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Dipietro L, Gonzalez-Mego P, Ramos-Estebanez C, Zukowski LH, Mikkilineni R, Rushmore RJ, Wagner T. The evolution of Big Data in neuroscience and neurology. JOURNAL OF BIG DATA 2023; 10:116. [PMID: 37441339 PMCID: PMC10333390 DOI: 10.1186/s40537-023-00751-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/08/2023] [Indexed: 07/15/2023]
Abstract
Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes. Supplementary Information The online version contains supplementary material available at 10.1186/s40537-023-00751-2.
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Affiliation(s)
| | - Paola Gonzalez-Mego
- Spaulding Rehabilitation/Neuromodulation Lab, Harvard Medical School, Cambridge, MA USA
| | | | | | | | | | - Timothy Wagner
- Highland Instruments, Cambridge, MA USA
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA USA
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Afief AR, Irham LM, Adikusuma W, Perwitasari DA, Brahmadhi A, Chong R. Integration of genomic variants and bioinformatic-based approach to drive drug repurposing for multiple sclerosis. Biochem Biophys Rep 2022; 32:101337. [PMID: 36105612 PMCID: PMC9464879 DOI: 10.1016/j.bbrep.2022.101337] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/25/2022] [Accepted: 08/25/2022] [Indexed: 01/04/2023] Open
Abstract
Multiple sclerosis (MS) is a chronic autoimmune disease in the central nervous system (CNS) marked by inflammation, demyelination, and axonal loss. Currently available MS medication is limited, thereby calling for a strategy to accelerate new drug discovery. One of the strategies to discover new drugs is to utilize old drugs for new indications, an approach known as drug repurposing. Herein, we first identified 421 MS-associated SNPs from the Genome-Wide Association Study (GWAS) catalog (p-value < 5 × 10-8), and a total of 427 risk genes associated with MS using HaploReg version 4.1 under the criterion r 2 > 0.8. MS risk genes were then prioritized using bioinformatics analysis to identify biological MS risk genes. The prioritization was performed based on six defined categories of functional annotations, namely missense mutation, cis-expression quantitative trait locus (cis-eQTL), molecular pathway analysis, protein-protein interaction (PPI), genes overlap with knockout mouse phenotype, and primary immunodeficiency (PID). A total of 144 biological MS risk genes were found and mapped into 194 genes within an expanded PPI network. According to the DrugBank and the Therapeutic Target Database, 27 genes within the list targeted by 68 new candidate drugs were identified. Importantly, the power of our approach is confirmed with the identification of a known approved drug (dimethyl fumarate) for MS. Based on additional data from ClinicalTrials.gov, eight drugs targeting eight distinct genes are prioritized with clinical evidence for MS disease treatment. Notably, CD80 and CD86 pathways are promising targets for MS drug repurposing. Using in silico drug repurposing, we identified belatacept as a promising MS drug candidate. Overall, this study emphasized the integration of functional genomic variants and bioinformatic-based approach that reveal important biological insights for MS and drive drug repurposing efforts for the treatment of this devastating disease.
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Key Words
- ARE, Antioxidant Response Element
- ASN, Asian
- Autoimmune disease
- Bioinformatics
- CNS, Central Nervous System
- Drug repurposing
- FDA, Food and Drug Administration
- FDR, False Discovery Rate
- GO, Gene Ontology
- GWAS, Genome-Wide Association Study
- Genomic variants
- HLA, Human Leukocyte Antigen
- KEGG, Kyoto Encyclopedia of Genes and Genomes
- MP, Mammalian Phenotype
- MS, Multiple Sclerosis
- Multiple sclerosis
- PID, Primary Immuno-deficiency
- PPI, Protein-Protein Interaction
- SNP, Single Nucleotide Polymorphism
- cis-eQTL, cis-expression Quantitative Trait Locus
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Affiliation(s)
| | | | - Wirawan Adikusuma
- Department of Pharmacy, University of Muhammadiyah Mataram, Mataram, Indonesia
| | | | - Ageng Brahmadhi
- Faculty of Medicine, Universitas Muhammadiyah Purwokerto, Purwokerto, Central Java, Indonesia
| | - Rockie Chong
- Department of Chemistry and Biochemistry, University of California, Los Angeles, USA
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Lucía Schmidt A, Rodriguez-Esteban R, Gottowik J, Leddin M. Applications of quantitative social media listening to patient-centric drug development. Drug Discov Today 2022; 27:1523-1530. [PMID: 35114364 DOI: 10.1016/j.drudis.2022.01.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/13/2021] [Accepted: 01/26/2022] [Indexed: 11/27/2022]
Abstract
Social media listening has been increasingly acknowledged as a tool with applications in many stages of the drug development process. These applications were created to meet the need for patient-centric therapies that are fit-for-purpose and meaningful to patients. Such applications, however, require the leverage of new quantitative approaches and analytical methods that draw from developments in artificial intelligence and real-world data (RWD) analysis. Here, we review the state-of-the-art in quantitative social media listening (QSML) methods applied to drug discovery from the perspective of the pharmaceutical industry.
