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Bagg MK, Hicks AJ, Hellewell SC, Ponsford JL, Lannin NA, O'Brien TJ, Cameron PA, Cooper DJ, Rushworth N, Gabbe BJ, Fitzgerald M. The Australian Traumatic Brain Injury Initiative: Statement of Working Principles and Rapid Review of Methods to Define Data Dictionaries for Neurological Conditions. Neurotrauma Rep 2024; 5:424-447. [PMID: 38660461 PMCID: PMC11040195 DOI: 10.1089/neur.2023.0116] [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] [Indexed: 04/26/2024] Open
Abstract
The Australian Traumatic Brain Injury Initiative (AUS-TBI) aims to develop a health informatics approach to collect data predictive of outcomes for persons with moderate-severe TBI across Australia. Central to this approach is a data dictionary; however, no systematic reviews of methods to define and develop data dictionaries exist to-date. This rapid systematic review aimed to identify and characterize methods for designing data dictionaries to collect outcomes or variables in persons with neurological conditions. Database searches were conducted from inception through October 2021. Records were screened in two stages against set criteria to identify methods to define data dictionaries for neurological conditions (International Classification of Diseases, 11th Revision: 08, 22, and 23). Standardized data were extracted. Processes were checked at each stage by independent review of a random 25% of records. Consensus was reached through discussion where necessary. Thirty-nine initiatives were identified across 29 neurological conditions. No single established or recommended method for defining a data dictionary was identified. Nine initiatives conducted systematic reviews to collate information before implementing a consensus process. Thirty-seven initiatives consulted with end-users. Methods of consultation were "roundtable" discussion (n = 30); with facilitation (n = 16); that was iterative (n = 27); and frequently conducted in-person (n = 27). Researcher stakeholders were involved in all initiatives and clinicians in 25. Importantly, only six initiatives involved persons with lived experience of TBI and four involved carers. Methods for defining data dictionaries were variable and reporting is sparse. Our findings are instructive for AUS-TBI and can be used to further development of methods for defining data dictionaries.
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Affiliation(s)
- Matthew K. Bagg
- Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Bentley, Western Australia, Australia
- Perron Institute for Neurological and Translational Science, Nedlands, Western Australia, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, New South Wales, Australia
- School of Health Sciences, University of Notre Dame Australia, Fremantle, Western Australia, Australia
| | - Amelia J. Hicks
- School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash-Epworth Rehabilitation Research Centre, Epworth Healthcare, Melbourne, Victoria, Australia
| | - Sarah C. Hellewell
- Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Bentley, Western Australia, Australia
- Perron Institute for Neurological and Translational Science, Nedlands, Western Australia, Australia
| | - Jennie L. Ponsford
- School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash-Epworth Rehabilitation Research Centre, Epworth Healthcare, Melbourne, Victoria, Australia
| | - Natasha A. Lannin
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Alfred Health, Melbourne, Victoria, Australia
| | - Terence J. O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Peter A. Cameron
- National Trauma Research Institute, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Emergency and Trauma Centre, The Alfred Hospital, Melbourne, Victoria, Australia
| | - D. Jamie Cooper
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Intensive Care and Hyperbaric Medicine, The Alfred Hospital, Melbourne, Victoria, Australia
| | - Nick Rushworth
- Brain Injury Australia, Sydney, New South Wales, Australia
| | - Belinda J. Gabbe
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Health Data Research UK, Swansea University Medical School, Swansea University, Singleton Park, United Kingdom
| | - Melinda Fitzgerald
- Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Bentley, Western Australia, Australia
- Perron Institute for Neurological and Translational Science, Nedlands, Western Australia, Australia
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Carmichael O. The Role of fMRI in Drug Development: An Update. ADVANCES IN NEUROBIOLOGY 2023; 30:299-333. [PMID: 36928856 DOI: 10.1007/978-3-031-21054-9_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Functional magnetic resonance imaging (fMRI) of the brain is a technology that holds great potential for increasing the efficiency of drug development for the central nervous system (CNS). In preclinical studies and both early- and late-phase human trials, fMRI has the potential to improve cross-species translation of drug effects, help to de-risk compounds early in development, and contribute to the portfolio of evidence for a compound's efficacy and mechanism of action. However, to date, the utilization of fMRI in the CNS drug development process has been limited. The purpose of this chapter is to explore this mismatch between potential and utilization. This chapter provides introductory material related to fMRI and drug development, describes what is required of fMRI measurements for them to be useful in a drug development setting, lists current capabilities of fMRI in this setting and challenges faced in its utilization, and ends with directions for future development of capabilities in this arena. This chapter is the 5-year update of material from a previously published workshop summary (Carmichael et al., Drug DiscovToday 23(2):333-348, 2018).
