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Related Concept Videos

Alzheimer's Disease: Treatment01:22

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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Related Experiment Video

Updated: Jan 11, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Multi-objective optimization formulation for Alzheimer's disease trial patient selection.

Alireza Moayedikia1, Sara Fin2, Uffe Kock Wiil3

  • 1Swinburne Business School, Swinburne University of Technology, Australia.

Journal of Biomedical Informatics
|November 17, 2025
PubMed
Summary
This summary is machine-generated.

Optimizing Alzheimer's disease clinical trial eligibility criteria using multi-objective optimization offers incremental efficiency gains. This computational approach validates existing practices and enhances recruitment feasibility, though outcomes show variability.

Keywords:
Alzheimer’s diseaseClinical trial designCost optimizationMulti-objective optimizationPatient selection

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Area of Science:

  • Computational Biology and Bioinformatics
  • Clinical Trial Design and Optimization
  • Neuroscience and Alzheimer's Disease Research

Background:

  • Alzheimer's disease (AD) clinical trials face high screen failure rates (>80%), hindering progress.
  • Current patient selection relies on expert consensus, lacking systematic evaluation of competing objectives.
  • There's a critical need to balance statistical power, recruitment feasibility, safety, and cost in AD trial design.

Purpose of the Study:

  • To develop and implement a multi-objective optimization framework for AD clinical trial eligibility criteria.
  • To systematically identify optimal criteria configurations balancing patient identification accuracy, recruitment feasibility, and economic efficiency.
  • To validate computational approaches against expert consensus in AD trial design.

Main Methods:

  • Utilized the Non-dominated Sorting Genetic Algorithm III (NSGA-III) for multi-objective optimization.
  • Employed National Alzheimer's Coordinating Center data (2,743 participants) with clinical and biomarker information.
  • Optimized 14 eligibility parameters and validated using Monte Carlo simulations, bootstrap analysis, and SHAP interpretability.

Main Results:

  • Identified 11 Pareto-optimal solutions balancing F1 scores (0.979-0.995) and eligible patient pools (108-327).
  • Optimized criteria identified similar patient cohorts to standard criteria but with potential cost savings ($1,048/patient).
  • Biomarker requirements were identified as the dominant cost driver; computational results converged with expert-designed criteria.

Conclusions:

  • Multi-objective optimization offers incremental value by systematically validating and probabilistically enhancing efficiency in AD trials.
  • Computational approaches serve as sophisticated validation tools, identifying concrete efficiency improvements within existing frameworks.
  • Site-specific evaluation and recruitment infrastructure quality are crucial; optimization enhances, rather than replaces, clinical expertise.