scholarly journals P4-015: CLINICAL TRIAL DESIGN FOR A PILOT STUDY EVALUATING THROMBIN INHIBITION IN ALZHEIMER'S DISEASE (THE BEACON TRIAL)

2019 ◽  
Vol 15 ◽  
pp. P1278-P1279
Author(s):  
Cláudia Y. Santos ◽  
Christine Getter ◽  
John Stoukides ◽  
Brian R. Ott ◽  
Stephen Salloway ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trial ◽  
Pilot Study ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Thrombin Inhibition

P3-037: CLINICAL TRIAL DESIGN FOR A PHASE II, RANDOMIZED, PLACEBO-CONTROLLED TRIAL OF AMX0035 IN ALZHEIMER'S DISEASE

2006 ◽  
Vol 14 (7S_Part_20) ◽  
pp. P1078-P1079
Author(s):  
Kent L. Leslie ◽  
Joshua Cohen ◽  
Justin Klee ◽  
Victoria Williams ◽  
Steven E. Arnold
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trial ◽  
Phase Ii ◽  
Trial Design ◽  
Controlled Trial ◽  
Clinical Trial Design

scholarly journals BIOMARKER AND CLINICAL TRIAL DESIGN SUPPORT FOR DISEASE-MODIFYING THERAPIES: REPORT OF A SURVEY OF THE EU/US: ALZHEIMER’S DISEASE TASK FORCE

2018 ◽  
pp. 1-7
Author(s):  
J. Cummings ◽  
N. Fox ◽  
B. Vellas ◽  
P. Aisen ◽  
G. Shan
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trial ◽  
Trial Design ◽  
Task Force ◽  
Clinical Trial Design ◽  
Disease Modification ◽  
Disease Modifying Therapies ◽  
Disease Modifying ◽  
The Eu

BACKGROUND: Disease-modifying therapies are urgently needed for the treatment of Alzheimer’s disease (AD). The European Union/United States (EU/US) Task Force represents a broad range of stakeholders including biopharma industry personnel, academicians, and regulatory authorities. OBJECTIVES: The EU/US Task Force represents a community of knowledgeable individuals who can inform views of evidence supporting disease modification and the development of disease-modifying therapies (DMTs). We queried their attitudes toward clinical trial design and biomarkers in support of DMTs. DESIGN/SETTING/PARTICIANTS: A survey of members of the EU/US Alzheimer’s Disease Task Force was conducted. Ninety-three members (87%) responded. The details were analyzed to understand what clinical trial design and biomarker data support disease modification. MEASUREMENTS/RESULTS/CONCLUSIONS: Task Force members favored the parallel group design compared to delayed start or staggered withdrawal clinical trial designs to support disease modification. Amyloid biomarkers were regarded as providing mild support for disease modification while tau biomarkers were regarded as providing moderate support. Combinations of biomarkers, particularly combinations of tau and neurodegeneration, were regarded as providing moderate to marked support for disease modification and combinations of all three classes of biomarkers were regarded by a majority as providing marked support for disease modification. Task Force members considered that evidence derived from clinical trials and biomarkers supports clinical meaningfulness of an intervention, and when combined with a single clinical trial outcome, nearly all regarded the clinical trial design or biomarker evidence as supportive of disease modification. A minority considered biomarker evidence by itself as indicative of disease modification in prevention trials. Levels of evidence (A,B,C) were constructed based on these observations. CONCLUSION: The survey indicates the view of knowledgeable stakeholders regarding evidence derived from clinical trial design and biomarkers in support of disease modification. Results of this survey can assist in designing clinical trials of DMTs.

Evaluation of Interventions in Alzheimer's Disease

1996 ◽  
Vol 8 (S1) ◽  
pp. 17-20 ◽  
Author(s):  
Cornelia Beck
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trial ◽  
Behavioral Problems ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Delivery Of Care ◽  
Organization Of Care

To address the development of studies for behavioral problems in patients with Alzheimer's disease (AD), a framework is used that includes the patient, the caregiver (formal or informal), the patient-caregiver interaction, the environment, the organization of care within institutions, and systems for the delivery of care to patients and caregivers. Within each of these components, there will be indicated the areas that are ready for testing using a clinical trial design and the areas that need further study using less controlled designs. Finally, recommendations will be made that address all components of the framework.

