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Izmailova ES, Maguire RP, McCarthy TJ, Müller MLTM, Murphy P, Stephenson D. Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies. Clin Transl Sci 2023; 16:383-397. [PMID: 36382716 PMCID: PMC10014695 DOI: 10.1111/cts.13461] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/27/2022] [Accepted: 11/03/2022] [Indexed: 11/17/2022] Open
Abstract
The US Food and Drug Administration (FDA) has publicly recognized the importance of improving drug development efficiency, deeming translational biomarkers a top priority. The use of imaging biomarkers has been associated with increased rates of drug approvals. An appropriate level of validation provides a pragmatic way to choose and implement these biomarkers. Standardizing imaging modality selection, data acquisition protocols, and image analysis (in ways that are agnostic to equipment and algorithms) have been key to imaging biomarker deployment. The best known examples come from studies done via precompetitive collaboration efforts, which enable input from multiple stakeholders and data sharing. Digital health technologies (DHTs) provide an opportunity to measure meaningful aspects of patient health, including patient function, for extended periods of time outside of the hospital walls, with objective, sensor-based measures. We identified the areas where learnings from the imaging biomarker field can accelerate the adoption and widespread use of DHTs to develop novel treatments. As with imaging, technical validation parameters and performance acceptance thresholds need to be established. Approaches amenable to multiple hardware options and data processing algorithms can be enabled by sharing DHT data and by cross-validating algorithms. Data standardization and creation of shared databases will be vital. Pre-competitive consortia (public-private partnerships and professional societies that bring together all stakeholders, including patient organizations, industry, academic experts, and regulators) will advance the regulatory maturity of DHTs in clinical trials.
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Schalkamp AK, Rahman N, Monzón-Sandoval J, Sandor C. Deep phenotyping for precision medicine in Parkinson's disease. Dis Model Mech 2022; 15:dmm049376. [PMID: 35647913 PMCID: PMC9178512 DOI: 10.1242/dmm.049376] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
A major challenge in medical genomics is to understand why individuals with the same disorder have different clinical symptoms and why those who carry the same mutation may be affected by different disorders. In every complex disorder, identifying the contribution of different genetic and non-genetic risk factors is a key obstacle to understanding disease mechanisms. Genetic studies rely on precise phenotypes and are unable to uncover the genetic contributions to a disorder when phenotypes are imprecise. To address this challenge, deeply phenotyped cohorts have been developed for which detailed, fine-grained data have been collected. These cohorts help us to investigate the underlying biological pathways and risk factors to identify treatment targets, and thus to advance precision medicine. The neurodegenerative disorder Parkinson's disease has a diverse phenotypical presentation and modest heritability, and its underlying disease mechanisms are still being debated. As such, considerable efforts have been made to develop deeply phenotyped cohorts for this disorder. Here, we focus on Parkinson's disease and explore how deep phenotyping can help address the challenges raised by genetic and phenotypic heterogeneity. We also discuss recent methods for data collection and computation, as well as methodological challenges that have to be overcome.
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Affiliation(s)
| | | | | | - Cynthia Sandor
- UK Dementia Research Institute at Cardiff University,Division of Psychological Medicine and Clinical Neuroscience, Haydn Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK
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Gaonkar B, Cook K, Yoo B, Salehi B, Macyszyn L. Imaging Biomarker Development for Lower Back Pain Using Machine Learning: How Image Analysis Can Help Back Pain. Methods Mol Biol 2022; 2393:623-640. [PMID: 34837203 DOI: 10.1007/978-1-0716-1803-5_33] [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] [Indexed: 06/13/2023]
Abstract
State-of-the-art diagnosis of radiculopathy relies on "highly subjective" radiologist interpretation of magnetic resonance imaging of the lower back. Currently, the treatment of lumbar radiculopathy and associated lower back pain lacks coherence due to an absence of reliable, objective diagnostic biomarkers. Using emerging machine learning techniques, the subjectivity of interpretation may be replaced by the objectivity of automated analysis. However, training computer vision methods requires a curated database of imaging data containing anatomical delineations vetted by a team of human experts. In this chapter, we outline our efforts to develop such a database of curated imaging data alongside the required delineations. We detail the processes involved in data acquisition and subsequent annotation. Then we explain how the resulting database can be utilized to develop a machine learning-based objective imaging biomarker. Finally, we present an explanation of how we validate our machine learning-based anatomy delineation algorithms. Ultimately, we hope to allow validated machine learning models to be used to generate objective biomarkers from imaging data-for clinical use to diagnose lumbar radiculopathy and guide associated treatment plans.
