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Su C, Hou Y, Xu J, Xu Z, Zhou M, Ke A, Li H, Xu J, Brendel M, Maasch JRMA, Bai Z, Zhang H, Zhu Y, Cincotta MC, Shi X, Henchcliffe C, Leverenz JB, Cummings J, Okun MS, Bian J, Cheng F, Wang F. Identification of Parkinson's disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data. NPJ Digit Med 2024; 7:184. [PMID: 38982243 PMCID: PMC11233682 DOI: 10.1038/s41746-024-01175-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Accepted: 06/21/2024] [Indexed: 07/11/2024] Open
Abstract
Parkinson's disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals' phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.
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Grants
- R21 AG083003 NIA NIH HHS
- R01 AG082118 NIA NIH HHS
- R56 AG074001 NIA NIH HHS
- R01AG076448 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1AG072449 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- MJFF-023081 Michael J. Fox Foundation for Parkinson's Research (Michael J. Fox Foundation)
- R01AG080991 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P30 AG072959 NIA NIH HHS
- 3R01AG066707-01S1 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R21AG083003 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG066707 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R35 AG071476 NIA NIH HHS
- RF1 AG082211 NIA NIH HHS
- R56AG074001 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG082118 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R25 AG083721 NIA NIH HHS
- RF1AG082211 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01 NS093334 NINDS NIH HHS
- AG083721-01 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1NS133812 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P20GM109025 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1 NS133812 NINDS NIH HHS
- R35AG71476 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01 AG073323 NIA NIH HHS
- R01 AG066707 NIA NIH HHS
- R01AG053798 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG076234 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01 AG076448 NIA NIH HHS
- R01 AG080991 NIA NIH HHS
- R01 AG076234 NIA NIH HHS
- U01NS093334 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P20 GM109025 NIGMS NIH HHS
- P30AG072959 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1 AG072449 NIA NIH HHS
- R01 AG053798 NIA NIH HHS
- 3R01AG066707-02S1 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01AG073323 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- ALZDISCOVERY-1051936 Alzheimer's Association
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Affiliation(s)
- Chang Su
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Yu Hou
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Manqi Zhou
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Alison Ke
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Haoyang Li
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Matthew Brendel
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Jacqueline R M A Maasch
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computer Science, Cornell Tech, Cornell University, New York, NY, USA
| | - Zilong Bai
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Haotan Zhang
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Yingying Zhu
- Department of Computer Science, University of Texas at Arlington, Arlington, TX, USA
| | - Molly C Cincotta
- Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Claire Henchcliffe
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Pam Quirk Brain Health and Biomarker Laboratory, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Michael S Okun
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA.
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA.
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2
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Kan-Tor Y, Ness L, Szlak L, Benninger F, Ravid S, Chorev M, Rosen-Zvi M, Shimoni Y, Fisher RS. Comparing the efficacy of anti-seizure medications using matched cohorts on a large insurance claims database. Epilepsy Res 2024; 201:107313. [PMID: 38417192 DOI: 10.1016/j.eplepsyres.2024.107313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/22/2024] [Accepted: 01/25/2024] [Indexed: 03/01/2024]
Abstract
Epilepsy is a severe chronic neurological disease affecting 60 million people worldwide. Primary treatment is with anti-seizure medicines (ASMs), but many patients continue to experience seizures. We used retrospective insurance claims data on 280,587 patients with uncontrolled epilepsy (UE), defined as status epilepticus, need for a rescue medicine, or admission or emergency visit for an epilepsy code. We conducted a computational risk ratio analysis between pairs of ASMs using a causal inference method, in order to match 1034 clinical factors and simulate randomization. Data was extracted from the MarketScan insurance claims Research Database records from 2011 to 2015. The cohort consisted of individuals over 18 years old with a diagnosis of epilepsy who took one of eight ASMs and had more than a year of history prior to the filling of the drug prescription. Seven ASM exposures were analyzed: topiramate, phenytoin, levetiracetam, gabapentin, lamotrigine, valproate, and carbamazepine or oxcarbazepine (treated as the same exposure). We calculated the risk ratio of UE between pairs of ASM after controlling for bias with inverse propensity weighting applied to 1034 factors, such as demographics, confounding illnesses, non-epileptic conditions treated by ASMs, etc. All ASMs exhibited a significant reduction in the prevalence of UE, but three drugs showed pair-wise differences compared to other ASMs. Topiramate consistently was associated with a lower risk of UE, with a mean risk ratio range of 0.68-0.93 (average 0.82, CI: 0.56-1.08). Phenytoin and levetiracetam were consistently associated with a higher risk of UE with mean risk ratio ranges of 1.11 to 1.47 (average 1.13, CI 0.98-1.65) and 1.15 to 1.43 (average 1.2, CI 0.72-1.69), respectively. Large-scale retrospective insurance claims data - combined with causal inference analysis - provides an opportunity to compare the effect of treatments in real-world data in populations 1,000-fold larger than those in typical randomized trials. Our causal analysis identified the clinically unexpected finding of topiramate as being associated with a lower risk of UE; and phenytoin and levetiracetam as associated with a higher risk of UE (compared to other studied drugs, not to baseline). However, we note that our data set for this study only used insurance claims events, which does not comprise actual seizure frequencies, nor a clear picture of side effects. Our results do not advocate for any change in practice but demonstrate that conclusions from large databases may differ from and supplement those of randomized trials and clinical practice and therefore may guide further investigation.
