P3-370: A pilot survey to explore attitudes toward primary prevention, secondary prevention and Alzheimer's disease treatment clinical trials

BACKGROUND: Screening to identify individuals with elevated brain amyloid (Aβ+) for clinical trials in Preclinical Alzheimer’s Disease (PAD), such as the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s disease (A4) trial, is slow and costly. The Trial-Ready Cohort in Preclinical/Prodromal Alzheimer’s Disease (TRC-PAD) aims to accelerate and reduce costs of AD trial recruitment by maintaining a web-based registry of potential trial participants, and using predictive algorithms to assess their likelihood of suitability for PAD trials. OBJECTIVES: Here we describe how algorithms used to predict amyloid burden within TRC-PAD project were derived using screening data from the A4 trial. DESIGN: We apply machine learning techniques to predict amyloid positivity. Demographic variables, APOE genotype, and measures of cognition and function are considered as predictors. Model data were derived from the A4 trial. SETTING: TRC-PAD data are collected from web-based and in-person assessments and are used to predict the risk of elevated amyloid and assess eligibility for AD trials. PARTICIPANTS: Pre-randomization, cross-sectional data from the ongoing A4 trial are used to develop statistical models. MEASUREMENTS: Models use a range of cognitive tests and subjective memory assessments, along with demographic variables. Amyloid positivity in A4 was confirmed using positron emission tomography (PET). RESULTS: The A4 trial screened N=4,486 participants, of which N=1323 (29%) were classified as Aβ+ (SUVR ≥ 1.15). The Area under the Receiver Operating Characteristic curves for these models ranged from 0.60 (95% CI 0.56 to 0.64) for a web-based battery without APOE to 0.74 (95% CI 0.70 to 0.78) for an in-person battery. The number needed to screen to identify an Aβ+ individual is reduced from 3.39 in A4 to 2.62 in the remote setting without APOE, and 1.61 in the remote setting with APOE. CONCLUSIONS: Predictive algorithms in a web-based registry can improve the efficiency of screening in future secondary prevention trials. APOE status contributes most to predictive accuracy with cross-sectional data. Blood-based assays of amyloid will likely improve the prediction of amyloid PET positivity.

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Aβ-Immunotherapeutic strategies: a wide range of approaches for Alzheimer's disease treatment

Expert Reviews in Molecular Medicine ◽

10.1017/erm.2016.11 ◽

2016 ◽

Vol 18 ◽

Cited By ~ 20

Author(s):

Laia Montoliu-Gaya ◽

Sandra Villegas

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Trials ◽

State Of The Art ◽

Phase Iii ◽

Disease Treatment ◽

Passive Immunotherapy ◽

Wide Range ◽

Current Therapies ◽

Underlying Causes

Current therapies to treat Alzheimer's disease (AD) are focused on ameliorating symptoms instead of treating the underlying causes of AD. The accumulation of amyloid β (Aβ) oligomers, whether by an increase in production or by a decrease in clearance, has been described as the seed that initiates the pathological cascade in AD. Developing therapies to target these species is a vital step in improving AD treatment. Aβ-immunotherapy, especially passive immunotherapy, is a promising approach to reduce the Aβ burden. Up to now, several monoclonal antibodies (mAbs) have been tested in clinical trials on humans, but none of them have passed Phase III. In all likelihood, these trials failed mainly because patients with mild-to-moderate AD were recruited, and thus treatment may have been too late to be effective. Therefore, many ongoing clinical trials are being conducted in patients at the prodromal stage. New structures based on antibody fragments have been engineered intending to improve efficacy and safety. This review presents the properties of this variety of developing treatments and provides a perspective on state-of-the-art of passive Aβ-immunotherapy in AD.

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Insights into the Drug Repositioning Applied to the Alzheimer's Disease Treatment and Future Perspectives

Current Alzheimer Research ◽

10.2174/1567205015666180813150703 ◽

2018 ◽

Vol 15 (12) ◽

pp. 1161-1178 ◽

Cited By ~ 11

Author(s):