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Affiliation(s)
- Bilwaj Gaonkar
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Kirstin Cook
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bryan Yoo
- Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Banafsheh Salehi
- Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Luke Macyszyn
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, USA
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Davis AM, Engkvist O, Fairclough RJ, Feierberg I, Freeman A, Iyer P. Public-Private Partnerships: Compound and Data Sharing in Drug Discovery and Development. SLAS DISCOVERY 2021; 26:604-619. [PMID: 33586501 DOI: 10.1177/2472555220982268] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Collaborative efforts between public and private entities such as academic institutions, governments, and pharmaceutical companies form an integral part of scientific research, and notable instances of such initiatives have been created within the life science community. Several examples of alliances exist with the broad goal of collaborating toward scientific advancement and improved public welfare. Such collaborations can be essential in catalyzing breaking areas of science within high-risk or global public health strategies that may have otherwise not progressed. A common term used to describe these alliances is public-private partnership (PPP). This review discusses different aspects of such partnerships in drug discovery/development and provides example applications as well as successful case studies. Specific areas that are covered include PPPs for sharing compounds at various phases of the drug discovery process-from compound collections for hit identification to sharing clinical candidates. Instances of PPPs to support better data integration and build better machine learning models are also discussed. The review also provides examples of PPPs that address the gap in knowledge or resources among involved parties and advance drug discovery, especially in disease areas with unfulfilled and/or social needs, like neurological disorders, cancer, and neglected and rare diseases.
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Affiliation(s)
- Andrew M Davis
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Rebecca J Fairclough
- Emerging Innovations Unit, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Isabella Feierberg
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Boston, USA
| | - Adrian Freeman
- Emerging Innovations Unit, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Preeti Iyer
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
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Bi XA, Wu H, Xie Y, Zhang L, Luo X, Fu Y. The exploration of Parkinson's disease: a multi-modal data analysis of resting functional magnetic resonance imaging and gene data. Brain Imaging Behav 2020; 15:1986-1996. [PMID: 32990896 DOI: 10.1007/s11682-020-00392-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/31/2020] [Indexed: 02/02/2023]
Abstract
Parkinson's disease (PD) is the most universal chronic degenerative neurological dyskinesia and an important threat to elderly health. At present, the researches of PD are mainly based on single-modal data analysis, while the fusion research of multi-modal data may provide more meaningful information in the aspect of comprehending the pathogenesis of PD. In this paper, 104 samples having resting functional magnetic resonance imaging (rfMRI) and gene data are from Parkinson's Progression Markers Initiative (PPMI) and Alzheimer's Disease Neuroimaging Initiative (ADNI) database to predict pathological brain areas and risk genes related to PD. In the experiment, Pearson correlation analysis is adopted to conduct fusion analysis from the data of genes and brain areas as multi-modal sample characteristics, and the clustering evolution random forest (CERF) method is applied to detect the discriminative genes and brain areas. The experimental results indicate that compared with several existing advanced methods, the CERF method can further improve the diagnosis of PD and healthy control, and can achieve a significant effect. More importantly, we find that there are some interesting associations between brain areas and genes in PD patients. Based on these associations, we notice that PD-related brain areas include angular gyrus, thalamus, posterior cingulate gyrus and paracentral lobule, and risk genes mainly include C6orf10, HLA-DPB1 and HLA-DOA. These discoveries have a significant contribution to the early prevention and clinical treatments of PD.