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Affiliation(s)
- Yoav Kan-Tor
- AI for Healthcare and Life Sciences Department, IBM Research, Haifa, Israel
| | - Lior Ness
- AI for Healthcare and Life Sciences Department, IBM Research, Haifa, Israel
| | - Liran Szlak
- AI for Healthcare and Life Sciences Department, IBM Research, Haifa, Israel
| | - Felix Benninger
- Department of Neurology, Rabin Medical Center, Petach Tikva, Israel; School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Sivan Ravid
- AI for Healthcare and Life Sciences Department, IBM Research, Haifa, Israel
| | - Michal Chorev
- AI for Healthcare and Life Sciences Department, IBM Research, Haifa, Israel; Centre for Applied Research, IBM Australia, Melbourne, Australia
| | - Michal Rosen-Zvi
- AI for Healthcare and Life Sciences Department, IBM Research, Haifa, Israel; Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Yishai Shimoni
- AI for Healthcare and Life Sciences Department, IBM Research, Haifa, Israel
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3
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Zang C, Zhang H, Xu J, Zhang H, Fouladvand S, Havaldar S, Cheng F, Chen K, Chen Y, Glicksberg BS, Chen J, Bian J, Wang F. High-throughput target trial emulation for Alzheimer's disease drug repurposing with real-world data. Nat Commun 2023; 14:8180. [PMID: 38081829 PMCID: PMC10713627 DOI: 10.1038/s41467-023-43929-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 11/24/2023] [Indexed: 12/18/2023] Open
Abstract
Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer's disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer's patients.
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Affiliation(s)
- Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY, USA
| | - Hao Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Hansi Zhang
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Sajjad Fouladvand
- Institude for Biomedical Informatics (IBI) and Department of Computer Science, University of Kentucky, Lexington, KY, USA
| | - Shreyas Havaldar
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kun Chen
- Department of Statistics, University of Connecticut, Storrs, CT, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics (DBEI), the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jin Chen
- Institude for Biomedical Informatics (IBI) and Department of Computer Science, University of Kentucky, Lexington, KY, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY, USA.
