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Rao SJA, Shetty NP. Structure-based screening of natural product libraries in search of potential antiviral drug-leads as first-line treatment to COVID-19 infection. Microb Pathog 2022; 165:105497. [PMID: 35337962 PMCID: PMC8938336 DOI: 10.1016/j.micpath.2022.105497] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 01/25/2022] [Accepted: 03/18/2022] [Indexed: 12/15/2022]
Abstract
The study focuses on identifying and screening natural products (NPs) based on their structural similarities with chemical drugs followed by their possible use in first-line treatment to COVID-19 infection. In the present study, the in-house natural product libraries, consisting of 26,311 structures, were screened against potential targets of SARS-CoV-2 based on their structural similarities with the prescribed chemical drugs. The comparison was based on molecular properties, 2 and 3-dimensional structural similarities, activity cliffs, and core fragments of NPs with chemical drugs. The screened NPs were evaluated for their therapeutic effects based on their predicted in-silico pharmacokinetic and pharmacodynamics properties, binding interactions with the appropriate targets, and structural stability of the bound complex using molecular dynamics simulations. The study yielded NPs with significant structural similarities to synthetic drugs currently used to treat COVID-19 infections. The study proposes the probable biological action of the selected NPs as Anti-retroviral protease inhibitors, RNA-dependent RNA polymerase inhibitors, and viral entry inhibitors.
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Affiliation(s)
- S J Aditya Rao
- Plant Cell Biotechnology Department, CSIR-Central Food Technological Research Institute, Mysore, 570020, Karnataka, India.
| | - Nandini P Shetty
- Plant Cell Biotechnology Department, CSIR-Central Food Technological Research Institute, Mysore, 570020, Karnataka, India
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2
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Dzobo K. The Role of Natural Products as Sources of Therapeutic Agents for Innovative Drug Discovery. COMPREHENSIVE PHARMACOLOGY 2022. [PMCID: PMC8016209 DOI: 10.1016/b978-0-12-820472-6.00041-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Emerging threats to human health require a concerted effort in search of both preventive and treatment strategies, placing natural products at the center of efforts to obtain new therapies and reduce disease spread and associated mortality. The therapeutic value of compounds found in plants has been known for ages, resulting in their utilization in homes and in clinics for the treatment of many ailments ranging from common headache to serious conditions such as wounds. Despite the advancement observed in the world, plant based medicines are still being used to treat many pathological conditions or are used as alternatives to modern medicines. In most cases, these natural products or plant-based medicines are used in an un-purified state as extracts. A lot of research is underway to identify and purify the active compounds responsible for the healing process. Some of the current drugs used in clinics have their origins as natural products or came from plant extracts. In addition, several synthetic analogues are natural product-based or plant-based. With the emergence of novel infectious agents such as the SARS-CoV-2 in addition to already burdensome diseases such as diabetes, cancer, tuberculosis and HIV/AIDS, there is need to come up with new drugs that can cure these conditions. Natural products offer an opportunity to discover new compounds that can be converted into drugs given their chemical structure diversity. Advances in analytical processes make drug discovery a multi-dimensional process involving computational designing and testing and eventual laboratory screening of potential drug candidates. Lead compounds will then be evaluated for safety, pharmacokinetics and efficacy. New technologies including Artificial Intelligence, better organ and tissue models such as organoids allow virtual screening, automation and high-throughput screening to be part of drug discovery. The use of bioinformatics and computation means that drug discovery can be a fast and efficient process and enable the use of natural products structures to obtain novel drugs. The removal of potential bottlenecks resulting in minimal false positive leads in drug development has enabled an efficient system of drug discovery. This review describes the biosynthesis and screening of natural products during drug discovery as well as methods used in studying natural products.
