Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer's disease

Abstract

We developed an endophenotype disease module-based methodology for Alzheimer's disease (AD) drug repurposing and identified sildenafil as a potential disease risk modifier. Based on retrospective case-control pharmacoepidemiologic analyses of insurance claims data for 7.23 million individuals, we found that sildenafil usage was significantly associated with a 69% reduced risk of AD (hazard ratio = 0.31, 95% confidence interval 0.25-0.39, P<1.0×10^-8). Propensity score stratified analyses confirmed that sildenafil is significantly associated with a decreased risk of AD across all four drug cohorts we tested (diltiazem, glimepiride, losartan and metformin) after adjusting age, sex, race, and disease comorbidities. We also found that sildenafil increases neurite growth and decreases phospho-tau expression in AD patient-induced pluripotent stem cells-derived neuron models, supporting mechanistically its potential beneficial effect in Alzheimer's disease. The association between sildenafil use and decreased incidence of AD does not establish causality or its direction, which requires a randomized clinical trial approach.

Extended Data Fig 1.. Proof-of-concept of network module of Alzheimer’s disease (AD).

A subnetwork highlighting disease module (AD seed module) characterized by both amyloidosis and tauopathy under the human protein-protein interactome model. The AD seed module includes 227 protein–protein interactions (PPIs) (edges or links) connecting 102 unique proteins (nodes).

Extended Data Fig 2.. Heatmap illustrates the network proximity between molecular targets of 21 ongoing repurposable AD drugs and 37 AD disease modules.

These drugs could ameliorate amyloid, tau or both of them (amyloid & tau) pathology or target amyloid, tau or amyloid & tau related pathways in vitro, in vivo mouse models or in patients with AD. We built three AD network modules by assembling experimentally validated (seed) genes in amyloidosis (amyloid), tauopathy (tau), and AD (characterized by both amyloid and tau). In addition, we also built disease modules from 34 differentially expressed gene (DEG) sets derived from transcriptomics data (including microarray, and bulk RNA-sequencing) from AD genetic mouse.

Extended Data Fig 3.. The efficacy of endophenotype-based drug repurposing in Alzheimer’s disease (AD).

Molecular targets of 21 ongoing repurposable AD drugs that target both Amyloid and Tau have significantly closer network distance with AD network modules built from transcriptomics (a) and proteomics (b) data from AD genetic mouse, in comparison to drugs targeting amyloid or tau alone. In a, we built AD modules from 34 differentially expressed gene sets derived from transcriptomics data (including microarray, and bulk RNA-sequencing) from AD genetic mouse. In b, we built AD modules for 10 differentially expressed protein sets from proteomics data in AD genetic mice. P values were calculated by Wilcoxon test (one-side) in a and b.

Extended Data Fig 4.. Network-based in silico drug repurposing for Alzheimer’s disease (AD).

13 AD disease modules, including 3 AD seed genes and 10 differentially expressed proteins (DEPs) sets from AD mouse proteomics, were used to screen FDA-approved drugs for AD. A sankey plot illustrates a global view of 66 drug candidates identified by network proximity. Drugs are grouped by their first-level Anatomical Therapeutic Chemical Classification (ATC) codes.

Extended Data Fig 5.. Longitudinal analyses reveal that sildenafil usage is significantly associated with reduced likelihood of AD in individuals with coronary artery disease (CAD).

Five comparator analyses were conducted including: (a) sildenafil vs. matched non- sildenafil, (b) sildenafil vs. diltiazem (an anti-hypertensive drug), (c) sildenafil vs. losartan (an anti-hypertensive drug candidate in an AD clinical trial [ClinicalTrials.gov Identifier: NCT02913664]), (d) sildenafil vs. glimepiride (an anti-diabetic drug), and (e) sildenafil vs. metformin (an anti-diabetic drug in an AD clinical trial [ClinicalTrials.gov Identifier: NCT00620191]). For each comparator, we estimated the propensity score by using the variables described in Table 1. Then, we estimated the un-stratified Kaplan-Meier curves, conducted propensity score stratified (n strata = 10) log-rank test and Cox model.

Extended Data Fig 6.. Longitudinal analyses reveal that sildenafil usage is significantly associated with reduced likelihood of AD in individuals with hypertension (HT).