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4
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Hansford HJ, Cashin AG, Jones MD, Swanson SA, Islam N, Douglas SRG, Rizzo RRN, Devonshire JJ, Williams SA, Dahabreh IJ, Dickerman BA, Egger M, Garcia-Albeniz X, Golub RM, Lodi S, Moreno-Betancur M, Pearson SA, Schneeweiss S, Sterne JAC, Sharp MK, Stuart EA, Hernán MA, Lee H, McAuley JH. Reporting of Observational Studies Explicitly Aiming to Emulate Randomized Trials: A Systematic Review. JAMA Netw Open 2023; 6:e2336023. [PMID: 37755828 PMCID: PMC10534275 DOI: 10.1001/jamanetworkopen.2023.36023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/22/2023] [Indexed: 09/28/2023] Open
Abstract
Importance Observational (nonexperimental) studies that aim to emulate a randomized trial (ie, the target trial) are increasingly informing medical and policy decision-making, but it is unclear how these studies are reported in the literature. Consistent reporting is essential for quality appraisal, evidence synthesis, and translation of evidence to policy and practice. Objective To assess the reporting of observational studies that explicitly aimed to emulate a target trial. Evidence Review We searched Medline, Embase, PsycINFO, and Web of Science for observational studies published between March 2012 and October 2022 that explicitly aimed to emulate a target trial of a health or medical intervention. Two reviewers double-screened and -extracted data on study characteristics, key predefined components of the target trial protocol and its emulation (eligibility criteria, treatment strategies, treatment assignment, outcome[s], follow-up, causal contrast[s], and analysis plan), and other items related to the target trial emulation. Findings A total of 200 studies that explicitly aimed to emulate a target trial were included. These studies included 26 subfields of medicine, and 168 (84%) were published from January 2020 to October 2022. The aim to emulate a target trial was explicit in 70 study titles (35%). Forty-three studies (22%) reported use of a published reporting guideline (eg, Strengthening the Reporting of Observational Studies in Epidemiology). Eighty-five studies (43%) did not describe all key items of how the target trial was emulated and 113 (57%) did not describe the protocol of the target trial and its emulation. Conclusion and Relevance In this systematic review of 200 studies that explicitly aimed to emulate a target trial, reporting of how the target trial was emulated was inconsistent. A reporting guideline for studies explicitly aiming to emulate a target trial may improve the reporting of the target trial protocols and other aspects of these emulation attempts.
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Affiliation(s)
- Harrison J. Hansford
- School of Health Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
| | - Aidan G. Cashin
- School of Health Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
| | - Matthew D. Jones
- School of Health Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
| | - Sonja A. Swanson
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Nazrul Islam
- Oxford Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
- Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | - Susan R. G. Douglas
- School of Health Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
| | - Rodrigo R. N. Rizzo
- School of Health Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
| | - Jack J. Devonshire
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
| | - Sam A. Williams
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
| | - Issa J. Dahabreh
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Barbra A. Dickerman
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Matthias Egger
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Centre for Infectious Disease Epidemiology and Research, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Xabier Garcia-Albeniz
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- RTI Health Solutions, Barcelona, Spain
| | - Robert M. Golub
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Sara Lodi
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Margarita Moreno-Betancur
- Clinical Epidemiology & Biostatistics Unit, Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, Victoria, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, Victoria, Australia
| | - Sallie-Anne Pearson
- School of Population Health, Faculty of Medicine and Health, UNSW Sydney, New South Wales, Australia
| | - Sebastian Schneeweiss
- Division of Pharmacoepidemiology, Department of Medicine, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jonathan A. C. Sterne
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- NIHR Bristol Biomedical Research Centre, Bristol, United Kingdom
- Health Data Research UK South-West, Bristol, United Kingdom
| | - Melissa K. Sharp
- Department of Public Health and Epidemiology, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Elizabeth A. Stuart
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Miguel A. Hernán
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Hopin Lee
- University of Exeter Medical School, Exeter, United Kingdom
| | - James H. McAuley
- School of Health Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
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5
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Tan GSQ, Botteri E, Wood S, Sloan EK, Ilomäki J. Using administrative healthcare data to evaluate drug repurposing opportunities for cancer: the possibility of using beta-blockers to treat breast cancer. Front Pharmacol 2023; 14:1227330. [PMID: 37637417 PMCID: PMC10448902 DOI: 10.3389/fphar.2023.1227330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 07/28/2023] [Indexed: 08/29/2023] Open
Abstract
Introduction: Cancer registries and hospital electronic medical records are commonly used to investigate drug repurposing candidates for cancer. However, administrative data are often more accessible than data from cancer registries and medical records. Therefore, we evaluated if administrative data could be used to evaluate drug repurposing for cancer by conducting an example study on the association between beta-blocker use and breast cancer mortality. Methods: A retrospective cohort study of women aged ≥50 years with incident breast cancer was conducted using a linked dataset with statewide hospital admission data and nationwide medication claims data. Women receiving beta blockers and first-line anti-hypertensives prior to and at diagnosis were compared. Breast cancer molecular subtypes and metastasis status were inferred by algorithms from commonly prescribed breast cancer antineoplastics and hospitalization diagnosis codes, respectively. Subdistribution hazard ratios (sHR) and corresponding 95% confidence intervals (CIs) for breast cancer mortality were estimated using Fine and Gray's competing risk models adjusted for age, Charlson comorbidity index, congestive heart failure, myocardial infraction, molecular subtype, presence of metastasis at diagnosis, and breast cancer surgery. Results: 2,758 women were hospitalized for incident breast cancer. 604 received beta-blockers and 1,387 received first-line antihypertensives. In total, 154 breast cancer deaths were identified over a median follow-up time of 2.7 years. We found no significant association between use of any beta-blocker and breast-cancer mortality (sHR 0.86, 95%CI 0.58-1.28), or when stratified by beta-blocker type (non-selective, sHR 0.42, 95%CI 0.14-1.25; selective, sHR 0.95, 95%CI 0.63-1.43). Results were not significant when stratified by molecular subtypes (e.g., triple negative breast cancer (TNBC), any beta blocker, sHR 0.16, 95%CI 0.02-1.51). Discussion: It is possible to use administrative data to explore drug repurposing opportunities. Although non-significant, an indication of an association was found for the TNBC subtype, which aligns with previous studies using registry data. Future studies with larger sample size, longer follow-up are required to confirm the association, and linkage to clinical data sources are required to validate our methodologies.
