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

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.

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.

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.

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.

Cerebrovascular Dysfunction in Alzheimer's Disease and Transgenic Rodent Models

Alzheimer's Disease (AD) and Alzheimer's Disease-Related Dementia (ADRD) are the primary causes of dementia that has a devastating effect on the quality of life and is a tremendous economic burden on the healthcare system. The accumulation of extracellular beta-amyloid (Aβ) plaques and intracellular hyperphosphorylated tau-containing neurofibrillary tangles (NFTs) in the brain are the hallmarks of AD. They are also thought to be the underlying cause of inflammation, neurodegeneration, brain atrophy, and cognitive impairments that accompany AD. The discovery of APP, PS1, and PS2 mutations that increase Aβ production in families with early onset familial AD led to the development of numerous transgenic rodent models of AD. These models have provided new insight into the role of Aβ in AD; however, they do not fully replicate AD pathology in patients. Familial AD patients with mutations that elevate the production of Aβ represent only a small fraction of dementia patients. In contrast, those with late-onset sporadic AD constitute the majority of cases. This observation, along with the failure of previous clinical trials targeting Aβ or Tau and the modest success of recent trials using Aβ monoclonal antibodies, has led to a reappraisal of the view that Aβ accumulation is the sole factor in the pathogenesis of AD. More recent studies have established that cerebral vascular dysfunction is one of the earliest changes seen in AD, and 67% of the candidate genes linked to AD are expressed in the cerebral vasculature. Thus, there is an increasing appreciation of the vascular contribution to AD, and the National Institute on Aging (NIA) and the Alzheimer's Disease Foundation recently prioritized it as a focused research area. This review summarizes the strengths and limitations of the most commonly used transgenic AD animal models and current views about the contribution of Aβ accumulation versus cerebrovascular dysfunction in the pathogenesis of AD.

Network Proximity Analysis Deciphers the Pharmacological Mechanism of Osthole against D-Galactose Induced Cognitive Disorder in Rats

Osthole, a natural coumarin found in various medicinal plants, has been previously reported to have neuroprotective effects. However, the specific mechanism by which Osthole alleviates dysmnesia associated with Alzheimer's disease (AD) remains unclear. This study aimed to investigate the neuroprotective properties of Osthole against cognitive impairment in rats induced by D-galactose and elucidate its pharmacological mechanism. The rat model was established by subcutaneously injecting D-galactose at a dose of 150 mg/kg/day for 56 days. The effect of Osthole on cognitive impairment was evaluated by behavior and biochemical analysis. Subsequently, a combination of in silico prediction and experimental validation was performed to verify the network-based predictions, using western blot, Nissl staining, and immunofluorescence. The results demonstrate that Osthole could improve memory dysfunction induced by D-galactose in Sprague Dawley male rats. A network proximity-based approach and integrated pathways analysis highlight two key AD-related pathological processes that may be regulated by Osthole, including neuronal apoptosis, i.e., neuroinflammation. Among them, the pro-apoptotic markers (Bax), anti-apoptotic protein (Bcl-2), the microgliosis (Iba-1), Astro-cytosis (GFAP), and inflammatory cytokines (TNF-R1) were evaluated in both hippocampus and cortex. The results indicated that Osthole significantly ameliorated neuronal apoptosis and neuroinflammation in D-galactose-induced cognitive impairment rats. In conclusion, this study sheds light on the pharmacological mechanism of Osthole in mitigating D-galactose-induced memory impairment and identifies Osthole as a potential drug candidate for AD treatment, targeting multiple signaling pathways through network proximity and integrated pathways analysis.

High-throughput target trial emulation for Alzheimer's disease drug repurposing with real-world data

Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer's disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer's patients.

