scholarly journals Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases

2021 ◽  
Vol 21 (S8) ◽  
Author(s):  
Qianlong Wen ◽  
Ruoqi Liu ◽  
Ping Zhang
Keyword(s):  
Drug Effect ◽  
Drug Repurposing ◽  
Clinical Disease ◽  
Therapeutic Effects ◽  
Connectivity Map ◽  
Electronic Health Record Data ◽  
Chemical Structures ◽  
Laboratory Results ◽  
Clinical Drug ◽  
Disease Sign

Abstract Background Drug repurposing, the process of identifying additional therapeutic uses for existing drugs, has attracted increasing attention from both the pharmaceutical industry and the research community. Many existing computational drug repurposing methods rely on preclinical data (e.g., chemical structures, drug targets), resulting in translational problems for clinical trials. Results In this study, we propose a novel framework based on clinical connectivity mapping for drug repurposing to analyze therapeutic effects of drugs on diseases. We firstly establish clinical drug effect vectors (i.e., drug-laboratory results associations) by applying a continuous self-controlled case series model on a longitudinal electronic health record data, then establish clinical disease sign vectors (i.e., disease-laboratory results associations) by applying a Wilcoxon rank sum test on a large-scale national survey data. Eventually, a repurposing possibility score for each drug-disease pair is computed by applying a dot product-based scoring function on clinical disease sign vectors and clinical drug effect vectors. During the experiment, we comprehensively evaluate 392 drugs for 6 important chronic diseases (include asthma, coronary heart disease, congestive heart failure, heart attack, type 2 diabetes, and stroke). The experiment results not only reflect known associations between diseases and drugs, but also include some hidden drug-disease associations. The code for this paper is available at: https://github.com/HoytWen/CCMDR Conclusions The proposed clinical connectivity map framework uses laboratory results found from electronic clinical information to bridge drugs and diseases, which make their relations explainable and has better translational power than existing computational methods. Experimental results demonstrate the effectiveness of our proposed framework, further case analysis also proves our method can be used to repurposing existing drugs opportunities.

scholarly journals Connectivity Map Analysis Indicates PI3K/Akt/mTOR Inhibitors as Potential Anti-Hypoxia Drugs in Neuroblastoma

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2809
Author(s):  
Paolo Uva ◽  
Maria Carla Bosco ◽  
Alessandra Eva ◽  
Massimo Conte ◽  
Alberto Garaventa ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Drug Repurposing ◽  
Enrichment Analysis ◽  
Mtor Inhibitors ◽  
Gene Set Enrichment Analysis ◽  
Low Oxygen ◽  
Connectivity Map ◽  
Hypoxic Conditions ◽  
Connectivity Score

Neuroblastoma (NB) is one of the deadliest pediatric cancers, accounting for 15% of deaths in childhood. Hypoxia is a condition of low oxygen tension occurring in solid tumors and has an unfavorable prognostic factor for NB. In the present study, we aimed to identify novel promising drugs for NB treatment. Connectivity Map (CMap), an online resource for drug repurposing, was used to identify connections between hypoxia-modulated genes in NB tumors and compounds. Two sets of 34 and 21 genes up- and down-regulated between hypoxic and normoxic primary NB tumors, respectively, were analyzed with CMap. The analysis reported a significant negative connectivity score across nine cell lines for 19 compounds mainly belonging to the class of PI3K/Akt/mTOR inhibitors. The gene expression profiles of NB cells cultured under hypoxic conditions and treated with the mTORC complex inhibitor PP242, referred to as the Mohlin dataset, was used to validate the CMap findings. A heat map representation of hypoxia-modulated genes in the Mohlin dataset and the gene set enrichment analysis (GSEA) showed an opposite regulation of these genes in the set of NB cells treated with the mTORC inhibitor PP242. In conclusion, our analysis identified inhibitors of the PI3K/Akt/mTOR signaling pathway as novel candidate compounds to treat NB patients with hypoxic tumors and a poor prognosis.

scholarly journals A Connectivity Map-Based Drug Repurposing Study and Integrative Analysis of Transcriptomic Profiling of SARS-CoV-2 Infection

2020 ◽  
Author(s):  
seyedeh zahra mousavi ◽  
mojdeh rahmanian ◽  
ashkan sami
Keyword(s):  
Network Analysis ◽  
Cell Line ◽  
Inhaled Corticosteroids ◽  
Drug Repurposing ◽  
Protein Network ◽  
New Drugs ◽  
Health Concern ◽  
Epithelial Cell Line ◽  
Connectivity Map ◽  
Pathway Enrichment