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3
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Iyamu I, Xu AXT, Gómez-Ramírez O, Ablona A, Chang HJ, Mckee G, Gilbert M. Defining Digital Public Health and the Role of Digitization, Digitalization, and Digital Transformation: Scoping Review. JMIR Public Health Surveill 2021; 7:e30399. [PMID: 34842555 PMCID: PMC8665390 DOI: 10.2196/30399] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 08/05/2021] [Accepted: 08/17/2021] [Indexed: 12/12/2022] Open
Abstract
Background The recent proliferation and application of digital technologies in public health has spurred interest in digital public health. However, as yet, there appears to be a lack of conceptual clarity and consensus on its definition. Objective In this scoping review, we seek to assess formal and informal definitions of digital public health in the literature and to understand how these definitions have been conceptualized in relation to digitization, digitalization, and digital transformation. Methods We conducted a scoping literature search in Ovid MEDLINE, Embase, Google Scholar, and 14 government and intergovernmental agency websites encompassing 6 geographic regions. Among a total of 409 full articles identified, we reviewed 11 publications that either formally defined digital public health or informally described the integration of digital technologies into public health in relation to digitization, digitalization, and digital transformation, and we conducted a thematic analysis of the identified definitions. Results Two explicit definitions of digital public health were identified, each with divergent meanings. The first definition suggested digital public health was a reimagination of public health using new ways of working, blending established public health wisdom with new digital concepts and tools. The second definition highlighted digital public health as an asset to achieve existing public health goals. In relation to public health, digitization was used to refer to the technical process of converting analog records to digital data, digitalization referred to the integration of digital technologies into public health operations, and digital transformation was used to describe a cultural shift that pervasively integrates digital technologies and reorganizes services on the basis of the health needs of the public. Conclusions The definition of digital public health remains contested in the literature. Public health researchers and practitioners need to clarify these conceptual definitions to harness opportunities to integrate digital technologies into public health in a way that maximizes their potential to improve public health outcomes. International Registered Report Identifier (IRRID) RR2-10.2196/preprints.27686
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Affiliation(s)
- Ihoghosa Iyamu
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada.,Clinical Prevention Services, British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Alice X T Xu
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Oralia Gómez-Ramírez
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada.,Clinical Prevention Services, British Columbia Centre for Disease Control, Vancouver, BC, Canada.,CIHR Canadian HIV Trials Network, Vancouver, BC, Canada
| | - Aidan Ablona
- Clinical Prevention Services, British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Hsiu-Ju Chang
- Clinical Prevention Services, British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Geoff Mckee
- Clinical Prevention Services, British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Mark Gilbert
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada.,Clinical Prevention Services, British Columbia Centre for Disease Control, Vancouver, BC, Canada
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4
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Tang C, Plasek JM, Zhang S, Xiong Y, Zhu Y, Ma J, Zhou LI, Bates DW. The intersection of big data and epidemiology for epidemiologic research: The impact of the COVID-19 pandemic. Int J Qual Health Care 2021; 33:mzab134. [PMID: 34508642 PMCID: PMC8499918 DOI: 10.1093/intqhc/mzab134] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 08/30/2021] [Accepted: 09/09/2021] [Indexed: 11/14/2022] Open
Abstract
Big data epidemiology facilitates pandemic response by providing data-driven insights by utilizing big data tools that differ from traditional methods. Aspects regarding 'garbage in, garbage out', such as insufficient data, inaccessibility of data, missing data, uncertainty in handling data and bias in analysis or common findings are addressable by combining techniques across disciplines.
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Affiliation(s)
- Chunlei Tang
- Division of General Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Clinical and Quality Analysis, Partners HealthCare System to Mass General Brigham, Boston, MA, USA
| | - Joseph M Plasek
- Division of General Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Suhua Zhang
- Department of Kidney Disease, Suzhou Kowloon Hospital, Jiangsu, China
| | - Yun Xiong
- Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China
| | - Yangyong Zhu
- Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China
| | - Jing Ma
- Harvard Medical School, Boston, MA, USA
| | - L I Zhou
- Division of General Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - David W Bates
- Division of General Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Clinical and Quality Analysis, Partners HealthCare System to Mass General Brigham, Boston, MA, USA
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5
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Rao SJA, Shetty NP. Evolutionary selectivity of amino acid is inspired from the enhanced structural stability and flexibility of the folded protein. Life Sci 2021; 281:119774. [PMID: 34197884 DOI: 10.1016/j.lfs.2021.119774] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/16/2021] [Accepted: 06/18/2021] [Indexed: 12/18/2022]
Abstract
AIM The present study attempts to decipher the site-specific amino acid alterations at certain positions experiencing preferential selectivity and their effect on proteins' stability and flexibility. The study examines the selection preferences by considering pair-wise non-bonded interaction energies of adjacent and interacting amino acids present at the interacting site, along with their evolutionary history. MATERIALS AND METHODS For the study, variations in the interacting residues of spike protein (S-Protein) receptor-binding domain (RBD) of different coronaviruses were examined. The MD simulation trajectory analysis revealed that, though all the variants studied were structurally stable at their native and bound confirmations, the RBD of 2019-nCoV/SARS-CoV-2 was found to be more flexible and more dynamic. Furthermore, a noticeable change observed in the non-bonded interaction energies of the amino acids interacting with the receptor corroborated their selection at respective positions. KEY FINDINGS The conformational changes exerted by the altered amino acids could be the reason for a broader range of interacting receptors among the selected proteins. SIGNIFICANCE The results envisage a strong indication that the residue selection at certain positions is governed by a well-orchestrated feedback mechanism, which follows increased stability and flexibility in the folded structure compared to its evolutionary predecessor.