Identification of Potential Therapeutic Targets for Myopic Choroidal Neovascularization via Discovery-Driven Data Mining

Purpose: Myopic choroidal neovascularization (mCNV) is a prevalent cause of vision loss. However, the development of effective therapeutic targets for mCNV has been hindered by the paucity of suitable animal models. Therefore, the aim of this study is to identify potential genes and pathways associated with mCNV and to unearth prospective therapeutic targets that can be utilized to devise efficacious treatments.Methods: Text data mining was used to identify genes linked to choroid, neovascularization, and myopia. g: Profiler was utilized to analyze the biological processes of gene ontology and the Reactome pathways. Protein interaction network analysis was performed using strings and visualized in Cytoscape. MCODE and cytoHubba were used for further screening.Results: Discovery-driven text data mining identified 55 potential genes related to choroid, neovascularization, and myopia. Gene enrichment analysis revealed 11 biological processes and seven Reactome pathways. A protein-protein interaction network with 47 nodes was constructed and analyzed using centrality ranking. Key clusters were identified through algorithm tools. Finally, 14 genes (IL6, FGF2, MMP9, IL10, TNF, MMP2, HGF, MMP3, IGF1, CCL2, CTNNB1, BDNF, NGF, and EDN1), in addition to VEGFA, were evaluated as targets with potential as future therapeutics.Conclusions: This study provides new potential therapeutic targets for mCNV, including IL6, FGF2, MMP9, IL10, TNF, MMP2, HGF, MMP3, IGF1, CCL2, CTNNB1, BDNF, NGF, and EDN1, which correspond to seven potential enriched pathways. These findings provide a basis for further research and offer new possibilities for developing therapeutic interventions for this condition.

A case-control study of phosphodiesterase-5 inhibitor use and Alzheimer's disease and related dementias among male and female patients aged 65 years and older supporting the need for a phase III clinical trial

BACKGROUND

Phosphodiesterase-5 inhibitors (PDE5i) have been evaluated as a novel treatment for Alzheimer's disease and related dementias (ADRD), but two recent cohort studies have offered opposing conclusions.

METHODS

We performed an unmatched case-control study using electronic medical records from a large healthcare system to evaluate the association of PDE5i use and ADRD in patients ≥65 years old.

RESULTS

Odds of PDE5i exposure were 64.2%, 55.7%, and 54.0% lower in patients with ADRD than controls among populations with erectile dysfunction, benign prostatic hyperplasia, and pulmonary hypertension, respectively. We observed odds ratios less than unity among males and females and with exposure to the PDE5i sildenafil (Viagra®) and tadalafil (Cialis®). We also evaluated the odds of exposure to two other common treatments for pulmonary hypertension: endothelin receptor antagonists (ERA) and calcium channel blockers (CCB). The odds of ERA exposure were 63.2% lower, but the odds of CCB exposure were 30.7% higher, in patients with ADRD than controls among the population with pulmonary hypertension.

CONCLUSIONS

Our results reconcile the opposing conclusions from the previous observational studies and support further research into using PDE5i for prevention and treatment of ADRD.

A multi-disciplinary commentary on preclinical research to investigate vascular contributions to dementia

Although dementia research has been dominated by Alzheimer's disease (AD), most dementia in older people is now recognised to be due to mixed pathologies, usually combining vascular and AD brain pathology. Vascular cognitive impairment (VCI), which encompasses vascular dementia (VaD) is the second most common type of dementia. Models of VCI have been delayed by limited understanding of the underlying aetiology and pathogenesis. This review by a multidisciplinary, diverse (in terms of sex, geography and career stage), cross-institute team provides a perspective on limitations to current VCI models and recommendations for improving translation and reproducibility. We discuss reproducibility, clinical features of VCI and corresponding assessments in models, human pathology, bioinformatics approaches, and data sharing. We offer recommendations for future research, particularly focusing on small vessel disease as a main underpinning disorder.

Modulating endoplasmic reticulum stress in APP/PS1 mice by Gomisin B and Osthole in Bushen-Yizhi formula: Synergistic effects and therapeutic implications for Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder with no effective cure. Targeting endoplasmic reticulum (ER) stress pathway may offer a novel approach to ameliorate cognitive deficits in AD. Bushen-Yizhi formula (BSYZ), a traditional Chinese medicine (TCM) prescription, has shown potential benefits for AD. To facilitate the development of new therapeutic agents for AD, it is important to identify the active components and the underlying mechanisms of BSYZ against AD.