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Affiliation(s)
- George S. Q. Tan
- Centre for Medicine Use and Safety, Monash University, Parkville, VIC, Australia
| | - Edoardo Botteri
- Section for Colorectal Cancer Screening, Cancer Registry of Norway, Oslo, Norway
- Research Department, Cancer Registry of Norway, Oslo, Norway
| | - Stephen Wood
- Centre for Medicine Use and Safety, Monash University, Parkville, VIC, Australia
| | - Erica K. Sloan
- Monash Institute of Pharmaceutical Sciences, Drug Discovery Biology Theme, Monash University, Parkville, VIC, Australia
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Jenni Ilomäki
- Centre for Medicine Use and Safety, Monash University, Parkville, VIC, Australia
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6
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Xu J, Wang F, Zang C, Zhang H, Niotis K, Liberman AL, Stonnington CM, Ishii M, Adekkanattu P, Luo Y, Mao C, Rasmussen LV, Xu Z, Brandt P, Pacheco JA, Peng Y, Jiang G, Isaacson R, Pathak J. Comparing the effects of four common drug classes on the progression of mild cognitive impairment to dementia using electronic health records. Sci Rep 2023; 13:8102. [PMID: 37208478 PMCID: PMC10199021 DOI: 10.1038/s41598-023-35258-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 05/15/2023] [Indexed: 05/21/2023] Open
Abstract
The objective of this study was to investigate the potential association between the use of four frequently prescribed drug classes, namely antihypertensive drugs, statins, selective serotonin reuptake inhibitors, and proton-pump inhibitors, and the likelihood of disease progression from mild cognitive impairment (MCI) to dementia using electronic health records (EHRs). We conducted a retrospective cohort study using observational EHRs from a cohort of approximately 2 million patients seen at a large, multi-specialty urban academic medical center in New York City, USA between 2008 and 2020 to automatically emulate the randomized controlled trials. For each drug class, two exposure groups were identified based on the prescription orders documented in the EHRs following their MCI diagnosis. During follow-up, we measured drug efficacy based on the incidence of dementia and estimated the average treatment effect (ATE) of various drugs. To ensure the robustness of our findings, we confirmed the ATE estimates via bootstrapping and presented associated 95% confidence intervals (CIs). Our analysis identified 14,269 MCI patients, among whom 2501 (17.5%) progressed to dementia. Using average treatment estimation and bootstrapping confirmation, we observed that drugs including rosuvastatin (ATE = - 0.0140 [- 0.0191, - 0.0088], p value < 0.001), citalopram (ATE = - 0.1128 [- 0.125, - 0.1005], p value < 0.001), escitalopram (ATE = - 0.0560 [- 0.0615, - 0.0506], p value < 0.001), and omeprazole (ATE = - 0.0201 [- 0.0299, - 0.0103], p value < 0.001) have a statistically significant association in slowing the progression from MCI to dementia. The findings from this study support the commonly prescribed drugs in altering the progression from MCI to dementia and warrant further investigation.