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Affiliation(s)
- S J Aditya Rao
- Plant Cell Biotechnology Department, CSIR-Central Food Technological Research Institute, Mysore, Karnataka, India.
| | - Nandini P Shetty
- Plant Cell Biotechnology Department, CSIR-Central Food Technological Research Institute, Mysore, Karnataka, India
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6
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D’Souza G, Golub ET, Gange SJ. The Changing Science of HIV Epidemiology in the United States. Am J Epidemiol 2019; 188:2061-2068. [PMID: 31595945 PMCID: PMC7036648 DOI: 10.1093/aje/kwz211] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 08/29/2019] [Accepted: 09/19/2019] [Indexed: 12/19/2022] Open
Abstract
In 1984, a large prospective study of the natural history of human immunodeficiency virus (HIV) infection, the Multicenter AIDS Cohort Study (MACS), was established; 10 years later, the Women's Interagency HIV Study (WIHS) was launched. Motivated by the merger and redesign of these long-standing HIV cohort studies in 2019, we review ways in which HIV epidemiology in the United States has transformed over the lives of these studies and how this evolution has influenced planning for enrollment and follow-up. We highlight changes that have occurred in the 3 major domains that are central to epidemiologic science: changes to key populations at highest risk for HIV, refinements in measurement and shifts in the outcomes of interest, and a new era in the tools and approaches that epidemiologists use to synthesize evidence from measurements made on populations. By embracing foundational principles with modern methods, the epidemiologic approach of analyzing the causes and distributions of diseases in contemporaneous populations will continue to advance HIV science over the next decade.
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Affiliation(s)
- Gypsyamber D’Souza
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore MD
| | - Elizabeth T Golub
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore MD
| | - Stephen J Gange
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore MD
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7
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Nissen F, Quint JK, Morales DR, Douglas IJ. How to validate a diagnosis recorded in electronic health records. Breathe (Sheff) 2019; 15:64-68. [PMID: 30838062 PMCID: PMC6395976 DOI: 10.1183/20734735.0344-2018] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Over the last decades, the adoption of electronic health records (EHR) by health services worldwide has facilitated the construction of large population-based patient databases. These routinely generated longitudinal records have an enormous potential for epidemiological and clinical research [1–3]. EHR contain information on the health of an individual and are an electronic version of a patient's medical history. This contrasts with administrative claims data, whose main purpose is administration of reimbursement of medical services to healthcare providers. Due to the immense size of EHR, they can offer high statistical power and can often be representative of a population. Linkage between different EHR can further improve the completeness of the data. However, the primary raison d’être of most of these EHR is for clinical, administrative or audit purposes, which is a major challenge to their use for health research. Data elements that would be useful for research can therefore be wrongly classified, insufficiently specified or missing. Misclassified data can lead to systematic measurement errors. Missing data can lead to selection bias and counteract the statistical power provided by the magnitude of EHR [4]. Systematic measurement errors in electronic health record databases can lead to large inferential errors. Validation techniques can help determine the degree of these errors and therefore aid in the interpretation of findings.http://ow.ly/iHQ630np4xU
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Affiliation(s)
- Francis Nissen
- Dept of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, Dundee, UK
| | - Ian J Douglas
- Dept of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
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8
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Velupillai S, Suominen H, Liakata M, Roberts A, Shah AD, Morley K, Osborn D, Hayes J, Stewart R, Downs J, Chapman W, Dutta R. Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances. J Biomed Inform 2018; 88:11-19. [PMID: 30368002 PMCID: PMC6986921 DOI: 10.1016/j.jbi.2018.10.005] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 10/14/2018] [Accepted: 10/15/2018] [Indexed: 12/27/2022]
Abstract