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Affiliation(s)
- Xia-An Bi
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, People's Republic of China. .,College of Information Science and Engineering, Hunan Normal University, Changsha, People's Republic of China.
| | - Hao Wu
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, People's Republic of China.,College of Information Science and Engineering, Hunan Normal University, Changsha, People's Republic of China
| | - Yiming Xie
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, People's Republic of China.,College of Information Science and Engineering, Hunan Normal University, Changsha, People's Republic of China
| | - Lixia Zhang
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, People's Republic of China.,College of Information Science and Engineering, Hunan Normal University, Changsha, People's Republic of China
| | - Xun Luo
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, People's Republic of China.,College of Information Science and Engineering, Hunan Normal University, Changsha, People's Republic of China
| | - Yu Fu
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, People's Republic of China.,College of Information Science and Engineering, Hunan Normal University, Changsha, People's Republic of China
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Li R, Wang X, Liu Y, Zhang S, Hanif O. Research status and collaboration analysis based on big data mining: an empirical study of Alzheimer's disease. TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT 2020. [DOI: 10.1080/09537325.2020.1815693] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Rongrong Li
- School of Economics and Management, China University of Petroleum (East China), Qingdao, People’s Republic of China
- School of Management & Economics, Beijing Institute of Technology, Haidian District, People’s Republic of China
| | - Xuefeng Wang
- School of Management & Economics, Beijing Institute of Technology, Haidian District, People’s Republic of China
| | - Yuqin Liu
- School of Journalism and Publication, Beijing Institue of Graphic Communication, Beijing, People’s Republic of China
| | - Shuo Zhang
- School of Management & Economics, Beijing Institute of Technology, Haidian District, People’s Republic of China
| | - Omer Hanif
- School of Management & Economics, Beijing Institute of Technology, Haidian District, People’s Republic of China
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Cummings J, Reiber C, Kumar P. The price of progress: Funding and financing Alzheimer's disease drug development. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2018; 4:330-343. [PMID: 30175227 PMCID: PMC6118094 DOI: 10.1016/j.trci.2018.04.008] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
INTRODUCTION Advancing research and treatment for Alzheimer's disease (AD) and the search for effective treatments depend on a complex financial ecosystem involving federal, state, industry, advocacy, venture capital, and philanthropy funding approaches. METHODS We conducted an expert review of the literature pertaining to funding and financing of translational research and drug development for AD. RESULTS The federal government is the largest public funder of research in AD. The National Institute on Aging, National Institute of Mental Health, National Institute of General Medical Sciences, and National Center for Advancing Translational Science all fund aspects of research in AD drug development. Non-National Institutes of Health federal funding comes from the National Science Foundation, Veterans Administration, Food and Drug Administration, and the Center for Medicare and Medicaid Services. Academic Medical Centers host much of the federally funded basic science research and are increasingly involved in drug development. Funding of the "Valley of Death" involves philanthropy and federal funding through small business programs and private equity from seed capital, angel investors, and venture capital companies. Advocacy groups fund both basic science and clinical trials. The Alzheimer Association is the advocacy organization with the largest research support portfolio relevant to AD drug development. Pharmaceutical companies are the largest supporters of biomedical research worldwide; companies are most interested in late stage de-risked drugs. Drugs progressing into phase II and III are candidates for pharmaceutical industry support through licensing, mergers and acquisitions, and co-development collaborations. DISCUSSION Together, the funding and financing entities involved in supporting AD drug development comprise a complex, interactive, dynamic financial ecosystem. Funding source interaction is largely unstructured and available funding is insufficient to meet all demands for new therapies. Novel approaches to funding such as mega-funds have been proposed and more integration of component parts would assist in accelerating drug development.