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Affiliation(s)
- Ana Lucía Schmidt
- Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Raul Rodriguez-Esteban
- Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland.
| | - Juergen Gottowik
- Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Mathias Leddin
- Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland
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Adikusuma W, Irham LM, Chou WH, Wong HSC, Mugiyanto E, Ting J, Perwitasari DA, Chang WP, Chang WC. Drug Repurposing for Atopic Dermatitis by Integration of Gene Networking and Genomic Information. Front Immunol 2021; 12:724277. [PMID: 34721386 PMCID: PMC8548825 DOI: 10.3389/fimmu.2021.724277] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 09/15/2021] [Indexed: 12/02/2022] Open
Abstract
Atopic Dermatitis (AD) is a chronic and relapsing skin disease. The medications for treating AD are still limited, most of them are topical corticosteroid creams or antibiotics. The current study attempted to discover potential AD treatments by integrating a gene network and genomic analytic approaches. Herein, the Single Nucleotide Polymorphism (SNPs) associated with AD were extracted from the GWAS catalog. We identified 70 AD-associated loci, and then 94 AD risk genes were found by extending to proximal SNPs based on r2 > 0.8 in Asian populations using HaploReg v4.1. Next, we prioritized the AD risk genes using in silico pipelines of bioinformatic analysis based on six functional annotations to identify biological AD risk genes. Finally, we expanded them according to the molecular interactions using the STRING database to find the drug target genes. Our analysis showed 27 biological AD risk genes, and they were mapped to 76 drug target genes. According to DrugBank and Therapeutic Target Database, 25 drug target genes overlapping with 53 drugs were identified. Importantly, dupilumab, which is approved for AD, was successfully identified in this bioinformatic analysis. Furthermore, ten drugs were found to be potentially useful for AD with clinical or preclinical evidence. In particular, we identified filgotinub and fedratinib, targeting gene JAK1, as potential drugs for AD. Furthermore, four monoclonal antibody drugs (lebrikizumab, tralokinumab, tocilizumab, and canakinumab) were successfully identified as promising for AD repurposing. In sum, the results showed the feasibility of gene networking and genomic information as a potential drug discovery resource.
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Affiliation(s)
- Wirawan Adikusuma
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
- Department of Pharmacy, Faculty of Health Science, University of Muhammadiyah Mataram, Mataram, Indonesia
| | - Lalu Muhammad Irham
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
- Faculty of Pharmacy, University of Ahmad Dahlan, Yogyakarta, Indonesia
| | - Wan-Hsuan Chou
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | - Henry Sung-Ching Wong
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | - Eko Mugiyanto
- Ph. D. Program in the Clinical Drug Development of Herbal Medicines, College of Pharmacy, Taipei Medical University, Taipei, Taiwan
- Department of Pharmacy, Faculty of Health Science, University of Muhammadiyah Pekajangan Pekalongan, Pekalongan, Indonesia
| | - Jafit Ting
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | | | - Wei-Pin Chang
- School of Health Care Administration, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Wei-Chiao Chang
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
- Taipei Medical University (TMU) Research Center of Cancer Translational Medicine, Taipei, Taiwan
- Department of Pharmacy, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Integrative Research Center for Critical Care, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Department of Pharmacology, National Defense Medical Center, Taipei, Taiwan
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Busl KM, Rubin MA, Tolchin BD, Larriviere D, Epstein L, Kirschen M, Taylor LP. Use of Social Media in Health Care-Opportunities, Challenges, and Ethical Considerations: A Position Statement of the American Academy of Neurology. Neurology 2021; 97:585-594. [PMID: 34864637 DOI: 10.1212/wnl.0000000000012557] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 06/30/2021] [Indexed: 01/26/2023] Open
Affiliation(s)
- Katharina M Busl
- From the Departments of Neurology and Neurosurgery (K.M.B.), College of Medicine, University of Florida, Gainesville; Department of Neurology (M.A.R.), UT Southwestern, Dallas, TX; Department of Neurology (B.D.T.), Yale University Medical School, New Haven, CT; Department of Neurology (D.L.), Ochsner Medical Center, Jefferson, LA; Department of Pediatric Neurology (L.E.), Northwestern University, Evanston, IL; Department of Pediatric Medicine (M.K.), Children's Hospital of Philadelphia, PA; and Department of Neurology (L.P.T.), University of Washington, Seattle.
