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

Uncovering New Therapeutic Targets for Amyotrophic Lateral Sclerosis and Neurological Diseases Using Real-World Data

Although attractive for relevance to real-world scenarios, real-world data (RWD) is typically used for drug repurposing and not therapeutic target discovery. Repurposing studies have identified few effective options in neurological diseases such as the rare disease, amyotrophic lateral sclerosis (ALS), which has no disease-modifying treatments available. We previously reclassified drugs by their simulated effects on proteins downstream of drug targets and observed class-level effects in the EHR, implicating the downstream protein as the source of the effect. Here, we developed a novel ALS-focused network medicine model using data from patient samples, the public domain, and consortia. With this model, we simulated drug effects on ALS and measured class effects on overall survival in retrospective EHR studies. We observed an increased but non-significant risk of death for patients taking drugs with complement system proteins downstream of their targets and experimentally validated drug effects on complement activation. We repeated this for six protein classes, three of which, including multiple chemokine receptors, were associated with a significantly increased risk for death, suggesting that targeting proteins such as CXCR5, CXCR3, chemokine signaling generally, or neuropeptide Y (NPY) could be advantageous therapeutic targets for these patients. We expanded our analysis to the neuroinflammatory condition, myasthenia gravis, and neurodegenerative disease, Parkinson's, and recovered similar effect sizes. We demonstrated the utility of network medicine for testing novel therapeutic effects using RWD and believe this approach may accelerate target discovery in neurological diseases, addressing the critical need for new therapeutic options.

Increased cGMP improves microvascular exercise training adaptations in diet-induced obesity

With the development of atherosclerosis, impaired microvascular function can result in diminished capacity for ambulation and is a risk factor for type 2 diabetes. Dynamic changes in vascular tone are determined, in large part, by the endothelial nitric oxide synthase (eNOS)/nitric oxide (NO)/cGMP axis. We used pharmacological gain of function of the eNOS/NO/cGMP axis in diet-induced obese (DIO) mice and reduced function in lean mice to test the hypothesis that functionality of this vascular control mechanism parallels the benefits of an exercise training regimen. DIO mice have 50% lower exercise capacity (P < 0.0001) than lean mice and were used for pharmacological gain of function. The phosphodiesterase-5a (PDE-5a) inhibitor, sildenafil, increases cGMP and was administered to DIO mice daily. In sedentary mice, neither acute nor chronic sildenafil improves exercise capacity. In contrast, chronic sildenafil synergizes with exercise training to improve performance during an incremental exercise test. Improved exercise performance was accompanied by a 40% increase in basal skeletal muscle capillary flow velocity and ∼20% increase in plasma-perfused capillary density measured via intravital microscopy. Loss of function was tested in lean mice hemizygous for endothelial cell (EC) specific eNOS creating an EC-eNOS knockdown (KD). EC-eNOS KD decreases capillary density and exercise tolerance in sedentary mice; however, it did not prevent exercise-training-induced improvements in endurance capacity. These data show that 1) increasing cGMP with sildenafil enhances microcirculatory function and exercise work tolerance that results from training; 2) eNOS KD does not prevent the microcirculatory or improvements in exercise tolerance with training. PDE-5a inhibitors combined with physical exercise are a potential mechanism for improving ambulation in patients with circulatory limitations.NEW & NOTEWORTHY This study used pharmacological gain-of-function and genetic loss-of-function approaches to test the hypothesis that the eNOS/NO/cGMP axis is central to exercise training adaptations in microcirculatory function and exercise capacity. Chronic but not acute treatment with the PDE5 inhibitor, sildenafil, synergizes with exercise training to improve performance with incremental exercise in obese mice; whereas endothelium-specific knockdown in eNOS does not blunt the microcirculatory adaptations and improvements in exercise tolerance with training.

Intervention points for the role of physical activity in prevention and treatment of Alzheimer's disease

Alzheimer's disease (AD) is a growing global health challenge with limited pharmacological treatments. Epidemiological studies link regular physical activity with a lower risk of AD and cognitive decline in general, whereas randomized controlled trials show that aerobic exercise slows disease progression and improves cognitive function. However the underlying mechanisms remain incompletely understood. In this review we discuss five likely intervention points through which physical activity may influence AD progression and pathology: (1) reducing neuroinflammation and amyloid beta (Aβ) aggregation, (2) enhancing clearance of Aβ aggregates, (3) increasing neuronal resilience, (4) promoting hippocampal neurogenesis and (5) strengthening cognitive reserve. Understanding which of these mechanistic links are most likely to drive the AD-protective effects of exercise could help refine lifestyle-based interventions to complement pharmacological treatments and inform future prevention strategies.

Multi-omics analysis reveals aspirin is associated with reduced risk of Alzheimer's disease

The urgent need for safe and effective therapies for Alzheimer's disease (AD) has spurred a growing interest in repurposing existing drugs to treat or prevent AD. In this study, we combined multi-omics and clinical data to investigate possible repurposing opportunities for AD. We performed transcriptome-wide association studies (TWAS) to construct gene expression signatures of AD from publicly available GWAS summary statistics, using both transcriptome prediction models for 49 tissues from the Genotype-Tissue Expression (GTEx) project and microglia-specific models trained on eQTL data from the Microglia Genomic Atlas (MiGA). We then identified compounds capable of reversing the AD-associated changes in gene expression observed in these signatures by querying the Connectivity Map (CMap) drug perturbation database. Out of >2,000 small-molecule compounds in CMap, aspirin emerged as the most promising AD repurposing candidate. To investigate the longitudinal effects of aspirin use on AD, we collected drug exposure and AD coded diagnoses from three independent sources of real-world data: electronic health records (EHRs) from Vanderbilt University Medical Center (VUMC) and the National Institutes of Health All of Us Research Program, along with national healthcare claims from the MarketScan Research Databases. In meta-analysis of EHR data from VUMC and All of Us , we found that aspirin use before age 65 was associated with decreased risk of incident AD (hazard ratio=0.76, 95% confidence interval [CI]: 0.64-0.89, P =0.001). Consistent with the findings utilizing EHR data, analysis of claims data from MarketScan revealed significantly lower odds of aspirin exposure among AD cases compared to matched controls (odds ratio=0.32, 95% CI: 0.28-0.38, P <0.001). Our results demonstrate the value of integrating genetic and clinical data for drug repurposing studies and highlight aspirin as a promising repurposing candidate for AD, warranting further investigation in clinical trials.

A Genetics-guided Integrative Framework for Drug Repurposing: Identifying Anti-hypertensive Drug Telmisartan for Type 2 Diabetes

Drug development is a long and costly process, and repurposing existing drugs for use toward a different disease or condition may serve as a cost-effective alternative. As drug targets with genetic support have a doubled success rate, genetics-informed drug repurposing holds promise in translating genetic findings into therapeutics. In this study, we developed a Genetics Informed Network-based Drug Repurposing via in silico Perturbation (GIN-DRIP) framework and applied the framework to repurpose drugs for type-2 diabetes (T2D). In GIN-DRIP for T2D, it integrates multi-level omics data to translate T2D GWAS signals into a genetics-informed network that simultaneously encodes gene importance scores and a directional effect (up/down) of risk genes for T2D; it then bases on the GIN to perform signature matching with drug perturbation experiments to identify drugs that can counteract the effect of T2D risk alleles. With this approach, we identified 3 high-confidence FDA-approved candidate drugs for T2D, and validated telmisartan, an anti-hypertensive drug, in our EHR data with over 3 million patients. We found that telmisartan users were associated with a reduced incidence of T2D compared to users of other anti-hypertensive drugs and non-users, supporting the therapeutic potential of telmisartan for T2D. Our framework can be applied to other diseases for translating GWAS findings to aid drug repurposing for complex diseases.

Sildenafil and risk of Alzheimer disease: a systematic review and meta-analysis

BACKGROUND

Alzheimer Disease (AD) affects more than 50 million people worldwide, with 10 million new diagnosis each year. The link between Sildenafil, a Phosphodiesterase-5 (PDE5) inhibitor, and risk of AD has been debated. We conducted the first meta-analysis on the association between Sildenafil use and risk of AD.