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Affiliation(s)
- Jeffrey Cummings
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
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Arnerić SP, Kern VD, Stephenson DT. Regulatory-accepted drug development tools are needed to accelerate innovative CNS disease treatments. Biochem Pharmacol 2018; 151:291-306. [PMID: 29410157 DOI: 10.1016/j.bcp.2018.01.043] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Accepted: 01/26/2018] [Indexed: 02/07/2023]
Abstract
Central Nervous System (CNS) diseases represent one of the most challenging therapeutic areas for successful drug approvals. Developing quantitative biomarkers as Drug Development Tools (DDTs) can catalyze the path to innovative treatments, and improve the chances of drug approvals. Drug development and healthcare management requires sensitive, reliable, validated, and regulatory accepted biomarkers and endpoints. This review highlights the regulatory paths and considerations for developing DDTs required to advance biomarker and endpoint use in clinical development (e.g., consensus CDISC [Clinical Data Interchange Standards Consortium] data standards, precompetitive sharing of anonymized patient-level data, and continual alignment with regulators). Summarized is the current landscape of biomarkers in a range of CNS diseases including Alzheimer disease, Parkinson Disease, Amyotrophic Lateral Sclerosis, Autism Spectrum Disorders, Depression, Huntington's disease, Multiple Sclerosis and Traumatic Brain Injury. Advancing DDTs for these devastating diseases that are both validated and qualified will require an integrated, cross-consortium approach to accelerate the delivery of innovative CNS therapeutics.
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Affiliation(s)
- Stephen P Arnerić
- Critical Path for Alzheimer's Disease, Crititcal Path Institute, United States.
| | - Volker D Kern
- Critical Path for Alzheimer's Disease, Crititcal Path Institute, United States
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Risacher SL, Anderson WH, Charil A, Castelluccio PF, Shcherbinin S, Saykin AJ, Schwarz AJ. Alzheimer disease brain atrophy subtypes are associated with cognition and rate of decline. Neurology 2017; 89:2176-2186. [PMID: 29070667 DOI: 10.1212/wnl.0000000000004670] [Citation(s) in RCA: 105] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 09/05/2017] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVE To test the hypothesis that cortical and hippocampal volumes, measured in vivo from volumetric MRI (vMRI) scans, could be used to identify variant subtypes of Alzheimer disease (AD) and to prospectively predict the rate of clinical decline. METHODS Amyloid-positive participants with AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) 1 and ADNI2 with baseline MRI scans (n = 229) and 2-year clinical follow-up (n = 100) were included. AD subtypes (hippocampal sparing [HpSpMRI], limbic predominant [LPMRI], typical AD [tADMRI]) were defined according to an algorithm analogous to one recently proposed for tau neuropathology. Relationships between baseline hippocampal volume to cortical volume ratio (HV:CTV) and clinical variables were examined by both continuous regression and categorical models. RESULTS When participants were divided categorically, the HpSpMRI group showed significantly more AD-like hypometabolism on 18F-fluorodeoxyglucose-PET (p < 0.05) and poorer baseline executive function (p < 0.001). Other baseline clinical measures did not differ across the 3 groups. Participants with HpSpMRI also showed faster subsequent clinical decline than participants with LPMRI on the Alzheimer's Disease Assessment Scale, 13-Item Subscale (ADAS-Cog13), Mini-Mental State Examination (MMSE), and Functional Assessment Questionnaire (all p < 0.05) and tADMRI on the MMSE and Clinical Dementia Rating Sum of Boxes (CDR-SB) (both p < 0.05). Finally, a larger HV:CTV was associated with poorer baseline executive function and a faster slope of decline in CDR-SB, MMSE, and ADAS-Cog13 score (p < 0.05). These associations were driven mostly by the amount of cortical rather than hippocampal atrophy. CONCLUSIONS AD subtypes with phenotypes consistent with those observed with tau neuropathology can be identified in vivo with vMRI. An increased HV:CTV ratio was predictive of faster clinical decline in participants with AD who were clinically indistinguishable at baseline except for a greater dysexecutive presentation.