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Affiliation(s)
- Owen Carmichael
- Pennington Biomedical Research Center, Baton Rouge, LA, USA.
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Na R, Bae JB, Jung SH, Kim KW. Clinical Data Interchange Standards in Clinical Trials on Alzheimer's Disease. Psychiatry Investig 2022; 19:814-823. [PMID: 36327961 PMCID: PMC9633174 DOI: 10.30773/pi.2022.0149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/04/2022] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE The Clinical Data Interchange Standards Consortium (CDISC) proposed outcome measures for clinical trials on Alzheimer's disease (AD) in the Therapeutic Area User Guide for AD (TAUG-AD). To investigate how well the clinical trials on AD registered in the ClinicalTrials.gov complied with the recommendations on outcome measures by the CDISC. METHODS We compared the outcome measures proposed in the TAUG-AD version 2.0.1 with those employed in the protocols of clinical trials on AD registered in ClinicalTrials.gov. RESULTS We analyzed 101 outcome measures from 305 protocols. The TAUG-AD listed ten scales for outcome measures of clinical trials on AD. The scales for cognition, activities of daily living, behavioral and psychological symptoms of dementia, and global severity listed in TAUG-AD were most frequently employed in the clinical trials on AD. However, TAUG-AD did not include any scale on quality of life. Also, several scales such as Montreal Cognitive Assessment, Alzheimer's Disease Cooperative Study-Activities of Daily Living, and Cohen- Mansfield Agitation Inventory not listed in the TAUG-AD were commonly employed in the clinical trials on AD and changed over time. CONCLUSION To properly standardize the data from clinical trials on AD, the gap between the TAUG-AD and the measures employed in real-world clinical trials should be filled.
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Affiliation(s)
- Riyoung Na
- Republic of Korea National Institute of Dementia, Seoul, Republic of Korea
| | - Jong Bin Bae
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Sue Hyun Jung
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Ki Woong Kim
- Republic of Korea National Institute of Dementia, Seoul, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.,Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Brain and Cognitive Science, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
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Salimi Y, Domingo-Fernández D, Bobis-Álvarez C, Hofmann-Apitius M, Birkenbihl C. ADataViewer: exploring semantically harmonized Alzheimer's disease cohort datasets. Alzheimers Res Ther 2022; 14:69. [PMID: 35598021 PMCID: PMC9123725 DOI: 10.1186/s13195-022-01009-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 04/27/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Currently, Alzheimer's disease (AD) cohort datasets are difficult to find and lack across-cohort interoperability, and the actual content of publicly available datasets often only becomes clear to third-party researchers once data access has been granted. These aspects severely hinder the advancement of AD research through emerging data-driven approaches such as machine learning and artificial intelligence and bias current data-driven findings towards the few commonly used, well-explored AD cohorts. To achieve robust and generalizable results, validation across multiple datasets is crucial. METHODS We accessed and systematically investigated the content of 20 major AD cohort datasets at the data level. Both, a medical professional and a data specialist, manually curated and semantically harmonized the acquired datasets. Finally, we developed a platform that displays vital information about the available datasets. RESULTS Here, we present ADataViewer, an interactive platform that facilitates the exploration of 20 cohort datasets with respect to longitudinal follow-up, demographics, ethnoracial diversity, measured modalities, and statistical properties of individual variables. It allows researchers to quickly identify AD cohorts that meet user-specified requirements for discovery and validation studies regarding available variables, sample sizes, and longitudinal follow-up. Additionally, we publish the underlying variable mapping catalog that harmonizes 1196 unique variables across the 20 cohorts and paves the way for interoperable AD datasets. CONCLUSIONS In conclusion, ADataViewer facilitates fast, robust data-driven research by transparently displaying cohort dataset content and supporting researchers in selecting datasets that are suited for their envisioned study. The platform is available at https://adata.scai.fraunhofer.de/ .
