Synthesize heterogeneous biological knowledge via representation learning for Alzheimer’s disease drug repurposing

Developing drugs for treating Alzheimer’s disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic targets. To address the challenge in AD drug development, we developed a multi-task deep learning pipeline that learns biological interactions...

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Published iniScience Vol. 26; no. 1; p. 105678
Main Authors Hsieh, Kang-Lin, Plascencia-Villa, German, Lin, Ko-Hong, Perry, George, Jiang, Xiaoqian, Kim, Yejin
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 20.01.2023
Elsevier
Subjects
Artificial intelligence
Drugs
Neuroscience
Drugs
Artificial intelligence
Neuroscience
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Abstract Developing drugs for treating Alzheimer’s disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic targets. To address the challenge in AD drug development, we developed a multi-task deep learning pipeline that learns biological interactions and AD risk genes, then utilizes multi-level evidence on drug efficacy to identify repurposable drug candidates. Using the embedding derived from the model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, efficacy in preclinical models, population-based treatment effects, and clinical trials. We mechanistically validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in reducing oxidative stress and safety in maintaining neuronal viability and morphology. Our neuronal response experiments confirmed several biologically efficacious drug combinations. This pipeline showed that harmonizing heterogeneous and complementary data/knowledge, including human interactome, transcriptome patterns, experimental efficacy, and real-world patient data shed light on the drug development of complex diseases. [Display omitted] •Our study aims to find AD drugs from biological interactome and structural associations•Our ranking model utilized multi-level drug evidence for drug prioritization•Our predicted drug combinations can reduce Aβ-induced ROS production in neuronal cells Drugs; Neuroscience; Artificial intelligence
AbstractList Developing drugs for treating Alzheimer's disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic targets. To address the challenge in AD drug development, we developed a multi-task deep learning pipeline that learns biological interactions and AD risk genes, then utilizes multi-level evidence on drug efficacy to identify repurposable drug candidates. Using the embedding derived from the model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, efficacy in preclinical models, population-based treatment effects, and clinical trials. We mechanistically validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in reducing oxidative stress and safety in maintaining neuronal viability and morphology. Our neuronal response experiments confirmed several biologically efficacious drug combinations. This pipeline showed that harmonizing heterogeneous and complementary data/knowledge, including human interactome, transcriptome patterns, experimental efficacy, and real-world patient data shed light on the drug development of complex diseases.
Developing drugs for treating Alzheimer’s disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic targets. To address the challenge in AD drug development, we developed a multi-task deep learning pipeline that learns biological interactions and AD risk genes, then utilizes multi-level evidence on drug efficacy to identify repurposable drug candidates. Using the embedding derived from the model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, efficacy in preclinical models, population-based treatment effects, and clinical trials. We mechanistically validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in reducing oxidative stress and safety in maintaining neuronal viability and morphology. Our neuronal response experiments confirmed several biologically efficacious drug combinations. This pipeline showed that harmonizing heterogeneous and complementary data/knowledge, including human interactome, transcriptome patterns, experimental efficacy, and real-world patient data shed light on the drug development of complex diseases. [Display omitted] •Our study aims to find AD drugs from biological interactome and structural associations•Our ranking model utilized multi-level drug evidence for drug prioritization•Our predicted drug combinations can reduce Aβ-induced ROS production in neuronal cells Drugs; Neuroscience; Artificial intelligence
Developing drugs for treating Alzheimer’s disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic targets. To address the challenge in AD drug development, we developed a multi-task deep learning pipeline that learns biological interactions and AD risk genes, then utilizes multi-level evidence on drug efficacy to identify repurposable drug candidates. Using the embedding derived from the model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, efficacy in preclinical models, population-based treatment effects, and clinical trials. We mechanistically validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in reducing oxidative stress and safety in maintaining neuronal viability and morphology. Our neuronal response experiments confirmed several biologically efficacious drug combinations. This pipeline showed that harmonizing heterogeneous and complementary data/knowledge, including human interactome, transcriptome patterns, experimental efficacy, and real-world patient data shed light on the drug development of complex diseases. • Our study aims to find AD drugs from biological interactome and structural associations • Our ranking model utilized multi-level drug evidence for drug prioritization • Our predicted drug combinations can reduce Aβ-induced ROS production in neuronal cells Drugs; Neuroscience; Artificial intelligence
ArticleNumber 105678
Author Kim, Yejin
Plascencia-Villa, German
Hsieh, Kang-Lin
Perry, George
Jiang, Xiaoqian
Lin, Ko-Hong
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Keywords Drugs
Artificial intelligence
Neuroscience
Language English
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Snippet Developing drugs for treating Alzheimer’s disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic...
Developing drugs for treating Alzheimer's disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic...
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SubjectTerms Artificial intelligence
Drugs
Neuroscience
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Title Synthesize heterogeneous biological knowledge via representation learning for Alzheimer’s disease drug repurposing
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