Five comparator analyses were conducted including: (a) sildenafil vs. matched non- sildenafil, (b) sildenafil vs. diltiazem (an anti-hypertensive drug), (c) sildenafil vs. losartan (an anti-hypertensive drug candidate in an AD clinical trial [ClinicalTrials.gov Identifier: NCT02913664]), (d) sildenafil vs. glimepiride (an anti-diabetic drug), and (e) sildenafil vs. metformin (an anti-diabetic drug in an AD clinical trial [ClinicalTrials.gov Identifier: NCT00620191]). For each comparator, we estimated the propensity score by using the variables described in Table 1. Then, we estimated the un-stratified Kaplan-Meier curves, conducted propensity score stratified (n strata = 10) log-rank test and Cox model.

Extended Data Fig 7.. Longitudinal analyses reveal that sildenafil usage is significantly associated with reduced likelihood of AD in individuals with type-2 diabetes (T2D).

Five comparator analyses were conducted including: (a) sildenafil vs. matched non- sildenafil, (b) sildenafil vs. diltiazem (an anti-hypertensive drug), (c) sildenafil vs. losartan (an anti-hypertensive drug candidate in an AD clinical trial [ClinicalTrials.gov Identifier: NCT02913664]), (d) sildenafil vs. glimepiride (an anti-diabetic drug), and (e) sildenafil vs. metformin (an anti-diabetic drug in an AD clinical trial [ClinicalTrials.gov Identifier: NCT00620191]). For each comparator, we estimated the propensity score by using the variables described in Table 1. Then, we estimated the un-stratified Kaplan-Meier curves, conducted propensity score stratified (n strata = 10) log-rank test and Cox model.

Figure 1.. A diagram illustrating an endophenotype network-based drug repurposing framework for Alzheimer’s disease (AD).

(a) Construction and validation of endophenotype disease modules for AD in the human protein-protein interactome network. (b) In silico drug repurposing by network proximity analysis (see Methods). (c) Population-based validation to test the drug user’s relationship with AD outcomes. (d) Network-based mechanistic observations in human microglia cells and AD patient induced pluripotent stem cells (iPSC)-derived neurons.

Figure 2.. Network-based in silico drug repurposing for Alzheimer’s disease (AD).

In total, 13 endophenotype disease modules, built by 3 experimentally validated (seed) gene sets in amyloidosis (Amyloid), tauopathy (Tau), and AD, as well as 10 differentially expressed protein from proteomics data generated in AD genetic mouse models, were evaluated to screen FDA-approved drugs using a network proximity measure (Methods). A Sankey diagram illustrates a global view of 66 high-confidence drug candidates, identified by network proximity analysis. Network proximity analysis measures the interplay between endophenotype disease modules (proteins) and drug targets in the human interactome. Drugs are grouped by their first-level Anatomical Therapeutic Chemical Classification (ATC) codes. The drugs with known anti-AD clinical trial status, in vivo animal model, and blood–brain barrier (BBB) properties data are highlighted. The z-score with AD seed module by network proximity is given for each drug. Subject matter expertise criterion-based prioritization resulted in sildenafil as the best candidate (z = −2.30), which has significant close network distance with eight endophenotype disease modules.

Figure 3.. Longitudinal analyses reveal that sildenafil usage is significantly associated with reduced likelihood of AD in a longitudinal patient database with 7.23 million subjects.

Five comparator analyses were conducted: (a) sildenafil (n = 116,412) vs. sex and comorbidities (hypertension, diabetes, and coronary artery disease) matched (ratio 1:4) non-sildenafil exposure population (n = 460,356), (b) sildenafil vs. diltiazem (an anti-hypertensive drug, n = 251,360), (c) sildenafil vs. losartan (an anti-hypertensive drug in AD clinical trial [ClinicalTrials.gov Identifier: NCT02913664], n = 664,265), (d) sildenafil vs. glimepiride (an anti-diabetic drug, n= 159,597), and (e) sildenafil vs. metformin (an anti-diabetic drug under an AD clinical trial [ClinicalTrials.gov Identifier: NCT00620191], n = 723,082). First, for each comparator, we estimated the propensity score by using the variables described in Table 1. We estimated the un-stratified Kaplan-Meier curves, conducted propensity score stratified (n strata = 10) two-sided log-rank test and applied a Cox model. (f) Hazard ratios (HR) and 95% confidence interval (CI) across five cohort studies. Propensity score stratified Cox-proportional hazards models were used for statistical inference of the hazard ratios (sildenafil n = 116,412; non-sildenafil n = 460,356; diltiazem n = 251,360; losartan n =664,265; glimepiride n = 159,597; metformin n = 723,082).