PURPOSE

The aim of this study was to systematically screen the active components of BSYZ that could improve learning and memory impairment in AD by modulating ER stress pathway.

METHODS

A drug-target (D-T) network was constructed to analyze the herbal components of BSYZ. Network proximity method was used to identify the potential anti-AD components that targeted ER stress and evaluate their synergistic effects. The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and the literature evidence were considered to select promising candidates for further validation. The selected components were tested in vitro using an AD cell model (APPswe-SH-SY5Y). In vivo anti-AD effects of the components were assessed in APP/PS1 double-transgenic mice.

RESULTS

58 potential anti-AD components targeting ER stress were detected by network proximity analysis, and 13 out of them were selected based on ADMET properties and literature evidence. In vitro experiments confirmed that 5 components, namely gomisin B, β-Carotene, imperatorin, chrysophanol, and osthole (OST), exhibited anti-AD effects on the APPswe-SH-SY5Y model. Moreover, network proximity analysis suggested that OST and Gomisin B might have synergistic effects on modulating ER stress. In vivo experiments demonstrated that OST, Gomisin B, OST+Gomisin B, and BSYZ all improved learning and memory function in APP/PS1 mice. Gomisin B and OST also restored cellular morphology and tissue structure in APP/PS1 mice. Thioflavine-S (Th-S) staining revealed that they reduced amyloid plaque deposition in the brain tissue of AD model mice. The qPCR results indicated that BSYZ, OST, and Gomisin B differentially regulated IRE1α, PERK, EIF2α, DDIT3, and Caspase 12 expression levels, while the OST and Gomisin B co-administration group showed better efficacy. This trend was further confirmed by immunofluorescence experiments.

CONCLUSION

This study identified the active components of BSYZ that could ameliorate learning and memory impairment in AD by targeting ER stress pathway. OST and Gomisin B exhibited synergistic effects on modulating ER stress and reducing amyloid plaque deposition in vivo. Overall, our study elucidated the molecular mechanisms of BSYZ and its active components in attenuating AD symptoms which suggested the therapeutic potential of TCM for AD.

Testing the cognitive effects of tadalafil. Neuropsychological secondary outcomes from the PASTIS trial

Cerebral small vessel disease (SVD) is a major cause of cognitive impairment in older people. As secondary endpoints in a phase-2 randomised clinical trial, we tested the effects of single administration of a widely-used PDE5 inhibitor, tadalafil, on cognitive performance in older people with SVD. In a double-blinded, placebo-controlled, cross-over trial, participants received tadalafil (20 mg) and placebo on two visits ≥ 7 days apart (randomised to order of treatment). The Montreal Cognitive Assessment (MOCA) was administered at baseline, alongside a measure to estimate optimal intellectual ability (Test of Premorbid Function). Then, before and after treatment, a battery of neuropsychological tests was administered, assessing aspects of attention, information processing speed, working memory and executive function. Sixty-five participants were recruited and 55 completed the protocol (N = 55, age: 66.8 (8.6) years, range 52-87; 15/40 female/male). Median MOCA score was 26 (IQR: 23, 27], range 15-30). No significant treatment effects were seen in any of the neuropsychological tests. There was a trend towards improved performance on Digit Span Forward (treatment effect 0.37, C.I. 0.01, 0.72; P = 0.0521). We did not identify significant treatment effects of single-administration tadalafil on neuropsychological performance in older people with SVD. The trend observed on Digit Span Forward may help to inform future studies.

Clinical trial registration

http://www.clinicaltrials.gov. Unique identifier: NCT00123456, https://eudract.ema.europa.eu. Unique identifier: 2015-001,235-20NCT00123456.