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Affiliation(s)
- Jie Xu
- University of Florida, Gainesville, FL, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Fei Wang
- Weill Cornell Medicine, New York, NY, USA
| | | | - Hao Zhang
- Weill Cornell Medicine, New York, NY, USA
| | | | | | | | | | | | - Yuan Luo
- Northwestern University, Chicago, IL, USA
| | | | | | | | | | | | - Yifan Peng
- Weill Cornell Medicine, New York, NY, USA
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7
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Su C, Hou Y, Zhou M, Rajendran S, Maasch JRA, Abedi Z, Zhang H, Bai Z, Cuturrufo A, Guo W, Chaudhry FF, Ghahramani G, Tang J, Cheng F, Li Y, Zhang R, DeKosky ST, Bian J, Wang F. Biomedical discovery through the integrative biomedical knowledge hub (iBKH). iScience 2023; 26:106460. [PMID: 37020958 PMCID: PMC10068563 DOI: 10.1016/j.isci.2023.106460] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/20/2022] [Accepted: 03/16/2023] [Indexed: 04/01/2023] Open
Abstract
The abundance of biomedical knowledge gained from biological experiments and clinical practices is an invaluable resource for biomedicine. The emerging biomedical knowledge graphs (BKGs) provide an efficient and effective way to manage the abundant knowledge in biomedical and life science. In this study, we created a comprehensive BKG called the integrative Biomedical Knowledge Hub (iBKH) by harmonizing and integrating information from diverse biomedical resources. To make iBKH easily accessible for biomedical research, we developed a web-based, user-friendly graphical portal that allows fast and interactive knowledge retrieval. Additionally, we also implemented an efficient and scalable graph learning pipeline for discovering novel biomedical knowledge in iBKH. As a proof of concept, we performed our iBKH-based method for computational in-silico drug repurposing for Alzheimer's disease. The iBKH is publicly available.
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Affiliation(s)
- Chang Su
- Department of Health Service Administration and Policy, College of Public Health, Temple University, Philadelphia, PA 19122, USA
| | - Yu Hou
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA
- Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA
| | - Manqi Zhou
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA
| | - Suraj Rajendran
- Tri-Institutional Computational Biology & Medicine Program, Cornell University, New York, NY 10065, USA
| | | | - Zehra Abedi
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA
| | - Haotan Zhang
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Zilong Bai
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA
| | | | - Winston Guo
- Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Fayzan F. Chaudhry
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Gregory Ghahramani
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Jian Tang
- Mila-Quebec AI Institute and HEC Montreal, Montreal, QC H2S 3H1, Canada
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Yue Li
- School of Computer Science, McGill University, Montreal, QC H3A 0C6, Canada
| | - Rui Zhang
- Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA
| | - Steven T. DeKosky
- Department of Neurology, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA
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8
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Zhang H, Zang C, Xu Z, Zhang Y, Xu J, Bian J, Morozyuk D, Khullar D, Zhang Y, Nordvig AS, Schenck EJ, Shenkman EA, Rothman RL, Block JP, Lyman K, Weiner MG, Carton TW, Wang F, Kaushal R. Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes. Nat Med 2023; 29:226-235. [PMID: 36456834 PMCID: PMC9873564 DOI: 10.1038/s41591-022-02116-3] [Citation(s) in RCA: 74] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 11/02/2022] [Indexed: 12/05/2022]
Abstract
The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated or newly incident in the period after acute SARS-CoV-2 infection. Most studies have examined these conditions individually without providing evidence on co-occurring conditions. In this study, we leveraged the electronic health record data of two large cohorts, INSIGHT and OneFlorida+, from the national Patient-Centered Clinical Research Network. We created a development cohort from INSIGHT and a validation cohort from OneFlorida+ including 20,881 and 13,724 patients, respectively, who were SARS-CoV-2 infected, and we investigated their newly incident diagnoses 30-180 days after a documented SARS-CoV-2 infection. Through machine learning analysis of over 137 symptoms and conditions, we identified four reproducible PASC subphenotypes, dominated by cardiac and renal (including 33.75% and 25.43% of the patients in the development and validation cohorts); respiratory, sleep and anxiety (32.75% and 38.48%); musculoskeletal and nervous system (23.37% and 23.35%); and digestive and respiratory system (10.14% and 12.74%) sequelae. These subphenotypes were associated with distinct patient demographics, underlying conditions before SARS-CoV-2 infection and acute infection phase severity. Our study provides insights into the heterogeneity of PASC and may inform stratified decision-making in the management of PASC conditions.