Figure 4.. Subgroup analyses of five drug cohort studies to evaluate confounding by disease comorbidities.

(a) Hazard ratios (HR) and 95% confidence intervals (CI) across five cohort studies after exclusion of individuals with coronary artery disease (CAD), hypertension (HT), and type-2 diabetes (T2D) (sildenafil n = 56,518; diltiazem n = 113,600; losartan n = 275,116; glimepiride n = 52,623; metformin n = 303,008). (b-d) HR and 95% CI plots across five cohort studies in individuals with CAD (b) (sildenafil n = 19,093; diltiazem n = 51,771; losartan n = 111,592; glimepiride n = 30,083; metformin n = 91,705), HT (c) (sildenafil n = 49,541; diltiazem n = 119,097; losartan n = 339,940; glimepiride n = 74,018; metformin n = 275,328), or T2D (d) (sildenafil n = 21,978; diltiazem n = 51,300; losartan n = 156,308; glimepiride n = 100,298; metformin n = 367,754). Non-sildenafil exposure population were matched to the exposures (ratio 4:1) by adjusting the initiation time of sildenafil, enrollment history, sex, and disease comorbidities (CAD, T2D, and HT). Propensity score stratified Cox-proportional hazards models and two-sided log-rank test were used to conduct statistical inference for the hazard ratios.

Figure 5.. Sex-specific and age-specific subgroup analyses across five drug cohort studies.

(a) Hazard ratios (HR) and 95% confidence intervals (CI) across five drug cohort studies for males and females respectively (sildenafil female n = 2,280 and male n = 114,132; non-sildenafil female n = 8,182 and male n = 452,174; diltiazem female n = 150,137 and male n = 101,223; losartan female n = 385,454 and male n = 278,811; glimepiride female n = 72,689 and male n = 86,908; metformin female n = 344,944 and male n = 378,138). (b) HR and 95% CI plots across five cohort studies for mild older individuals (65–74 years) and older individuals (75–100 years) (sildenafil 65–74 n = 89,875 and ≥75 n = 26,537; non-sildenafil 65–74 n = 264,209 and ≥75 n = 196,147; diltiazem 65–74 n = 115,892 and ≥75 n = 135,468; losartan 65–74 n = 389,084 and ≥75 n = 275,181; glimepiride 65–74 n = 97,243 and ≥75 n = 62,354; metformin 65–74 n = 505,210 and ≥75 n = 217,872). Non-sildenafil exposure population were matched to the exposures (ratio 4:1) by adjusting the initiation time of sildenafil, enrollment history, sex, and disease comorbidities (hypertension, type 2 diabetes, and coronary artery disease). Propensity score stratified Cox-proportional hazards models and two-sided log-rank test were used to conduct statistical inference for the hazard ratios.

Figure 6.. Experimental validation of sildenafil’s likely mechanism-of-action in Alzheimer’s disease (AD).

(a) Network analyses highlighting the inferred mechanism-of-action for sildenafil in AD. The molecular mechanisms of sildenafil were investigated via integration of known drug targets and experimentally validated AD seed genes into brain-specific human protein-protein interactome network (see Methods). Node size indicates the protein-coding gene expression level in brain compared with other 31 tissues from GTEx database. Larger size highlighting the high expression level in brain compared with other tissues. (b) Effects of sildenafil on LPS-induced activation of glycogen synthase kinase 3 beta (GSK3β) (c) and cyclin-dependent kinase 5 (CDK5) (d) in human microglia HMC3 cells. HMC3 cells were pretreated with sildenafil and followed LPS treatment (1 μg/mL, 30 min). The total cell lysates were collected and subjected to Western blot analysis. (e) Timeline of differentiation of AD iPSCs into forebrain neurons and downstream drug treatment. (f) iPSC HVRDi001-A-1 colonies, 5× magnification. Scale bar, 300 μm. (g) NPCs at day 7, 20× magnification. Scale bar, 100 μm. (h) Neural precursors at day 27, 20× magnification. Scale bar, 100 μm. (i) Anti-Tuj1 immunostaining of AD neurons after 6 days of treatment with DMSO or sildenafil (30 μM). Scale bar, 100 μm. (j) Decreased level of p-tau (181) after sildenafil treatment in neuronal lysates. Values are normalized to DMSO treatment group. Quantification data represent mean ± standard deviation (SD) of three independent experiments (n=3). P-value was computed by two-tailed student’s t-test. ***p < 0.001.