scholarly journals NRF2 drug repurposing using a question-answer artificial intelligence system

2019 ◽  
Author(s):  
Michel-Edwar Mickael ◽  
Marta Pajares ◽  
Ioana Enache ◽  
Gina Manda ◽  
Antonio Cuadrado
Keyword(s):  
Artificial Intelligence ◽  
Drug Repurposing ◽  
Computational Method ◽  
Lipoxygenase Inhibitor ◽  
Nrf2 Pathway ◽  
Intelligence System ◽  
Drug Compounds ◽  
Potential Applications ◽  
Inhibitor Drug

AbstractDrug repurposing represents an innovative approach to reduce the drug development timeline. Text mining using artificial intelligence methods offers great potential in the context of drug repurposing. Here, we present a question-answer artificial intelligence (QAAI) system that is capable of repurposing drug compounds. Our system employs a Google semantic AI universal encoder to compute the sentence embedding of an imposed text question in relation to publications stored in our RedBrain JSON database. Sentences similarity is calculated using a sorting function to identify drug compounds. We demonstrate our system’s ability to predict new indications for already existing drugs. Activation of the NRF2 pathway seems critical for enhancing several diseases prognosis. We experimentally validated the prediction for the lipoxygenase inhibitor drug zileuton as a modulator of the NRF2 pathway in vitro, with potential applications to reduce macrophage M1 phenotype and ROS production. This novel computational method provides a new approach to reposition of known drugs in order to treat neurodegenerative diseases. Github for the database and the code can be downloaded fromhttps://gist.github.com/micheledw/5a165b44345d45105d715340b88c756b

scholarly journals Repurposing Zileuton as a Depression Drug Using an AI and In Vitro Approach

Molecules ◽  
2020 ◽  
Vol 25 (9) ◽  
pp. 2155 ◽  
Author(s):  
Norwin Kubick ◽  
Marta Pajares ◽  
Ioana Enache ◽  
Gina Manda ◽  
Michel-Edwar Mickael
Keyword(s):  
Therapeutic Option ◽  
Drug Repurposing ◽  
Macrophage Phenotype ◽  
Lipoxygenase Inhibitor ◽  
M1 Macrophages ◽  
Macrophage Response ◽  
Nrf2 Pathway ◽  
M1 Macrophage ◽  
Inflammatory Profile

Repurposing drugs to target M1 macrophages inflammatory response in depression constitutes a bright alternative for commonly used antidepressants. Depression is a significant type of mood disorder, where patients suffer from pathological disturbances associated with a proinflammatory M1 macrophage phenotype. Presently, the most commonly used antidepressants such as Zoloft and Citalopram can reduce inflammation, but suffer from dangerous side effects without offering specificity toward macrophages. We employed a new strategy for drug repurposing based on the integration of RNA-seq analysis and text mining using deep neural networks. Our system employs a Google semantic AI universal encoder to compute sentences embedding. Sentences similarity is calculated using a sorting function to identify drug compounds. Then sentence relevance is computed using a custom-built convolution differential network. Our system highlighted the NRF2 pathway as a critical drug target to reprogram M1 macrophage response toward an anti-inflammatory profile (M2). Using our approach, we were also able to predict that lipoxygenase inhibitor drug zileuton could modulate NRF2 pathway in vitro. Taken together, our results indicate that reorienting zileuton usage to modulate M1 macrophages could be a novel and safer therapeutic option for treating depression.

scholarly journals Resveratrol Inhibits HCoV-229E and SARS-CoV-2 Coronavirus Replication In Vitro

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 354
Author(s):  
Sébastien Pasquereau ◽  
Zeina Nehme ◽  
Sandy Haidar Ahmad ◽  
Fadoua Daouad ◽  
Jeanne Van Assche ◽  
...  
Keyword(s):  
Drug Repurposing ◽  
Effective Concentration ◽  
Selectivity Index ◽  
Viral Titer ◽  
Human Coronavirus ◽  
Candidate Drug ◽  
Drug Compounds ◽  
Low Cytotoxicity ◽  
Novel Coronavirus

A novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in China at the end of 2019 causing a large global outbreak. As treatments are of the utmost importance, drug repurposing embodies a rich and rapid drug discovery landscape, where candidate drug compounds could be identified and optimized. To this end, we tested seven compounds for their ability to reduce replication of human coronavirus (HCoV)-229E, another member of the coronavirus family. Among these seven drugs tested, four of them, namely rapamycin, disulfiram, loperamide and valproic acid, were highly cytotoxic and did not warrant further testing. In contrast, we observed a reduction of the viral titer by 80% with resveratrol (50% effective concentration (EC50) = 4.6 µM) and lopinavir/ritonavir (EC50 = 8.8 µM) and by 60% with chloroquine (EC50 = 5 µM) with very limited cytotoxicity. Among these three drugs, resveratrol was less cytotoxic (cytotoxic concentration 50 (CC50) = 210 µM) than lopinavir/ritonavir (CC50 = 102 µM) and chloroquine (CC50 = 67 µM). Thus, among the seven drugs tested against HCoV-229E, resveratrol demonstrated the optimal antiviral response with low cytotoxicity with a selectivity index (SI) of 45.65. Similarly, among the three drugs with an anti-HCoV-229E activity, namely lopinavir/ritonavir, chloroquine and resveratrol, only the latter showed a reduction of the viral titer on SARS-CoV-2 with reduced cytotoxicity. This opens the door to further evaluation to fight Covid-19.