Drug repurposing for Alzheimer's disease from 2012-2022-a 10-year literature review

Background: Alzheimer's disease (AD) is a debilitating neurodegenerative condition with few treatment options available. Drug repurposing studies have sought to identify existing drugs that could be repositioned to treat AD; however, the effectiveness of drug repurposing for AD remains unclear. This review systematically analyzes the progress made in drug repurposing for AD throughout the last decade, summarizing the suggested drug candidates and analyzing changes in the repurposing strategies used over time. We also examine the different types of data that have been leveraged to validate suggested drug repurposing candidates for AD, which to our knowledge has not been previous investigated, although this information may be especially useful in appraising the potential of suggested drug repurposing candidates. We ultimately hope to gain insight into the suggested drugs representing the most promising repurposing candidates for AD. Methods: We queried the PubMed database for AD drug repurposing studies published between 2012 and 2022. 124 articles were reviewed. We used RxNorm to standardize drug names across the reviewed studies, map drugs to their constituent ingredients, and identify prescribable drugs. We used the Anatomical Therapeutic Chemical (ATC) Classification System to group drugs. Results: 573 unique drugs were proposed for repurposing in AD over the last 10 years. These suggested repurposing candidates included drugs acting on the nervous system (17%), antineoplastic and immunomodulating agents (16%), and drugs acting on the cardiovascular system (12%). Clozapine, a second-generation antipsychotic medication, was the most frequently suggested repurposing candidate (N = 6). 61% (76/124) of the reviewed studies performed a validation, yet only 4% (5/124) used real-world data for validation. Conclusion: A large number of potential drug repurposing candidates for AD has accumulated over the last decade. However, among these drugs, no single drug has emerged as the top candidate, making it difficult to establish research priorities. Validation of drug repurposing hypotheses is inconsistently performed, and real-world data has been critically underutilized for validation. Given the urgent need for new AD therapies, the utility of real-world data in accelerating identification of high-priority candidates for AD repurposing warrants further investigation.

Association of Proton Pump Inhibitor Use With Incident Dementia and Cognitive Decline in Older Adults: A Prospective Cohort Study

BACKGROUND & AIMS

Prior studies have suggested that proton pump inhibitor (PPI) use is associated with increased risk of dementia; however, these have been limited by incomplete assessment of medication use and failure to account for confounders. Furthermore, prior studies have relied on claims-based diagnoses for dementia, which can lead to misclassification. We investigated the associations of PPI and histamine-2 receptor antagonist (H2RA) use with dementia and cognitive decline.

METHODS

We conducted a post hoc analysis of ASPirin in Reducing Events in the Elderly (ASPREE), a randomized trial of aspirin in the United States and Australia, including 18,934 community-based adults ≥65 years of all races/ethnicities. Baseline and recent PPI and H2RA use were determined according to review of medications during annual in-person study visits. Incident dementia was defined according to Diagnostic and Statistical Manual for Mental Disorders, Fourth Edition, criteria. Secondary endpoints include cognitive impairment, no dementia (CIND) and changes in cognition. Associations of medication use with dementia and CIND outcomes were examined using Cox proportional hazards models. Changes in cognitive test scores were examined using linear mixed-effects models.

RESULTS

Baseline PPI use vs nonuse was not associated with incident dementia (multivariable hazard ratio, 0.88; 95% confidence interval, 0.72-1.08), CIND (multivariable hazard ratio, 1.00; 95% confidence interval, 0.92-1.09), or with changes in overall cognitive test scores over time (multivariable B, -0.002; standard error, 0.01; P = .85). Similarly, no associations were observed between H2RA use and all cognitive endpoints.

CONCLUSIONS

In adults ≥65 years of age, PPI and H2RA use were not associated with incident dementia, CIND, or decline in cognition over time. These data provide reassurance about the safety of long-term use of PPIs among older adults.

Neuroprotective Effect of NO-Delivery Dinitrosyl Iron Complexes (DNICs) on Amyloid Pathology in the Alzheimer's Disease Cell Model