The importance of incorporating Natural Language Processing (NLP) methods in clinical informatics research has been increasingly recognized over the past years, and has led to transformative advances. Typically, clinical NLP systems are developed and evaluated on word, sentence, or document level annotations that model specific attributes and features, such as document content (e.g., patient status, or report type), document section types (e.g., current medications, past medical history, or discharge summary), named entities and concepts (e.g., diagnoses, symptoms, or treatments) or semantic attributes (e.g., negation, severity, or temporality). From a clinical perspective, on the other hand, research studies are typically modelled and evaluated on a patient- or population-level, such as predicting how a patient group might respond to specific treatments or patient monitoring over time. While some NLP tasks consider predictions at the individual or group user level, these tasks still constitute a minority. Owing to the discrepancy between scientific objectives of each field, and because of differences in methodological evaluation priorities, there is no clear alignment between these evaluation approaches. Here we provide a broad summary and outline of the challenging issues involved in defining appropriate intrinsic and extrinsic evaluation methods for NLP research that is to be used for clinical outcomes research, and vice versa. A particular focus is placed on mental health research, an area still relatively understudied by the clinical NLP research community, but where NLP methods are of notable relevance. Recent advances in clinical NLP method development have been significant, but we propose more emphasis needs to be placed on rigorous evaluation for the field to advance further. To enable this, we provide actionable suggestions, including a minimal protocol that could be used when reporting clinical NLP method development and its evaluation.
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Affiliation(s)
- Sumithra Velupillai
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; School of Electrical Engineering and Computer Science, KTH, Stockholm, Sweden.
| | - Hanna Suominen
- College of Engineering and Computer Science, The Australian National University, Data61/CSIRO, University of Canberra, Australia; University of Turku, Finland.
| | - Maria Liakata
- Department of Computer Science, University of Warwick/Alan Turing Institute, UK.
| | - Angus Roberts
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK.
| | - Anoop D Shah
- Institute of Health Informatics, University College London, UK; University College London NHS Foundation Trust, London, UK.
| | - Katherine Morley
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; Melbourne School of Population and Global Health, The University of Melbourne, Australia.
| | - David Osborn
- Division of Psychiatry, University College London, UK; Camden and Islington NHS Foundation Trust, London, UK.
| | - Joseph Hayes
- Division of Psychiatry, University College London, UK; Camden and Islington NHS Foundation Trust, London, UK.
| | - Robert Stewart
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; South London and Maudsley NHS Foundation Trust, London, UK.
| | - Johnny Downs
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; South London and Maudsley NHS Foundation Trust, London, UK.
| | - Wendy Chapman
- Department of Biomedical Informatics, University of Utah, United States.
| | - Rina Dutta
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; South London and Maudsley NHS Foundation Trust, London, UK.
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9
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Dissing AS, Lakon CM, Gerds TA, Rod NH, Lund R. Measuring social integration and tie strength with smartphone and survey data. PLoS One 2018; 13:e0200678. [PMID: 30138354 PMCID: PMC6107109 DOI: 10.1371/journal.pone.0200678] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 05/23/2018] [Indexed: 01/11/2023] Open
Abstract
Recordings of smartphone use for contacts are increasingly being used as alternative or supplementary measurement methods for social interactions and social relations in the health sciences. Less work has been done to understand how these measures compare with widely used survey-based information. Using data from the Copenhagen Network Study, we investigated whether derived survey and smartphone measures on two widely studied concepts; Social integration and Tie strength were associated. The study population included 737 college students (mean age 21.6 years, Standard deviation: 2.6), who were followed with surveys and continuous recordings of smartphone usage over a one-month period. We derived self-reported and smartphone measures of social integration (social role diversity, social network size), and tie strength (contact frequency, duration and tie reciprocity). Logistic regression models were used to assess the associations between smartphone derived and self-reported measures adjusting for gender, age and co-habitation. Larger call and text message networks were associated with having a high self-reported social role diversity, and a high self-reported social contact frequency was likewise associated with having both frequent call and text message interactions, longer call duration and a higher level of reciprocity in call and text message communication. Self-reported aspects of social relations and smartphone measures of social interactions have considerable overlap supporting a measurement of similar underlying concepts.
