Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer’s disease

Fang, Jiansong; Zhang, Pengyue; Zhou, Yadi; Chiang, Chun‐Pin; Tan, Juan; Hou, Yuan; Stauffer, Shaun R.; Li, Lang; Pieper, Andrew A.; Cummings, Jeffrey L.; Cheng, Feixiong

doi:10.1038/s43587-021-00138-z

Cited by 125 publications

(132 citation statements)

References 89 publications

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“…Systems pharmacology is an emerging discipline that combines in silico network-based approaches and experimental assays, with the aim to elucidate the therapeutic mechanisms of complex diseases [ 12 , 13 ]. For instance, systems pharmacology-based approaches are effective for exploring the multipharmacological effects of natural products on various diseases, such as Alzheimer's disease [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

In Silico Identification and Mechanism Exploration of Active Ingredients against Stroke from An-Gong-Niu-Huang-Wan (AGNHW) Formula

Song

et al. 2022

Oxidative Medicine and Cellular Longevity

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An-Gong-Niu-Huang-Wan (AGNHW) is a well-known formula for treating cerebrovascular diseases, with roles including clearing away heat, detoxification, and wake-up consciousness. In recent years, AGNHW has been commonly used for the treatment of ischemic stroke, but the mechanism by which AGNHW relieves stroke has not been clearly elucidated. In the current study, we developed a multiple systems pharmacology-based framework to identify the potential antistroke ingredients in AGNHW and explore the underlying mechanisms of action (MOA) of AGNHW against stroke from a holistic perspective. Specifically, we performed a network-based method to identify the potential antistroke ingredients in AGNHW by integrating the drug-target network and stroke-associated genes. Furthermore, the oxygen-glucose deprivation/reoxygenation (OGD/R) model was used to validate the anti-inflammatory effects of the key ingredients by determining the levels of inflammatory cytokines, including interleukin (IL)-6, IL-1β, and tumor necrosis factor (TNF)-α. The antiapoptotic effects of the key ingredients were also confirmed in vitro. Integrated pathway analysis of AGNHW revealed that it might regulate three biological signaling pathways, including IL-17, TNF, and PI3K-AKT, to play a protective role in stroke. Moreover, 30 key antistroke ingredients in AGNHW were identified via network-based in silico prediction and were confirmed to have known neuroprotective effects. After drug-like property evaluation and pharmacological validation in vitro, scutellarein (SCU) and caprylic acid (CA) were selected for further antistroke investigation. Finally, systems pharmacology-based analysis of CA and SCU indicated that they might exert antistroke effects via the apoptotic signaling pathway and inflammatory response, which was further validated in an in vitro stroke model. Overall, the current study proposes an integrative systems pharmacology approach to identify antistroke ingredients and demonstrate the underlying pharmacological MOA of AGNHW in stroke, which provides an alternative strategy to investigate novel traditional Chinese medicine formulas for complex diseases.

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Section: Introductionmentioning

confidence: 99%

In Silico Identification and Mechanism Exploration of Active Ingredients against Stroke from An-Gong-Niu-Huang-Wan (AGNHW) Formula

Song

et al. 2022

Oxidative Medicine and Cellular Longevity

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“…Additionally, some related articles published may not be included due to the selection of search strings, databases, and language. For instance, in some studies 57 – 59 , where usage of EHR is not explicitly mentioned in the text and inferred by a human reader, thus causing them to not be retrieved by our search query. Secondly, this review only focused on the studies that directly used EHR for drug repurposing, and studies that used EHR (e.g., a follow-up study based on a study utilizing EHR to explore epilepsy and twelve autoimmune diseases 60 ) indirectly to facilitate drug repurposing are excluded.…”

Section: Discussionmentioning

confidence: 99%

Computational drug repurposing based on electronic health records: a scoping review

et al. 2022

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Computational drug repurposing methods adapt Artificial intelligence (AI) algorithms for the discovery of new applications of approved or investigational drugs. Among the heterogeneous datasets, electronic health records (EHRs) datasets provide rich longitudinal and pathophysiological data that facilitate the generation and validation of drug repurposing. Here, we present an appraisal of recently published research on computational drug repurposing utilizing the EHR. Thirty-three research articles, retrieved from Embase, Medline, Scopus, and Web of Science between January 2000 and January 2022, were included in the final review. Four themes, (1) publication venue, (2) data types and sources, (3) method for data processing and prediction, and (4) targeted disease, validation, and released tools were presented. The review summarized the contribution of EHR used in drug repurposing as well as revealed that the utilization is hindered by the validation, accessibility, and understanding of EHRs. These findings can support researchers in the utilization of medical data resources and the development of computational methods for drug repurposing.

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“…• Last, compared with existing AD repurposing studies which typically focused on validating one or two hypotheses with a single type of RWD 31,37,38 , our study offered a high-throughput way of generating and validating AD repurposing hypotheses using both EHRs and claims 39 , which would further catalyze innovation in AD drug discovery at scale, or can be broadly applied to other diseases.…”

Section: Discussionmentioning

confidence: 99%

High-Throughput Clinical Trial Emulation with Real World Data and Machine Learning: A Case Study of Drug Repurposing for Alzheimer’s Disease

Zang

Zhang

et al. 2022

Preprint

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Clinical trial emulation, which is the process of mimicking targeted randomized controlled trials (RCT) with real world data (RWD), has attracted growing attentions and interests in recent years from pharmaceutical industry. Different from RCTs which have stringent eligibility criteria for recruiting participants, RWD are more representative of real world patients whom the drugs will be prescribed to. One technical challenge for trial emulation is how to conduct effective confounding control with complex RWD so that the treatment effects can be objectively derived. Recently many approaches, including deep learning algorithms, have been proposed for this goal, but there is still no systematic evaluation and practical guidance on them. In this paper, we emulate $430,000$ trials from two large-scale RWD warehouses, covering both electronic health records (EHR) and general claims, over 170 million patients spanning more than 10 years, aiming to identify new indications of approved drugs for Alzheimer's disease (AD). We have investigated the behaviors of multiple different approaches including logistic regression and deep learning models, and propose a new model selection strategy that can significantly improve the performance of confounding balance of the participants in different arms of emulated trials. We demonstrate that regularized logistic regression based propensity score (PS) model outperforms deep learning based PS model and others, which contradicts with our intuitions to certain extent. Finally, we identified 8 drugs whose original indications are not AD (pantoprazole, gabapentin, acetaminophen, atorvastatin, albuterol, fluticasone, amoxicillin and omeprazole), hold great potential of being beneficial to AD patients.

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Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer’s disease

Cited by 125 publications

References 89 publications

In Silico Identification and Mechanism Exploration of Active Ingredients against Stroke from An-Gong-Niu-Huang-Wan (AGNHW) Formula

In Silico Identification and Mechanism Exploration of Active Ingredients against Stroke from An-Gong-Niu-Huang-Wan (AGNHW) Formula

Computational drug repurposing based on electronic health records: a scoping review

High-Throughput Clinical Trial Emulation with Real World Data and Machine Learning: A Case Study of Drug Repurposing for Alzheimer’s Disease

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