Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing

Abstract

Following two decades of more than 400 clinical trials centered on the "one drug, one target, one disease" paradigm, there is still no effective disease-modifying therapy for Alzheimer's disease (AD). The inherent complexity of AD may challenge this reductionist strategy. Recent observations and advances in network medicine further indicate that AD likely shares common underlying mechanisms and intermediate pathophenotypes, or endophenotypes, with other diseases. In this review, we consider AD pathobiology, disease comorbidity, pleiotropy, and therapeutic development, and construct relevant endophenotype networks to guide future therapeutic development. Specifically, we discuss six main endophenotype hypotheses in AD: amyloidosis, tauopathy, neuroinflammation, mitochondrial dysfunction, vascular dysfunction, and lysosomal dysfunction. We further consider how this endophenotype network framework can provide advances in computational and experimental strategies for drug-repurposing and identification of new candidate therapeutic strategies for patients suffering from or at risk for AD. We highlight new opportunities for endophenotype-informed, drug discovery in AD, by exploiting multi-omics data. Integration of genomics, transcriptomics, radiomics, pharmacogenomics, and interactomics (protein-protein interactions) are essential for successful drug discovery. We describe experimental technologies for AD drug discovery including human induced pluripotent stem cells, transgenic mouse/rat models, and population-based retrospective case-control studies that may be integrated with multi-omics in a network medicine methodology. In summary, endophenotype-based network medicine methodologies will promote AD therapeutic development that will optimize the usefulness of available data and support deep phenotyping of the patient heterogeneity for personalized medicine in AD.

Keywords: Alzheimer's disease; amyloidosis; drug repurposing; endophenotype; network medicine; omics; pathobiology; systems biology; tauopathy.

Figure 1.. Statistics of current drug development status in Alzheimer’s disease (AD).

(A) Distribution of clinically failed drugs versus approved drugs in the past 20 years (1998–2017). The data are collected from Adis R&D Insight Database; (B) Distribution of mechanistic classes in development of AD drugs. The data are collected from U.S. AD clinical trials from the Alzforum database in March 2016.

Figure 2.. Reductionist versus network medicine paradigm for AD pathogenesis and drug discovery.

(A) The traditional reductionist paradigm that utilizes routine single omics approaches resulting in large bodies of distinct data that are not integrated. (B) Network medicine is based on the utilization of state-of-the-art network science tools and systems biology approaches to build the integrative model (AD endophenotype) from multi-omics data under the human interactome network framework.

Figure 3.. Statistics of repurposed drugs in nonclinical or clinical investigations for Alzheimer therapy.

All repurposed drugs in AD clinical trials are collected from clinicaltrials.gov database as of July 31, 2019. The inner ring represents Phase III agents; the middle ring shows Phase II agents; the outer ring presents Phase I or preclinical drugs. All the drugs are classified into four types: cardiovascular drugs, metabolic drugs, nervous systems or mental drugs, and others, according to their original indication. The mechanism-of-action of drugs are classified into four types: amyloid-related, tau-related, amyloid & tau related, and others.

Figure 4.. A diagram illustrates drug repurposing approach under endophenotype networks of amyloid and tau in AD.

Endophenotype networks (i.e. amyloid or tau network) are generated using endophenotype-specific genetic or genomic data. Drug repurposing opportunities are revealed via integration of endophenotype network and drug-target network in human protein-protein interactome.

Figure 5.. Six illustrated endophenotype hypotheses for Alzheimer’s disease.

Six endophenotype hypotheses including amyloidosis (A), tauopathy (B), neuroinflammation (C), mitochondrial dysfunction (D), vascular dysfunction (E), and lysosomal dysfunction (F). TNFα: tumor necrosis factor alpha; iNOS: inducible nitric oxide synthase; IL-6: Interleukin 6; IL-1b: Interleukin 1 beta; APP: amyloid precursor protein.

Figure 6.. Proof-of-concept of disease module for Alzheimer’s disease (AD).

This AD module highlights several network features under the human protein-protein interactome: 1) both amyloidosis and tauopathy are the key components of AD module; 2) there is a significant gene overlap (12 overlapping genes, P = 1.82 × 10⁻²⁷, Fisher’s exact test) between the two endophenotypes (amyloidosis and tauopathy). The AD module includes 227 protein-protein interactions (PPIs) (edges or links) connecting 102 unique proteins (including 31 amyloid genes, 11 tau genes, 12 amyloid & tau gene, and 48 other genes). AD module is generated based on 144 AD seed genes collected from the literatures.

Figure 7.. An endophenotype network-based drug repurposing infrastructure for AD.

The entire infrastructure is consisted: (A) develop AD endophenotype-based disease module from multi-omics data; (B) experimental or clinical validation of disease modules; (C) in silico prediction of highly repurposable drugs via network proximity analysis; (D) in vitro/in vivo preclinical validation, and population-based validation of in silico drug repurposing.

Figure 8.. Two case studies illustrate proof-of-concept of network-based drug combinations for anti-Alzheimer therapy.

(A) The possible exposure mode of disease module to the pairwise drug combinations. An effective drug combination will be captured by the “Complementary Exposure” pattern: the targets of the drugs both hit disease module, but target separate neighborhoods in the human interactome network. Z_CA and Z_CB denote the network proximity (Z-score) between targets (Drugs A and B) and disease module. S_AB denotes separation score of targets between Drug A and Drug B. (B) Network proximity analysis illustrates potential drug combination (carvedilol plus riluzole) for AD. (C) Network proximity analysis illustrates a potential drug combination (acamprosate plus baclofen) for AD. Disease module is generated based on 54 genes related to Amyloid hypothesis of Alzheimer’s disease collected from the literatures.

Figure 9.. An individualized network-based approach for personalized medicine in Alzheimer’s disease (AD).

Individualized disease network modules built from multi-omics data of individual patients under the human interatcome network model could improve the success rate of AD drug discovery, guide therapeutic evaluation during clinical trials, and optimize new treatment for patients with AD and those at risk for AD dementia.