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Affiliation(s)
- Shannon L Risacher
- From the Department of Radiology and Imaging Sciences (S.L.R., A.J. Saykin, A.J. Schwarz), Indiana Alzheimer Disease Center (S.L.R., A.J. Saykin), and Department of Biostatistics (P.F.C.), Indiana University School of Medicine; Eli Lilly and Company (W.H.A., A.C., S.S., A.J. Schwarz), Indianapolis; and Department of Psychological and Brain Sciences (A.J. Schwarz), Indiana University, Bloomington
| | - Wesley H Anderson
- From the Department of Radiology and Imaging Sciences (S.L.R., A.J. Saykin, A.J. Schwarz), Indiana Alzheimer Disease Center (S.L.R., A.J. Saykin), and Department of Biostatistics (P.F.C.), Indiana University School of Medicine; Eli Lilly and Company (W.H.A., A.C., S.S., A.J. Schwarz), Indianapolis; and Department of Psychological and Brain Sciences (A.J. Schwarz), Indiana University, Bloomington
| | - Arnaud Charil
- From the Department of Radiology and Imaging Sciences (S.L.R., A.J. Saykin, A.J. Schwarz), Indiana Alzheimer Disease Center (S.L.R., A.J. Saykin), and Department of Biostatistics (P.F.C.), Indiana University School of Medicine; Eli Lilly and Company (W.H.A., A.C., S.S., A.J. Schwarz), Indianapolis; and Department of Psychological and Brain Sciences (A.J. Schwarz), Indiana University, Bloomington
| | - Peter F Castelluccio
- From the Department of Radiology and Imaging Sciences (S.L.R., A.J. Saykin, A.J. Schwarz), Indiana Alzheimer Disease Center (S.L.R., A.J. Saykin), and Department of Biostatistics (P.F.C.), Indiana University School of Medicine; Eli Lilly and Company (W.H.A., A.C., S.S., A.J. Schwarz), Indianapolis; and Department of Psychological and Brain Sciences (A.J. Schwarz), Indiana University, Bloomington
| | - Sergey Shcherbinin
- From the Department of Radiology and Imaging Sciences (S.L.R., A.J. Saykin, A.J. Schwarz), Indiana Alzheimer Disease Center (S.L.R., A.J. Saykin), and Department of Biostatistics (P.F.C.), Indiana University School of Medicine; Eli Lilly and Company (W.H.A., A.C., S.S., A.J. Schwarz), Indianapolis; and Department of Psychological and Brain Sciences (A.J. Schwarz), Indiana University, Bloomington
| | - Andrew J Saykin
- From the Department of Radiology and Imaging Sciences (S.L.R., A.J. Saykin, A.J. Schwarz), Indiana Alzheimer Disease Center (S.L.R., A.J. Saykin), and Department of Biostatistics (P.F.C.), Indiana University School of Medicine; Eli Lilly and Company (W.H.A., A.C., S.S., A.J. Schwarz), Indianapolis; and Department of Psychological and Brain Sciences (A.J. Schwarz), Indiana University, Bloomington.
| | - Adam J Schwarz
- From the Department of Radiology and Imaging Sciences (S.L.R., A.J. Saykin, A.J. Schwarz), Indiana Alzheimer Disease Center (S.L.R., A.J. Saykin), and Department of Biostatistics (P.F.C.), Indiana University School of Medicine; Eli Lilly and Company (W.H.A., A.C., S.S., A.J. Schwarz), Indianapolis; and Department of Psychological and Brain Sciences (A.J. Schwarz), Indiana University, Bloomington.
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de Vrueh RLA, Crommelin DJA. Reflections on the Future of Pharmaceutical Public-Private Partnerships: From Input to Impact. Pharm Res 2017; 34:1985-1999. [PMID: 28589444 PMCID: PMC5579142 DOI: 10.1007/s11095-017-2192-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Accepted: 05/23/2017] [Indexed: 01/08/2023]
Abstract
Public Private Partnerships (PPPs) are multiple stakeholder partnerships designed to improve research efficacy. We focus on PPPs in the biomedical/pharmaceutical field, which emerged as a logical result of the open innovation model. Originally, a typical PPP was based on an academic and an industrial pillar, with governmental or other third party funding as an incentive. Over time, other players joined in, often health foundations, patient organizations, and regulatory scientists. This review discusses reasons for initiating a PPP, focusing on precompetitive research. It looks at typical expectations and challenges when starting such an endeavor, the characteristics of PPPs, and approaches to assessing the success of the concept. Finally, four case studies are presented, of PPPs differing in size, geographical spread, and research focus.