METHODS

We searched MEDLINE and Embase from inception to March 11, 2024 to identify cohort, case-control studies comparing the frequency of AD in patients taking Sildenafil with those without. We computed risk ratios (RR) and hazard ratios (HR) with accompanying 95% Confidence Intervals (CIs) for each study, and pooled the results using a random-effects meta-analysis.

RESULTS

Out of 415 studies that were screened initially, 5 studies comprising 885,380 patients were included for analysis. Sildenafil use was associated with a reduced risk of developing AD by two-fold compared to non-use (HR: 0.47, 95% CI: 0.27-0.82, p<0.001). There was a similar association in risk reduction of AD in patients on PDE5 inhibitors compared to non-use (RR: 0.55, 95% CI: 0.38-0.80, p=0.002).

CONCLUSIONS

Our meta-analysis showed that the use of Sildenafil is associated with a reduced risk of developing AD by two-fold. Further randomized control trials to ascertain the effect of Sildenafil on AD pathology would be useful.

Drug repurposing for Alzheimer's disease and other neurodegenerative disorders

Repurposed drugs provide a rich source of potential therapies for Alzheimer's disease (AD) and other neurodegenerative disorders (NDD). Repurposed drugs have information from non-clinical studies, phase 1 dosing, and safety and tolerability data collected with the original indication. Computational approaches, "omic" studies, drug databases, and electronic medical records help identify candidate therapies. Generic repurposed agents lack intellectual property protection and are rarely advanced to late-stage trials for AD/NDD. In this review we define repurposing, describe the advantages and challenges of repurposing, offer strategies for overcoming the obstacles, and describe the key contributions of repurposing to the drug development ecosystem.

A novel phosphodiesterase 5 inhibitor, RF26, improves memory impairment and ameliorates tau aggregation and neuroinflammation in the P301S tauopathy mouse model of Alzheimer's disease

Phosphodiesterase-5 (PDE5) inhibitors are primarily used in the treatment of erectile dysfunction and pulmonary hypertension, but have also been reported to have a potential therapeutic effect for the treatment of Alzheimer's disease (AD). This is likely to be through stimulation of nitric oxide (NO)/cyclic guanosine monophosphate (cGMP) signaling by elevating cGMP, a secondary messenger involved in processes of neuroplasticity. In the present study, we evaluated the efficacy of a novel PDE5 inhibitor, RF26, using P301S tauopathy mice model. A body of experimental evidence suggests that the development of tau inclusions leads to the neurodegeneration observed in tauopathies, including AD, Frontotemporal dementia (FTD), Supranuclear palsy and others. RF26 successfully targeted NO/cGMP signaling pathway and showed a significant improvement of spatial memory task performance of P301S mice using Morris Water Maze and T-maze. Furthermore, RF26 -treated mice showed a significant reduction of phosphorylated tau load, gliosis and downregulated pro-inflammatory cytokines. The presented data support the efficacy of RF26 as a potent PDE5 inhibitor and calls for further investigation as a potential therapeutic drug for Alzheimer's and other tauopathy related neurological disorders.

A network-based systems genetics framework identifies pathobiology and drug repurposing in Parkinson's disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder. However, current treatments only manage symptoms and lack the ability to slow or prevent disease progression. We utilized a systems genetics approach to identify potential risk genes and repurposable drugs for PD. First, we leveraged non-coding genome-wide association studies (GWAS) loci effects on five types of brain-specific quantitative trait loci (xQTLs, including expression, protein, splicing, methylation and histone acetylation) under the protein-protein interactome (PPI) network. We then prioritized 175 PD likely risk genes (pdRGs), such as SNCA, CTSB, LRRK2, DGKQ, and CD44, which are enriched in druggable targets and differentially expressed genes across multiple human brain-specific cell types. Integrating network proximity-based drug repurposing and patient electronic health record (EHR) data observations, we identified Simvastatin as being significantly associated with reduced incidence of PD (hazard ratio (HR) = 0.91 for fall outcome, 95% confidence interval (CI): 0.87-0.94; HR = 0.88 for dementia outcome, 95% CI: 0.86-0.89) after adjusting for 267 covariates. In summary, our network-based systems genetics framework identifies potential risk genes and repurposable drugs for PD and other neurodegenerative diseases if broadly applied.

DrugMAP 2.0: molecular atlas and pharma-information of all drugs

The escalating costs and high failure rates have decelerated the pace of drug development, which amplifies the research interests in developing combinatorial/repurposed drugs and understanding off-target adverse drug reaction (ADR). In other words, it is demanded to delineate the molecular atlas and pharma-information for the combinatorial/repurposed drugs and off-target interactions. However, such invaluable data were inadequately covered by existing databases. In this study, a major update was thus conducted to the DrugMAP, which accumulated (a) 20831 combinatorial drugs and their interacting atlas involving 1583 pharmacologically important molecules; (b) 842 repurposed drugs and their interacting atlas with 795 molecules; (c) 3260 off-targets relevant to the ADRs of 2731 drugs and (d) various types of pharmaceutical information, including diverse ADMET properties, versatile diseases, and various ADRs/off-targets. With the growing demands for discovering combinatorial/repurposed therapies and the rapidly emerging interest in AI-based drug discovery, DrugMAP was highly expected to act as an indispensable supplement to existing databases facilitating drug discovery, which was accessible at: https://idrblab.org/drugmap/.

Network Medicine-Based Strategy Identifies Maprotiline as a Repurposable Drug by Inhibiting PD-L1 Expression via Targeting SPOP in Cancer

Immune checkpoint inhibitors (ICIs) are drugs that inhibit immune checkpoint (ICP) molecules to restore the antitumor activity of immune cells and eliminate tumor cells. Due to the limitations and certain side effects of current ICIs, such as programmed death protein-1, programmed cell death-ligand 1, and cytotoxic T lymphocyte-associated antigen 4 (CTLA4) antibodies, there is an urgent need to find new drugs with ICP inhibitory effects. In this study, a network-based computational framework called multi-network algorithm-driven drug repositioning targeting ICP (Mnet-DRI) is developed to accurately repurpose novel ICIs from ≈3000 Food and Drug Administration-approved or investigational drugs. By applying Mnet-DRI to PD-L1, maprotiline (MAP), an antidepressant drug is repurposed, as a potential PD-L1 modifier for colorectal and lung cancers. Experimental validation revealed that MAP reduced PD-L1 expression by targeting E3 ubiquitin ligase speckle-type zinc finger structural protein (SPOP), and the combination of MAP and anti-CTLA4 in vivo significantly enhanced the antitumor effect, providing a new alternative for the clinical treatment of colorectal and lung cancer.

Data-driven discovery of associations between prescribed drugs and dementia risk: A systematic review

Abstract

Recent clinical trials on slowing dementia progression have led to renewed focus on finding safer, more effective treatments. One approach to identify plausible candidates is to assess whether existing medications for other conditions may affect dementia risk. We conducted a systematic review to identify studies adopting a data-driven approach to investigate the association between a wide range of prescribed medications and dementia risk. We included 14 studies using administrative or medical records data for more than 130 million individuals and 1 million dementia cases. Despite inconsistencies in identifying specific drugs that may modify Alzheimer's or dementia risk, some themes emerged for drug classes with biological plausibility. Antimicrobials, vaccinations, and anti-inflammatories were associated with reduced risk, while diabetes drugs, vitamins and supplements, and antipsychotics were associated with increased risk. We found conflicting evidence for antihypertensives and antidepressants. Drug repurposing for use in dementia is an urgent priority. Our findings offer a basis for prioritizing candidates and exploring underlying mechanisms.

Highlights

·We present a systematic review of studies reporting association between drugs prescribed for other conditions and risk of dementia including 139 million people and 1 million cases of dementia.·Our work supports some previously reported associations, for example, showing decreased risk of dementia with drugs to treat inflammatory disease and increased risk with antipsychotic treatment.·Antimicrobial treatment was perhaps more surprisingly associated with decreased risk, supportive of recent increased interest in this potential therapeutic avenue.·Our work should help prioritize drugs for entry into adaptive platform trials in Alzheimer's disease and will serve as a useful resource for those investigating drugs or classes of drugs and risk of dementia.