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Affiliation(s)
- Hao Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Yongkang Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Dmitry Morozyuk
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Dhruv Khullar
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Yiye Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Anna S Nordvig
- Department of Neurology, Weill Cornell Medicine, New York, NY, USA
| | - Edward J Schenck
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Elizabeth A Shenkman
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Russell L Rothman
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jason P Block
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA
| | - Kristin Lyman
- Louisiana Public Health Institute, New Orleans, LA, USA
| | - Mark G Weiner
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | | | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
| | - Rainu Kaushal
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
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9
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Wyss R, Yanover C, El-Hay T, Bennett D, Platt RW, Zullo AR, Sari G, Wen X, Ye Y, Yuan H, Gokhale M, Patorno E, Lin KJ. Machine learning for improving high-dimensional proxy confounder adjustment in healthcare database studies: an overview of the current literature. Pharmacoepidemiol Drug Saf 2022; 31:932-943. [PMID: 35729705 PMCID: PMC9541861 DOI: 10.1002/pds.5500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 06/01/2022] [Accepted: 06/05/2022] [Indexed: 11/10/2022]
Abstract
Controlling for large numbers of variables that collectively serve as 'proxies' for unmeasured factors can often improve confounding control in pharmacoepidemiologic studies utilizing administrative healthcare databases. There is a growing body of evidence showing that data-driven machine learning algorithms for high-dimensional proxy confounder adjustment can supplement investigator-specified variables to improve confounding control compared to adjustment based on investigator-specified variables alone. Consequently, there has been a recent focus on the development of data-driven methods for high-dimensional proxy confounder adjustment. In this paper, we discuss the considerations underpinning three areas for data-driven high-dimensional proxy confounder adjustment: 1) feature generation-transforming raw data into covariates (or features) to be used for proxy adjustment; 2) covariate prioritization, selection and adjustment; and 3) diagnostic assessment. We survey current approaches and recent advancements within each area, including the most widely used approach to proxy confounder adjustment in healthcare database studies (the high-dimensional propensity score or hdPS). We also discuss limitations of the hdPS and outline recent advancements that incorporate the principles of proxy adjustment with machine learning extensions to improve performance. We further discuss challenges and avenues of future development within each area. This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE). This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Richard Wyss
- Division of Pharmacoepidemioogy and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Tal El-Hay
- KI Research Institute, Kfar Malal, Israel.,IBM Research-Haifa Labs, Haifa, Israel
| | - Dimitri Bennett
- Global Evidence and Outcomes, Takeda Pharmaceutical Company Ltd., Cambridge, MA, USA
| | | | - Andrew R Zullo
- Department of Health Services, Policy, and Practice, Brown University School of Public Health and Center of Innovation in Long-Term Services and Supports, Providence Veterans Affairs Medical Center, Providence, RI, USA
| | - Grammati Sari
- Real World Evidence Strategy Lead, Visible Analytics Ltd, Oxford, UK
| | - Xuerong Wen
- Health Outcomes, Pharmacy Practice, College of Pharmacy, University of Rhode Island, Kingston, RI, USA
| | - Yizhou Ye
- Global Epidemiology, AbbVie Inc. North Chicago, IL, USA
| | - Hongbo Yuan
- Canadian Agency for Drugs and Technologies in Health, Ottawa, Canada
| | - Mugdha Gokhale
- Pharmacoepidemiology, Center for Observational and Real-world Evidence, Merck, PA, USA
| | - Elisabetta Patorno
- Division of Pharmacoepidemioogy and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kueiyu Joshua Lin
- Division of Pharmacoepidemioogy and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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10
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Zong N, Wen A, Moon S, Fu S, Wang L, Zhao Y, Yu Y, Huang M, Wang Y, Zheng G, Mielke MM, Cerhan JR, Liu H. Computational drug repurposing based on electronic health records: a scoping review. NPJ Digit Med 2022; 5:77. [PMID: 35701544 PMCID: PMC9198008 DOI: 10.1038/s41746-022-00617-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 05/19/2022] [Indexed: 11/30/2022] Open
Abstract
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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Affiliation(s)
- Nansu Zong
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.