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Affiliation(s)
- Yasamin Salimi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany.
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany.
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany
| | - Carlos Bobis-Álvarez
- University Hospital Ntra. Sra. de Candelaria, Santa Cruz de Tenerife, 38010, Spain
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany
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Xiaojin L, Licong C, Fei W, Schulz PE, Zhang GQ. Identifying Sleep-Related Factors Associated with Cognitive Function in a Hispanics/Latinos Cohort: A Dual Random Forest Approach. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2021:744-753. [PMID: 35308908 PMCID: PMC8861662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Disordered sleep is associated with poor cognitive function and cognitive decline. However, little is known regarding the association of sleep-related factors with cognitive function in underrepresented cohorts such as the Hispanic/Latino population. Leveraging the National Sleep Research Resource, one of the most comprehensive collections of sleep studies, we identified a Hispanic/Latino cohort of 1,031 lower cognitive function cases and 2,062 normal controls. We developed a novel dual random forest (DRF) approach to discriminate cases against controls for estimating the potential impact of sleep-related variables related to the decline of cognitive function. Several important sleep-related factors were identified which may be associated with cognitive function in the Hispanics/Latinos cohort, such as heart rate, sleep duration, trouble falling asleep, and apnea/hypopnea index, which are consistent with existing research findings. Our DRF approach is effective in validating the association between disordered sleep and cognitive decline in this unique minority population.
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Affiliation(s)
- Li Xiaojin
- The University of Texas Health Science Center at Houston, Houston, TX 77030
| | - Cui Licong
- The University of Texas Health Science Center at Houston, Houston, TX 77030
| | - Wang Fei
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065
| | - Paul E Schulz
- The University of Texas Health Science Center at Houston, Houston, TX 77030
| | - Guo-Qiang Zhang
- The University of Texas Health Science Center at Houston, Houston, TX 77030
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Birkenbihl C, Salimi Y, Domingo‐Fernándéz D, Lovestone S, Fröhlich H, Hofmann‐Apitius M. Evaluating the Alzheimer's disease data landscape. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2020; 6:e12102. [PMID: 33344750 PMCID: PMC7744022 DOI: 10.1002/trc2.12102] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 09/21/2020] [Indexed: 01/08/2023]
Abstract
INTRODUCTION Numerous studies have collected Alzheimer's disease (AD) cohort data sets. To achieve reproducible, robust results in data-driven approaches, an evaluation of the present data landscape is vital. METHODS Previous efforts relied exclusively on metadata and literature. Here, we evaluate the data landscape by directly investigating nine patient-level data sets generated in major clinical cohort studies. RESULTS The investigated cohorts differ in key characteristics, such as demographics and distributions of AD biomarkers. Analyzing the ethnoracial diversity revealed a strong bias toward White/Caucasian individuals. We described and compared the measured data modalities. Finally, the available longitudinal data for important AD biomarkers was evaluated. All results are explorable through our web application ADataViewer (https://adata.scai.fraunhofer.de). DISCUSSION Our evaluation exposed critical limitations in the AD data landscape that impede comparative approaches across multiple data sets. Comparison of our results to those gained by metadata-based approaches highlights that thorough investigation of real patient-level data is imperative to assess a data landscape.