Single-microglia transcriptomic transition network-based prediction and real-world patient data validation identifies ketorolac as a repurposable drug for Alzheimer's disease

INTRODUCTION

High microglial heterogeneities hinder the development of microglia-targeted treatment for Alzheimer's disease (AD).

METHODS

We integrated 0.7 million single-nuclei RNA-sequencing transcriptomes from human brains using a variational autoencoder. We predicted AD-relevant microglial subtype-specific transition networks for disease-associated microglia (DAM), tau microglia, and neuroinflammation-like microglia (NIM). We prioritized drugs by specifically targeting microglia-specific transition networks and validated drugs using two independent real-world patient databases.

RESULTS

We identified putative AD molecular drivers (e.g., SYK, CTSB, and INPP5D) in transition networks of DAM and NIM. Via specifically targeting NIM, we identified that usage of ketorolac was associated with reduced AD incidence in both MarketScan (hazard ratio [HR] = 0.89) and INSIGHT (HR = 0.83) Clinical Research Network databases, mechanistically supported by ketorolac-treated transcriptomic data from AD patient induced pluripotent stem cell-derived microglia.

DISCUSSION

This study offers insights into the pathobiology of AD-relevant microglial subtypes and identifies ketorolac as a potential anti-inflammatory treatment for AD.

HIGHLIGHTS

An integrative analysis of ≈ 0.7 million single-nuclei RNA-sequencing transcriptomes from human brains identified Alzheimer's disease (AD)-relevant microglia subtypes. Network-based analysis identified putative molecular drivers (e.g., SYK, CTSB, INPP5D) of transition networks between disease-associated microglia (DAM) and neuroinflammation-like microglia (NIM). Via network-based prediction and population-based validation, we identified that usage of ketorolac (a US Food and Drug Administration-approved anti-inflammatory medicine) was associated with reduced AD incidence in two independent patient databases. Mechanistic observation showed that ketorolac treatment downregulated the Type-I interferon signaling in patient induced pluripotent stem cell-derived microglia, mechanistically supporting its protective effects in real-world patient databases.

Network-based drug repurposing for psychiatric disorders using single-cell genomics

Neuropsychiatric disorders lack effective treatments due to a limited understanding of underlying cellular and molecular mechanisms. To address this, we integrated population-scale single-cell genomics data and analyzed cell-type-level gene regulatory networks across schizophrenia, bipolar disorder, and autism (23 cell classes/subclasses). Our analysis revealed potential druggable transcription factors co-regulating known risk genes that converge into cell-type-specific co-regulated modules. We applied graph neural networks on those modules to prioritize novel risk genes and leveraged them in a network-based drug repurposing framework to identify 220 drug molecules with the potential for targeting specific cell types. We found evidence for 37 of these drugs in reversing disorder-associated transcriptional phenotypes. Additionally, we discovered 335 drug-associated cell-type eQTLs, revealing genetic variation's influence on drug target expression at the cell-type level. Our results provide a single-cell network medicine resource that provides mechanistic insights for advancing treatment options for neuropsychiatric disorders.

Association between prescription drugs and all-cause mortality risk in the UK population

Although most drugs currently approved are meant to treat specific diseases or symptoms, it has been hypothesized that some might bear a beneficial effect on lifespan in healthy older individuals, outside of their specific disease indication. Such drugs include, among others, metformin, SGLT2 inhibitors and rapamycin. Since 2006, the UK biobank has recorded prescription medication and mortality data for over 500'000 participants, aged between 40 and 70 years old. In this work, we examined the impact of the top 406 prescribed medications on overall mortality rates within the general population of the UK. As expected, most drugs were linked to a shorter lifespan, likely due to the life-limiting nature of the diseases they are prescribed to treat. Importantly, a few drugs were associated with increased lifespans, including notably Sildenafil, Atorvastatin, Naproxen and Estradiol. These retrospective results warrant further investigation in randomized controlled trials.

Ras, RhoA, and vascular pharmacology in neurodevelopment and aging

Small GTPases Ras, Rac, and RhoA are crucial regulators of cellular functions. They also act in dysregulated cell proliferation and transformation. Multiple publications have focused on illuminating their roles and mechanisms, including in immune system pathologies. Their functions in neurology-related diseases, neurodegeneration and neurodevelopment, are also emerging, as well as their potential as pharmacological targets in both pathologies. Observations increasingly suggest that these pathologies may relate to activation (or suppression) of signaling by members of the Ras superfamily, especially Ras, Rho, and Rac isoforms, and components of their signaling pathways. Germline (or embryonic) mutations that they harbor are responsible for neurodevelopmental disorders, such as RASopathies, autism spectrum disorder, and dilated cardiomyopathy. In aging, they promote neurodegenerative diseases, with Rho GTPase featuring in their pharmacology, as in the case of Alzheimer's disease (AD). Significantly, drugs with observed anti-AD activity, particularly those involved in cardiovascular systems, are associated with the RhoA signaling, as well as cerebral vasculature in brain development and aging. This leads us to suggest that anti-AD drugs could inform neurodevelopmental disorders, including pediatric low-grade gliomas pharmacology. Neurodevelopmental disorders associated with RhoA, like autism, are also connected with vascular systems, thus could be targets of vascular system-connected drugs.

An endophenotype network strategy uncovers YangXue QingNao Wan suppresses Aβ deposition, improves mitochondrial dysfunction and glucose metabolism

BACKGROUND

Alzheimer's disease (AD), an escalating global health issue, lacks effective treatments due to its complex pathogenesis. YangXue QingNao Wan (YXQNW) is a China Food and Drug Administration (CFDA)- approved TCM formula that has been repurposed in clinical Phase II for the treatment of AD. Identifying YXQNW's active ingredients and their mechanisms is crucial for developing effective AD treatments.

PURPOSE

This study aims to elucidate the anti-AD effects of YXQNW and to explore its potential therapeutic mechanisms employing an endophenotype network strategy.

METHODS

Herein we present an endophenotype network strategy that combines active ingredient identification in rat serum, network proximity prediction, metabolomics, and in vivo experimental validation in two animal models. Specially, utilizing UPLC-Q-TOF-MS/MS, active ingredients are identified in YXQNW to build a drug-target network. We applied network proximity to identify potential AD pathological mechanisms of YXQNW via integration of drug-target network, AD endophenotype gene sets, and human protein interactome, and validated related mechanisms in two animal models. In a d-galactose-induced senescent rat model, YXQNW was administered at varying doses for cognitive and neuronal assessments through behavioral tests, Nissl staining, and transmission electron microscopy (TEM). Metabolomic analysis with LC-MS revealed YXQNW's influence on brain metabolites, suggesting therapeutic pathways. Levels of key proteins and biochemicals were measured by WB and ELISA, providing insights into YXQNW's neuroprotective mechanisms. In addition, 5×FAD model mice were used and administered YXQNW by gavage for 14 days at two doses. Amyloid-β levels, transporter expression, and cerebral blood flow have been detected by MRI and biochemical assays.

RESULTS

The network proximity analysis showed that the effect of YXQNW on AD was highly correlated with amyloid β, synaptic function, glucose metabolism and mitochondrial function. The results of metabolomics combined with in vivo experimental validation suggest that YXQNW has the potential to ameliorate glucose transport abnormalities in the brain by upregulating the expression of GLUT1 and GLUT3, while further enhancing glucose metabolism through increased O-GlcNAcylation and mitigating mitochondrial dysfunction via the AMPK/Sirt1 pathway, thereby improving d-galactose-induced cognitive deficits in rats. Additionally, YXQNW treatment significantly decreased Aβ1-42 levels and enhanced cerebral blood flow (CBF) in the hippocampus of 5×FAD mice. while mechanistic findings indicated that YXQNW treatment increased the expression of ABCB1, an Aβ transporter, in 5×FAD model mice to promote the clearance of Aβ from the brain and alleviate AD-like symptoms.

CONCLUSIONS

This study reveals that YXQNW may mitigate AD by inhibiting Aβ deposition and ameliorating mitochondrial dysfunction and glucose metabolism, thus offering a promising therapeutic approach for AD.

