Accelerating drug development for Alzheimer's disease through the use of data standards

Neville, Jon; Kopko, Steve; Romero, Klaus; Corrigan, Brian; Stafford, Bob; Leroy, Elisabeth; Broadbent, Steve; Cisneroz, Martin; Wilson, Ethan; Reiman, Eric M.; Vanderstichele, Hugo; Arnerić, Stephen P.; Stephenson, Diane

doi:10.1016/j.trci.2017.03.006

Cited by 12 publications

(11 citation statements)

References 48 publications

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“…Our analysis exposed major challenges that severely impede comparative approaches on AD cohort data. Although there has been work on standardizing data collection 21,22 as well as on guidelines defining an AD‐specific data model, 23 we still experience a deficit in interoperability across AD data sets. The investigated cohort data sets neither followed a common naming system for variables nor represented values of the same measurement in an equal manner.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Alzheimer's disease data landscape

Birkenbihl

Salimi

Domingo‐Fernándéz

et al. 2020

A&D Transl Res & Clin Interv

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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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Section: Discussionmentioning

confidence: 99%

Evaluating the Alzheimer's disease data landscape

Birkenbihl

Salimi

Domingo‐Fernándéz

et al. 2020

A&D Transl Res & Clin Interv

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“…CDISC standards for DMD and HD were developed through collaborations between the CDISC and consortia at the Critical Path Institute (C-Path), Duchenne Regulatory Science Consortium (D-RSC), and Huntington's Disease Regulatory Science Consortium (HD-RSC), respectively, according to the methods described in other diseases 7,8,11 and summarized in Figure 1. In each case, controlled terminology was developed for predefined measurements commonly collected in clinical research by engaging subject matter experts.…”

Section: Methodsmentioning

confidence: 99%

“…For HD, the measures that were selected for CDISC standards development included genetics, imaging biomarkers, and biofluid biomarkers. Except for magnetic resonance spectroscopy (MRS), discussed below, which used pre‐existing data structures but required the creation of two new parameters, the biomarker domains had pre‐existing data standards covered in other neurodegenerative therapeutic areas, 7 which served to minimize the workflow required for development. In most cases, even when pre‐existing concept maps existed, subject matter experts reviewed the biomarker applications to assure alignment with use in HD clinical research (e.g., phosphodiesterase 10 positron emission tomography, mutant huntingtin in cerebral spinal fluid).…”

Section: Methodsmentioning

confidence: 99%

“…The database structure and terminology described by the CDISC Study Data Tabulation Model (SDTM) are ideal for integration of datasets. [7][8][9] Where possible, new data collected should be collected using CDISC Clinical Data Acquisition Standards Harmonization (CDASH) standard format, which describes how to design case report forms that include the needed metadata associated with each data element. If data are collected using the CDASH standards, they may be more readily mapped into CDISC SDTM format.…”

Section: What Question Did This Study Address?mentioning

confidence: 99%

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Standardized Data Structures in Rare Diseases: CDISC User Guides for Duchenne Muscular Dystrophy and Huntington’s Disease

Mullin

Corey

Turner

et al. 2020

Clinical Translational Sci

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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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“…Several such vocabularies (e.g., NIFT Iyappan et al, 2017 andPTS Iyappan et al, 2016) have been established, but significant improvements in interoperability will only come with widespread adoption (Neu et al, 2012). The most prominent example might be the AD specific standard developed by the Clinical Data Interchange Standards Consortium (CDISC; Neville et al, 2017). At the subject level, mapping between training and validation cohorts can be accomplished by identifying, in the validation cohort, a subset of subjects that is statistically comparable to the training cohort.…”

Section: Interoperabilitymentioning

confidence: 99%

Challenges of Integrative Disease Modeling in Alzheimer's Disease

Khatami

Robinson

Birkenbihl

et al. 2020

Front. Mol. Biosci.

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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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Accelerating drug development for Alzheimer's disease through the use of data standards

Cited by 12 publications

References 48 publications

Evaluating the Alzheimer's disease data landscape

Evaluating the Alzheimer's disease data landscape

Standardized Data Structures in Rare Diseases: CDISC User Guides for Duchenne Muscular Dystrophy and Huntington’s Disease

Challenges of Integrative Disease Modeling in Alzheimer's Disease

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