Develop a mathematical model for Alzheimer’s disease treatment study

Lead Investigator: Wenrui Hao, Penn State University
Title of Proposal Research: Develop a mathematical model for Alzheimer’s disease treatment study.
Vivli Data Request: 8848
Funding Source: Government Funding – NSF grant
Potential Conflicts of Interest: None
Summary of the Proposed Research:
Personalized medicine is an innovative approach that aims to tailor healthcare to individual patients based on their unique characteristics, such as genetic makeup, lifestyle factors, and environmental exposures. Alzheimer’s disease is a prevalent neurodegenerative disorder that affects millions of people worldwide, causing cognitive decline and memory loss. The disease is estimated to affect more than 50 million people worldwide, and this number is expected to triple by 2050 due to the aging population. The development of personalized treatments for Alzheimer’s disease is critical due to the complexity of the disease and the significant variability in how it manifests in different individuals.

In this project, we have developed a mathematical model that integrates cognitive, cerebrospinal fluid (CSF) biomarkers (this refers to abnormal clumps of proteins known as amyloid and tau, which are present in the brain; changes in the levels of these proteins are reflected in the CSF), positron emission tomography (PET) and magnetic resonance imaging (MRI) biomarkers of Alzheimer’s disease. The “mathematical model 2019” refers to the Alzheimer’s Disease Biomarker Cascade (ADBC) model that was published in 2019 [1]. The term “validated mathematical model” indicates that the ADBC model’s parameters were trained using clinical data, enabling the model to accurately reflect clinical reality. The ADBC model incorporates four clinical biomarkers: Abeta, tau, neuron degeneration, and cognitive decline. “Individualized risk profiles” in this context refers to the personalized risk assessment for each biomarker in each patient. To “parameterize” the model means to train the mathematical model using optimization algorithms to ensure a good fit to the clinical data.

Regarding the relationship to dementia, CSF biomarkers provide valuable insights into the development and progression of dementia. Abnormal levels of amyloid and tau proteins in the CSF have been associated with Alzheimer’s disease and other forms of dementia. Analyzing these biomarkers can help in the diagnosis, monitoring, and understanding of the underlying pathological processes in dementia.
It is worth noting that the term “imaging” mentioned in the context of the ADBC model refers to brain imaging techniques such as positron emission tomography (PET) and magnetic resonance imaging (MRI). These imaging modalities provide visual representations of the brain and can be used to complement the biomarker data in Alzheimer’s disease research.

Our model is intended to help healthcare professionals personalize treatment plans for Alzheimer’s patients by providing them with individualized risk profiles and potential prevention and treatment strategies. To validate and refine our model further, we plan to use clinical trial datasets to parameterize it. Ultimately, we aim to use this validated mathematical model to conduct research on potential treatments for Alzheimer’s disease, potentially benefiting millions of people affected by this devastating condition.

Requested Studies:
Effect of LY3202626 on Alzheimer’s Disease Progression as Measured by Cerebral ¹⁸F-AV-1451 Tau-PET in Mild Alzheimer’s Disease Dementia
Data Contributor: Lilly
Study ID: NCT02791191
Sponsor ID: 16223

Effect of γ-Secretase Inhibition on the Progression of Alzheimer’s Disease: LY450139 Versus Placebo
Data Contributor: Lilly
Study ID: NCT00594568
Sponsor ID: 7666

Effect of Passive Immunization on the Progression of Mild Alzheimer’s Disease: Solanezumab (LY2062430) Versus Placebo
Data Contributor: Lilly
Study ID: NCT01900665
Sponsor ID: 15136

Continued Efficacy and Safety Monitoring of Solanezumab, an Anti-Amyloid β Antibody in Patients With Alzheimer’s Disease
Data Contributor: Lilly
Study ID: NCT01127633
Sponsor ID: 11935
Effect of LY2062430, an Anti-Amyloid Beta Monoclonal Antibody, on the Progression of Alzheimer’s Disease as Compared With Placebo
Data Contributor: Lilly
Study ID: NCT00905372
Sponsor ID: 6747

Effect of Passive Immunization on the Progression of Alzheimer’s Disease: LY2062430 Versus Placebo
Data Contributor: Lilly
Study ID: NCT00904683
Sponsor ID: 11934