Network-based identification and mechanism exploration of active ingredients against Alzheimer's disease via targeting endoplasmic reticulum stress from traditional chinese medicine

Alzheimer's disease is a neurodegenerative disease that leads to dementia and poses a serious threat to the health of the elderly. Traditional Chinese medicine (TCM) presents as a promising novel therapeutic therapy for preventing and treating dementia. Studies have shown that natural products derived from kidney-tonifying herbs can effectively inhibit AD. Furthermore, endoplasmic reticulum (ER) stress is a critical factor in the pathology of AD. Regulation of ER stress is a crucial approach to prevent and treat AD. Thus, in this study, we first collected kidney-tonifying herbs, integrated chemical ingredients from multiple TCM databases, and constructed a comprehensive drug-target network. Subsequently, we employed the endophenotype network (network proximity) method to identify potential active ingredients in kidney-tonifying herbs that prevented AD via regulating ER stress. By combining the predicted outcomes, we discovered that 32 natural products could ameliorate AD pathology via regulating ER stress. After a comprehensive evaluation of the multi-network model and systematic pharmacological analyses, we further selected several promising compounds for in vitro testing in the APP-SH-SY5Y cell model. Experimental results showed that echinacoside and danthron were able to effectively reduce ER stress-mediated neuronal apoptosis by inhibiting the expression levels of BIP, p-PERK, ATF6, and CHOP in APP-SH-SY5Y cells. Overall, this study utilized the endophenotype network to preliminarily decipher the effective material basis and potential molecular mechanism of kidney-tonifying Chinese medicine for prevention and treatment of AD.

3DSTarPred: A Web Server for Target Prediction of Bioactive Small Molecules Based on 3D Shape Similarity

Target identification plays a critical role in preclinical drug development. The in silico approach has been developed and widely applied to assist medicinal chemists and pharmacologists in drug target identification. There are many target prediction web servers available today that have revealed both advantages and shortcomings in practical applications. Here, we present 3DSTarPred, a web server for three-dimensional (3D) shape similarity-based target prediction of small molecules. A benchmark study showed that 3DSTarPred achieved a target prediction success rate of 76.27%, which was higher than that of existing target prediction web servers. In addition, the performance of 3DSTarPred in the target prediction of diverse substructures/superstructures was also better than that of the existing target prediction web servers. In case studies, 3DSTarPred was used to identify the potential targets of two small molecules, one being kaempferol, a natural lead compound for the treatment of Alzheimer's disease (AD), and the other being sildenafil, a candidate for drug repurposing in AD. The case studies further demonstrated the reliability and success of 3DSTarPred in practice. The 3DSTarPred server is freely available at http://3dstarpred.pumc.wecomput.com.

Network medicine informed multiomics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. We developed a network medicine methodology via integrating human brain multi-omics data to prioritize drug targets and repurposable treatments for ALS. We leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on human brain expression quantitative trait loci (QTL) (eQTL), protein QTL (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Using a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in known ALS pathobiological pathways. Applying network proximity analysis of predicted ALS-associated genes and drug-target networks under the human protein-protein interactome (PPI) model, we identified potential repurposable drugs (i.e., Diazoxide and Gefitinib) for ALS. Subsequent validation established preclinical evidence for top-prioritized drugs. In summary, we presented a network-based multi-omics framework to identify drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

Multimodal action of phosphodiesterase 5 inhibitors against neurodegenerative disorders: An update review

Phosphodiesterase type 5 (PDE5) is an enzyme primarily found in the smooth muscle of the corpus cavernosum and also highly expressed in the substantia nigra, cerebellum, caudate, hippocampal regions and cerebellar purkinje cells, responsible for selectively breaking down cyclic guanosine monophosphate (cGMP) into 5'-GMP and regulate intracellular cGMP levels. As a second messenger, cyclic GMP enhances signals at postsynaptic receptors and triggers downstream effector molecules, leading to changes in gene expression and neuronal responses. Additionally, cGMP signaling transduction cascade, present in the brain, is also essential for learning and memory processes. Mechanistically, PDE5 inhibitors share structural similarities with cGMP, competitively binding to PDE5 and inhibiting cGMP hydrolysis. This action enhances the effects of nitric oxide, resulting in anti-inflammatory and neuroprotective effects. Neurodegenerative disorders entail the progressive loss of neuron structure, culminating in neuronal cell death, with currently available drugs providing only limited symptomatic relief, rendering neurodegeneration considered incurable. PDE5 inhibitors have recently emerged as a potential therapeutic approach for neurodegeneration, neuroinflammation, and diseases involving cognitive impairment. This review elucidates the principal roles of 3',5'-cyclic adenosine monophosphate (cAMP) and cGMP signaling pathways in neuronal functions, believed to play pivotal roles in the pathogenesis of various neurodegenerative disorders. It provides an updated assessment of PDE5 inhibitors as disease-modifying agents for conditions such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, cerebral ischemia, Huntington's disease, and neuroinflammation. The paper aims to review the current understanding of PDE5 inhibitors, which concurrently regulate both cAMP and cGMP signaling pathways, positing that they may exert complementary and synergistic effects in modifying neurodegeneration, thus presenting a novel direction in therapeutic discovery. Moreover, the review provides critical about biological functions, therapeutic potentials, limitations, challenges, and emerging applications of selective PDE5 inhibitors. This comprehensive overview aims to guide future academic and industrial endeavors in this field.

Unlocking the potential of signature-based drug repurposing for anticancer drug discovery

Cancer is the leading cause of death worldwide and is often associated with tumor relapse even after chemotherapeutics. This reveals malignancy is a complex process, and high-throughput omics strategies in recent years have contributed significantly in decoding the molecular mechanisms of these complex events in cancer. Further, the omics studies yield a large volume of cancer-specific molecular signatures that promote the discovery of cancer therapy drugs by a method termed signature-based drug repurposing. The drug repurposing method identifies new uses for approved drugs beyond their intended initial therapeutic use, and there are several approaches to it. In this review, we discuss signature-based drug repurposing in cancer, how cancer omics have revolutionized this method of drug discovery, and how one can use the cancer signature data for repurposed drug identification by providing a step-by-step procedural handout. This modern approach maximizes the use of existing therapeutic agents for cancer therapy or combination therapy to overcome chemotherapeutics resistance, making it a pragmatic and efficient alternative to traditional resource-intensive and time-consuming methods.

Phosphodiesterase-5 inhibitors use and the risk of alzheimer's disease: a systematic review and meta-analysis

BACKGROUND

The management of Alzheimer's disease (AD) poses considerable challenges, necessitating the pursuit of innovative therapeutic approaches. Recent research has spotlighted the promising role of phosphodiesterase type 5 inhibitors (PDE5Is) in reducing the prevalence of AD, utilizing their vasodilatory properties to suggest a potential neuroprotective effect. This meta-analysis and systematic review aims to assess the relationship between the use of PDE5Is and the risk of AD.

METHODS

A detailed examination was carried out across several electronic databases till March 2024, including PubMed, Web of Science, Scopus, CENTRAL, and Embase. The focus was on identifying studies that compare the occurrence of AD among PDE5I users vs non-users. Through a random-effects model, pooled hazard ratios (HRs) were calculated, in alignment with guidelines from the Cochrane Handbook for Systematic Reviews and Meta-Analysis and the PRISMA standards.

RESULTS

This analysis included six studies, cumulating a participant count of 8,337,313, involving individuals treated with sildenafil, tadalafil, and vardenafil, against a control group undergoing other or no treatments. The cumulative HR for AD risk among PDE5I users versus the control group was 0.53 (95% CI: 0.32-0.86, p = 0.008), signaling a markedly reduced likelihood of AD development in the PDE5I group. Particularly, sildenafil usage showed a significant risk reduction (HR: 0.46, 95% CI: 0.31-0.70, p < 0.001), while findings for tadalafil and vardenafil were not significant. Test of subgroup differences found no difference between male and female participants in the risk of AD.

CONCLUSIONS

Our findings suggest that the use of PDE5Is is associated with a reduced risk of AD, highlighting its potential as a protective agent against neurodegenerative diseases. Given the very low quality of evidence and the heterogeneity among the included studies, further high-quality research is warranted to confirm these findings and elucidate the underlying mechanisms. Register number PROSPERO 2024: CRD42024522197.

