Drug repositioning in the AI-driven era: data, approaches, and challenges DOI
Jing Wang,

Siming Kong,

Xiaochen Bo

et al.

IntechOpen eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 27, 2024

The advent of artificial intelligence (AI) has revolutionized drug repositioning, transforming it into an indispensable strategy for accelerating discovery. This chapter offers in-depth exploration the multifaceted landscape repositioning in AI era, emphasizing profound influence on this domain and providing a roadmap future research. Beginning with brief summary data that form bedrock field, biomedical databases encompassing drugs, diseases, molecular targets, clinical are introduced detail. Then experimental computational approaches underpin further dissected, ranging from binding assays or phenotypic screening to multi-omics methodologies silico technologies, emphasis AI-driven methods. Subsequently, successful cases across diverse diseases highlighted. Finally, importance fully leveraging address challenges is underscored.

Language: Английский

Repurposing lipid-lowering drugs on asthma and lung function: evidence from a genetic association analysis DOI Creative Commons
Yue Zhang,

Zichao Jiang,

Lingli Chen

et al.

Journal of Translational Medicine, Journal Year: 2024, Volume and Issue: 22(1)

Published: July 3, 2024

Abstract Objective To explore the correlation between asthma risk and genetic variants affecting expression or function of lipid-lowering drug targets. Methods We conducted Mendelian randomization (MR) analyses using in several genes associated with medication targets: HMGCR (statin target), PCSK9 (alirocumab NPC1L1 (ezetimibe APOB (mipomersen ANGPTL3 (evinacumab PPARA (fenofibrate APOC3 (volanesorsen as well LDLR LPL. Our objective was to investigate relationship drugs through MR. Finally, we assessed efficacy stability MR analysis Egger inverse variance weighted (IVW) methods. Results The elevated triglyceride (TG) levels APOC3, LPL targets were found increase risk. Conversely, higher LDL-C driven by decrease Additionally, (driven APOB, targets) TG target) improved lung (FEV1/FVC). decreased Conclusion In conclusion, our findings suggest a likely causal drugs. Moreover, there is compelling evidence indicating that therapies could play crucial role future management asthma.

Language: Английский

Citations

2
Genetic causality of lipidomic and immune cell profiles in ischemic stroke DOI Creative Commons

Haohao Chen,

Zequn Zheng,

Xiaorui Cai

et al.

Frontiers in Neurology, Journal Year: 2024, Volume and Issue: 15

Published: Sept. 30, 2024

Background Ischemic stroke (IS) is a global health issue linked to lipid metabolism and immune cell responses. This study uses Mendelian randomization (MR) identify genetic risk factors for IS subtypes using comprehensive data from lipidomic profiles. Methods We assessed susceptibility across 179 lipids 731 phenotypes instrumental variables (IVs) recent genome-wide association studies. A two-sample MR approach evaluated correlations, two-step mediation analysis explored the role of in lipid-IS pathway. Sensitivity analyses, including MR-Egger Cochran Q tests, ensured robust results. Results Genetic IVs 162 614 were identified. Significant causality was found between 35 large artery (LAS), with 12 as (sterol esters, phosphatidylcholines, phosphatidylethanolamines) 23 protective (phosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols). For small vessel (SVS), 8 phosphatidylcholines), 2 (phosphatidylinositol, sphingomyelin). cardioembolic (CS), factors, 4 factors. Mediation revealed that CCR2 on granulocytes, CD11c CD62L + myeloid dendritic cells, FSC-A granulocytes mediated lipid-immune cell-LAS pathway, while CD4 activated regulatory T cells & secreting cell-SVS Conclusion identifies links specific subtypes, highlights cells’ mediation, suggests new therapeutic targets, uncovers drivers.

Language: Английский

Citations

1
Network-based drug repurposing for psychiatric disorders using single-cell genomics DOI
Chirag Gupta, Noah Cohen Kalafut, Declan Clarke

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 2, 2024

Neuropsychiatric disorders lack effective treatments due to a limited understanding of underlying cellular and molecular mechanisms. To address this, we integrated population-scale single-cell genomics data analyzed cell-type-level gene regulatory networks across schizophrenia, bipolar disorder, autism (23 cell classes/subclasses). Our analysis revealed potential druggable transcription factors co-regulating known risk genes that converge into cell-type-specific co-regulated modules. We applied graph neural on those modules prioritize novel leveraged them in network-based drug repurposing framework identify 220 molecules with the for targeting specific types. found evidence 37 these drugs reversing disorder-associated transcriptional phenotypes. Additionally, discovered 335 drug-associated cell-type eQTLs, revealing genetic variation's influence target expression at level. results provide network medicine resource provides mechanistic insights advancing treatment options neuropsychiatric disorders.

Language: Английский

Citations

1
Current approaches in identification of a novel drug targets for drug repurposing DOI

Khushal Khambhati,

Vijai Singh

Progress in molecular biology and translational science, Journal Year: 2024, Volume and Issue: unknown, P. 213 - 220

Published: Jan. 1, 2024

Language: Английский

Citations

0
Navigating the Intersection of Technology and Depression Precision Medicine DOI

M Burcu Irmak-Yazicioglu,

Ayla Arslan

Advances in experimental medicine and biology, Journal Year: 2024, Volume and Issue: unknown, P. 401 - 426

Published: Jan. 1, 2024

Language: Английский

Citations

0
Drug repositioning in the AI-driven era: data, approaches, and challenges DOI
Jing Wang,

Siming Kong,

Xiaochen Bo

et al.

IntechOpen eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 27, 2024

The advent of artificial intelligence (AI) has revolutionized drug repositioning, transforming it into an indispensable strategy for accelerating discovery. This chapter offers in-depth exploration the multifaceted landscape repositioning in AI era, emphasizing profound influence on this domain and providing a roadmap future research. Beginning with brief summary data that form bedrock field, biomedical databases encompassing drugs, diseases, molecular targets, clinical are introduced detail. Then experimental computational approaches underpin further dissected, ranging from binding assays or phenotypic screening to multi-omics methodologies silico technologies, emphasis AI-driven methods. Subsequently, successful cases across diverse diseases highlighted. Finally, importance fully leveraging address challenges is underscored.

Language: Английский

Citations

0