A Novel Subject Synchronization Clinical Trial Design for Alzheimer's Disease

2012 ◽  
Vol 31 (3) ◽  
pp. 507-516
Author(s):  
Timothy Schultz ◽  
Eric Yang ◽  
Michael Farnum ◽  
Victor Lobanov ◽  
Rudi Verbeeck ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trial ◽  
Trial Design ◽  
Clinical Trial Design

scholarly journals Can we improve clinical trial design in Alzheimer’s disease? The participants point of view

2021 ◽  
Vol 17 (S9) ◽  
Author(s):  
Lois Ottenhoff ◽  
Everard G.B. Vijverberg ◽  
Leonie N.C. Visser ◽  
Merike Verrijp ◽  
Niels D. Prins ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trial ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Point Of View

scholarly journals RE-ENGINEERING ALZHEIMER CLINICAL TRIALS: GLOBAL ALZHEIMER’S PLATFORM NETWORK

2016 ◽  
pp. 1-7
Author(s):  
J. Cummings ◽  
P. Aisen ◽  
R. Barton ◽  
J. Bork ◽  
R. Doody ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trial ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Fund Raising ◽  
Highly Qualified ◽  
Trial Site ◽  
Trial Efficiency ◽  
New Treatments

Alzheimer’s disease (AD) drug development is costly, time-consuming, and inefficient. Trial site functions, trial design, and patient recruitment for trials all require improvement. The Global Alzheimer Platform (GAP) was initiated in response to these challenges. Four GAP work streams evolved in the US to address different trial challenges: 1) registry-to-cohort web-based recruitment; 2) clinical trial site activation and site network construction (GAP-NET); 3) adaptive proof-of-concept clinical trial design; and 4) finance and fund raising. GAP-NET proposes to establish a standardized network of continuously funded trial sites that are highly qualified to perform trials (with established clinical, biomarker, imaging capability; certified raters; sophisticated management system. GAP-NET will conduct trials for academic and biopharma industry partners using standardized instrument versions and administration. Collaboration with the Innovative Medicines Initiative (IMI) European Prevention of Alzheimer’s Disease (EPAD) program, the Canadian Consortium on Neurodegeneration in Aging (CCNA) and other similar international initiatives will allow conduct of global trials. GAP-NET aims to increase trial efficiency and quality, decrease trial redundancy, accelerate cohort development and trial recruitment, and decrease trial costs. The value proposition for sites includes stable funding and uniform training and trial execution; the value to trial sponsors is decreased trial costs, reduced time to execute trials, and enhanced data quality. The value for patients and society is the more rapid availability of new treatments for AD.

scholarly journals Predicting future regional tau accumulation in asymptomatic and early Alzheimer’s disease

2020 ◽  
Author(s):  
Joseph Giorgio ◽  
William J Jagust ◽  
Suzanne Baker ◽  
Susan M. Landau ◽  
Peter Tino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trial ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Learning Approach ◽  
Multimodal Data ◽  
Machine Learning Approach ◽  
Β Amyloid

AbstractThe earliest stages of Alzheimer’s disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future tau accumulation, translating predictive information from deep phenotyping cohorts at early stages of AD to cognitively normal individuals. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal atrophy, tau and APOE 4) at early and asymptomatic stages of AD. We next derive a predictive index that stratifies individuals based on future pathological tau accumulation, highlighting two critical features for optimal clinical trial design. First, future tau accumulation provides a better outcome measure compared to changes in cognition. Second, stratification based on multimodal data compared to β-amyloid alone reduces the sample size required to detect a clinically meaningful change in tau accumulation. Further, we extend our machine learning approach to derive individualised trajectories of future pathological tau accumulation in early AD patients and accurately predict regional future rate of tau accumulation in an independent sample of cognitively unimpaired individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design at asymptomatic and early stages of AD.One Sentence SummaryOur machine learning approach combines baseline multimodal data to make individualised predictions of future pathological tau accumulation at prodromal and asymptomatic stages of Alzheimer’s disease with high accuracy and regional specificity.

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