A network-based systems genetics framework identifies pathobiology and drug repurposing in Parkinson's disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder. However, current treatments are directed at symptoms and lack ability to slow or prevent disease progression. Large-scale genome-wide association studies (GWAS) have identified numerous genomic loci associated with PD, which may guide the development of disease-modifying treatments. We presented a systems genetics approach to identify potential risk genes and repurposable drugs for PD. First, we leveraged non-coding GWAS loci effects on multiple human brain-specific quantitative trait loci (xQTLs) under the protein-protein interactome (PPI) network. We then prioritized a set of PD likely risk genes (pdRGs) by integrating five types of molecular xQTLs: expression (eQTLs), protein (pQTLs), splicing (sQTLs), methylation (meQTLs), and histone acetylation (haQTLs). We also integrated network proximity-based drug repurposing and patient electronic health record (EHR) data observations to propose potential drug candidates for PD treatments. We identified 175 pdRGs from QTL-regulated GWAS findings, such as SNCA, CTSB, LRRK2, DGKQ, CD38 and CD44. Multi-omics data validation revealed that the identified pdRGs are likely to be druggable targets, differentially expressed in multiple cell types and impact both the parkin ubiquitin-proteasome and alpha-synuclein (a-syn) pathways. Based on the network proximity-based drug repurposing followed by EHR data validation, we identified usage of simvastatin as being significantly associated with reduced incidence of PD (fall outcome: hazard ratio (HR) = 0.91, 95% confidence interval (CI): 0.87-0.94; for dementia outcome: HR = 0.88, 95% CI: 0.86-0.89), after adjusting for 267 covariates. Our network-based systems genetics framework identifies potential risk genes and repurposable drugs for PD and other neurodegenerative diseases if broadly applied.

Network Medicine Approach Unravels Endophenotype Signature in Alzheimer's Disease through Large-Scale Comparative Proteomics Analysis: Vascular Dysfunction as a Prime Example

Alzheimer's disease (AD) is the most common neurodegenerative disease burdening public health. We proposed a network-based infrastructure to identify protein signatures for five AD pathological endophenotypes: amyloidosis, tauopathy, vascular dysfunction, lysosomal dysfunction, and neuroinflammation. We analyzed 23 proteomic data sets from AD patients and transgenic mouse models, using network proximity to measure associations between endophenotype modules and differentially expressed proteins (DEPs) in the integrated AD proteome. We focused on the vascular dysfunction signature with 21 DEPs by integrating RNA-seq, single-cell transcriptomics, GWAS, and literature. Experiments on APP/PS1 and MCAO models highlighted three proteins (SEPT5, SNAP25, STXBP1) as novel AD biomarker candidates. This study demonstrates a network medicine framework for deciphering endophenotype signatures in AD.

Polypharmacological Potential of Phosphodiesterase 5 Inhibitors for the Treatment of Neurocognitive Disorders

The prevalence of neurocognitive disorders (NCD) increases every year as the population continues to age, leading to significant global health concerns. Overcoming this challenge requires identifying biomarkers, risk factors, and effective therapeutic interventions that might provide meaningful clinical benefits. For Alzheimer's disease (AD), one of the most studied NCD, approved drugs include acetylcholinesterase inhibitors (rivastigmine, donepezil, and galantamine), an NMDA receptor antagonist (memantine), and anti-amyloid monoclonal antibodies (aducanumab and lecanemab). These drugs offer limited relief, targeting singular pathological processes of the AD. Given the multifactorial nature of the NCDs, a poly-pharmacological strategy may lead to improved outcomes compared to the current standard of care. In this regard, phosphodiesterase 5 (PDE5) inhibitors emerged as promising drug candidates for the treatment of neurocognitive disorders. These inhibitors increase cGMP levels and CREB signaling, thus enhancing learning, memory and neuroprotection, while reducing Aβ deposition, tau phosphorylation, oxidative stress, and neuroinflammation. In the present article, we evaluate the therapeutic potential of different PDE5 inhibitors to outline their multifaceted impact in the NCDs.

Increased cGMP improves microvascular exercise training adaptations independent of endothelial nitric oxide synthase

Impaired microvascular function is a hallmark of pre-diabetes. With development of atherosclerosis this impaired microvascular function can result in diminished capacity for ambulation and is a risk factor for Type 2 Diabetes. Dynamic changes in vascular tone are determined, in large part, by the eNOS/NO/cGMP axis. We used gain of function of the eNOS/NO/cGMP axis in diet-induced obese (DIO) mice and reduced function in lean mice to test the hypothesis that functionality of this vascular control mechanism parallels the benefits of an exercise training regimen. DIO mice have lower exercise capacity than lean mice and were used for pharmacological gain of function. The PDE-5a inhibitor - sildenafil - increases cGMP and was administered to DIO mice daily. In sedentary mice, we find that sildenafil does not improve exercise capacity. In contrast, it amplifies the microcirculatory effects of exercise training. Sildenafil synergizes with exercise training to improve performance during an incremental exercise test. Improved exercise performance was accompanied by increased skeletal muscle capillary flow velocity and capillary density measured via intravital microscopy. Loss of function was tested in lean mice hemizygous for endothelial cell (EC) specific eNOS creating an EC-eNOS knockdown (KD). EC-eNOS KD decreases capillary density and exercise tolerance in sedentary mice; however, it did not prevent exercise-training induced improvements in endurance capacity. These data show that 1) increasing cGMP with sildenafil enhances microcirculatory function and exercise work tolerance that results from training; 2) eNOS KD does not prevent the microcirculatory or improvements in exercise tolerance with training. PDE-5a inhibitors combined with physical exercise are a potential mechanism for improving ambulation in patients with circulatory limitations.

Identification of Parkinson's disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data

Parkinson's disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals' phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.

Network Medicine: A Potential Approach for Virtual Drug Screening

Traditional drug screening methods typically focus on a single protein target and exhibit limited efficiency due to the multifactorial nature of most diseases, which result from disturbances within complex networks of protein-protein interactions rather than single gene abnormalities. Addressing this limitation requires a comprehensive drug screening strategy. Network medicine is rooted in systems biology and provides a comprehensive framework for understanding disease mechanisms, prevention, and therapeutic innovations. This approach not only explores the associations between various diseases but also quantifies the relationships between disease genes and drug targets within interactome networks, thus facilitating the prediction of drug-disease relationships and enabling the screening of therapeutic drugs for specific complex diseases. An increasing body of research supports the efficiency and utility of network-based strategies in drug screening. This review highlights the transformative potential of network medicine in virtual therapeutic screening for complex diseases, offering novel insights and a robust foundation for future drug discovery endeavors.

Cerebrovascular Effects of Sildenafil in Small Vessel Disease: The OxHARP Trial

BACKGROUND

Vascular cognitive impairment due to cerebral small vessel disease is associated with cerebral pulsatility, white matter hypoperfusion, and reduced cerebrovascular reactivity (CVR), and is potentially improved by endothelium-targeted drugs such as cilostazol. Whether sildenafil, a phosphodiesterase-5 inhibitor, improves cerebrovascular dysfunction is unknown.

METHODS

OxHARP trial (Oxford Haemodynamic Adaptation to Reduce Pulsatility) was a double-blind, randomized, placebo-controlled, 3-way crossover trial after nonembolic cerebrovascular events with mild-moderate white matter hyperintensities (WMH), the most prevalent manifestation of cerebral small vessel disease. The primary outcome assessed the superiority of 3 weeks of sildenafil 50 mg thrice daily versus placebo (mixed-effect linear models) on middle cerebral artery pulsatility, derived from peak systolic and end-diastolic velocities (transcranial ultrasound), with noninferiority to cilostazol 100 mg twice daily. Secondary end points included the following: cerebrovascular reactivity during inhalation of air, 4% and 6% CO2 on transcranial ultrasound (transcranial ultrasound-CVR); blood oxygen-level dependent-magnetic resonance imaging within WMH (CVR-WMH) and normal-appearing white matter (CVR-normal-appearing white matter); cerebral perfusion by arterial spin labeling (magnetic resonance imaging pseudocontinuous arterial spin labeling); and resistance by cerebrovascular conductance. Adverse effects were compared by Cochran Q.