| | - Michael A Rubin
- From the Departments of Neurology and Neurosurgery (K.M.B.), College of Medicine, University of Florida, Gainesville; Department of Neurology (M.A.R.), UT Southwestern, Dallas, TX; Department of Neurology (B.D.T.), Yale University Medical School, New Haven, CT; Department of Neurology (D.L.), Ochsner Medical Center, Jefferson, LA; Department of Pediatric Neurology (L.E.), Northwestern University, Evanston, IL; Department of Pediatric Medicine (M.K.), Children's Hospital of Philadelphia, PA; and Department of Neurology (L.P.T.), University of Washington, Seattle
| | - Benjamin D Tolchin
- From the Departments of Neurology and Neurosurgery (K.M.B.), College of Medicine, University of Florida, Gainesville; Department of Neurology (M.A.R.), UT Southwestern, Dallas, TX; Department of Neurology (B.D.T.), Yale University Medical School, New Haven, CT; Department of Neurology (D.L.), Ochsner Medical Center, Jefferson, LA; Department of Pediatric Neurology (L.E.), Northwestern University, Evanston, IL; Department of Pediatric Medicine (M.K.), Children's Hospital of Philadelphia, PA; and Department of Neurology (L.P.T.), University of Washington, Seattle
| | - Dan Larriviere
- From the Departments of Neurology and Neurosurgery (K.M.B.), College of Medicine, University of Florida, Gainesville; Department of Neurology (M.A.R.), UT Southwestern, Dallas, TX; Department of Neurology (B.D.T.), Yale University Medical School, New Haven, CT; Department of Neurology (D.L.), Ochsner Medical Center, Jefferson, LA; Department of Pediatric Neurology (L.E.), Northwestern University, Evanston, IL; Department of Pediatric Medicine (M.K.), Children's Hospital of Philadelphia, PA; and Department of Neurology (L.P.T.), University of Washington, Seattle
| | - Leon Epstein
- From the Departments of Neurology and Neurosurgery (K.M.B.), College of Medicine, University of Florida, Gainesville; Department of Neurology (M.A.R.), UT Southwestern, Dallas, TX; Department of Neurology (B.D.T.), Yale University Medical School, New Haven, CT; Department of Neurology (D.L.), Ochsner Medical Center, Jefferson, LA; Department of Pediatric Neurology (L.E.), Northwestern University, Evanston, IL; Department of Pediatric Medicine (M.K.), Children's Hospital of Philadelphia, PA; and Department of Neurology (L.P.T.), University of Washington, Seattle
| | - Matthew Kirschen
- From the Departments of Neurology and Neurosurgery (K.M.B.), College of Medicine, University of Florida, Gainesville; Department of Neurology (M.A.R.), UT Southwestern, Dallas, TX; Department of Neurology (B.D.T.), Yale University Medical School, New Haven, CT; Department of Neurology (D.L.), Ochsner Medical Center, Jefferson, LA; Department of Pediatric Neurology (L.E.), Northwestern University, Evanston, IL; Department of Pediatric Medicine (M.K.), Children's Hospital of Philadelphia, PA; and Department of Neurology (L.P.T.), University of Washington, Seattle
| | - Lynne P Taylor
- From the Departments of Neurology and Neurosurgery (K.M.B.), College of Medicine, University of Florida, Gainesville; Department of Neurology (M.A.R.), UT Southwestern, Dallas, TX; Department of Neurology (B.D.T.), Yale University Medical School, New Haven, CT; Department of Neurology (D.L.), Ochsner Medical Center, Jefferson, LA; Department of Pediatric Neurology (L.E.), Northwestern University, Evanston, IL; Department of Pediatric Medicine (M.K.), Children's Hospital of Philadelphia, PA; and Department of Neurology (L.P.T.), University of Washington, Seattle
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Adly AS, Adly AS, Adly MS. Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review. J Med Internet Res 2020; 22:e19104. [PMID: 32584780 PMCID: PMC7423390 DOI: 10.2196/19104] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 06/24/2020] [Accepted: 06/25/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) and the Internet of Intelligent Things (IIoT) are promising technologies to prevent the concerningly rapid spread of coronavirus disease (COVID-19) and to maximize safety during the pandemic. With the exponential increase in the number of COVID-19 patients, it is highly possible that physicians and health care workers will not be able to treat all cases. Thus, computer scientists can contribute to the fight against COVID-19 by introducing more intelligent solutions to achieve rapid control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the disease. OBJECTIVE The objectives of this review were to analyze the current literature, discuss the applicability of reported ideas for using AI to prevent and control COVID-19, and build a comprehensive view of how current systems may be useful in particular areas. This may be of great help to many health care administrators, computer scientists, and policy makers worldwide. METHODS We conducted an electronic search of articles in the MEDLINE, Google Scholar, Embase, and Web of Knowledge databases to formulate a comprehensive review that summarizes different categories of the most recently