<div>Aims: The recent outbreak of COVID-19 has become a global health concern. There are currently no effective treatment strategies and vaccines for the treatment or prevention of this fatal disease. The current study aims to determine promising treatment options for the COVID-19 through a computational drug repurposing approach.</div><div>Materials and methods: In this study, we focus on differentially expressed genes (DEGs), detected in SARS-CoV-2 infected cell lines including “the primary human lung epithelial cell line NHBE” and “the transformed lung alveolar cell line A549”. Next, the identified DEGs are used in the connectivity map (CMap) analysis to identify similarly acting therapeutic candidates. Furthermore, to interpret lists of DEGs, pathway enrichment and protein network analysis are performed. Genes are categorized into easily interpretable pathways based on their biological functions, and overrepresentations of each pathway are tested in comparison to what is expected randomly.</div><div>Key findings: The results suggest the effectiveness of Saquinavir, lansoprazole, folic acid, ebselen, aminocaproic acid, simvastatin, surfactant stimulant drugs, heat shock protein 90 (HSP90) inhibitors, histone deacetylase (HDAC) inhibitors, metronidazole, inhaled corticosteroids (ICS) and many other clinically approved drugs and investigational compounds as potent drugs against COVID-19 outbreak.</div><div>Significance: Making new drugs remain a lengthy process, so the drug repurposing approach provides an insight into the therapeutics that might be helpful in this pandemic. In this study, pathway enrichment and protein network analysis are also performed, and the effectiveness of some drugs obtained from the CMap analysis has been investigated according to previous research.</div>

scholarly journals COMPARE Analysis, a Bioinformatic Approach to Accelerate Drug Repurposing against Covid-19 and Other Emerging Epidemics

2020 ◽  
pp. 247255522097567
Author(s):  
Imad Naasani
Keyword(s):  
Human Cancer ◽  
Drug Repurposing ◽  
Data Repository ◽  
Human Cancer Cell Lines ◽  
Chemical Structures ◽  
Drug Molecules ◽  
Growth Inhibitory ◽  
Antiviral Activities ◽  
Bioinformatic Approach ◽  
Compare Analysis

A novel bioinformatic approach for drug repurposing against emerging viral epidemics like Covid-19 is described. It exploits the COMPARE algorithm, a public program from the National Cancer Institute (NCI) to sort drugs according to their patterns of growth inhibitory profiles from a diverse panel of human cancer cell lines. The data repository of the NCI includes the growth inhibitory patterns of more than 55,000 molecules. When candidate drug molecules with ostensible anti-SARS-CoV-2 activities were used as seeds (e.g., hydroxychloroquine, ritonavir, and dexamethasone) in COMPARE, the analysis uncovered several molecules with fingerprints similar to the seeded drugs. Interestingly, despite the fact that the uncovered drugs were from various pharmacological classes (antiarrhythmic, nucleosides, antipsychotic, alkaloids, antibiotics, and vitamins), they were all reportedly known from published literature to exert antiviral activities via different modes, confirming that COMPARE analysis is efficient for predicting antiviral activities of drugs from various pharmacological classes. Noticeably, several of the uncovered drugs can be readily tested, like didanosine, methotrexate, vitamin A, nicotinamide, valproic acid, uridine, and flucloxacillin. Unlike pure in silico methods, this approach is biologically more relevant and able to pharmacologically correlate compounds regardless of their chemical structures. This is an untapped resource, reliable and readily exploitable for drug repurposing against current and future viral outbreaks.

scholarly journals Identify potent SARS-CoV-2 main protease inhibitors via accelerated free energy perturbation-based virtual screening of existing drugs

2020 ◽  
Vol 117 (44) ◽  
pp. 27381-27387 ◽  
Author(s):  
Zhe Li ◽  
Xin Li ◽  
Yi-You Huang ◽  
Yaoxing Wu ◽  
Runduo Liu ◽  
...  
Keyword(s):  
Free Energy ◽  
Virtual Screening ◽  
Binding Free Energy ◽  
Drug Repurposing ◽  
Free Energy Perturbation ◽  
Therapeutic Effects ◽  
Screening Approach ◽  
Main Protease ◽  
Energy Perturbation ◽  
Virtual Screening Approach

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and thus repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a restraint energy distribution (RED) function, making the practical FEP-ABFE−based virtual screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 Mpro. The most potent one is dipyridamole (inhibitory constant Ki= 0.04 µM) which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki= 0.36 µM) and chloroquine (Ki= 0.56 µM) were also found to potently inhibit SARS-CoV-2 Mpro. We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts.

scholarly journals A SARS-CoV-2 (COVID-19) biological network to find targets for drug repurposing

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mahnaz Habibi ◽  
Golnaz Taheri ◽  
Rosa Aghdam
Keyword(s):  
Biological Networks ◽  
Drug Repurposing ◽  
Diabetes Type 2 ◽  
Minimum Size ◽  
Essential Proteins ◽  
Fast Recognition ◽  
Associated Proteins ◽  
Significant Enrichment ◽  
Effective Drugs ◽  
Clinical Drug