RESULTS

In 65/75 (87%) patients (median, 70 years;79% male) with valid primary outcome data, cerebral pulsatility was unchanged on sildenafil versus placebo (0.02, -0.01 to 0.05; P=0.18), or versus cilostazol (-0.01, -0.04 to 0.02; P=0.36), despite increased blood flow (∆ peak systolic velocity, 6.3 cm/s, 3.5-9.07; P<0.001; ∆ end-diastolic velocity, 1.98, 0.66-3.29; P=0.004). Secondary outcomes improved on sildenafil versus placebo for CVR-transcranial ultrasound (0.83 cm/s per mm Hg, 0.23-1.42; P=0.007), CVR-WMH (0.07, 0-0.14; P=0.043), CVR-normal-appearing white matter (0.06, 0.00-0.12; P=0.048), perfusion (WMH: 1.82 mL/100 g per minute, 0.5-3.15; P=0.008; and normal-appearing white matter, 2.12, 0.66-3.6; P=0.006) and cerebrovascular resistance (sildenafil-placebo: 0.08, 0.05-0.10; P=4.9×10-8; cilostazol-placebo, 0.06, 0.03-0.09; P=5.1×10-5). Both drugs increased headaches (P=1.1×10-4), while cilostazol increased moderate-severe diarrhea (P=0.013).

CONCLUSIONS

Sildenafil did not reduce pulsatility but increased cerebrovascular reactivity and perfusion. Sildenafil merits further study to determine whether it prevents the clinical sequelae of small vessel disease.

REGISTRATION

URL: https://www.clinicaltrials.gov/study/NCT03855332; Unique identifier: NCT03855332.

In-silico approaches to assessing multiple high-level drug-drug and drug-disease adverse drug effects

INTRODUCTION

Pharmacovigilance plays a pivotal role in monitoring adverse events (AEs) related to chemical substances in human/animal populations. With increasing spontaneous-reporting systems, researchers turned to in-silico approaches to efficiently analyze drug safety profiles. Here, we review in-silico methods employed for assessing multiple drug-drug/drug-disease AEs covered by comparative analyses and visualization strategies.

AREAS COVERED

Disproportionality, involving multi-stage statistical methodologies and data processing, identifies safety signals among drug-AE pairs. By stratifying data based on disease indications/demographics, researchers address confounders and assess drug safety. Comparative analyses, including clustering techniques and visualization techniques, assess drug similarities, patterns, and trends, calculate correlations, and identify distinct toxicities. Furthermore, we conducted a thorough Scopus search on 'pharmacovigilance,' yielding 5,836 publications spanning 2003 to 2023.

EXPERT OPINION

Pharmacovigilance relies on diverse data sources, presenting challenges in the integration of in-silico approaches and requiring compliance with regulations and AI adoption. Systematic use of statistical analyses enables identifications of potential risks with drugs. Frequentist and Bayesian methods are used in disproportionalities, each with its strengths and weaknesses. Integration of pharmacogenomics with pharmacovigilance enables personalized medicine, with AI further enhancing patient engagement. This multidisciplinary approach holds promise, improving drug efficacy and safety, and should be a core mission of One-Health studies.

Phosphodiesterase-5 Inhibitors and Dementia Risk: A Real-World Study

INTRODUCTION

Biological and scarce epidemiological evidence suggested that phosphodiesterase-5 inhibitors (PDE5i) might reduce dementia risk. We aimed to examine the association between PDE5i and dementia using real-world data.

METHODS

Two retrospective cohorts within the database of Clalit, the largest healthcare provider in Israel (2005-2023), were studied. The first cohort included new daily users, older than 50 years of age, of low-dose tadalafil, prescribed for benign prostatic hypertrophy (BPH), propensity-score matched to new-users of alpha-1 blockers, and analyzed using 2-year lag time. The second cohort included patients with erectile dysfunction, with/without any PDE5i treatment, using time-dependent analysis. Individuals in the cohorts were followed through May 2023 for the occurrence of dementia.

RESULTS

The first cohort included 5,204 tadalafil initiators propensity-score matched to 18,565 alpha-1 blockers initiators. There was no association between tadalafil use and dementia risk, HR = 0.99 (95% CI: 0.88-1.12), p = 0.927. Similar results were obtained in a competing risk analysis, and in a sensitivity analysis in which we restricted the cohort to patients older than 60 years at cohort entry. The second cohort of 133,336 patients with erectile dysfunction included new users and nonusers of any PDE5i. In a mean follow-up of 7.9 years, 8,631 patients were newly diagnosed with dementia. In a time-dependent multivariable analysis, PDE5i use was not associated with reduced dementia risk, HR = 0.95 (95% CI: 0.86-1.04). Results were not changed in sensitivity analyses (patients older than 60 years or stratification by PDE5i type).

CONCLUSION

This study suggests that the use of PDE5 inhibitors is not associated with decreased risk of dementia.

Network medicine-based analysis of the hepatoprotective effects of Amomum villosum Lour. on alcoholic liver disease in rats

Alcoholic liver disease (ALD) is characterized by high morbidity and mortality, and mainly results from prolonged and excessive alcohol use. Amomum villosum Lour. (A. villosum), a well-known traditional Chinese medicine (TCM), has hepatoprotective properties. However, its ability to combat alcohol-induced liver injury has not been fully explored. The objective of this study was to investigate the hepatoprotective effects of A. villosum in a rat model of alcohol-induced liver disease, thereby establishing a scientific foundation for the potential preventive use of A. villosum in ALD. We established a Chinese liquor (Baijiu)-induced liver injury model in rats. Hematoxylin and eosin (HE) staining, in combination with biochemical tests, was used to evaluate the protective effects of A. villosum on the liver. The integration of network medicine analysis with experimental validation was used to explore the hepatoprotective effects and potential mechanisms of A. villosum in rats. Our findings showed that A. villosum ameliorated alcohol-induced changes in body weight, liver index, hepatic steatosis, inflammation, blood lipid metabolism, and liver function in rats. Network proximity analysis was employed to identify 18 potentially active ingredients of A. villosum for ALD treatment. These potentially active ingredients in the blood were further identified using mass spectrometry (MS). Our results showed that A. villosum plays a hepatoprotective role by modulating the protein levels of estrogen receptor 1 (ESR1), anti-nuclear receptor subfamily 3 group C member 1 (NR3C1), interleukin 6 (IL-6), and tumor necrosis factor-α (TNF-α). In conclusion, the results of the current study suggested that A. villosum potentially exerts hepatoprotective effects on ALD in rats, possibly through regulating the protein levels of ESR1, NR3C1, IL-6, and TNF-α.

Innovative target mining stratagems to navigate drug repurposing endeavours

The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.

Reimagining old drugs with new tricks: Mechanisms, strategies and notable success stories in drug repurposing for neurological diseases

Recent evolution in drug repurposing has brought new anticipation, especially in the conflict against neurodegenerative diseases (NDDs). The traditional approach to developing novel drugs for these complex disorders is laborious, time-consuming, and often abortive. However, drug reprofiling which is the implementation of illuminating novel therapeutic applications of existing approved drugs, has shown potential as a promising strategy to accelerate the hunt for therapeutics. The advancement of computational approaches and artificial intelligence has expedited drug repurposing. These progressive technologies have enabled scientists to analyse extensive datasets and predict potential drug-disease interactions. By prospecting into the existing pharmacological knowledge, scientists can recognise potential therapeutic candidates for reprofiling, saving precious time and resources. Preclinical models have also played a pivotal role in this field, confirming the effectiveness and mechanisms of action of repurposed drugs. Several studies have occurred in recent years, including the discovery of available drugs that demonstrate significant protective effects in NDDs, relieve debilitating symptoms, or slow down the progression of the disease. These findings highlight the potential of repurposed drugs to change the landscape of NDD treatment. Here, we present an overview of recent developments and major advances in drug repurposing intending to provide an in-depth analysis of traditional drug discovery and the strategies, approaches and technologies that have contributed to drug repositioning. In addition, this chapter attempts to highlight successful case studies of drug repositioning in various therapeutic areas related to NDDs and explore the clinical trials, challenges and limitations faced by researchers in the field. Finally, the importance of drug repositioning in drug discovery and development and its potential to address discontented medical needs is also highlighted.