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive impairment, memory loss, and behavioral deficits. β-amyloid1-42 (Aβ1-42) aggregation is a significant cause of the pathogenesis in AD. Despite the numerous types of research, the current treatment efficacy remains insufficient. Hence, a novel therapeutic strategy is required. Nitric oxide (NO) is a multifunctional gaseous molecule. NO displays a neuroprotective role in the central nervous system by inhibiting the Aβ aggregation and rescuing memory and learning deficit through the NO signaling pathway. Targeting the NO pathway might be a therapeutic option; however, NO has a limited half-life under the biological system. To address this issue, a biomimetic dinitrosyl iron complex [(NO)2Fe(μ-SCH2CH2COOH)2Fe(NO)2] (DNIC-COOH) that could stably deliver NO was explored in the current study. To determine whether DNIC-COOH exerts anti-AD efficacy, DNIC-COOH was added to neuron-like cells and primary cortical neurons along with Aβ1-42. This study found that DNIC-COOH protected neuronal cells from Aβ-induced cytotoxicity, potentiated neuronal functions, and facilitated Aβ1-42 degradation through the NO-sGC-cGMP-AKT-GSK3β-CREB/MMP-9 pathway.

Systems pharmacology-based mechanism exploration of Acanthopanax senticosusin for Alzheimer's disease using UPLC-Q-TOF-MS, network analysis, and experimental validation

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disease, characterized by progressive cognitive dysfunction and memory loss. However, the disease-modifying treatments for AD are still lacking. Traditional Chinese herbs, have shown their potentials as novel treatments for complex diseases, such as AD.

PURPOSE

This study was aimed at investigating the mechanism of action (MOA) of Acanthopanax senticosusin (AS) for treatment of AD.

METHODS

In this study, we firstly identified the chemical constituents in Acanthopanax senticosusin (AS) utilizing ultra-high performance liquid chromatography coupled with Q-TOF-mass spectrometry (UPLC-Q-TOF-MS), and next built the drug-target network of these compounds. We next performed the systems pharmacology-based analysis to preliminary explore the MOA of AS against AD. Moreover, we applied the network proximity approach to identify the potential anti-AD components in AS. Finally, experimental validations, including animal behavior test, ELISA and TUNEL staining, were conducted to verify our systems pharmacology-based analysis.

RESULTS

60 chemical constituents in AS were identified via the UPLC-Q-TOF-MS approach. The systems pharmacology-based analysis indicated that AS might exert its therapeutic effects on AD via acetylcholinesterase and apoptosis signaling pathway. To explore the material basis of AS against AD, we further identified 15 potential anti-AD components in AS. Consistently, in vivo experiments demonstrated that AS could protect cholinergic nervous system damage and decrease neuronal apoptosis caused by scopolamine.

CONCLUSION

Overall, this study applied systems pharmacology approach, via UPLC-Q-TOF-MS, network analysis, and experimental validation to decipher the potential molecular mechanism of AS against AD.

PDE5 inhibitor drugs for use in dementia

Alzheimer's disease and related dementias (ADRD) remain a major health-care challenge with few licensed medications. Repurposing existing drugs may afford prevention and treatment. Phosphodiesterase-5 (PDE5) is widely expressed in vascular myocytes, neurons, and glia. Potent, selective, Food and Drug Administration-approved PDE5 inhibitors are already in clinical use (sildenafil, vardenafil, tadalafil) as vasodilators in erectile dysfunction and pulmonary arterial hypertension. Animal data indicate cognitive benefits of PDE5 inhibitors. In humans, real-world patient data suggest that sildenafil and vardenafil are associated with reduced dementia risk. While a recent clinical trial of acute tadalafil on cerebral blood flow was neutral, there may be chronic actions of PDE5 inhibition on cerebrovascular or synaptic function. We provide a perspective on the potential utility of PDE5 inhibitors for ADRD. We conclude that further prospective clinical trials with PDE5 inhibitors are warranted. The choice of drug will depend on brain penetration, tolerability in older people, half-life, and off-target effects.

HIGHLIGHTS

Potent phosphodiesterase-5 (PDE5) inhibitors are in clinical use as vasodilators.In animals PDE5 inhibitors enhance synaptic function and cognitive ability.In humans the PDE5 inhibitor sildenafil is associated with reduced risk of Alzheimer's disease.Licensed PDE5 inhibitors have potential for repurposing in dementia.Prospective clinical trials of PDE5 inhibitors are warranted.

