug repurposing is an effective alternative to de novo drug discovery and offers many benefits such as reducing the time and money required to 5-7 years 4,5 and $200-$300 million, respectively, since much of the toxicity and efficacy information is already known. It also increases the success rate in clinical development and testing phases. 6 Success stories of drug repurposing have resulted in serendipity and by physicians' observation. 7 One such example is

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“…The researchers connected e-cigarettes and traditional smoking devices to the airway organ on a chip platform that contained cultured healthy and COPD patient HBEC and primary human airway epithelial cells (hAECs) in an air-liquid interface. During the study, the expression of various genes such as SPRR3, IL-8, NRCAM, MT1H, ATP6V0D2, TMPRSS11E and TMPRSS11F, MMP1, RPTN, ANKRD22, and TSPAN7 were detected ( Ahmed et al, 2022d ). Longlong et al investigated the impact of coronavirus-2 and influenza A infections on the bronchial airway using an organ-on-chip platform.…”

Section: Design and Materialsmentioning

confidence: 99%

Revolutionizing drug development: harnessing the potential of organ-on-chip technology for disease modeling and drug discovery

et al. 2023

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The inefficiency of existing animal models to precisely predict human pharmacological effects is the root reason for drug development failure. Microphysiological system/organ-on-a-chip technology (organ-on-a-chip platform) is a microfluidic device cultured with human living cells under specific organ shear stress which can faithfully replicate human organ-body level pathophysiology. This emerging organ-on-chip platform can be a remarkable alternative for animal models with a broad range of purposes in drug testing and precision medicine. Here, we review the parameters employed in using organ on chip platform as a plot mimic diseases, genetic disorders, drug toxicity effects in different organs, biomarker identification, and drug discoveries. Additionally, we address the current challenges of the organ-on-chip platform that should be overcome to be accepted by drug regulatory agencies and pharmaceutical industries. Moreover, we highlight the future direction of the organ-on-chip platform parameters for enhancing and accelerating drug discoveries and personalized medicine.

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A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19

Cited by 48 publications

References 184 publications

Drug repurposing in psoriasis, performed by reversal of disease-associated gene expression profiles

Drug repurposing in psoriasis, performed by reversal of disease-associated gene expression profiles

Drug repurposing for viral cancers: A paradigm of machine learning, deep learning, and virtual screening‐based approaches

Revolutionizing drug development: harnessing the potential of organ-on-chip technology for disease modeling and drug discovery

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