Alexandre A. de Castro ◽

Elaine F.F. da Cunha ◽

Ander F. Pereira ◽

Flavia V. Soares ◽

Daniel H.S. Leal ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Trials ◽

Late Onset ◽

Drug Repositioning ◽

Amyloid Β ◽

Disease Treatment ◽

Beneficial Effects ◽

Relevant Evidence ◽

Treatment Conditions

Introduction: Alzheimer's disease is known to be a chronic disease, with an estimated prevalence of about 10-30%, considering the population over 60 years of age. Most patients with this disorder (> 95%) present the sporadic form, being characterized by a late onset (80-90 years of age), and it is the consequence of the failure to clear the amyloid-β (Aβ) peptide from the interstices of the brain. Significant numbers of genetic risk factors for the sporadic disease have been researched. Some existing drugs for Alzheimer's disease provide symptomatic benefit for up to 12 months, but there are no approved disease- modifying therapies. In this line, a complementary strategy based on repositioning drugs which are approved for the treatment of other disorders could be interesting. It is noteworthy the fact that some clinical trials indicate that several classes of drugs own potent and beneficial effects on the Alzheimer's disease treatment. In this present work, we present the details and evaluation of these alternative treatments. It has highlighted several compounds with relevant evidence for this purpose, which deserves further investigation to clarify optimal treatment conditions in the clinical trials of patients with Alzheimer's disease.

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ADVANCING ALZHEIMER’S DISEASE TREATMENT: LESSONS FROM CTAD 2018

Journal of Prevention of Alzheimer's Disease ◽

10.14283/jpad.2019.11 ◽

2019 ◽

pp. 1-6

Author(s):

B. Vellas ◽

L.J. Bain ◽

J. Touchon ◽

P.S. Aisen

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Trials ◽

Predictive Modeling ◽

Learning Algorithms ◽

Synaptic Function ◽

Machine Learning Algorithms ◽

Disease Treatment ◽

Efficiency And Productivity

The 2018 Clinical Trials on Alzheimer’s Disease (CTAD) conference showcased recent successes and failures in trials of Alzheimer’s disease treatments. More importantly, the conference provided opportunities for investigators to share what they have learned from those studies with the goal of designing future trials with a greater likelihood of success. Data from studies of novel and non-amyloid treatment approaches were also shared, including neuroprotective and regenerative strategies and those that target neuroinflammation and synaptic function. New tools to improve the efficiency and productivity of clinical trials were described, including biomarkers and machine learning algorithms for predictive modeling.

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Faculty Opinions recommendation of Potential for primary prevention of Alzheimer's disease: an analysis of population-based data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718500180.793501897 ◽

2014 ◽

Author(s):

Gerd Kempermann

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Primary Prevention ◽

Population Based

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A Biomarker Combining Imaging and Neuropsychological Assessment for Tracking Early Alzheimer's Disease in Clinical Trials

Current Alzheimer Research ◽

10.2174/1567205014666171106150309 ◽

2018 ◽

Vol 15 (5) ◽

pp. 429-442 ◽

Cited By ~ 3

Author(s):

Nishant Verma ◽

S. Natasha Beretvas ◽

Belen Pascual ◽

Joseph C. Masdeu ◽

Mia K. Markey ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Trials ◽

Cognitive Impairment ◽

Latent Variable ◽

Brain Regions ◽

Disease Assessment ◽

Latent Variable Analysis ◽

The Relationship ◽

Selection Of

Background: Combining optimized cognitive (Alzheimer's Disease Assessment Scale- Cognitive subscale, ADAS-Cog) and atrophy markers of Alzheimer's disease for tracking progression in clinical trials may provide greater sensitivity than currently used methods, which have yielded negative results in multiple recent trials. Furthermore, it is critical to clarify the relationship among the subcomponents yielded by cognitive and imaging testing, to address the symptomatic and anatomical variability of Alzheimer's disease. Method: Using latent variable analysis, we thoroughly investigated the relationship between cognitive impairment, as assessed on the ADAS-Cog, and cerebral atrophy. A biomarker was developed for Alzheimer's clinical trials that combines cognitive and atrophy markers. Results: Atrophy within specific brain regions was found to be closely related with impairment in cognitive domains of memory, language, and praxis. The proposed biomarker showed significantly better sensitivity in tracking progression of cognitive impairment than the ADAS-Cog in simulated trials and a real world problem. The biomarker also improved the selection of MCI patients (78.8±4.9% specificity at 80% sensitivity) that will evolve to Alzheimer's disease for clinical trials. Conclusion: The proposed biomarker provides a boost to the efficacy of clinical trials focused in the mild cognitive impairment (MCI) stage by significantly improving the sensitivity to detect treatment effects and improving the selection of MCI patients that will evolve to Alzheimer’s disease.

