Lirong Wang, Ph.D. - US grants

Project summary Our goal is to develop artificial intelligent (AI) analytics models to facilitate personalized treatment plans for Alzheimer?s disease (AD) patients with most common comorbidities, such as cardiovascular diseases (CVD), diabetes mellitus (DM) and depression. AD is a neurodegenerative disease that progressively causes 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 patients. Moreover, disease-modifying drugs based on our 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. Additionally, increasing evidence has indicated that comorbidities of AD share common disease pathways with AD, and medications used for these diseases may also alter the cognitive functions in AD patients. However, few studies have assessed combinations of these medications in treatments for AD. In this study, we will address this problem by retrospectively analyzing the observational data collected by the University of Pittsburgh Alzheimer?s Disease Research Center (ADRC). In Aim 1, we plan to statistically investigate the effects of different medications when used in combination with anti-AD medications on the trajectory of cognitive decline. If specific drug combination(s) are found to have a potential synergistic effect against cognitive decline, we will further study the underlying mechanisms using molecular systems pharmacology methods in Aim 2. In Aim 3, we will focus on establishing a clinical decision support system that facilitates individualized treatment for AD patients with these common comorbidities. We will build a Bayesian Network model that can predict the disease progression based patient and treatment information provided by the ADRC data set. The model will be learned and tested based on the ADRC dataset using the Tetrad software package. We will then apply methodologies of decision theory and search for a treatment combination that leads to the optimal treatment outcomes for specific patients. Collectively, these studies will contribute to a discovery of novel drug combinations for treating AD patients with comorbidities, and generate ideas for a clinical decision support system that can facilitate personalized medicine for these patients.

PROJECT SUMMARY: Psychotic symptoms occur in ~ 40-60% of individuals with Alzheimer Disease (AD with psychosis, AD+P). Numerous studies have found that the AD+P phenotype is associated with more rapid cognitive decline than AD subjects without psychosis (AD-P). Current, empirically developed, treatments for psychosis in AD have limited efficacy, do not alter the more rapid disease progression, and are associated with substantial toxicity, including excess mortality. Because the annual incidence of psychosis in AD is only ~ 10%, there is a window of opportunity to intervene to prevent psychosis onset if resilience factors can be identified. Multiple brain imaging studies have shown that relative to AD+P, subjects with AD-P have preserved indices of cortical synaptic function, especially in the dorsolateral prefrontal cortex (DLPFC). Our recent genetic and proteomic findings in patients and model systems have converged on a possible mechanism to explain this synaptic resilience in AD-P: Preservation of postsynaptic density (PSD) protein levels in DLPFC. First, using targeted mass spectrometry (MS) in DLPFC grey matter homogenates from mild to moderate AD subjects, we found a robust increase in homogenate levels of canonical PSD proteins in AD-P subjects relative to both AD+P and Control subjects. Second, we identified and independently confirmed a polygenic protection against psychosis in AD which included an allele associated with reduced DLPFC expression of TOM1L2. TOM1L2 is an adaptor protein that facilitates degradation of synaptic proteins via actin-based endocytic trafficking. Finally, in the APPswe/PSEN1dE9 mouse model of A? overproduction, we found that reduction of Kalrn, a Rac1/RhoA guanine nucleotide exchange factor that regulates endocytic trafficking, elevated canonical PSD protein levels in cortical homogenates, preserved these proteins' levels in PSD enrichments, and protected against psychosis-associated behaviors. We thus hypothesize: resilience to psychosis onset in AD is conferred by preservation of protein levels in PSD enrichments, due to reduced trafficking of PSD proteins for degradation, and can be used to identify novel therapeutics. We will test this hypothesis in three Aims: Aim 1) To determine if PSD proteome alterations and gene-protein interactions are associated with resilience to AD+P; Aim 2) To test the effect of reduction in Tom1l2 on the synaptic proteome in a mouse model, and; Aim 3) To use computational chemogenomics to identify drugs that induce synaptic proteome compensations which confer resilience to AD+P, providing for rational prevention and/or treatment. The above aims benefit from the tight integration and leveraging of Multiple PIs with expertise in the synaptic pathology of psychosis (Sweet), the neuropathology of AD (Kofler), and the use of computation for novel therapeutic discovery (Wang). Upon completion, we will have delineated the synaptic protein compensations associated with resilience to psychosis in AD and discovered leads to compounds that generate synaptic resilience for future testing in future studies.