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Affiliation(s)
- Agnete S. Dissing
- Section of Social Medicine, Department of Public Health, University of Copenhagen, Copenhagen K, Denmark
- * E-mail:
| | - Cynthia M. Lakon
- Program in Public Health, University of California Irvine, Irvine, CA, United States of America
| | - Thomas A. Gerds
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen K, Denmark
| | - Naja H. Rod
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen K, Denmark
- Copenhagen Stress Research Centre, Copenhagen, Denmark
| | - Rikke Lund
- Section of Social Medicine, Department of Public Health, University of Copenhagen, Copenhagen K, Denmark
- Center for Healthy Aging, Faculty of Health Sciences, University of Copenhagen, Copenhagen N, Denmark
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10
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Thomford NE, Senthebane DA, Rowe A, Munro D, Seele P, Maroyi A, Dzobo K. Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery. Int J Mol Sci 2018; 19:E1578. [PMID: 29799486 PMCID: PMC6032166 DOI: 10.3390/ijms19061578] [Citation(s) in RCA: 558] [Impact Index Per Article: 93.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 05/16/2018] [Accepted: 05/18/2018] [Indexed: 12/12/2022] Open
Abstract
The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.
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Affiliation(s)
- Nicholas Ekow Thomford
- Pharmacogenomics and Drug Metabolism Group, Division of Human Genetics, Department of Pathology and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Anzio Road, Observatory, Cape Town 7925, South Africa.
- School of Medical Sciences, University of Cape Coast, PMB, Cape Coast, Ghana.
| | - Dimakatso Alice Senthebane
- International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town Component, Wernher and Beit Building (South), University of Cape Town Medical Campus, Anzio Road, Observatory, Cape Town 7925, South Africa.
- Division of Medical Biochemistry and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Anzio Road, Observatory, Cape Town 7925, South Africa.
| | - Arielle Rowe
- International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town Component, Wernher and Beit Building (South), University of Cape Town Medical Campus, Anzio Road, Observatory, Cape Town 7925, South Africa.
| | - Daniella Munro
- Pharmacogenomics and Drug Metabolism Group, Division of Human Genetics, Department of Pathology and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Anzio Road, Observatory, Cape Town 7925, South Africa.
| | - Palesa Seele
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Anzio Road, Observatory, Cape Town 7925, South Africa.
| | - Alfred Maroyi
- Department of Botany, University of Fort Hare, Private Bag, Alice X1314, South Africa.
| | - Kevin Dzobo
- International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town Component, Wernher and Beit Building (South), University of Cape Town Medical Campus, Anzio Road, Observatory, Cape Town 7925, South Africa.
- Division of Medical Biochemistry and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Anzio Road, Observatory, Cape Town 7925, South Africa.
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Abstract
PURPOSE OF REVIEW Cardiovascular disease is a leading cause of morbidity and mortality worldwide and is the focus of extensive biomedical research. Large genetic consortia combining data from many traditional prospective cohort and ascertained case-control study designs have facilitated the discovery of genetic associations for a variety of cardiovascular diseases including diabetes, coronary artery disease, and hypertension. Biobank-based genetic studies offer an alternative whereby large populations are genotyped and linked to electronic health records. RECENT FINDINGS Biobank sample sizes worldwide have surpassed even the largest genetic consortia and have yielded key insights into the genetic determinants of both common and rare cardiovascular phenotypes. Herein, we provide an overview of the largest genomic biobanks and discuss the relevant advantages and challenges inherent to the biobank model of cohort generation and genomic study design.
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12
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Bui AAT, Van Horn JD. Envisioning the future of 'big data' biomedicine. J Biomed Inform 2017; 69:115-117. [PMID: 28366789 PMCID: PMC5613673 DOI: 10.1016/j.jbi.2017.03.017] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 03/17/2017] [Accepted: 03/29/2017] [Indexed: 01/23/2023]
Abstract
Through the increasing availability of more efficient data collection procedures, biomedical scientists are now confronting ever larger sets of data, often finding themselves struggling to process and interpret what they have gathered. This, while still more data continues to accumulate. This torrent of biomedical information necessitates creative thinking about how the data are being generated, how they might be best managed, analyzed, and eventually how they can be transformed into further scientific understanding for improving patient care. Recognizing this as a major challenge, the National Institutes of Health (NIH) has spearheaded the "Big Data to Knowledge" (BD2K) program - the agency's most ambitious biomedical informatics effort ever undertaken to date. In this commentary, we describe how the NIH has taken on "big data" science head-on, how a consortium of leading research centers are developing the means for handling large-scale data, and how such activities are being marshalled for the training of a new generation of biomedical data scientists. All in all, the NIH BD2K program seeks to position data science at the heart of 21st Century biomedical research.