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Recent Updates in the Alzheimer’s Disease Etiopathology and Possible Treatment Approaches: A Narrative Review of Current Clinical Trials

Current Molecular Pharmacology ◽

10.2174/1874467213666200422090135 ◽

2020 ◽

Vol 13 (4) ◽

pp. 273-294 ◽

Cited By ~ 2

Author(s):

Elahe Zarini-Gakiye ◽

Javad Amini ◽

Nima Sanadgol ◽

Gholamhassan Vaezi ◽

Kazem Parivar

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Trials ◽

Small Molecules ◽

Clinical Trial Design ◽

Treatment Approaches ◽

Drug Candidates ◽

Novel Treatments ◽

Approved Drugs ◽

Tau Aggregation

Background: Alzheimer’s disease (AD) is the most frequent subtype of incurable neurodegenerative dementias and its etiopathology is still not clearly elucidated. Objective: Outline the ongoing clinical trials (CTs) in the field of AD, in order to find novel master regulators. Methods: We strictly reviewed all scientific reports from Clinicaltrials.gov and PubMed databases from January 2010 to January 2019. The search terms were “Alzheimer's disease” or “dementia” and “medicine” or “drug” or “treatment” and “clinical trials” and “interventions”. Manuscripts that met the objective of this study were included for further evaluations. Results: Drug candidates have been categorized into two main groups including antibodies, peptides or hormones (such as Ponezumab, Interferon β-1a, Solanezumab, Filgrastim, Levemir, Apidra, and Estrogen), and naturally-derived ingredients or small molecules (such as Paracetamol, Ginkgo, Escitalopram, Simvastatin, Cilostazo, and Ritalin-SR). The majority of natural candidates acted as anti-inflammatory or/and anti-oxidant and antibodies exert their actions via increasing amyloid-beta (Aβ) clearance or decreasing Tau aggregation. Among small molecules, most of them that are present in the last phases act as specific antagonists (Suvorexant, Idalopirdine, Intepirdine, Trazodone, Carvedilol, and Risperidone) or agonists (Dextromethorphan, Resveratrol, Brexpiprazole) and frequently ameliorate cognitive dysfunctions. Conclusion: The presences of a small number of candidates in the last phase suggest that a large number of candidates have had an undesirable side effect or were unable to pass essential eligibility for future phases. Among successful treatment approaches, clearance of Aβ, recovery of cognitive deficits, and control of acute neuroinflammation are widely chosen. It is predicted that some FDA-approved drugs, such as Paracetamol, Risperidone, Escitalopram, Simvastatin, Cilostazoand, and Ritalin-SR, could also be used in off-label ways for AD. This review improves our ability to recognize novel treatments for AD and suggests approaches for the clinical trial design for this devastating disease in the near future.

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On the design of early-phase Alzheimer’s disease clinical trials with cerebrospinal fluid tau outcomes

Clinical Trials ◽

10.1177/17407745211034497 ◽

2021 ◽

pp. 174077452110344

Author(s):

Michelle M Nuño ◽

Joshua D Grill ◽

Daniel L Gillen ◽

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Trials ◽

Cerebrospinal Fluid ◽

Phosphorylated Tau ◽

Inclusion Criteria ◽

Eligibility Criteria ◽

Total Tau ◽

Prodromal Alzheimer’S Disease ◽

Study Power

Background/Aims: The focus of Alzheimer’s disease studies has shifted to earlier disease stages, including mild cognitive impairment. Biomarker inclusion criteria are often incorporated into mild cognitive impairment clinical trials to identify individuals with “prodromal Alzheimer’s disease” to ensure appropriate drug targets and enrich for participants likely to develop Alzheimer’s disease dementia. The use of these eligibility criteria may affect study power. Methods: We investigated outcome variability and study power in the setting of proof-of-concept prodromal Alzheimer’s disease trials that incorporate cerebrospinal fluid levels of total tau (t-tau) and phosphorylated (p-tau) as primary outcomes and how differing biomarker inclusion criteria affect power. We used data from the Alzheimer’s Disease Neuroimaging Initiative to model trial scenarios and to estimate the variance and within-subject correlation of total and phosphorylated tau. These estimates were then used to investigate the differences in study power for trials considering these two surrogate outcomes. Results: Patient characteristics were similar for all eligibility criteria. The lowest outcome variance and highest within-subject correlation were obtained when phosphorylated tau was used as an eligibility criterion, compared to amyloid beta or total tau, regardless of whether total tau or phosphorylated tau were used as primary outcomes. Power increased when eligibility criteria were broadened to allow for enrollment of subjects with either low amyloid beta or high phosphorylated tau. Conclusion: Specific biomarker inclusion criteria may impact statistical power in trials using total tau or phosphorylated tau as the primary outcome. In concert with other important considerations such as treatment target and population of clinical interest, these results may have implications to the integrity and efficiency of prodromal Alzheimer’s disease trial designs.

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