Recent progress of nanomedicine in the treatment of Alzheimer's disease

Alzheimer's disease (AD) is the most common cause of memory disruption in elderly subjects, with the prevalence continuing to rise mainly because of the aging world population. Unfortunately, no efficient therapy is currently available for the AD treatment, due to low drug potency and several challenges to delivery, including low bioavailability and the impediments of the blood-brain barrier. Recently, nanomedicine has gained considerable attention among researchers all over the world and shown promising developments in AD treatment. A wide range of nano-carriers, such as polymer nanoparticles, liposomes, solid lipid nanoparticles, dendritic nanoparticles, biomimetic nanoparticles, magnetic nanoparticles, etc., have been adapted to develop successful new treatment strategies. This review comprehensively summarizes the recent advances of different nanomedicine for their efficacy in pre-clinical studies. Finally, some insights and future research directions are proposed. This review can provide useful information to guide the future design and evaluation of nanomedicine in AD treatment.

Association study between drug prescriptions and Alzheimer's disease claims in a commercial insurance database

In the ongoing effort to discover treatments for Alzheimer's disease (AD), there has been considerable focus on investigating the use of repurposed drug candidates. Mining of electronic health record data has the potential to identify novel correlated effects between commonly used drugs and AD. In this study, claims from members with commercial health insurance coverage were analyzed to determine the correlation between the use of various drugs on AD incidence and claim frequency. We found that, within the insured population, several medications for psychotic and mental illnesses were associated with higher disease incidence and frequency, while, to a lesser extent, antibiotics and anti-inflammatory drugs were associated with lower AD incidence rates. The observations thus provide a general overview of the prescription and claim relationships between various drug types and Alzheimer's disease, with insights into which drugs have possible implications on resulting AD diagnosis.

Integrating Comorbidity Knowledge for Alzheimer's Disease Drug Repurposing using Multi-task Graph Neural Network

Alzheimer's Disease (AD) is a multifactorial disease that shares common etiologies with its multiple comorbidities, especially vascular diseases. To predict repurposable drugs for AD utilizing the relatively well-investigated comorbidities' knowledge, we proposed a multi-task graph neural network (GNN)-based pipeline that incorporates the corresponding biomedical interactome of these diseases with their genetic markers and effective therapeutics. Our pipeline can accurately capture the interactions and disease classification in the network. Next, we predicted drugs that might interact with the AD module by the node embedding similarity. Our candidates are mostly BBB permeable, and literature evidence showed their potential for treating AD pathologies, accompanying symptoms, or cotreating AD pathology and its common comorbidities. Our pipeline demonstrated a workable strategy that predicts drug candidates with current knowledge of biological interplays between AD and several vascular diseases.

Network medicine framework identified drug-repurposing opportunities of pharmaco-active compounds of Angelica acutiloba (Siebold & Zucc.) Kitag. for skin aging

Increasing incidence of skin aging has highlighted the importance of identifying effective drugs with repurposed opportunities for skin aging. We aimed to identify pharmaco-active compounds with drug-repurposing opportunities for skin aging from Angelica acutiloba (Siebold & Zucc.) Kitag. (AAK). The proximity of network medicine framework (NMF) firstly identified 8 key AAK compounds with repurposed opportunities for skin aging, which may exert by regulating 29 differentially expressed genes (DGEs) of skin aging, including 13 up-regulated targets and 16 down-regulated targets. Connectivity MAP (cMAP) analysis revealed 8 key compounds were involved in regulating the process of cell proliferation and apoptosis, mitochondrial energy metabolism and oxidative stress of skin aging. Molecular docking analysis showed that 8 key compounds had a high docked ability with AR, BCHE, HPGD and PI3, which were identified as specific biomarker for the diagnosis of skin aging. Finally, the mechanisms of these key compounds were predicted to be involved in inhibiting autophagy pathway and activating Phospholipase D signaling pathway. In conclusion, this study firstly elucidated the drug-repurposing opportunities of AAK compounds for skin aging, providing a theoretical reference for identifying repurposing drugs from Chinese medicine and new insights for our future research.

