# SmartAD for Intelligent Alzheimer’s Disease(AD) Personalized Combination Therapy

> **NIH NIH R56** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $395,417

## Abstract

Alzheimer’s Disease (AD) is a complex neurodegenerative disease that causes progressive memory loss and
cognitive impairment. While current treatments have shown some amelioration of symptoms, the effects have
been transient and limited to a small percentage of AD patients. Moreover, disease-modifying drugs based on
current understanding of disease mechanisms have all shown negative results in clinical trials. Part of the
failure is due to the heterogeneity in the disease mechanism, of which we do not yet have a clear
understanding. Increasing evidence has indicated that medical comorbidities share common disease pathways
with AD, and the medications used for these diseases can also alter the cognitive functions of AD patients.
However, limited studies have assessed combinations of these medications as treatments for AD with common
comorbidities. Thus, the goal of this proposal is to develop artificial intelligence (AI) analytics models and a
SmartAD app to facilitate cognitive function evaluation and personalized treatment plans for AD patients with
the most common comorbidities, such as cardiovascular diseases (CVD)/hypertension (HTN), diabetes
mellitus (DM), and depression (DPN). To achieve our goal, we will carry out retrospective analysis of
observational clinical data collected by the University of Pittsburgh Alzheimer’s Disease Research Center
(ADRC). First, we will statistically investigate the effects of different comorbidity medications when used in
combination with anti-AD medications on the trajectory of cognitive decline (Aim1). By identifying specific drug
combination(s) that have a synergistic effect against cognitive decline, we will then study the underlying
mechanisms using molecular systems pharmacology methods and validate the findings using in vitro iPSC and
other bioassays as needed (Aim2). Subsequently, we will build a clinical decision support system, SmartAD,
that will facilitate cognitive function evaluation and individualized treatment for AD patients with these common
comorbidities. We will build a Bayesian Network model that can predict patient-tailored disease progression
and treatment information provided by ADRC at the University of Pittsburgh (Aims 3 & 4). This model will be
intelligently machine-learned and trained on the ADRC dataset using causal machine-learning approaches.
Methodologies of decision theory will then be applied to search for a treatment combination that leads to the
optimal outcome for that patient. Finally, we will use external medical data from AD Neuroimaging Initiative
(ADNI) and National Alzheimer’s Coordinating Center (NACC) for model systems test validation (Aims 3 and
4). Taken all together, these studies will contribute to the discovery of novel drug combinations for AD patients
with comorbidities and develop SmartAD as an intelligent clinical decision support system that can facilitate
paperless cognitive function evaluation, progression prediction, as well as assist optimal personaliz...

## Key facts

- **NIH application ID:** 10670481
- **Project number:** 1R56AG074951-01A1
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Xiang-Qun Xie
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $395,417
- **Award type:** 1
- **Project period:** 2022-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10670481

## Citation

> US National Institutes of Health, RePORTER application 10670481, SmartAD for Intelligent Alzheimer’s Disease(AD) Personalized Combination Therapy (1R56AG074951-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10670481. Licensed CC0.

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