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Affiliation(s)
- Colin Birkenbihl
- Department of BioinformaticsFraunhofer Institute for Algorithms and Scientific Computing (SCAI)Sankt AugustinGermany
- Bioinformatics GroupRheinische Friedrich‐Wilhelms‐Universität BonnBonnGermany
| | - Yasamin Salimi
- Department of BioinformaticsFraunhofer Institute for Algorithms and Scientific Computing (SCAI)Sankt AugustinGermany
- Bioinformatics GroupRheinische Friedrich‐Wilhelms‐Universität BonnBonnGermany
| | - Daniel Domingo‐Fernándéz
- Department of BioinformaticsFraunhofer Institute for Algorithms and Scientific Computing (SCAI)Sankt AugustinGermany
- Bioinformatics GroupRheinische Friedrich‐Wilhelms‐Universität BonnBonnGermany
| | | | | | - Holger Fröhlich
- Department of BioinformaticsFraunhofer Institute for Algorithms and Scientific Computing (SCAI)Sankt AugustinGermany
- Bioinformatics GroupRheinische Friedrich‐Wilhelms‐Universität BonnBonnGermany
| | - Martin Hofmann‐Apitius
- Department of BioinformaticsFraunhofer Institute for Algorithms and Scientific Computing (SCAI)Sankt AugustinGermany
- Bioinformatics GroupRheinische Friedrich‐Wilhelms‐Universität BonnBonnGermany
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Mullin AP, Corey D, Turner EC, Liwski R, Olson D, Burton J, Sivakumaran S, Hudson LD, Romero K, Stephenson DT, Larkindale J. Standardized Data Structures in Rare Diseases: CDISC User Guides for Duchenne Muscular Dystrophy and Huntington's Disease. Clin Transl Sci 2020; 14:214-221. [PMID: 32702147 PMCID: PMC7877853 DOI: 10.1111/cts.12845] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 06/14/2020] [Indexed: 12/13/2022] Open
Abstract
Interest in drug development for rare diseases has expanded dramatically since the Orphan Drug Act was passed in 1983, with 40% of new drug approvals in 2019 targeting orphan indications. However, limited quantitative understanding of natural history and disease progression hinders progress and increases the risks associated with rare disease drug development. Use of international data standards can assist in data harmonization and enable data exchange, integration into larger datasets, and a quantitative understanding of disease natural history. The US Food and Drug Administration (FDA) requires the use of Clinical Data Interchange Consortium (CDISC) Standards in new drug submissions to help the agency efficiently and effectively receive, process, review, and archive submissions, as well as to help integrate data to answer research questions. Such databases have been at the core of biomarker qualification efforts and fit‐for‐purpose models endorsed by the regulators. We describe the development of CDISC therapeutic area user guides for Duchenne muscular dystrophy and Huntington’s disease through Critical Path Institute consortia. These guides describe formalized data structures and controlled terminology to map and integrate data from different sources. This will result in increased standardization of data collection and allow integration and comparison of data from multiple studies. Integration of multiple data sets enables a quantitative understanding of disease progression, which can help overcome common challenges in clinical trial design in these and other rare diseases. Ultimately, clinical data standardization will lead to a faster path to regulatory approval of urgently needed new therapies for patients.
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Affiliation(s)
| | - Diane Corey
- Critical Path Institute, Tucson, Arizona, USA
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Golriz Khatami S, Robinson C, Birkenbihl C, Domingo-Fernández D, Hoyt CT, Hofmann-Apitius M. Challenges of Integrative Disease Modeling in Alzheimer's Disease. Front Mol Biosci 2020; 6:158. [PMID: 31993440 PMCID: PMC6971060 DOI: 10.3389/fmolb.2019.00158] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 12/18/2019] [Indexed: 12/15/2022] Open
Abstract
Dementia-related diseases like Alzheimer's Disease (AD) have a tremendous social and economic cost. A deeper understanding of its underlying pathophysiologies may provide an opportunity for earlier detection and therapeutic intervention. Previous approaches for characterizing AD were targeted at single aspects of the disease. Yet, due to the complex nature of AD, the success of these approaches was limited. However, in recent years, advancements in integrative disease modeling, built on a wide range of AD biomarkers, have taken a global view on the disease, facilitating more comprehensive analysis and interpretation. Integrative AD models can be sorted in two primary types, namely hypothetical models and data-driven models. The latter group split into two subgroups: (i) Models that use traditional statistical methods such as linear models, (ii) Models that take advantage of more advanced artificial intelligence approaches such as machine learning. While many integrative AD models have been published over the last decade, their impact on clinical practice is limited. There exist major challenges in the course of integrative AD modeling, namely data missingness and censoring, imprecise human-involved priori knowledge, model reproducibility, dataset interoperability, dataset integration, and model interpretability. In this review, we highlight recent advancements and future possibilities of integrative modeling in the field of AD research, showcase and discuss the limitations and challenges involved, and finally, propose avenues to address several of these challenges.