Population-based discovery and Mendelian randomization analysis identify telmisartan as a candidate medicine for Alzheimer's disease in African Americans

INTRODUCTION

African Americans (AAs) and European Americans (EAs) differ in Alzheimer's disease (AD) prevalence, risk factors, and symptomatic presentation and AAs are less likely to enroll in AD clinical trials.

METHODS

We conducted race-conscious pharmacoepidemiologic studies of 5.62 million older individuals (age ≥60) to investigate the association of telmisartan exposure and AD outcome using Cox analysis, Kaplan-Meier analysis, and log-rank test. We performed Mendelian randomization (MR) analysis of large ethnically diverse genetic data to test likely causal relationships between telmisartan's target and AD.

RESULTS

We identified that moderate/high telmisartan exposure was significantly associated with a reduced incidence of AD in the AAs compared to low/no telmisartan exposure (hazard ratio [HR] = 0.77, 95% CI: 0.65-0.91, p-value = 0.0022), but not in the non-Hispanic EAs (HR = 0.97, 95% CI: 0.89-1.05, p-value = 0.4110). Sensitivity and sex-/age-stratified patient subgroup analyses identified that telmisartan's medication possession ratio (MPR) and average hypertension daily dosage were significantly associated with a stronger reduction in the incidence of both AD and dementia in AAs. Using MR analysis from large genome-wide association studies (GWAS) (over 2 million individuals) across AD, hypertension, and diabetes, we further identified AA-specific beneficial effects of telmisartan for AD.

DISCUSSION

Randomized controlled trials with ethnically diverse patient cohorts are warranted to establish causality and therapeutic outcomes of telmisartan and AD.

HIGHLIGHTS

Telmisartan is associated with lower risk of Alzheimer's disease (AD) in African Americans (AAs). Telmisartan is the only angiotensin II receptor blockers having PPAR-γ agonistic properties with beneficial anti-diabetic and renal function effects, which mitigate AD risk in AAs. Mendelian randomization (MR) analysis demonstrates the specificity of telmisartan's protective mechanism to AAs.

Biomedical discovery through the integrative biomedical knowledge hub (iBKH)

The abundance of biomedical knowledge gained from biological experiments and clinical practices is an invaluable resource for biomedicine. The emerging biomedical knowledge graphs (BKGs) provide an efficient and effective way to manage the abundant knowledge in biomedical and life science. In this study, we created a comprehensive BKG called the integrative Biomedical Knowledge Hub (iBKH) by harmonizing and integrating information from diverse biomedical resources. To make iBKH easily accessible for biomedical research, we developed a web-based, user-friendly graphical portal that allows fast and interactive knowledge retrieval. Additionally, we also implemented an efficient and scalable graph learning pipeline for discovering novel biomedical knowledge in iBKH. As a proof of concept, we performed our iBKH-based method for computational in-silico drug repurposing for Alzheimer's disease. The iBKH is publicly available.

Negative and positive allosteric modulators of the α7 nicotinic acetylcholine receptor regulates the ability of adolescent binge alcohol exposure to enhance adult alcohol consumption

Rationale and Objectives: Ethanol acts directly on the α7 Nicotinic acetylcholine receptor (α7). Adolescent-binge alcohol exposure (ABAE) produces deleterious consequences during adulthood, and data indicate that the α7 receptor regulates these damaging events. Administration of an α7 Negative Allosteric Modulator (NAM) or the cholinesterase inhibitor galantamine can prophylactically prevent adult consequences of ABAE. The goals of the experiments were to determine the effects of co-administration of ethanol and a α7 agonist in the mesolimbic dopamine system and to determine if administration of an α7 NAM or positive allosteric modulator (PAM) modulates the enhancement of adult alcohol drinking produced by ABAE. Methods: In adult rats, ethanol and the α7 agonist AR-R17779 (AR) were microinjected into the posterior ventral tegmental area (VTA), and dopamine levels were measured in the nucleus accumbens shell (AcbSh). In adolescence, rats were treated with the α7 NAM SB-277011-A (SB) or PNU-120596 (PAM) 2 h before administration of EtOH (ABAE). Ethanol consumption (acquisition, maintenance, and relapse) during adulthood was characterized. Results: Ethanol and AR co-administered into the posterior VTA stimulated dopamine release in the AcbSh in a synergistic manner. The increase in alcohol consumption during the acquisition and relapse drinking during adulthood following ABAE was prevented by administration of SB, or enhanced by administration of PNU, prior to EtOH exposure during adolescence. Discussion: Ethanol acts on the α7 receptor, and the α7 receptor regulates the critical effects of ethanol in the brain. The data replicate the findings that cholinergic agents (α7 NAMs) can act prophylactically to reduce the alterations in adult alcohol consumption following ABAE.