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Affiliation(s)
- Alex A T Bui
- BD2K Centers Coordinating Center (BD2K CCC), University of California, Los Angeles, Los Angeles, CA, USA. http://www.bd2kccc.org
| | - John Darrell Van Horn
- BD2K Training Coordinating Center (BD2K TCC), University of Southern California, Los Angeles, CA, USA. http://www.bigdatau.org
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13
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Ehrenstein V, Nielsen H, Pedersen AB, Johnsen SP, Pedersen L. Clinical epidemiology in the era of big data: new opportunities, familiar challenges. Clin Epidemiol 2017; 9:245-250. [PMID: 28490904 PMCID: PMC5413488 DOI: 10.2147/clep.s129779] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Routinely recorded health data have evolved from mere by-products of health care delivery or billing into a powerful research tool for studying and improving patient care through clinical epidemiologic research. Big data in the context of epidemiologic research means large interlinkable data sets within a single country or networks of multinational databases. Several Nordic, European, and other multinational collaborations are now well established. Advantages of big data for clinical epidemiology include improved precision of estimates, which is especially important for reassuring (“null”) findings; ability to conduct meaningful analyses in subgroup of patients; and rapid detection of safety signals. Big data will also provide new possibilities for research by enabling access to linked information from biobanks, electronic medical records, patient-reported outcome measures, automatic and semiautomatic electronic monitoring devices, and social media. The sheer amount of data, however, does not eliminate and may even amplify systematic error. Therefore, methodologies addressing systematic error, clinical knowledge, and underlying hypotheses are more important than ever to ensure that the signal is discernable behind the noise.
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Affiliation(s)
- Vera Ehrenstein
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus N, Denmark
| | - Henrik Nielsen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus N, Denmark
| | - Alma B Pedersen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus N, Denmark
| | - Søren P Johnsen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus N, Denmark
| | - Lars Pedersen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus N, Denmark
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14
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Zhang X, Pérez-Stable EJ, Bourne PE, Peprah E, Duru OK, Breen N, Berrigan D, Wood F, Jackson JS, Wong DWS, Denny J. Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century. Ethn Dis 2017; 27:95-106. [PMID: 28439179 DOI: 10.18865/ed.27.2.95] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health disparities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them.
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Affiliation(s)
- Xinzhi Zhang
- Office of the Director, National Institute on Minority Health and Health Disparities, National Institutes of Health (NIH)
| | - Eliseo J Pérez-Stable
- Office of the Director, National Institute on Minority Health and Health Disparities, National Institutes of Health (NIH)
| | | | | | | | - Nancy Breen
- Office of the Director, National Institute on Minority Health and Health Disparities, National Institutes of Health (NIH)
| | | | | | - James S Jackson
- College of Literature, Science and the Arts, University of Michigan
| | - David W S Wong
- Department of Geography and GeoInformation Science, College of Science, George Mason University
| | - Joshua Denny
- Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center
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15
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Reade S, Spencer K, Sergeant JC, Sperrin M, Schultz DM, Ainsworth J, Lakshminarayana R, Hellman B, James B, McBeth J, Sanders C, Dixon WG. Cloudy with a Chance of Pain: Engagement and Subsequent Attrition of Daily Data Entry in a Smartphone Pilot Study Tracking Weather, Disease Severity, and Physical Activity in Patients With Rheumatoid Arthritis. JMIR Mhealth Uhealth 2017; 5:e37. [PMID: 28341616 PMCID: PMC5384994 DOI: 10.2196/mhealth.6496] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 11/02/2016] [Accepted: 11/23/2016] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The increasing ownership of smartphones provides major opportunities for epidemiological research through self-reported and passively collected data. OBJECTIVE This pilot study aimed to codesign a smartphone app to assess associations between weather and joint pain in patients with rheumatoid arthritis (RA) and to study the success of daily self-reported data entry over a 60-day period and the enablers of and barriers to data collection. METHODS A patient and public involvement group (n=5) and 2 focus groups of patients with RA (n=9) supported the codesign of the app collecting self-reported symptoms. A separate "capture app" was designed to collect global positioning system (GPS) and continuous raw accelerometer data, with the GPS-linking providing local weather data. A total of 20 patients with RA were then recruited to collect daily data for 60 days, with entry and exit interviews. Of these, 17 were loaned an Android smartphone, whereas 3 used their own Android smartphones. RESULTS Of the 20 patients, 6 (30%) withdrew from the study: 4 because of technical challenges and 2 for health reasons. The mean completion of daily entries was 68% over 2 months. Patients entered data at least five times per week 65% of the time. Reasons for successful engagement included a simple graphical user interface, automated reminders, visualization of data, and eagerness to contribute to this easily understood research question. The main barrier to continuing engagement was impaired battery life due to the accelerometer data capture app. For some, successful engagement required ongoing support in using the smartphones. CONCLUSIONS This successful pilot study has demonstrated that daily data collection using smartphones for health research is feasible and achievable with high levels of ongoing engagement over 2 months. This result opens important opportunities for large-scale longitudinal epidemiological research.