Artificial intelligence for drug discovery and development in Alzheimer's disease

The complex molecular mechanism and pathophysiology of Alzheimer's disease (AD) limits the development of effective therapeutics or prevention strategies. Artificial Intelligence (AI)-guided drug discovery combined with genetics/multi-omics (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) analysis contributes to the understanding of the pathophysiology and precision medicine of the disease, including AD and AD-related dementia. In this review, we summarize the AI-driven methodologies for AD-agnostic drug discovery and development, including de novo drug design, virtual screening, and prediction of drug-target interactions, all of which have shown potentials. In particular, AI-based drug repurposing emerges as a compelling strategy to identify new indications for existing drugs for AD. We provide several emerging AD targets from human genetics and multi-omics findings and highlight recent AI-based technologies and their applications in drug discovery using AD as a prototypical example. In closing, we discuss future challenges and directions in AI-based drug discovery for AD and other neurodegenerative diseases.

Network medicine informed multi-omics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. To date, the challenge to establishing effective treatment for ALS remains formidable, partly due to inadequate translation of existing human genetic findings into actionable ALS-specific pathobiology for subsequent therapeutic development. This study evaluates the feasibility of network medicine methodology via integrating human brain-specific multi-omics data to prioritize drug targets and repurposable treatments for ALS. Using human brain-specific genome-wide quantitative trait loci (x-QTLs) under a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in various known ALS pathobiological pathways, including regulation of T cell activation, monocyte differentiation, and lymphocyte proliferation. Specifically, we leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on brain-specific expression quantitative trait loci (QTL) (eQTL), protein QTLs (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Applying network proximity analysis of predicted ALS-associated gene-coding targets and existing drug-target networks under the human protein-protein interactome (PPI) model, we identified a set of potential repurposable drugs (including Diazoxide, Gefitinib, Paliperidone, and Dimethyltryptamine) for ALS. Subsequent validation established preclinical and clinical evidence for top-prioritized repurposable drugs. In summary, we presented a network-based multi-omics framework to identify potential drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

Are trait-associated genes clustered together in a gene network?

Genome-wide association studies (GWAS) have provided an abundance of information about the genetic variants and their loci that are associated to complex traits and diseases. However, due to linkage disequilibrium (LD) and noncoding regions of loci, it remains a challenge to pinpoint the causal genes. Gene network-based approaches, paired with network diffusion methods, have been proposed to prioritize causal genes and to boost statistical power in GWAS based on the assumption that trait-associated genes are clustered in a gene network. Due to the difficulty in mapping trait-associated variants to genes in GWAS, this assumption has never been directly or rigorously tested empirically. On the other hand, whole exome sequencing (WES) data focuses on the protein-coding regions, directly identifying trait-associated genes. In this study, we tested the assumption by leveraging the recently available exome-based association statistics from the UK Biobank WES data along with two types of networks. We found that almost all trait-associated genes were significantly more proximal to each other than randomly selected genes within both networks. These results support the assumption that trait-associated genes are clustered in gene networks, which can be further leveraged to boost the power of GWAS such as by introducing less stringent p value thresholds.

Pharmacological interventions in human aging

Pharmacological interventions are emerging as potential avenues of alleviating age-related disease. However, the knowledge of ongoing clinical trials as they relate to aging and pharmacological interventions is dispersed across a variety of mediums. In this review we summarize 136 age-related clinical trials that have been completed or are ongoing. Furthermore, we establish a database that describe the trials (AgingDB, www.agingdb.com) keeping track of the previous and ongoing clinical trials, alongside their outcomes. The aim of this review and database is to give people the ability to easily query for their trial of interest and stay up to date on the latest results. In sum, herein we give an overview of the current pharmacological strategies that have been applied to target human aging.

Alzheimer's Disease and COVID-19 Pathogenic Overlap: Implications for Drug Repurposing

As COVID-19 continues, a safe, cost-effective treatment strategy demands continued inquiry. Chronic neuroinflammatory disorders may appear to be of little relevance in this regard; often indolent and progressive disorders characterized by neuroinflammation (such as Alzheimer's disease (AD)) are fundamentally dissimilar in etiology and symptomology to COVID-19's rapid infectivity and pathology. However, the two disorders share extensive pathognomonic features, including at membrane, cytoplasmic, and extracellular levels, culminating in analogous immunogenic destruction of their respective organ parenchyma. We hypothesize that these mechanistic similarities may extent to therapeutic targets, namely that it is conceivable an agent against AD's immunopathy may have efficacy against COVID-19 and vice versa. It is notable that while extensively investigated, no agent has yet demonstrated significant therapeutic efficacy against AD's cognitive and memory declines. Yet this very failure has driven the development of numerous agents with strong mechanistic potential and clinical characteristics. Having already approved for clinical trials, these agents may be an expedient starting point in the urgent search for an effective COVID-19 therapy. Herein, we review the overlapping Alzheimer's/ COVID-19 targets and theorize several initial platforms.

Machine learning models identify predictive features of patient mortality across dementia types

BACKGROUND

Dementia care is challenging due to the divergent trajectories in disease progression and outcomes. Predictive models are needed to flag patients at risk of near-term mortality and identify factors contributing to mortality risk across different dementia types.

METHODS

Here, we developed machine-learning models predicting dementia patient mortality at four different survival thresholds using a dataset of 45,275 unique participants and 163,782 visit records from the U.S. National Alzheimer's Coordinating Center (NACC). We built multi-factorial XGBoost models using a small set of mortality predictors and conducted stratified analyses with dementiatype-specific models.

RESULTS

Our models achieved an area under the receiver operating characteristic curve (AUC-ROC) of over 0.82 utilizing nine parsimonious features for all 1-, 3-, 5-, and 10-year thresholds. The trained models mainly consisted of dementia-related predictors such as specific neuropsychological tests and were minimally affected by other age-related causes of death, e.g., stroke and cardiovascular conditions. Notably, stratified analyses revealed shared and distinct predictors of mortality across eight dementia types. Unsupervised clustering of mortality predictors grouped vascular dementia with depression and Lewy body dementia with frontotemporal lobar dementia.

CONCLUSIONS

This study demonstrates the feasibility of flagging dementia patients at risk of mortality for personalized clinical management. Parsimonious machine-learning models can be used to predict dementia patient mortality with a limited set of clinical features, and dementiatype-specific models can be applied to heterogeneous dementia patient populations.

Phosphodiesterase Type 5 Inhibitors in Men With Erectile Dysfunction and the Risk of Alzheimer Disease: A Cohort Study

BACKGROUND AND OBJECTIVES

Repurposing phosphodiesterase type 5 inhibitors (PDE5Is) as drugs for Alzheimer disease (AD) risk reduction has shown promise based on animal studies. However, evidence in humans remains inconclusive. Therefore, we conducted a cohort study to evaluate the association between PDE5I initiation compared with nonuse and the risk of developing AD in men with erectile dysfunction (ED).

METHODS

Using electronic health records from IQVIA Medical Research Data UK (formerly known as the THIN database), we identified men aged ≥40 years with a new diagnosis of ED between 2000 and 2017. Individuals with a previous diagnosis of dementia, cognitive impairment, confusion, or prescription for dementia symptoms were excluded. The occurrence of incident AD was identified using diagnostic read codes. To minimize immortal-time bias, PDE5I initiation was treated as a time-varying exposure variable. Potential confounders were adjusted using inverse probability of treatment weighting based on propensity scores. Cox proportional hazard models were used to estimate the adjusted hazard ratio (HR) with 95% CIs. A secondary analysis explored the association between AD and the cumulative number of PDE5I prescriptions. Sensitivity analyses included lag (delay) periods of 1 and 3 years after cohort entry to address the prodromal stage of AD.

RESULTS

The study included 269,725 men, with 1,119 newly diagnosed with AD during a median follow-up of 5.1 (interquartile range 2.9-8.9) years. The adjusted HR in PDE5I initiators compared with nonuse was 0.82 (95% CI 0.72-0.93). The associated risk of AD decreased in individuals issued >20 prescriptions: HR 0.56 (95% CI 0.43-0.73) for 21-50 prescriptions and HR 0.65 (95% CI 0.49-0.87) for >50 prescriptions. Sensitivity analysis with a 1-year lag period supported the primary findings (HR 0.82, 95% CI 0.72-0.94), but the results differed with the inclusion of a 3-year lag period (HR 0.93, 95% CI 0.80-1.08).