reported AI-based approaches to prevent and control the spread of COVID-19. RESULTS Our search identified the 10 most recent AI approaches that were suggested to provide the best solutions for maximizing safety and preventing the spread of COVID-19. These approaches included detection of suspected cases, large-scale screening, monitoring, interactions with experimental therapies, pneumonia screening, use of the IIoT for data and information gathering and integration, resource allocation, predictions, modeling and simulation, and robotics for medical quarantine. CONCLUSIONS We found few or almost no studies regarding the use of AI to examine COVID-19 interactions with experimental therapies, the use of AI for resource allocation to COVID-19 patients, or the use of AI and the IIoT for COVID-19 data and information gathering/integration. Moreover, the adoption of other approaches, including use of AI for COVID-19 prediction, use of AI for COVID-19 modeling and simulation, and use of AI robotics for medical quarantine, should be further emphasized by researchers because these important approaches lack sufficient numbers of studies. Therefore, we recommend that computer scientists focus on these approaches, which are still not being adequately addressed.
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Affiliation(s)
- Aya Sedky Adly
- Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt
| | - Afnan Sedky Adly
- Faculty of Physical Therapy, Cardiovascular-Respiratory Disorders and Geriatrics, Laser Applications in Physical Medicine, Cairo University, Cairo, Egypt
- Faculty of Physical Therapy, Internal Medicine, Beni-Suef University, Beni-Suef, Egypt
| | - Mahmoud Sedky Adly
- Faculty of Oral and Dental Medicine, Cairo University, Cairo, Egypt
- Royal College of Surgeons of Edinburgh, Scotland, United Kingdom
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Li X, Lin X, Ren H, Guo J. Ontological Organization and Bioinformatic Analysis of Adverse Drug Reactions From Package Inserts: Development and Usability Study. J Med Internet Res 2020; 22:e20443. [PMID: 32706718 PMCID: PMC7400033 DOI: 10.2196/20443] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/11/2020] [Accepted: 06/14/2020] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Licensed drugs may cause unexpected adverse reactions in patients, resulting in morbidity, risk of mortality, therapy disruptions, and prolonged hospital stays. Officially approved drug package inserts list the adverse reactions identified from randomized controlled clinical trials with high evidence levels and worldwide postmarketing surveillance. Formal representation of the adverse drug reaction (ADR) enclosed in semistructured package inserts will enable deep recognition of side effects and rational drug use, substantially reduce morbidity, and decrease societal costs. OBJECTIVE This paper aims to present an ontological organization of traceable ADR information extracted from licensed package inserts. In addition, it will provide machine-understandable knowledge for bioinformatics analysis, semantic retrieval, and intelligent clinical applications. METHODS Based on the essential content of package inserts, a generic ADR ontology model is proposed from two dimensions (and nine subdimensions), covering the ADR information and medication instructions. This is followed by a customized natural language processing method programmed with Python to retrieve the relevant information enclosed in package inserts. After the biocuration and identification of retrieved data from the package insert, an ADR ontology is automatically built for further bioinformatic analysis. RESULTS We collected 165 package inserts of quinolone drugs from the National Medical Products Administration and other drug databases in China, and built a specialized ADR ontology containing 2879 classes and 15,711 semantic relations. For each quinolone drug, the reported ADR information and medication instructions have been logically represented and formally organized in an ADR ontology. To demonstrate its usage, the source data were further bioinformatically analyzed. For example, the number of drug-ADR triples and major ADRs associated with each active ingredient were recorded. The 10 ADRs most frequently observed among quinolones were identified and categorized based on the 18 categories defined in the proposal. The occurrence frequency, severity, and ADR mitigation method explicitly stated in package inserts were also analyzed, as well as the top 5 specific populations with contraindications for quinolone drugs. CONCLUSIONS Ontological representation and organization using officially approved information from drug package inserts enables the identification and bioinformatic analysis of adverse reactions caused by a specific drug with regard to predefined ADR ontology classes and semantic relations. The resulting ontology-based ADR knowledge source classifies drug-specific adverse reactions, and supports a better understanding of ADRs and safer prescription of medications.