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Affiliation(s)
| | - Daan J A Crommelin
- Department of Pharmaceutics, Utrecht Institute for Pharmaceutical Sciences, UIPS, Utrecht University, Utrecht, The Netherlands.
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Neville J, Kopko S, Romero K, Corrigan B, Stafford B, LeRoy E, Broadbent S, Cisneroz M, Wilson E, Reiman E, Vanderstichele H, Arnerić SP, Stephenson D. Accelerating drug development for Alzheimer's disease through the use of data standards. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2017; 3:273-283. [PMID: 29067333 PMCID: PMC5651436 DOI: 10.1016/j.trci.2017.03.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
INTRODUCTION The exceedingly high rate of failed trials in Alzheimer's disease (AD) calls for immediate attention to improve efficiencies and learning from past, ongoing, and future trials. Accurate, highly rigorous standardized data are at the core of meaningful scientific research. Data standards allow for proper integration of clinical data sets and represent the essential foundation for regulatory endorsement of drug development tools. Such tools increase the potential for success and accuracy of trial results. METHODS The development of the Clinical Data Interchange Standards Consortium (CDISC) AD therapeutic area data standard was a comprehensive collaborative effort by CDISC and Coalition Against Major Diseases, a consortium of the Critical Path Institute. Clinical concepts for AD and mild cognitive impairment were defined and a data standards user guide was created from various sources of input, including data dictionaries used in AD clinical trials and observational studies. RESULTS A comprehensive collection of AD-specific clinical data standards consisting of clinical outcome measures, leading candidate genes, and cerebrospinal fluid and imaging biomarkers was developed. The AD version 2.0 (V2.0) Therapeutic Area User Guide was developed by diverse experts working with data scientists across multiple consortia through a comprehensive review and revision process. The AD CDISC standard is a publicly available resource to facilitate widespread use and implementation. DISCUSSION The AD CDISC V2.0 data standard serves as a platform to catalyze reproducible research, data integration, and efficiencies in clinical trials. It allows for the mapping and integration of available data and provides a foundation for future studies, data sharing, and long-term registries in AD. The availability of consensus data standards for AD has the potential to facilitate clinical trial initiation and increase sharing and aggregation of data across observational studies and among clinical trials, thereby improving our understanding of disease progression and treatment.
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Affiliation(s)
| | | | | | - Brian Corrigan
- Division of Pharmacometrics, Pfizer Global Research and Development, Groton, CT, USA
| | | | | | | | - Martin Cisneroz
- College of Medicine, The University of Arizona, Tucson, AZ, USA
| | | | - Eric Reiman
- Banner Medical institute, Arizona State University, Phoenix, AZ, USA
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimers Dement 2017; 13:e1-e85. [PMID: 28342697 DOI: 10.1016/j.jalz.2016.11.007] [Citation(s) in RCA: 170] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS We used standard searches to find publications using ADNI data. RESULTS (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Thies WH. Alzheimer's Disease Neuroimaging Initiative: A decade of progress in Alzheimer's disease. Alzheimers Dement 2016; 11:727-9. [PMID: 26194307 DOI: 10.1016/j.jalz.2015.06.1883] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Hodes RJ, Buckholtz N. Accelerating Medicines Partnership: Alzheimer’s Disease (AMP-AD) Knowledge Portal Aids Alzheimer’s Drug Discovery through Open Data Sharing. Expert Opin Ther Targets 2016; 20:389-91. [DOI: 10.1517/14728222.2016.1135132] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Richard J. Hodes
- National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Neil Buckholtz
- National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
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