AbstractThe Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus needs a fast recognition of effective drugs to save lives. In the COVID-19 situation, finding targets for drug repurposing can be an effective way to present new fast treatments. We have designed a two-step solution to address this approach. In the first step, we identify essential proteins from virus targets or their associated modules in human cells as possible drug target candidates. For this purpose, we apply two different algorithms to detect some candidate sets of proteins with a minimum size that drive a significant disruption in the COVID-19 related biological networks. We evaluate the resulted candidate proteins sets with three groups of drugs namely Covid-Drug, Clinical-Drug, and All-Drug. The obtained candidate proteins sets approve 16 drugs out of 18 in the Covid-Drug, 273 drugs out of 328 in the Clinical-Drug, and a large number of drugs in the All-Drug. In the second step, we study COVID-19 associated proteins sets and recognize proteins that are essential to disease pathology. This analysis is performed using DAVID to show and compare essential proteins that are contributed between the COVID-19 comorbidities. Our results for shared proteins show significant enrichment for cardiovascular-related, hypertension, diabetes type 2, kidney-related and lung-related diseases.

scholarly journals Computationally Repurposing Drugs for Breast Cancer Subtypes Using a Network-Based Approach

2021 ◽  
Author(s):  
Forough Firoozbakht ◽  
Iman Rezaeian ◽  
Luis Rueda ◽  
Alioune Ngom
Keyword(s):  
Breast Cancer ◽  
Large Scale ◽  
De Novo ◽  
Low Cost ◽  
Drug Repurposing ◽  
Breast Cancer Subtypes ◽  
Therapeutic Effects ◽  
Phenotypic Data ◽  
Cancer Subtypes ◽  
Experimental Drugs

Abstract 'De novo' drug discovery is costly, slow, and with high risk. Repurposing known drugs for treatment of other diseases offers a fast, low-cost/risk and highly-efficient method toward development of efficacious treatments. The emergence of large-scale heterogeneous biomolecular networks, molecular, chemical and bioactivity data, and genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called 'in silico' drug repurposing, i.e., computational drug repurposing (CDR). The aim of CDR is to discover new indications for an existing drug (drug-centric) or to identify effective drugs for a disease (disease-centric). Both drug-centric and disease-centric approaches have the common challenge of either assessing the similarity or connections between drugs and diseases. However, traditional CDR is fraught with many challenges due to the underlying complex pharmacology and biology of diseases, genes, and drugs, as well as the complexity of their associations. As such, capturing highly non-linear associations among drugs, genes, diseases by most existing CDR methods has been challenging.We propose a network-based integration approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes.

scholarly journals Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients

2021 ◽  
Author(s):  
Hong Wang ◽  
Jingqing Zhang ◽  
Zhigang Lu ◽  
Weina Dai ◽  
Chuanjiang Ma ◽  
...  
Keyword(s):  
Drug Targets ◽  
Drug Repurposing ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Protein Protein Interaction ◽  
Disease Module ◽  
Approved Drugs ◽  
Data Driven Modeling ◽  
The Relationship

Abstract After experiencing the COVID-19 pandemic, it is widely acknowledged that a rapid drug repurposing method is highly needed. A series of useful drug repurposing tools have been developed based on data-driven modeling and network pharmacology. Based on the disease module, we identified several hub proteins that play important roles in the onset and development of the COVID-19, which are potential targets for repositioning approved drugs. Moreover, different network distance metrics were applied to quantify the relationship between drug targets and COVID-19 disease targets in the protein–protein-interaction (PPI) network and predict COVID-19 therapeutic effects of bioactive herbal ingredients and chemicals. Furthermore, the tentative mechanisms of candidates were illustrated through molecular docking and gene enrichment analysis. We obtained 15 chemical and 15 herbal ingredient candidates and found that different drugs may play different roles in the process of virus invasion and the onset and development of the COVID-19 disease. Given pandemic outbreaks, our method has an undeniable immense advantage in the feasibility analysis of drug repurposing or drug screening, especially in the analysis of herbal ingredients.

scholarly journals Integrating gene expression and clinical data to identify drug repurposing candidates for hyperlipidemia and hypertension

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Patrick Wu ◽  
QiPing Feng ◽  
Vern Eric Kerchberger ◽  
Scott D. Nelson ◽  
Qingxia Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Development ◽  
Clinical Data ◽  
High Throughput ◽  
Research Program ◽  
Human Gene ◽  
Drug Repurposing ◽  
Therapeutic Effects ◽  
New Drug Development ◽  
Approved Drugs

AbstractDiscovering novel uses for existing drugs, through drug repurposing, can reduce the time, costs, and risk of failure associated with new drug development. However, prioritizing drug repurposing candidates for downstream studies remains challenging. Here, we present a high-throughput approach to identify and validate drug repurposing candidates. This approach integrates human gene expression, drug perturbation, and clinical data from publicly available resources. We apply this approach to find drug repurposing candidates for two diseases, hyperlipidemia and hypertension. We screen >21,000 compounds and replicate ten approved drugs. We also identify 25 (seven for hyperlipidemia, eighteen for hypertension) drugs approved for other indications with therapeutic effects on clinically relevant biomarkers. For five of these drugs, the therapeutic effects are replicated in the All of Us Research Program database. We anticipate our approach will enable researchers to integrate multiple publicly available datasets to identify high priority drug repurposing opportunities for human diseases.

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