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Affiliation(s)
- Sepehr Golriz Khatami
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Christine Robinson
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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Boxer AL, Gold M, Feldman H, Boeve BF, Dickinson SLJ, Fillit H, Ho C, Paul R, Pearlman R, Sutherland M, Verma A, Arneric SP, Alexander BM, Dickerson BC, Dorsey ER, Grossman M, Huey ED, Irizarry MC, Marks WJ, Masellis M, McFarland F, Niehoff D, Onyike CU, Paganoni S, Panzara MA, Rockwood K, Rohrer JD, Rosen H, Schuck RN, Soares HD, Tatton N. New directions in clinical trials for frontotemporal lobar degeneration: Methods and outcome measures. Alzheimers Dement 2020; 16:131-143. [PMID: 31668596 PMCID: PMC6949386 DOI: 10.1016/j.jalz.2019.06.4956] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Frontotemporal lobar degeneration (FTLD) is the most common form of dementia for those under 60 years of age. Increasing numbers of therapeutics targeting FTLD syndromes are being developed. METHODS In March 2018, the Association for Frontotemporal Degeneration convened the Frontotemporal Degeneration Study Group meeting in Washington, DC, to discuss advances in the clinical science of FTLD. RESULTS Challenges exist for conducting clinical trials in FTLD. Two of the greatest challenges are (1) the heterogeneity of FTLD syndromes leading to difficulties in efficiently measuring treatment effects and (2) the rarity of FTLD disorders leading to recruitment challenges. DISCUSSION New personalized endpoints that are clinically meaningful to individuals and their families should be developed. Personalized approaches to analyzing MRI data, development of new fluid biomarkers and wearable technologies will help to improve the power to detect treatment effects in FTLD clinical trials and enable new, clinical trial designs, possibly leveraged from the experience of oncology trials. A computational visualization and analysis platform that can support novel analyses of combined clinical, genetic, imaging, biomarker data with other novel modalities will be critical to the success of these endeavors.
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Affiliation(s)
- Adam L. Boxer
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA
| | | | - Howard Feldman
- Department of Neurosciences, University of California San Diego, San Diego, CA
| | | | | | | | - Carole Ho
- Denali Therapeutics, San Francisco, CA
| | | | | | | | | | | | | | | | - Earl Ray Dorsey
- Center for Health and Technology, University of Rochester, Rochester, NY
| | - Murray Grossman
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
| | - Edward D. Huey
- Departments of Psychiatry and Neurology, Columbia University, NY
| | | | - William J. Marks
- Clinical Neurology, Verily Life Sciences, South San Francisco, CA
| | - Mario Masellis
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, ON, Canada; Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, ON, Canada
| | | | - Debra Niehoff
- Association for Frontotemporal Degeneration, Radnor, PA
| | - Chiadi U. Onyike
- Department Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University, Baltimore, MD
| | - Sabrina Paganoni
- Healey Center for ALS, Massachusetts General Hospital, Boston, MA
| | | | - Kenneth Rockwood
- Division of Geriatric Medicine, Dalhousie University, Halifax, NS
| | - Jonathan D. Rohrer
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, UK
| | - Howard Rosen
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Robert N. Schuck
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, FDA, Silver Spring, MD
| | | | - Nadine Tatton
- Association for Frontotemporal Degeneration, Radnor, PA
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New Frontiers in Parkinson's Disease: From Genetics to the Clinic. J Neurosci 2019; 38:9375-9382. [PMID: 30381429 DOI: 10.1523/jneurosci.1666-18.2018] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 09/15/2018] [Accepted: 09/18/2018] [Indexed: 12/30/2022] Open
Abstract
The greatest unmet therapeutic need in Parkinson's disease (PD) is a treatment that slows the relentless progression of the symptoms and the neurodegenerative process. This review highlights the utility of genetics to understand the pathogenic mechanisms and develop novel therapeutic approaches for PD. The focus is on strategies provided by genetic studies: notably via the reduction and clearance of α-synuclein, inhibition of LRRK2 kinase activity, and modulation of glucocerebrosidase-related substrates. In addition, the critical role of precompetitive public-private partnerships in supporting trial design optimization, overall drug development, and regulatory approvals is illustrated. With these great advances, the promise of developing transformative therapies that halt or slow disease progression is a tangible goal.
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