Azithromycin and Sildenafil May Have Protective Effects on Retinal Ganglion Cells via Different Pathways: Study in a Rodent Microbead Model

Decreased blood flow to the optic nerve (ON) and neuroinflammation are suggested to play an important role in the pathophysiology of glaucoma. This study investigated the potential neuroprotective effect of azithromycin, an anti-inflammatory macrolide, and sildenafil, a selective phosphodiesterase-5 inhibitor, on retinal ganglion cell survival in a glaucoma model, which was induced by microbead injection into the right anterior chamber of 50 wild-type (WT) and 30 transgenic toll-like receptor 4 knockout (TLR4KO) mice. Treatment groups included intraperitoneal azithromycin 0.1 mL (1 mg/0.1 mL), intravitreal sildenafil 3 µL, or intraperitoneal sildenafil 0.1 mL (0.24 μg/3 µL). Left eyes served as controls. Microbead injection increased intraocular pressure (IOP), which peaked on day 7 in all groups and on day 14 in azithromycin-treated mice. Furthermore, the retinas and ON of microbead-injected eyes showed a trend of increased expression of inflammatory- and apoptosis-related genes, mainly in WT and to a lesser extent in TLR4KO mice. Azithromycin reduced the BAX/BCL2 ratio, TGFβ, and TNFα levels in the ON and CD45 expression in WT retina. Sildenafil activated TNFα-mediated pathways. Both azithromycin and sildenafil exerted a neuroprotective effect in WT and TLR4KO mice with microbead-induced glaucoma, albeit via different pathways, without affecting IOP. The relatively low apoptotic effect observed in microbead-injected TLR4KO mice suggests a role of inflammation in glaucomatous damage.

Network modeling helps to tackle the complexity of drug-disease systems

From the (patho)physiological point of view, diseases can be considered as emergent properties of living systems stemming from the complexity of these systems. Complex systems display some typical features, including the presence of emergent behavior and the organization in successive hierarchic levels. Drug treatments increase this complexity scenario, and from some years the use of network models has been introduced to describe drug-disease systems and to make predictions about them with regard to several aspects related to drug discovery. Here, we review some recent examples thereof with the aim to illustrate how network science tools can be very effective in addressing both tasks. We will examine the use of bipartite networks that lead to the important concept of "disease module", as well as the introduction of more articulated models, like multi-scale and multiplex networks, able to describe disease systems at increasing levels of organization. Examples of predictive models will then be discussed, considering both those that exploit approaches purely based on graph theory and those that integrate machine learning methods. A short account of both kinds of methodological applications will be provided. Finally, the point will be made on the present situation of modeling complex drug-disease systems highlighting some open issues. This article is categorized under: Neurological Diseases > Computational Models Infectious Diseases > Computational Models Cardiovascular Diseases > Computational Models.

Translating precision medicine for autism spectrum disorder: A pressing need

Autism spectrum disorder (ASD) is a heterogenous group of neurodevelopmental disorders (NDDs) with a high unmet medical need. Currently, ASD is diagnosed according to behavior-based criteria that overlook clinical and genomic heterogeneity, thus repeatedly resulting in failed clinical trials. Here, we summarize the scientific evidence pointing to the pressing need to create a precision medicine framework for ASD and other NDDs. We discuss the role of omics and systems biology to characterize more homogeneous disease subtypes with different underlying pathophysiological mechanisms and to determine corresponding tailored treatments. Finally, we provide recent initiatives towards tackling the complexity in NDDs for precision medicine and cost-effective drug discovery.