| | - Andrew Wen
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Sungrim Moon
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Sunyang Fu
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Liwei Wang
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Yiqing Zhao
- Department of Preventive Medicine, Northwestern Medicine, Northwestern University, Chicago, IL, USA
| | - Yue Yu
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Ming Huang
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Yanshan Wang
- Department of Health Information Management, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Gang Zheng
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | | | - James R Cerhan
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Hongfang Liu
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
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11
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Lee S, Jeon S, Kim HS. A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus. Endocrinol Metab (Seoul) 2022; 37:195-207. [PMID: 35413782 PMCID: PMC9081315 DOI: 10.3803/enm.2022.1404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/21/2022] [Indexed: 11/11/2022] Open
Abstract
Drug repositioning is a strategy for identifying new applications of an existing drug that has been previously proven to be safe. Based on several examples of drug repositioning, we aimed to determine the methodologies and relevant steps associated with drug repositioning that should be pursued in the future. Reports on drug repositioning, retrieved from PubMed from January 2011 to December 2020, were classified based on an analysis of the methodology and reviewed by experts. Among various drug repositioning methods, the network-based approach was the most common (38.0%, 186/490 cases), followed by machine learning/deep learningbased (34.3%, 168/490 cases), text mining-based (7.1%, 35/490 cases), semantic-based (5.3%, 26/490 cases), and others (15.3%, 75/490 cases). Although drug repositioning offers several advantages, its implementation is curtailed by the need for prior, conclusive clinical proof. This approach requires the construction of various databases, and a deep understanding of the process underlying repositioning is quintessential. An in-depth understanding of drug repositioning could reduce the time, cost, and risks inherent to early drug development, providing reliable scientific evidence. Furthermore, regarding patient safety, drug repurposing might allow the discovery of new relationships between drugs and diseases.
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Affiliation(s)
- Suehyun Lee
- Department of Biomedical Informatics, Konyang University College of Medicine, Daejeon, Korea
- Health Care Data Science Center, Konyang University Hospital, Daejeon, Korea
| | - Seongwoo Jeon
- Health Care Data Science Center, Konyang University Hospital, Daejeon, Korea
| | - Hun-Sung Kim
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Corresponding author: Hun-Sung Kim Department of Medical Informatics, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Korea Tel: +82-2-2258-8262, Fax: +82-2-2258-8297, E-mail:
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12
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Xu Z, Su C, Xiao Y, Wang F. Artificial intelligence for COVID-19: battling the pandemic with computational intelligence. INTELLIGENT MEDICINE 2022; 2:13-29. [PMID: 34697578 PMCID: PMC8529224 DOI: 10.1016/j.imed.2021.09.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/15/2021] [Accepted: 09/29/2021] [Indexed: 12/15/2022]
Abstract
The new coronavirus disease 2019 (COVID-19) has become a global pandemic leading to over 180 million confirmed cases and nearly 4 million deaths until June 2021, according to the World Health Organization. Since the initial report in December 2019 , COVID-19 has demonstrated a high transmission rate (with an R0 > 2), a diverse set of clinical characteristics (e.g., high rate of hospital and intensive care unit admission rates, multi-organ dysfunction for critically ill patients due to hyperinflammation, thrombosis, etc.), and a tremendous burden on health care systems around the world. To understand the serious and complex diseases and develop effective control, treatment, and prevention strategies, researchers from different disciplines have been making significant efforts from different aspects including epidemiology and public health, biology and genomic medicine, as well as clinical care and patient management. In recent years, artificial intelligence (AI) has been introduced into the healthcare field to aid clinical decision-making for disease diagnosis and treatment such as detecting cancer based on medical images, and has achieved superior performance in multiple data-rich application scenarios. In the COVID-19 pandemic, AI techniques have also been used as a powerful tool to overcome the complex diseases. In this context, the goal of this study is to review existing studies on applications of AI techniques in combating the COVID-19 pandemic. Specifically, these efforts can be grouped into the fields of epidemiology, therapeutics, clinical research, social and behavioral studies and are summarized. Potential challenges, directions, and open questions are discussed accordingly, which may provide new insights into addressing the COVID-19 pandemic and would be helpful for researchers to explore more related topics in the post-pandemic era.
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Affiliation(s)
- Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York 10065, United States
| | - Chang Su
- Department of Health Service Administration and Policy, Temple University, Philadelphia 19122, United States
| | - Yunyu Xiao
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York 10065, United States
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York 10065, United States
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13
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Aronskyy I, Masoudi-Sobhanzadeh Y, Cappuccio A, Zaslavsky E. Advances in the computational landscape for repurposed drugs against COVID-19. Drug Discov Today 2021; 26:2800-2815. [PMID: 34339864 PMCID: PMC8323501 DOI: 10.1016/j.drudis.2021.07.026] [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: 03/17/2021] [Revised: 04/30/2021] [Accepted: 07/26/2021] [Indexed: 02/07/2023]
Abstract
The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.