scholarly journals A Systematic Review on the Contribution of Artificial Intelligence in the Development of Medicines for COVID-2019

2021 ◽  
Vol 11 (9) ◽  
pp. 926
Author(s):  
Carla Pires
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Drug Repurposing ◽  
Cochrane Library ◽  
Angiotensin Converting Enzyme 2 ◽  
Drug Databases ◽  
Repurposed Drugs ◽  
Meta Analyses ◽  
New Treatments

Background: COVID-2019 pandemic lead to a raised interest on the development of new treatments through Artificial Intelligence (AI). Aim: to carry out a systematic review on the development of repurposed drugs against COVID-2019 through the application of AI. Methods: The Systematic Reviews and Meta-Analyses (PRISMA) checklist was applied. Keywords: [“Artificial intelligence” and (COVID or SARS) and (medicine or drug)]. Databases: PubMed®, DOAJ and SciELO. Cochrane Library was additionally screened to identify previous published reviews on the same topic. Results: From the 277 identified records [PubMed® (n = 157); DOAJ (n = 119) and SciELO (n = 1)], 27 studies were included. Among other, the selected studies on new treatments against COVID-2019 were classified, as follows: studies with in-vitro and/or clinical data; association of known drugs; and other studies related to repurposing of drugs. Conclusion: Diverse potentially repurposed drugs against COVID-2019 were identified. The repurposed drugs were mainly from antivirals, antibiotics, anticancer, anti-inflammatory, and Angiotensin-converting enzyme 2 (ACE2) groups, although diverse other pharmacologic groups were covered. AI was a suitable tool to quickly analyze large amounts of data or to estimate drug repurposing against COVID-2019.

scholarly journals Artificial Intelligence-driven drug repurposing and structural biology for SARS-CoV-2

2021 ◽  
Author(s):  
Kartikay Prasad ◽  
Vijay Kumar
Keyword(s):  
Artificial Intelligence ◽  
Drug Development ◽  
Structural Biology ◽  
Drug Repurposing ◽  
Current Status ◽  
Full Potential ◽  
Potential Applications ◽  
Infectious Proteins ◽  
Applications Of Ai

Abstract It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing and development and motivate researchers in harnessing AI potentials in the fight against COVID-19.

The Antiviral and Antimalarial Drug Repurposing in Quest of Chemotherapeutics to Combat COVID-19 Utilizing Structure-Based Molecular Docking

2020 ◽  
Vol 23 ◽  
Author(s):  
Sisir Nandi ◽  
Mohit Kumar ◽  
Mridula Saxena ◽  
Anil Kumar Saxena
Keyword(s):  
Molecular Docking ◽  
In Silico ◽  
Drug Repurposing ◽  
Clinical Settings ◽  
The Novel ◽  
In Silico Docking ◽  
Biochemical Mechanisms ◽  
Novel Coronavirus ◽  
In Silico Molecular Docking

Background: The novel coronavirus disease (COVID-19) is caused by a new strain (SARS-CoV-2) erupted in 2019. Nowadays, it is a great threat that claims uncountable lives worldwide. There is no specific chemotherapeutics developed yet to combat COVID-19. Therefore, scientists have been devoted in the quest of the medicine that can cure COVID- 19. Objective: Existing antivirals such as ASC09/ritonavir, lopinavir/ritonavir with or without umifenovir in combination with antimalarial chloroquine or hydroxychloroquine have been repurposed to fight the current coronavirus epidemic. But exact biochemical mechanisms of these drugs towards COVID-19 have not been discovered to date. Method: In-silico molecular docking can predict the mode of binding to sort out the existing chemotherapeutics having a potential affinity towards inhibition of the COVID-19 target. An attempt has been made in the present work to carry out docking analyses of 34 drugs including antivirals and antimalarials to explain explicitly the mode of interactions of these ligands towards the COVID-19protease target. Results: 13 compounds having good binding affinity have been predicted towards protease binding inhibition of COVID-19. Conclusion: Our in silico docking results have been confirmed by current reports from clinical settings through the citation of suitable experimental in vitro data available in the published literature.

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