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Affiliation(s)
- Samuel Reade
- Manchester Medical School, University of Manchester, Manchester, United Kingdom.,Manchester Academic Health Science Centre, Arthritis Research UK Centre for Epidemiology, University of Manchester, Manchester, United Kingdom
| | - Karen Spencer
- Manchester Academic Health Science Centre, Arthritis Research UK Centre for Epidemiology, University of Manchester, Manchester, United Kingdom
| | - Jamie C Sergeant
- Manchester Academic Health Science Centre, Arthritis Research UK Centre for Epidemiology, University of Manchester, Manchester, United Kingdom.,National Institute of Health Research, Manchester Musculoskeletal Biomedical Research Unit, Central Manchester NHS Foundation Trust, Manchester, United Kingdom
| | - Matthew Sperrin
- The Farr Institute @ Health eResearch Centre, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - David M Schultz
- School of Earth, Atmospheric & Environmental Sciences, The University of Manchester, United Kingdom
| | - John Ainsworth
- National Institute of Health Research, Manchester Musculoskeletal Biomedical Research Unit, Central Manchester NHS Foundation Trust, Manchester, United Kingdom
| | | | | | - Ben James
- uMotif Limited, London, United Kingdom
| | - John McBeth
- Manchester Academic Health Science Centre, Arthritis Research UK Centre for Epidemiology, University of Manchester, Manchester, United Kingdom
| | - Caroline Sanders
- Manchester Academic Health Science Centre, Centre for Primary Care, The University of Manchester, Manchester, United Kingdom
| | - William G Dixon
- Manchester Academic Health Science Centre, Arthritis Research UK Centre for Epidemiology, University of Manchester, Manchester, United Kingdom.,The Farr Institute @ Health eResearch Centre, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.,Salford Royal NHS Foundation Trust, Rheumatology Department, Salford, United Kingdom
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16
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Lynch SM, Moore JH. A call for biological data mining approaches in epidemiology. BioData Min 2016; 9:1. [PMID: 26734074 PMCID: PMC4700596 DOI: 10.1186/s13040-015-0079-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Accepted: 12/29/2015] [Indexed: 12/28/2022] Open
Affiliation(s)
- Shannon M Lynch
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA 19111 USA
| | - Jason H Moore
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104-6021 USA
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17
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O'Leary DP, Zaheer A, Redmond HP, Corrigan MA. Integration of advances in social media and mHealth technology are pivotal to successful cancer prevention and control. Mhealth 2016; 2:38. [PMID: 28293611 PMCID: PMC5344161 DOI: 10.21037/mhealth.2016.09.02] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 09/22/2016] [Indexed: 01/09/2023] Open
Abstract
The successful prevention and treatment of cancer is dependent upon efficient and reliable communication between healthcare workers and patients. Advances in social media and mHealth platforms have provided new ways in which to enhance the sharing of cancer related information. Other benefits of embracing this technology include utilising its analytic capabilities which can process the vast quantity of information generated from genome exploration in a highly efficient manner. The aim of this review is to provide an overview of the rapidly evolving areas through which digital engagement is proving useful in the prevention and control of cancer.
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Affiliation(s)
- D Peter O'Leary
- Cork Breast Research Centre, Cork University Hospital, Wilton, Cork, Ireland
| | - Amir Zaheer
- Cork Breast Research Centre, Cork University Hospital, Wilton, Cork, Ireland
| | - H Paul Redmond
- Cork Breast Research Centre, Cork University Hospital, Wilton, Cork, Ireland
| | - Mark A Corrigan
- Cork Breast Research Centre, Cork University Hospital, Wilton, Cork, Ireland
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