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Affiliation(s)
- Illya Aronskyy
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yosef Masoudi-Sobhanzadeh
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Antonio Cappuccio
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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14
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Park K. The use of real-world data in drug repurposing. Transl Clin Pharmacol 2021; 29:117-124. [PMID: 34621704 PMCID: PMC8492393 DOI: 10.12793/tcp.2021.29.e18] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 09/24/2021] [Accepted: 09/24/2021] [Indexed: 02/02/2023] Open
Abstract
Drug repurposing, or repositioning, is to identify new uses for existing drugs. Significantly reducing the costs and time-to-market of a medication, drug repurposing has been an alternative tool to accelerate drug development process. On the other hand, 'real world data (RWD)' has been also increasingly used to support drug development process owing to its better representing actual pattern of drug treatment and outcome in real world. In the healthcare domain, RWD refers to data collected from sources other than traditional clinical trials; for example, in electronic health records or claims and billing data. With the enactment of the 21st Century Cures Act, which encourages the use of RWD in drug development and repurposing as well, such increasing trend in RWD use will be expedited. In this context, this review provides an overview of recent progresses in the area of drug repurposing where RWD was used by firstly introducing the increasing trend and regulatory change in the use of RWD in drug development, secondly reviewing published works using RWD in drug repurposing, classifying them in the repurposing strategy, and lastly addressing limitations and advantages of RWDs.
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Affiliation(s)
- Kyungsoo Park
- Department of Pharmacology, Yonsei University College of Medicine, Seoul 03722, Korea
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15
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Laifenfeld D, Yanover C, Ozery-Flato M, Shaham O, Rosen-Zvi M, Lev N, Goldschmidt Y, Grossman I. Emulated Clinical Trials from Longitudinal Real-World Data Efficiently Identify Candidates for Neurological Disease Modification: Examples from Parkinson's Disease. Front Pharmacol 2021; 12:631584. [PMID: 33967767 PMCID: PMC8100658 DOI: 10.3389/fphar.2021.631584] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 02/01/2021] [Indexed: 01/29/2023] Open
Abstract
Real-world healthcare data hold the potential to identify therapeutic solutions for progressive diseases by efficiently pinpointing safe and efficacious repurposing drug candidates. This approach circumvents key early clinical development challenges, particularly relevant for neurological diseases, concordant with the vision of the 21st Century Cures Act. However, to-date, these data have been utilized mainly for confirmatory purposes rather than as drug discovery engines. Here, we demonstrate the usefulness of real-world data in identifying drug repurposing candidates for disease-modifying effects, specifically candidate marketed drugs that exhibit beneficial effects on Parkinson's disease (PD) progression. We performed an observational study in cohorts of ascertained PD patients extracted from two large medical databases, Explorys SuperMart (N = 88,867) and IBM MarketScan Research Databases (N = 106,395); and applied two conceptually different, well-established causal inference methods to estimate the effect of hundreds of drugs on delaying dementia onset as a proxy for slowing PD progression. Using this approach, we identified two drugs that manifested significant beneficial effects on PD progression in both datasets: rasagiline, narrowly indicated for PD motor symptoms; and zolpidem, a psycholeptic. Each confers its effects through distinct mechanisms, which we explored via a comparison of estimated effects within the drug classification ontology. We conclude that analysis of observational healthcare data, emulating otherwise costly, large, and lengthy clinical trials, can highlight promising repurposing candidates, to be validated in prospective registration trials, beneficial against common, late-onset progressive diseases for which disease-modifying therapeutic solutions are scarce.
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Affiliation(s)
- Daphna Laifenfeld
- Formerly Global Research and Development, Teva Pharmaceutical Industries, Netanya, Israel
| | | | | | | | - Michal Rosen-Zvi
- AI for Healthcare, IBM Research ‐ Haifa, Israel
- Faculty of Medicine, the Hebrew University, Jerusalem, Israel
| | - Nirit Lev
- Formerly Global Research and Development, Teva Pharmaceutical Industries, Netanya, Israel
| | | | - Iris Grossman
- Formerly Global Research and Development, Teva Pharmaceutical Industries, Netanya, Israel
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