DISCUSSION

PDE5I initiation in men with ED was associated with a lower risk of AD, particularly in those most frequently issued prescriptions. The differences between primary and sensitivity analyses highlight the need to explore the optimal lag period. To enhance the generalizability of our findings, a randomized controlled trial including both sexes and exploring various PDE5I doses would be beneficial to confirm the association between PDE5I and AD.

A network-based drug prioritization and combination analysis for the MEK5/ERK5 pathway in breast cancer

BACKGROUND

Prioritizing candidate drugs based on genome-wide expression data is an emerging approach in systems pharmacology due to its holistic perspective for preclinical drug evaluation. In the current study, a network-based approach was proposed and applied to prioritize plant polyphenols and identify potential drug combinations in breast cancer. We focused on MEK5/ERK5 signalling pathway genes, a recently identified potential drug target in cancer with roles spanning major carcinogenesis processes.

RESULTS

By constructing and identifying perturbed protein-protein interaction networks for luminal A breast cancer, plant polyphenols and drugs from transcriptome data, we first demonstrated their systemic effects on the MEK5/ERK5 signalling pathway. Subsequently, we applied a pathway-specific network pharmacology pipeline to prioritize plant polyphenols and potential drug combinations for use in breast cancer. Our analysis prioritized genistein among plant polyphenols. Drug combination simulations predicted several FDA-approved drugs in breast cancer with well-established pharmacology as candidates for target network synergistic combination with genistein. This study also highlights the concept of target network enhancer drugs, with drugs previously not well characterised in breast cancer being prioritized for use in the MEK5/ERK5 pathway in breast cancer.

CONCLUSION

This study proposes a computational framework for drug prioritization and combination with the MEK5/ERK5 signaling pathway in breast cancer. The method is flexible and provides the scientific community with a robust method that can be applied to other complex diseases.

Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease

The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit may be addressed by applying artificial intelligence (AI) to "big data" to rapidly and effectively expand therapeutic development efforts. Recent accelerations in computing power and availability of big data, including electronic health records and multi-omics profiles, have converged to provide opportunities for scientific discovery and treatment development. Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD. We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, and computational scientists. AI and open data science expedite drug discovery and development of disease-modifying therapeutics for AD/ADRD and other neurodegenerative diseases.

Protein Misfolding in Pregnancy: Current Insights, Potential Mechanisms, and Implications for the Pathogenesis of Preeclampsia

Protein misfolding disorders are a group of diseases characterized by supra-physiologic accumulation and aggregation of pathogenic proteoforms resulting from improper protein folding and/or insufficiency in clearance mechanisms. Although these processes have been historically linked to neurodegenerative disorders, such as Alzheimer's disease, evidence linking protein misfolding to other pathologies continues to emerge. Indeed, the deposition of toxic protein aggregates in the form of oligomers or large amyloid fibrils has been linked to type 2 diabetes, various types of cancer, and, in more recent years, to preeclampsia, a life-threatening pregnancy-specific disorder. While extensive physiological mechanisms are in place to maintain proteostasis, processes, such as aging, genetic factors, or environmental stress in the form of hypoxia, nutrient deprivation or xenobiotic exposures can induce failure in these systems. As such, pregnancy, a natural physical state that already places the maternal body under significant physiological stress, creates an environment with a lower threshold for aberrant aggregation. In this review, we set out to discuss current evidence of protein misfolding in pregnancy and potential mechanisms supporting a key role for this process in preeclampsia pathogenesis. Improving our understanding of this emerging pathophysiological process in preeclampsia can lead to vital discoveries that can be harnessed to create better diagnoses and treatment modalities for the disorder.

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

Uncovering the Oxidative Stress Mechanisms and Targets in Alzheimer's Disease by Integrating Phenotypic Screening Data and Polypharmacology Networks

Background

The oxidative stress hypothesis is challenging the dominant position of amyloid-β (Aβ) in the field of understanding the mechanisms of Alzheimer's disease (AD), a complicated and untreatable neurodegenerative disease.

Objective

The goal of the present study was to uncover the oxidative stress mechanisms causing AD, as well as the potential therapeutic targets and neuroprotective drugs against oxidative stress mechanisms.

Methods

In this study, a systematic workflow combining pharmacological experiments and computational prediction was proposed. 222 drugs and natural products were collected first and then tested on SH-SY5Y cells to obtain phenotypic screening data on neuroprotection. The preliminary screening data were integrated with drug-target interactions (DTIs) and multi-scale biomedical data, which were analyzed with statistical tests and gene set enrichment analysis. A polypharmacology network was further constructed for investigation.

Results

340 DTIs were matched in multiple databases, and 222 cell viability ratios were calculated for experimental compounds. We identified significant potential therapeutic targets based on oxidative stress mechanisms for AD, including NR3C1, SHBG, ESR1, PGR, and AVPR1A, which might be closely related to neuroprotective effects and pathogenesis. 50% of the top 14 enriched pathways were found to correlate with AD, such as arachidonic acid metabolism and neuroactive ligand-receptor interaction. Several approved drugs in this research were also found to exert neuroprotective effects against oxidative stress mechanisms, including beclometasone, methylprednisolone, and conivaptan.

Conclusion

Our results indicated that NR3C1, SHBG, ESR1, PGR, and AVPR1A were promising therapeutic targets and several drugs may be repurposed from the perspective of oxidative stress and AD.

Phosphodiesterase-5 Inhibitors and Dementia Risk: Confounding by Indication in Real-World Studies

Pharmacoepidemiologic studies using routinely collected data allow researchers to propose drugs for repurposing trials for dementia prevention or treatment. A recent cohort study reported a 54% lower dementia risk among users of sildenafil compared to users of certain cardiovascular medications. We caution that "confounding by indication" can arise when outcomes are compared between a drug of interest and an inappropriate comparator. Here, we emphasize important considerations in selecting an active comparator. We assess the implications of substantial risk of confounding by indication in pharmacoepidemiologic studies linking phosphodiesterase-5 inhibitors to lower dementia risk.

Sildenafil as a Candidate Drug for Alzheimer's Disease: Real-World Patient Data Observation and Mechanistic Observations from Patient-Induced Pluripotent Stem Cell-Derived Neurons

Background

Alzheimer's disease (AD) is a chronic neurodegenerative disease needing effective therapeutics urgently. Sildenafil, one of the approved phosphodiesterase-5 inhibitors, has been implicated as having potential effect in AD.

Objective

To investigate the potential therapeutic benefit of sildenafil on AD.

Methods

We performed real-world patient data analysis using the MarketScan® Medicare Supplemental and the Clinformatics® databases. We conducted propensity score-stratified analyses after adjusting confounding factors (i.e., sex, age, race, and comorbidities). We used both familial and sporadic AD patient induced pluripotent stem cells (iPSC) derived neurons to evaluate the sildenafil's mechanism-of-action.

Results

We showed that sildenafil usage is associated with reduced likelihood of AD across four new drug compactor cohorts, including bumetanide, furosemide, spironolactone, and nifedipine. For instance, sildenafil usage is associated with a 54% reduced incidence of AD in MarketScan® (hazard ratio [HR] = 0.46, 95% CI 0.32- 0.66) and a 30% reduced prevalence of AD in Clinformatics® (HR = 0.70, 95% CI 0.49- 1.00) compared to spironolactone. We found that sildenafil treatment reduced tau hyperphosphorylation (pTau181 and pTau205) in a dose-dependent manner in both familial and sporadic AD patient iPSC-derived neurons. RNA-sequencing data analysis of sildenafil-treated AD patient iPSC-derived neurons reveals that sildenafil specifically target AD related genes and pathobiological pathways, mechanistically supporting the beneficial effect of sildenafil in AD.

Conclusions

These real-world patient data validation and mechanistic observations from patient iPSC-derived neurons further suggested that sildenafil is a potential repurposable drug for AD. Yet, randomized clinical trials are warranted to validate the causal treatment effects of sildenafil in AD.