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Affiliation(s)
- Xiaoying Li
- Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
| | - Xin Lin
- Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
| | - Huiling Ren
- Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
| | - Jinjing Guo
- Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
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Du J, Li X. A Knowledge Graph of Combined Drug Therapies Using Semantic Predications From Biomedical Literature: Algorithm Development. JMIR Med Inform 2020; 8:e18323. [PMID: 32343247 PMCID: PMC7218597 DOI: 10.2196/18323] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 03/26/2020] [Accepted: 03/29/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Combination therapy plays an important role in the effective treatment of malignant neoplasms and precision medicine. Numerous clinical studies have been carried out to investigate combination drug therapies. Automated knowledge discovery of these combinations and their graphic representation in knowledge graphs will enable pattern recognition and identification of drug combinations used to treat a specific type of cancer, improve drug efficacy and treatment of human disorders. OBJECTIVE This paper aims to develop an automated, visual approach to discover knowledge about combination therapies from biomedical literature, especially from those studies with high-level evidence such as clinical trial reports and clinical practice guidelines. METHODS Based on semantic predications, which consist of a triple structure of subject-predicate-object (SPO), we proposed an automated algorithm to discover knowledge of combination drug therapies using the following rules: 1) two or more semantic predications (S1-P-O and Si-P-O, i = 2, 3…) can be extracted from one conclusive claim (sentence) in the abstract of a given publication, and 2) these predications have an identical predicate (that closely relates to human disease treatment, eg, "treat") and object (eg, disease name) but different subjects (eg, drug names). A customized knowledge graph organizes and visualizes these combinations, improving the traditional semantic triples. After automatic filtering of broad concepts such as "pharmacologic actions" and generic disease names, a set of combination drug therapies were identified and characterized through manual interpretation. RESULTS We retrieved 22,263 clinical trial reports and 31 clinical practice guidelines from PubMed abstracts by searching "antineoplastic agents" for drug restriction (published between Jan 2009 and Oct 2019). There were 15,603 conclusive claims locally parsed using the search terms "conclusion*" and "conclude*" ready for semantic predications extraction by SemRep, and 325 candidate groups of semantic predications about combined medications were automatically discovered within 316 conclusive claims. Based on manual analysis, we determined that 255/316 claims (78.46%) were accurately identified as describing combination therapies and adopted these to construct the customized knowledge graph. We also identified two categories (and 4 subcategories) to characterize the inaccurate results: limitations of SemRep and limitations of proposal. We further learned the predominant patterns of drug combinations based on mechanism of action for new combined medication studies and discovered 4 obvious markers ("combin*," "coadministration," "co-administered," and "regimen") to identify potential combination therapies to enable development of a machine learning algorithm. CONCLUSIONS Semantic predications from conclusive claims in the biomedical literature can be used to support automated knowledge discovery and knowledge graph construction for combination therapies. A machine learning approach is warranted to take full advantage of the identified markers and other contextual features.
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Affiliation(s)
- Jian Du
- National Institute of Health Data Science, Peking University, Beijing, China
| | - Xiaoying Li
- Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
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Serendipitous drug repurposing through social media. Drug Discov Today 2019; 24:1321-1323. [PMID: 31102729 DOI: 10.1016/j.drudis.2019.05.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 05/10/2019] [Indexed: 11/21/2022]
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